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Bronchiectasis is a condition Natural weight loss drinks which inflammayion bronchial tubes airways are thickened due inflammatikn long-term inflammation ijflammation scarring.
This infalmmation to a build-up of mucus, frequent inflwmmation Airway inflammation, and inflsmmation, a ijflammation in lung function.
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This can cause innflammation chronic Anti-obesity initiatives. Chronic Inflammatino Pulmonary Disease IArway is a group of lung diseases, including emphysema Anthocyanins and athletic performance chronic bronchitis.
These diseases make Airday difficult. COPD inflammationn make it hard to catch your breath. Because breathing takes such hard work when you have COPD, you can become exhausted. Cystic fibrosis is an inherited disorder resulting inflammatino a gene mutation a flaw in a gene.
Cystic fibrosis can have severe complications, but ways to manage the disease have improved greatly over the past few decades. Emphysema happens when the air sacs in the lungs become damaged. The air sacs, called alveoli, inflate and deflate as you breathe, exchanging the oxygen in the lungs. When the alveoli are damaged, it is difficult to breathe.
Pulmonary hypertension is high blood pressure in the arteries in your lungs. Pulmonary hypertension is dangerous because it can result in heart failure. Interstitial lung disease ILD is a group of respiratory disorders that vary in severity and affect adults of all ages.
Because interstitial lung disease varies with respect to the severity of symptoms and prognosis, being diagnosed with ILD can be confusing and frightening to patients. There are two main types of lung cancer — small cell lung cancer and non-small cell lung cancer. Although the same techniques radiation therapy, chemotherapy, surgery are used to treat both, your treatment will be tailored depending on your diagnosis and clinical stage.
Ninety percent of lung cancers occur in individuals with a history of smoking cigarettes. Cigar and pipe smoke are potentially more dangerous; they are associated with a lung cancer risk twice that for cigarette smokers.
Exposure to air pollution, radiation and industrial chemicals such as asbestos, arsenic, nickel and chromium may also increase your risk for developing lung cancer. Mycobacterial diseases are infectious diseases caused by bacteria.
TB refers to the bacteria Mycobacterium tuberculosis. MAI refers to Mycobacterium avium-intracellulare MAIwhich is a term for two species of bacteria. Pneumonia is a common infection that inflames the alveoli air sacs in one or both lungs.
Bacteria, viruses or fungi cause the air sacs in the lungs to fill with fluid or pus. There are many different types of sleep disorders. You may find it hard to fall asleep or stay asleep insomnia. Parasomnias such as excessive night terrors or nightmares and a number of other sleep disorders can impact your mood, productivity and health.
Tracheobronchomalacia TBM is a condition of the airways that causes them to become weak and floppy and collapse with breathing. Normally the central airways the trachea and bronchi remain open when you breathe.
Search Submit Search. Find a Doctor. Sign up now. Medical Records Pay Hospital Bill Now available: new PatientSite design and features for a simpler user experience.
If you are experiencing a medical emergency, call Asthma Asthma is a chronic disease that affects your airways tubes that carry air in and out of your lungs. Bronchiectasis Bronchiectasis is a condition in which the bronchial tubes airways are thickened due to long-term inflammation and scarring.
Bronchitis Bronchitis is an inflammation of the airways. Chronic Obstructive Pulmonary Disease COPD Chronic Obstructive Pulmonary Disease COPD is a group of lung diseases, including emphysema and chronic bronchitis.
Cystic Fibrosis Cystic fibrosis is an inherited disorder resulting from a gene mutation a flaw in a gene. Emphysema Emphysema happens when the air sacs in the lungs become damaged.
Hypertension Pulmonary hypertension is high blood pressure in the arteries in your lungs. Interstitial Lung Disease ILD Interstitial lung disease ILD is a group of respiratory disorders that vary in severity and affect adults of all ages. Lung Cancer There are two main types of lung cancer — small cell lung cancer and non-small cell lung cancer.
Mycobacterial Diseases TB and MAI Mycobacterial diseases are infectious diseases caused by bacteria. Pneumonia Pneumonia is a common infection that inflames the alveoli air sacs in one or both lungs. Sleep Disorders There are many different types of sleep disorders. Tracheobronchomalacia TBM Tracheobronchomalacia TBM is a condition of the airways that causes them to become weak and floppy and collapse with breathing.
: Airway inflammation| Inflammation mechanism in COPD | Hypertension and heart disease Airway inflammation Fibros. There were 5 studies with inflammatuon asthma inflammtion Airway inflammation controlled [ 44 onflammation, 51 ] and 3 with unknown control status inflammtaion 535558 Airway inflammation, inflammxtion studies Aiway mild to moderate asthma Airway inflammation with controlled [ 212346 ] and Airway inflammation with unknown control status [ 222543505760 ], 6 studies with moderate to severe asthma 1 with controlled [ 47 ] and 5 with unknown control status 24;45;52;54;561 study with severe asthma with unknown control status [ 59 ] and 2 studies did not clearly mention asthma severity or control [ 4849 ]. Pathology of asthma. A three-dimensional mathematical and computational model of necrotizing enterocolitis. Discrete-time models such as agent-based models ABMs represent an inflammatory mediator as an agent i. PubMed Google Scholar. Airway remodeling involves activation of structural airway cells. |
| Publication types | We do not yet know why people with asthma have this inflammation response. Experts think it involves an imbalance between type 1 and type 2 helper T cells. An immune response that involves mostly type 2 helper T cells is linked to conditions like asthma. Genetic factors and environmental exposures increase the risk of developing asthma. They may do so by influencing the balance of types of immune cells. This may make some people more likely to have a type 2 inflammatory response. Some risk factors include: 1,2,6. By providing your email address, you are agreeing to our privacy policy. Skip to Accessibility Menu Skip to Login Skip to Content Skip to Footer. What Causes Asthma? By Editorial Team 3 min read. Share to Facebook Share to Twitter print page Bookmark for later. How is inflammation related to asthma? Airway narrowing A major result of inflammation in asthma is airway narrowing. This includes: 3,4 Swelling of the airway edema Excess mucus Thickening of airway muscles hypertrophy and hyperplasia These factors combine to narrow and block the airways. Airway remodeling For many people with asthma, airway narrowing and obstruction are reversible. Some risk factors include: 1,2,6 Family history of asthma History of other allergic conditions Viral respiratory infections during early childhood Exposure to certain allergens during early childhood Exposure to cigarette smoke in the womb or early childhood Air pollution or workplace exposures Obesity. Recommended Article Not Your Usual Asthma Symptoms Reactions 0 reactions. Comments 28 comments. Recommended Article What Causes Asthma Itch? Reactions 0 reactions. English Usage. Teaching Resources. Video Guides. Video Learn English. Video pronunciations. Build your vocabulary. Quiz English grammar. English collocations. English confusables. English idioms. English images. English usage. English synonyms. Thematic word lists. English Dictionary Grammar. Example sentences airway inflammation. These examples have been automatically selected and may contain sensitive content that does not reflect the opinions or policies of Collins, or its parent company HarperCollins. We welcome feedback: you can select the flag against a sentence to report it. In one study fritillaria reduced airway inflammation by suppressing cytokines, histamines, and other compounds of inflammatory response. Retrieved from Wikipedia CC BY-SA 3. Dust mite-induced signals are then propagated through epithelium, which enhance allergic airway inflammation. Loss of this enzyme leads to enhanced allergic eosinophilic airway inflammation. These microbial infections result in chronic lower airway inflammation , impaired mucociliary clearance, an increase in mucous production and eventually asthma. Other treatment options may target airway inflammation or may promote mucus expectoration. It is not useful It is offensive. Read more on how we generate our sentences. eə ʳweɪ. countable noun A person's airways are the passages from their nose and mouth down to their lungs , through which air enters and leaves their body. See full entry for 'airway'. Copyright © HarperCollins Publishers. variable noun An inflammation is a painful redness or swelling of a part of your body that results from an infection , injury , or illness. See full entry for 'inflammation'. COBUILD Collocations airway inflammation. airway inflammation. airway obstruction. clear the airway. narrow the airway. nasal airway. obstruct the airway. open the airway. positive airway pressure. upper airway. You may also like. English Quiz. Browse alphabetically airway inflammation. Definition of airway inflammation from the Collins English Dictionary. Read about the team of authors behind Collins Dictionaries. Quick word challenge Quiz Review. negative view or ocean views? Just a few early nights can transform your negative view of the world. Just a few early nights can transform your ocean views of the world. complex plot or vegetable plot? Acute respiratory distress syndrome. Eur Respir Rev. Horak F, Doberer D, Eber E, et al. Diagnosis and management of asthma - statement on the GINA Guidelines. Wien Klin Wochenschr. Centers for Disease Control and Prevention. Chronic obstructive pulmonary disease. Popper H, Stacher-Priehse E, Brcic L, Nerlich A. Lung fibrosis in autoimmune diseases and hypersensitivity: how to separate these from idiopathic pulmonary fibrosis. Rheumatol Int. Jeganathan N, Sathananthan M. The prevalence and burden of interstitial lung diseases in the USA. ERJ Open Res. Morley EJ, Johnson S, Leibner E, Shahid J. Emergency department evaluation and management of blunt chest and lung trauma trauma CME. Emerg Med Pract. Giacalone VD, Dobosh BS, Gaggar A, Tirouvanziam R, Margaroli C. Immunomodulation in cystic fibrosis: why and how? Int J Mol Sci. Adler Y, Charron P, Imazio M, et al. Eur Heart J. Lee JS, Moon T, Kim TH, et al. Deep vein thrombosis in patients with pulmonary embolism: prevalence, clinical significance and outcome. Vasc Specialist Int. American Lung Association. Lung cancer fact sheet. Scherer PM, Chen DL. Imaging pulmonary inflammation. J Nucl Med. Using oxygen at home. National Cancer Institute. Non-small cell lung cancer treatment: health professional version. Surgery for COPD. By James Myhre James is a writer who has worked with community-based HIV organizations since and who has previously held a faculty position with the USAID-funded Foundation for Professional Development. Use limited data to select advertising. Create profiles for personalised advertising. Use profiles to select personalised advertising. Create profiles to personalise content. Use profiles to select personalised content. Measure advertising performance. Measure content performance. Understand audiences through statistics or combinations of data from different sources. 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| We Care About Your Privacy | Mycobacterial diseases are infectious diseases caused by bacteria. TB refers to the bacteria Mycobacterium tuberculosis. MAI refers to Mycobacterium avium-intracellulare MAI , which is a term for two species of bacteria. Pneumonia is a common infection that inflames the alveoli air sacs in one or both lungs. Bacteria, viruses or fungi cause the air sacs in the lungs to fill with fluid or pus. There are many different types of sleep disorders. You may find it hard to fall asleep or stay asleep insomnia. Parasomnias such as excessive night terrors or nightmares and a number of other sleep disorders can impact your mood, productivity and health. Tracheobronchomalacia TBM is a condition of the airways that causes them to become weak and floppy and collapse with breathing. Normally the central airways the trachea and bronchi remain open when you breathe. Search Submit Search. Find a Doctor. Sign up now. Medical Records Pay Hospital Bill Now available: new PatientSite design and features for a simpler user experience. If you are experiencing a medical emergency, call Asthma Asthma is a chronic disease that affects your airways tubes that carry air in and out of your lungs. Bronchiectasis Bronchiectasis is a condition in which the bronchial tubes airways are thickened due to long-term inflammation and scarring. Bronchitis Bronchitis is an inflammation of the airways. Chronic Obstructive Pulmonary Disease COPD Chronic Obstructive Pulmonary Disease COPD is a group of lung diseases, including emphysema and chronic bronchitis. Cystic Fibrosis Cystic fibrosis is an inherited disorder resulting from a gene mutation a flaw in a gene. Emphysema Emphysema happens when the air sacs in the lungs become damaged. Vempati R, Deepak K: Effect of yogic practices on airway inflammation, mast cell activation and exercise induced asthma: a randomized controlled trial. Vempati R, Bijlani RL, Deepak KK: The efficacy of a comprehensive lifestyle modification programme based on yoga in the management of bronchial asthma: a randomized controlled trial. Scott HA, Gibson PG, Garg ML, Wood LG: Airway inflammation is augmented by obesity and fatty acids in asthma. Boyd AW, Estell K, Dransfield M, Schwiebert L: The effect of aerobic exercise on asthma-related responses in adults. Bonsignore MR, La Grutta S, Cibella F: Effects of exercise training and montelukast in children with mild asthma. Med Sci Sports Exerc. Bundgaard A, Ingemann-Hansen T, Schmidt A, Halkjaer-Kristensen J: Effect of physical training on peak oxygen consumption rate and exercise-induced asthma in adult asthmatics. Scand J Clin Lab Invest. Cochrane LM, Clark CJ: Benefits and problems of a physical training programme for asthmatic patients. Fanelli A, Cabral ALB, Neder JA: Exercise training on disease control and quality of life in asthmatic children. Fitch KD, Blitvich JD MAR: The effect of running training on exercise-induced asthma. Ann Allergy. Gunay O, Onur E, Yilmaz O, Dundar PE, Tikiz C, Var A, et al: Effects of physical exercise on lung injury and oxidant stress in children with asthma. Allergologia et Immunopathologia. Henriksen JM, Toftegaard NT: Effect of physical training on exercise-induced bronchoconstriction. Acta Peadiatric Scan. Moreira A, Delgado L, Haahtela T, Fonseca J, Moreira P, Lopes C, et al: Physical training does not increase allergic inflammation in asthmatic children. Neder JA, Nery LE, AnC S, Cabral ALB, Fernandes ALG: Short term effects of aerobic training in the clinical management of moderate to severe asthma in children. Onur E, Kabaro A, Ylu C, GA¼nay O, Var A, Yilmaz O, DA¼ndar P, et al: The beneficial effects of physical exercise on antioxidant status in asthmatic children. Allergol Immunopathol. Sly R, Harper R, Rosselot I: The effect of physical conditioning upon asthmatic children. Svenonius E, Kautto R, Arborelius JM: Improvement after training of children with exercise-induced asthma. Mendes FAR, Almeida FM, Cukier A: Effects of aerobic training on airway inflammation in asthmatic patients. Juvonen R, Bloigu A, Peitso A, Silvennoinen-Kassinen S, Saikku P, Leinonen M, et al: Training improves physical fitness and decrease CRP also in asthmatic conscripts. J Asthma. Newcomb P, Hunt A, Rast P, Cauble D, Rowe N, Li J: Acute effects of walking environment and GSTM1 variants in children with asthma. Biol Res Nurs. Nickerson BG, Bautista DB, Namey MA, Richards W, Keens TG: Distance running improves fitness in asthmatic children without pulmonary complications of changes in exercise-induced bronchospasm. Silva PL, Mello MT, Cheik NC, Sanches PL, Correia FA, Pian A, et al: Interdisciplinary therapy improves biomarkers profile and lung function in asthmatic obese adolescents. Pediatr Pulmonol. Segal RJ, Reid RD, Courneya KS, Sigal RJ, Kenny GP, Prud'Homme DG, et al: Randomized controlled trial of resistance or aerobic exercise in men receiving radiation therapy for prostate cancer. J Clin Oncol. Boule NG, Haddad E, Kenny GP, Wells GA, Sigal RJ: Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: a meta-analysis of controlled clinical trials. Diabetes Care. Heijink IH, Van Oosterhout AJM: Strategies for targeting T-cells in allergic diseases and asthma. Pharmacol Ther. Sood A, Ford ES, Camargo CA: Association between leptin and asthma in adults. Sutherland TJT, Cowan JO, Young S, Goulding A, Grant AM, Williamson A, et al: The Association between Obesity and Asthma: interactions between systemic and airway inflammation. Am J Respir Crit Care Med. Mosen DM, Schatz M, Magid DJ, Camargo J: The relationship between obesity and asthma severity and control in adults. Sutherland ER: Obesity and asthma. Immunol Allergy Clin North Am. Beuther DA, Sutherland ER: Overweight, obesity, and incident asthma: a meta-analysis of prospective epidemiologic studies. Franklin PJ, Stick SM: The value of FeNO measurement in asthma management: the motion against FeNO to help manage childhood asthma—reality bites. Paediatr Respir Rev. Jartti T, Wendelin-Saarenhovi M, Heinonen I, Hartiala J, Vanto T: Childhood asthma management guided by repeated FeNO measurements: a meta-analysis. Aaron SD, Vandemheen KL, Boulet LP, McIvor RA, FitzGerald JM, Hernandez P, et al: Overdiagnosis of asthma in obese and nonobese adults. Can Med Assoc J. Pakhale S, Doucette S, Vandemheen K, Boulet LP, McIvor RA, FitzGerald JM, et al: A comparison of obese and nonobese people with asthma: exploring an asthma-obesity interaction. Pakhale S, Sumner A, Coyle D, Vandemheen K, Aaron S: Correcting misdiagnoses of asthma: a cost effectiveness analysis. Dogra S, Kuk JL, Baker J, Jamnik V: Exercise is associated with improved asthma control in adults. Garcia-Aymerich J, Varraso R, Anto JM, Camargo CA: Prospective study of physical activity and risk of asthma exacerbations in older women. Download references. We would like to acknowledge the following people for their contributions: Dr. Moher The Ottawa Hospital Research Institute for his thoughtful input during the systematic review process and manuscript edits; Dr. Emtner Uppsala University, Sweden for supplying raw data from her studies and Ms. Risa Shorr The Ottawa Hospital Library for her help with the search strategies and peer review of searches. Pakhale is supported by the Ottawa Hospital Research Institute and the Department of Medicine, The Ottawa Hospital, Ottawa, Canada. Moher is supported by a University of Ottawa Research Chair. There was no role of the funders in conducting this systematic review. All authors declare no conflict of interest. The Ottawa Hospital, Smyth Road, Ottawa, Ontario, K1H 8L6, Canada. You can also search for this author in PubMed Google Scholar. Correspondence to Smita Pakhale. SP had full access to the data and takes full responsibility for the integrity of the data and the accuracy of the data analysis, contributed to the concept, design, implementation, statistical analysis, interpretation and writing. VL contributed to the data management, statistical analysis, interpretation and writing. AB contributed to the data management, statistical analysis, interpretation and writing. LT contributed to the data analysis, interpretation and writing. All authors read and approved the final manuscript. Additional File 1: Summary of included studies. This file provides details of the studies included in this systematic review including the number of subjects, mode of asthma diagnosis, asthma severity and control, current asthma pharmacotherapy, study design, training intervention dose, duration, site, intensity and frequency of physical exercise , control intervention and outcome measures. PDF 95 KB. This article is published under license to BioMed Central Ltd. Reprints and permissions. Pakhale, S. et al. Effect of physical training on airway inflammation in bronchial asthma: a systematic review. BMC Pulm Med 13 , 38 Download citation. Received : 26 November Accepted : 04 June Published : 13 June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background The majority of the global population cannot afford existing asthma pharmacotherapy. Methods A peer reviewed search was applied to Medline, Embase, Web of Science, Cochrane, and DARE databases. Results From the initial studies; 23 studies 16 RCTs and 7 prospective cohort studies were included. Conclusion Due to reporting issues, lack of information and heterogeneity there was no definite conclusion; however, some findings suggest physical training may reduce airway inflammation in asthmatics. Background Asthma is a multifactorial disease with genetic, environmental and inflammatory components in its etiology. Methods A separate protocol for this review was not previously published. Data sources and search Studies were identified by searching electronic databases, and scanning reference lists of articles. Study selection A scoping search revealed few studies in this area pertaining to either humans or animal models of asthma; therefore, we decided to investigate this question for both animal models and human asthmatic subjects of all age groups the former published separately [ 20 ]. Data extraction and quality assessment The search was applied to each database and the results were combined. Data synthesis and analysis We anticipated heterogeneity in studies in the primary outcome measures, methods of assessing outcome measures, and study designs. Results Search The initial search yielded a total of citations. Figure 1. Flow chart of systematic search. Full size image. Table 1 Risk of bias within randomized control trials Full size table. Table 2 Risk of bias within cohort studies Full size table. Figure 2. Discussion Physical exercise has multi-faceted benefits in health and disease [ 61 , 62 ] and is therefore part of many guidelines for chronic diseases [ 63 ]. Strengths and limitations of the review The current review has several strengths. Conclusions Effectiveness of physical exercise on airway inflammation is yet unproven. References Hargreave FE, Nair P: The definition and diagnosis of Asthma. Article CAS PubMed Google Scholar Global Initiative for Asthma Report, Global Strategy for Asthma Management and Prevention: Updates December pdf Lougheed MD, Lemiere C, Ducharme FM, Licskai C, Dell SD, Rowe BH, et al: Canadian Thoracic Society guideline update: diagnosis and management of asthma in preschoolers, children and adults. Article PubMed PubMed Central Google Scholar Chakir J, Hamid Q, Bosse M, Boulet LP, Laviolette M: Bronchial inflammation in corticosteroid-sensitive and corticosteroid-resistant asthma at baseline and on oral corticosteroid treatment. Article CAS PubMed Google Scholar Sumi Y, Hamid Q: Airway remodeling in asthma. Article CAS PubMed Google Scholar Editorial: Asthma: still more questions than answers. Article CAS PubMed Google Scholar Holgate ST: Has the time come to rethink the pathogenesis of asthma? Article PubMed Google Scholar Chandratilleke MG, Carson KV, Picot J, Brinn MP, Esterman AJ, Smith BJ: Physical training for asthma. Article PubMed PubMed Central Google Scholar Orie NGM, Slutter HJ, De V, Tammeling GJ: Chronic nonspecific respiratory diseases. Article CAS PubMed Google Scholar Hunninghake GM, Cho MH, Tesfaigzi Y, Soto-Quiros ME, Avila L, Lasky-Su J, et al: MMP12, lung function, and COPD in high-risk populations. Article CAS PubMed PubMed Central Google Scholar O'Donnell DE, Hernandez P, Kaplan A, Aaron S, Bourbeau J, Marciniuk D, et al: Canadian Thoracic Society recommendations for management of chronic obstructive pulmonary disease - update - highlights for primary care. PubMed PubMed Central Google Scholar Maltais F, Bourbeau J, Shapiro S, Lacasse Y, Perrault H, Baltzan M, et al: Effects of home-based pulmonary rehabilitation in patients with chronic obstructive pulmonary disease. Article PubMed Google Scholar Lacasse YF, Goldstein RF, Lasserson TJ FAU, Martin S: Pulmonary rehabilitation for chronic obstructive pulmonary disease. Article PubMed PubMed Central Google Scholar Dreyer NA: Making observational studies count: shaping the future of comparative effectiveness research. Article PubMed Google Scholar Luks V, Burkett A, Turner L, Pakhale S: Effect of physical training on airway inflammation in animal models of asthma: a systematic review. Article CAS PubMed PubMed Central Google Scholar Emtner M, Finne M, Stalenheim G: High-intensity physical training in adults with asthma. Article CAS PubMed Google Scholar Emtner M, Finne M, Stσlenheim G: A 3-year follow-up of asthmatic patients participating in a week rehabilitation program with emphasis on physical training. Article CAS PubMed Google Scholar Engstrom I, Fallstrom K, Karlberg E, Sten G, Bjure J: Psychological and respiratory physiological effects of a physical exercise programme on boys with severe asthma. Article CAS Google Scholar Matsumoto I, Araki H, Tsuda K, Odajima H, Nishima S, Higaki Y, et al: Effects of swimming training on aerobic capacity and exercise induced bronchoconstriction in children with bronchial asthma. Article CAS PubMed PubMed Central Google Scholar Higgins JPT, Altman DG, Sterne JAC: Chapter 8: Assessing risk of bias inincluded studies. Chapter Google Scholar Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al: The Newcastle-Ottawa Scale NOS for assessing the quality of nonrandomised studies in meta-analyses. asp Deeks JJ, Higgins JPT, Altman DG: Chapter 9: Analysing data andundertaking meta-analyses. Google Scholar Borenstein M, Hedges L, Higgins J, Rothstein H: Comprehensive Meta-analysis. Article Google Scholar Bijl D, Speelberg B, Folgering HT: Pulmonary rehabilitation at moderate altitude: a 1-year follow-up. CAS PubMed Google Scholar Bougault V, Turmel J, St-Laurant J, Bertrand M, Boulet LP: Asthma, airway inflammation and epithelial damage in swimmers and cold-air athletes. Article CAS PubMed Google Scholar Durigan JLQ, Peviani SM, Russo TL, Duarte AC, Vieira RP, Martins MA, et al: Physical training leads to remodeling of diaphragm muscle in asthma model. Article CAS PubMed Google Scholar Durigan JLQ, Peviani SM, Russo TL, Silva ACD, Vieira RP, Martins MA, et al: Effects of exercise training on atrophy gene expression in skeletal muscle of mice with chronic allergic lung inflammation. Article CAS PubMed Google Scholar Helenius I, Rytila P, Sarna S, Lumme A, Helenius M, Remes V, et al: Effect of continuing or finishing high-level sports on airway inflammation, bronchial hyperresponsiveness, and asthma: a 5-year prospective follow-up study of 42 highly trained swimmers. Article PubMed Google Scholar Holzer FJ, Schnall R, Landau L: The effect of a home exercise programme in children with cystic fibrosis and asthma. Article CAS Google Scholar Koul PA: Effects of exposure to cold and exercise on bronchial asthma. Article Google Scholar Nystad W, Stigum H, Carlsen KH: Increased level of bronchial responsiveness in inactive children with asthma. Article CAS PubMed Google Scholar Vempati R, Deepak K: Effect of yogic practices on airway inflammation, mast cell activation and exercise induced asthma: a randomized controlled trial. Article CAS PubMed Google Scholar Boyd AW, Estell K, Dransfield M, Schwiebert L: The effect of aerobic exercise on asthma-related responses in adults. Article PubMed Google Scholar Bundgaard A, Ingemann-Hansen T, Schmidt A, Halkjaer-Kristensen J: Effect of physical training on peak oxygen consumption rate and exercise-induced asthma in adult asthmatics. Article CAS PubMed Google Scholar Cochrane LM, Clark CJ: Benefits and problems of a physical training programme for asthmatic patients. Article CAS PubMed PubMed Central Google Scholar Fanelli A, Cabral ALB, Neder JA: Exercise training on disease control and quality of life in asthmatic children. Article PubMed Google Scholar Fitch KD, Blitvich JD MAR: The effect of running training on exercise-induced asthma. CAS PubMed Google Scholar Gunay O, Onur E, Yilmaz O, Dundar PE, Tikiz C, Var A, et al: Effects of physical exercise on lung injury and oxidant stress in children with asthma. Article PubMed Google Scholar Henriksen JM, Toftegaard NT: Effect of physical training on exercise-induced bronchoconstriction. Article CAS Google Scholar Moreira A, Delgado L, Haahtela T, Fonseca J, Moreira P, Lopes C, et al: Physical training does not increase allergic inflammation in asthmatic children. Article CAS PubMed Google Scholar Neder JA, Nery LE, AnC S, Cabral ALB, Fernandes ALG: Short term effects of aerobic training in the clinical management of moderate to severe asthma in children. Recruited inflammatory cells to the injured site release elastase and MMP9, which results in mucus hypersecretion and elastin degradation and emphysema, respectively. Macrophages discharge TGF-β which triggers fibroblast proliferation Tissue remodeling. Airway smooth muscle produces inflammatory cytokines, proteases, and growth factors, which may contribute to the remodeling process. TLRs: Toll-like-receptors, DAMPs: damage-associated molecular patterns, MMP9: matrix metalloproteinase-9 , TGF-β: transforming growth factor-β. Asthma is one of the most serious pulmonary-system diseases and it affects more than million individuals around the world. The presence of airway inflammation in asthma was detected in the nineteenth century. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production and airway-wall remodeling [ 88 ]. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma [ 89 ]. Airway epithelial cells activate epithelial TLRs to recognize patterns of inflammatory stimuli in allergic disease [ 90 , 91 ]. Then resolution process starts where antigen presenting cells APCs endocytose inhaled allergens, present them to naïve T cells, and activate mast cells by crosslinking surface-bound IgE molecules to release several bronchoconstrictor mediators, including cysteinyl leukotrienes and prostaglandin D 2 [ 92 ]. Myeloid dendritic cells process allergens and release CCL17 and CCL22, which act on CCR4 to attract TH 2 cells. TH 2 cells release IL-4 and IL, IL-5 and IL-9 and have a central role in the pathogenesis of allergic asthma [ 93 ]. Epithelial cells release CCL11, which recruits eosinophils via CCR3. Eosinophils secrete a wide array of cytotoxic and pro-inflammatory mediators [ 94 ]. LXA4 inhibits NK-cell cytotoxicity and increases eosinophil-induced apoptosis by NK cells, and inhibits interleukin IL release by ILC2s. In addition, eosinophils may contribute to resolution of inflammation in asthma and produce pro-resolving lipid mediators PD1 and RvE3. Where PD1 and IL secrete interleukin IL and promote macrophage activation [ 96 ]. Patients with asthma may have a defect in regulatory T TReg cells, which may lead to further TH 2 -cell proliferation [ 97 ]. TGF-β is introduced as a main regulator of remodeling in the airways of asthmatics [ 93 ]. Platelet-derived growth factor PDGF promotes fibroblasts and ASM proliferation in the asthmatic lung [ 98 , 99 ]. Injured epithelial cells releases stem-cell factor SCF which promote myofibroblasts differentiation and induce structural changes throughout airway-wall remodeling [ ] Fig. Increase in angiogenesis, pro-angiogenic cytokine vascular endothelial growth factor VEGF and its receptors [ , ] and dysregulation in production of extracellular matrix metalloproteinase MMPs [ ] have been reported as proteinases responsible for the degradation of the extracellular matrix during tissue remodeling in asthmatic airways [ ]. Inflammatory response in asthma: TLRs recognize patterns of allergens Pattern recognition. Myeloid DC process allergens and release CCL17 and CCL22 to attract TH2 to injured region. IgE molecules sensitize mast cells to release cysteinyl leukotrienes and PGD2. Damaged epithelial cells release CCL11 to recruit eosinophils which attract more proinflammatory mediators to the damaged region. Eosinophils produce PD1 and PD1 secrets IL10 which promotes macrophage activation Resolution. Damaged epithelial cells releases SCF to activate myofibroblast to repair damaged epithelial cells. TLRs: Toll-like-receptors, CCL: CC-chemokine ligand, TH2: T helper cells type 2, IgE: immunoglobulin E, PGD2: prostaglandin D2, SCF: stem-cell factor, PD1: pro-resolving lipid mediators. Apart from COPD and asthma, cystic fibrosis CF is an inherited chronic disease that affects the lungs of about 70, children and adults worldwide 30, in the US. Mutation of the CF transmembrane conductance regulator CFTR gene results in CF [ ]. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens [ ]. CFTR deficiency is associated with altered fluid and electrolyte homeostasis of epithelial cells and leads to unusually thick and viscose mucus that clogs small airways, and contributes to the development of persistent lung inflammation and an increased risk of lung infections [ ]. Pathogen-associated molecular patterns PAMPs activate TLR-MyD88 signaling to increase NF-κB signaling [ ]. TLRs and bacterial colonization activate neutrophils, macrophages and NF-κB-mediated inflammatory response to initiate the pathological process. Activated NF-κB result in production of inflammatory cytokines, such as IL-8 and High mobility group box 1 HMGB1 protein, and recruitment of polymorphonuclear leukocyte PMNs. HMGB1 increases pro-inflammatory cytokine expression via its cellular receptors. Increase in pro-inflammatory cytokine expression promotes toll-like receptor TLR-2 and TLR-4 production [ ]. Intracellular TLR4 activation prevents interferon regulatory factor 3 IRF3 translocation to the nucleus to activate type I IFN gene products, which are required for the activation of dendritic cells DCs and the clearance of some cystic fibrosis-related pathogens [ ]. TH 2 skews the inflammatory environment in cystic fibrosis. Abundant IL-8 stimulates airway epithelial and smooth muscle remodeling and induces greater contraction in CF airway smooth muscle than non-cystic fibrosis airway smooth muscle, which results in airway hyper-responsiveness [ ]. Decreased function of peroxisome proliferator-activated receptor-g PPARg associates with low levels of carbonic anhydrases that contribute to increased mucus viscosity and results in enhanced pro-inflammatory signaling and cytokine secretion in CF cells Fig. High numbers of neutrophils at the site of chronic infection and decreased neutrophil apoptosis, phagocytic capacity of macrophage and levels of pro-resolving mediators suggest an impaired inflammatory resolution that promotes sustained infection [ , ]. In addition, defective cilia function, increased mucus viscosity, hypoxia, free nutrients, damage to lung architecture, defective or decreased antimicrobials, TH 2 and TH 17 responses and ineffective cellular mediators, changes in virulence and direct downregulation of antimicrobial pathways contribute to infection and pulmonary decline in cystic fibrosis [ ]. Airway remodeling in CF is presented as secondary to infection and inflammation [ ]. MMPs are involved in tissue breakdown and repair and MMP-8 and MMP-9, which are mainly derived from neutrophils in the lower respiratory tract, are the most important group of endopeptidases in CF remodeling [ , , ]. In addition, TGFα is thought to play a role in the regulation of airways remodeling with CF [ ] and TH2 cytokines especially IL has been found during cycles of epithelial injury and repair of CF airways [ ]. Inflammatory response in CF: TLRs recognize PAMPs pattern recognition. TLRs and bacterial colonization activate inflammatory mediators like; neutrophils, macrophages and NF-κB. NF-κB produce IL8 and HMGB1 and recruit monocyte. Type I IFN activates DCs to clear cystic fibrosis-related pathogens and TH2 skew CF Resolution. IL8 stimulate damaged epithelial cells Tissue remodeling. PAMPs: Pathogen-associated molecular patterns, TLRs: Toll-like-receptors, NF-κB: nuclear factor kappa-light-chain-enhancer of activated B-cells, IL proinflammatory cytokines interleukin 8, HMGB1: High mobility group box 1, IFN: Interferon, DCs: dendritic cells. The contribution of harmful stimuli and inflammatory mediators in pattern recognition, resolution and remodeling process are classified for discussed disease condition in Table 1. All vital organs lose their function with age. Human lung matures up to age 20—25 years and will start to lose functionality after about 35 years. Breathing existent pollutants in the environment or exogenous oxidants in young or healthy individuals causes cellular damage in lung tissue [ ]. If the damage is too extreme, cells would sustain senescence to prevent oncogenic changes. Senescence signaling activates stem cells to replace damaged cells [ ]. An increase in senescent cells and corresponding senescence-associated secretory phenotype can induce further inflammation, alveolar destruction, endothelial dysfunction [ ]. In addition, excessive ROS will increase damage to cells by a defective repair mechanism in the elderly. In classic aging pathways, growth factor signaling activates PI3K, phospho-AKT and mTOR, which accelerate aging [ , , ]. Inhibition of mTOR signaling extends life span [ ]. Antiaging molecules such as phosphatase and tensin homolog PTEN inhibits PI3K and AMPK prevent hyperactivation of the mTOR signaling pathway. Sirtuins SIRT1 and SIRT6 upregulate FOXO3A and promotes autophagy [ , , ]. Defective mechanism of positive regulators SIRT1, SIRT6, PTEN, and AMPK will induce cytokine, chemokine, and ribosomal synthesis and secrete growth factors favoring cell proliferation and growth Fig. COPD is identified with an elevated ROS level and ROS are able to change biological molecules, signaling pathways and antioxidant molecule function. A decrease in the level of PTEN and SIRT1 in COPD would lead to activation of the mTOR-aging pathway via PI3K activation by ROS. This results in reduced antioxidant defense by FOXO3A inhibition and a loss of autophagy. Loss of autophagy can prevent the clearance of defective mitochondria and further increase ROS production [ ]. Defective autophagy decreases immune response to bacteria and cellular homoeostasis in COPD. Inflammaging mechanism in airways: ROS increase damage in airways epithelial cells. Growth factor signaling activate PI3K, phospho-AKT and mTOR signaling which accelerate aging in airways. PTEN and AMPK inhibit discussed factors that can lead to increase in life span. SIRT1 upregulate FOXO3A that functions as a trigger for apoptosis of damaged cells. SIRT1 also promotes autophagy. Effecting mechanism of SIRT1 will induce cytokine, chemokine and ribosomal synthesis and secrete growth factors favoring cell proliferation and growth. ROS: Reactive oxygen species, PI3K: Phosphoinositide 3-kinase, mTOR: mechanistic target of rapamycin, PTEN: phosphatase and tensin homolog , FOXO3: Forkhead box O3, SIRT1: Sirtuin 1. Asthma and COPD occur due to chronic inflammation of the airways. However, the mechanism of action is different. In asthma, mast cells, eosinophils and CD4 T lymphocytes represent the predominant cell types in the inflammatory process. In COPD, neutrophils, macrophages and CD8 T lymphocytes are the predominant cell types in the inflammatory process [ , , ]. In CF, neutrophils are the predominant cell types in the inflammatory process and they release oxidants, proteases, and elastase that causes respiratory exacerbations [ ]. Patients with COPD exhibit reduced airway caliber because of cell damage induced by external toxic agents such as cigarette smoke [ , , ]. There is a positive correlation between inflammation intensity and COPD severity. At the final stages of the disease, the inflammatory process becomes very intense. The intensity of inflammation may be combated through the application of anti-inflammatory therapies [ , ]. Anti-inflammatory therapies have the potential of combating CF and asthma, but care must be taken to avoid suppressing critical elements of the inflammatory response, which in turn may worsen the disease [ , ]. Inflammatory responses are numerous and include transport of plasma from the blood into the injured tissues, biochemical signaling cascades, and the mobilization of cytokines, such as interleukins [ ]. The complexity of the inflammation process suggests that strategies for developing effective and efficient therapeutic interventions for combating airway diseases would greatly benefit from predictions obtained from mathematical and computational modeling. For example, correlation of imaging measurements with disease severity would be useful in understanding the pathophysiology behind different airway diseases and guide the development of therapeutic interventions. In addition, computational models of lung tissue may aid in the study of lung tissue mechanics during an inflammatory process. Aging is a complex process that occurs in different cell types and tissues and is controlled by environmental, genetic, stochastic, epigenetic events and their long-term interactions [ ]. Inflammaging is associated with most of the age-related diseases but its precise etiology and potential causal role remain largely unknown [ ]. An understanding of the mechanism of lung inflammaging is therefore important in determining whether treatments that modulate inflammaging may be beneficial in combating age-related airway diseases. Mathematical models represent the essential characteristics of a system as a set of mathematical equations. They are useful in testing different hypotheses about the working of a system and their utility is established by matching their outputs with experimental observations. The study of inflammation is somewhat difficult because of the myriad inflammatory mediators involved and their effects on target tissues. The coordinated functions of these mediators and their multiple modes of regulation remain largely unknown [ ]. Mathematical models are vital tools that would help in deciphering the dynamic behavior of these networks. Analysis of a model often provides insights into the underlying mechanisms for the regulation of the system, and this may drive formulation of new hypotheses that would in turn lead to new rounds of experiments [ ]. Mechanistic models of inflammation may be classified as discrete-time or continuous-time models. Discrete-time models describe the changes in the system at certain time points with no information of its behavior at intervals between these time points. Discrete-time models such as agent-based models ABMs represent an inflammatory mediator as an agent i. Agent simulations are governed by local interactions among agents and can incorporate the stochasticity of the inflammatory process. Continuous-time models represent the system as continuous over time and usually manifest as differential equations [ ]. Most continuum models of inflammation use ordinary differential equations ODEs to describe the dynamics of an inflammation response. Some models use partial differential equations PDEs in place of ODEs or a combination of both [ 54 , ]. Thus, ODEs may be better suited for modeling the inflammation process over several days. However, to study the spatial distribution of inflammatory mediators and their effect on the progress of an inflammation process, PDE models would be a better option. ODEs and PDEs that model the complex dynamics of an inflammatory response are mostly nonlinear, and their exact or analytical solutions are difficult and sometimes impossible to obtain. The application of techniques for solving differential equations based on numerical approximations is required for finding approximate solutions for nonlinear differential equations. Numerical algorithms for the numerical approximation of nonlinear differential equations produce computational models that are easily simulated on computers to obtain approximate solutions. Mathematical and computational models have been developed to study the physiological functioning of the lungs and relatively few have focused on obstructive lung diseases [ 69 , ]. Most of the models of obstructive lung diseases do not incorporate the effect of inflammation [ 72 , 73 , 74 , 75 ]. Chernyavsky et al. They present a mathematical model that describes qualitatively the growth dynamics of airway smooth muscle cells over short and long terms in the normal and inflammatory environments often observed in asthma. Their model predicts that long-term airway smooth muscle growth is influenced by the inflammation resolution speed, the inflammation magnitude, and the frequency of inflammatory episodes. Their model highlights the importance of the resolution speed of inflammation in the long-term management of asthma. A limitation of their model is that it does not account for the mechanical interaction of the cells between each other and with the extracellular matrix that could affect the growth and apoptosis rates as well as the total capacity of an airway wall. In addition, the model neglects the spatially heterogeneous and anisotropic growth observed in micrographs and cell hypertrophy [ , ]. A study by Lee et al. Their model describes two types of macrophages that play complementary roles in fighting viral infections: classical-activated macrophages and alternative-activated macrophages. Classical-activated macrophages destroy infected cells and tissues to remove viruses, while alternative-activated macrophages repair damaged tissues. They describe populations of viruses and airway epithelial cells, concentrations of cytokines such as IFN- β and IL-4 and enzymes such as iNOS and arginase-1 secreted by the cells. After an infection, the airway epithelial cells are directly infected by the virus and the type I interferon they produce. Airway epithelial cells are defined to be in two states, dormant and activated. Dormant epithelial cells transition to the activated state upon exposure to virus. After epithelial cells have been infected and begun to respond, alveolar macrophages take control of the defense system. The balance between classically activated macrophages and alternatively activated macrophages is controlled by the cytokines IFNb and IL4. They investigate how viral infections alter the balance of the alveolar macrophage system and potentially trigger asthma exacerbations. In particular, they investigate how respiratory viral infection changes the balance between classical-activated macrophages and alternative-activated macrophages and how this response differs in hosts with asthma-like conditions, and how those differences can lead to accentuated symptoms. Their simulation results show that a higher viral load or longer duration of infection provokes a stronger immune response from the macrophage system. Their result also showed that the differences in response to respiratory viral infection in normal and asthmatic subjects skews the system toward a response that generates more severe symptoms in asthmatic patients. Thus, respiratory viral infection can aggravate symptoms in asthmatic patients [ ]. Kim et al. Airway exposure levels of lipopolysaccharide LPS determined type I versus type II helper T cell-induced experimental asthma. While high LPS levels induce Th1-dominant responses, low LPS levels derive Th2 cell-induced asthma. Their model describes the behaviors of T cells Th0, Th1, Th2 and macrophages and regulatory molecules IFN-γ, IL-4, IL, TNF-α in response to high, intermediate, and low levels of LPS. The simulation results showed how variations in the levels of injected LPS affect the development of Th1 or Th2 cell responses through differential cytokine induction. A few mathematical models have also been developed to study COPD. An example is the model presented by Cheng et al. The model investigated coinfection interactions between influenza and Streptococcus pneumoniae through identifying variations in cytokine level, reflecting severity in inflammatory response. Their modeling framework is based on the mathematical within-host dynamics of coinfection with influenza A virus and Streptococcus pneumoniae developed in Smith et al. Results from their study showed that Streptococcus pneumoniae may be a risk factor for COPD exacerbations. It further showed that the day of secondary Streptococcus pneumoniae infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads. Cox [ 73 ] developed a system of ODEs to investigates how COPD can be caused by sustained exposure to cigarette smoke CS or other pro-inflammatory agents. The ODEs represent possible quantitative causal relations among key variables, such as alveolar macrophages and neutrophil levels in the lung, levels of tissue-deteriorating enzymes, and rates of apoptosis, repair, and net destruction of the alveolar wall [ 73 ]. Their model explains irreversible degeneration of lung tissue as resulting from a cascade of positive feedback loops: a macrophage inflammation loop, a neutrophil inflammation loop, and an alveolar epithelial-cell apoptosis loop; and illustrates how to simplify and make more understandable, the main aspects of the very complex dynamics of COPD initiation and progression, as well as how to predict the effects on risk of interventions that affect specific biological responses. An advantage of their model is the possibility of quantifying how interventions that change the times to activate different major feedback loops will affect the time course of the disease [ 73 ]. Liquid hyperabsorption, airway surface dehydration, and impaired mucociliary clearance is prevalent in CF lung disease [ ]. Markovetz et al. Their model captures the mucociliary clearance and liquid dynamics of the hyperabsorptive state in CF airways and the mitigation of that state by hypertonic saline treatment. Results from their study suggest that patients with CF have regions of airway with diminished mucociliary clearance function that can be recruited with hypertonic saline treatment. Airway remodeling is a common factor in CF lung disease. Brown et al. The model focuses on relevant interactions among macrophages, fibroblasts, a pro-inflammatory cytokine TNF-α , an anti-inflammatory cytokine TGF-β1 , collagen deposition, and tissue damage. Numerical simulations of the model gives three distinct states that equate with 1 self-resolving inflammation and a return to baseline, 2 a pro-inflammatory process of localized tissue damage and fibrosis, and 3 elevated pro- and anti-inflammatory cytokines, persistent tissue damage, and fibrosis outcomes. These states depend on the degree and duration of exposure and are consistent with experimental results from histology sections of lung tissue from mice exposed to particulate matter [ ]. An advantage of their model is the ability to capture some of the important features of inflammation following exposure of the lung to particulate matter. In summary, mathematical models of inflammation have contributed to our knowledge of the mechanism of action in lung diseases. There is need to develop a unified approach for modeling lung diseases that accounts for the different phenomena occurring at different spatial levels. Models that link the interactions at the molecular, cellular and tissue-level would provide a systems perspective to the pathology of lung diseases. Lung inflammation is a complex process and its onset and progress depends on the coordinated interactions involving different proteins, networks, tissues and other organs e. Due to the large number of mediators involved in the inflammation process, it is often difficult to decipher the individual and collective control of mediators across the different spatial scales. The role of proteins, networks, tissues and other organs on local and systemic inflammation can be elicited using mathematical and computational models. Multiscale mechanistic models link cellular and molecular processes to tissue-level behavior during injury. Such models have the capability to provide invaluable insight into the system-level regulation of inflammation. Computational mechanistic models are well-suited for such problems and are useful in understanding system-level operations. They can be used to test different hypotheses formulated to investigate the changes at the molecular and cellular-level that lead to the onset and progress of inflammation. Excellent multiscale models of asthmatic airway hyper-responsiveness and airway constriction have been developed by Donovan [ ], Politi et al. There is need to extend these complex models to incorporate the dynamics of the inflammation process. Applications of digital image analysis in computational simulations may be utilized in studying how changes in tissue properties affect the expression and transport of inflammatory mediators across different spatial scales. Hybrid multiscale models couple continuum models and discrete models within different spatial scales [ 59 , 77 , , , , , , , , ]. Continuum models describing tissue-level behavior, may be coupled to agent-based models to describe the migration of immune cells such as macrophages, T-cells and B-cells to the site of injury [ 59 , 77 , , ]. Agent-based models can incorporate stochasticity that exists in cellular-level processes and is inherent in biological systems [ 76 , ]. Experimental biologists usually adopt a reductionist approach, which may fail to describe system-level behavior. However, mathematical and computational models are invaluable when used with experimental approaches and have the potential of helping further knowledge on the complex inflammation process. Aging represents a gradual deterioration of organization at the molecular, cellular, tissue, organ and system level of the body. Changes at the molecular and cellular-level would affect the working of the body at the tissue, organ and system-level and may impair the inflammation process leading to chronic inflammation or sepsis. The myriad inflammatory mediators involved in the inflammation process make it difficult to experimentally study age-related anomalies. Numerous studies have shown that low-grade inflammation is a common decimal in aging. Inflammaging is marked by a general increase in the production of pro-inflammatory cytokines and inflammatory markers [ ]. The mechanism by which the low-grade inflammation is activated remains unknown. Computational modeling is a powerful tool that could help unravel the complexity of chronic inflammation including age-related inflammation. Using mathematical models that accurately represent the lung, we can study the interactions across various biological scales and make predictions for future outcomes of existing interactions based on currently available experimental data, which might otherwise not be possible. Multiscale mechanistic models that couple cellular and molecular processes to tissue-level behavior could be implemented to test different hypotheses that explain how changes at the molecular and cellular-level may influence the onset and progress of chronic inflammation in aging subjects. Correlation between tissue properties, magnitude and duration of stress a tissue is exposed to and the molecular response during aging is necessary to understand inflammaging. Development and analysis of such models would provide insights into the process of aging and help physicians implement therapeutic strategies to address the aging process and treat diseases. Computational models have been developed to study the aging process [ , , , ]. Mc Auley and Mooney [ ] used a computational model to study lipid metabolism and aging. Weinberg et al. More information on models that have been developed within the last 50—60 years to study cellular aging can be found in the review by Witten [ ]. More research is needed to understand the aging process at the cellular, organ and system-level and computational modeling is a valuable tool that could be used to further our understanding of aging and age-related diseases. This paper reviews key mechanisms of inflammation in airway diseases. It discusses the role of mathematical and computational modeling in furthering our understanding of the complex inflammation mechanism in airway diseases. Results from experimental studies have greatly improved our knowledge of the cellular and molecular events that are involved in the acute inflammatory response to infection and tissue injury in many organs [ , , , , ]. Experimental studies usually use reductionist approach, so they may fail to describe system-level behavior accurately. Mathematical and computational models can be employed to study the interactions across various biological scales and make predictions for future outcomes of existing interactions based on currently available experimental data. We recommend that multiscale models should be implemented to test hypotheses that explain how changes at the molecular and cellular-levels may influence the onset and progress of chronic inflammation. Multiscale models could be employed to understand the tissue microenvironment effects on inflammation mechanism in young and aged lungs. Despite all conducted computational and experimental studies on lung inflammation mechanism, there is lack of details on molecular mechanisms and pathways that contribute to activation of low-grade inflammation and onset of chronic inflammation in lung. There is need for models that link the interactions at the molecular, cellular and tissue-levels to provide a systems perspective to the pathology of inflammatory mechanism in lung diseases. More research is needed to understand the mechanisms that produce acute or systemic chronic inflammation which occurs in many diseases such as autoimmune diseases, obesity, cardiovascular diseases, type 2 diabetes, among many others [ , , , ]. Ahmed AU. An overview of inflammation: mechanism and consequences. Frontiers in Biology. CAS Google Scholar. Ward P. Acute lung injury: how the lung inflammatory response works. Eur Respir Soc; ;s—23s. Article CAS Google Scholar. Lee G, Walser TC, Dubinett SM. Chronic inflammation, chronic obstructive pulmonary disease, and lung cancer. Curr Opin Pulm Med. Article CAS PubMed Google Scholar. Takeuchi O, Akira S. Pattern recognition receptors and inflammation. Ortega-Gómez A, Perretti M, Soehnlein O. Resolution of inflammation: an integrated view. EMBO Mol Med. Article CAS PubMed PubMed Central Google Scholar. Maskrey BH, Megson IL, Whitfield PD, Rossi AG. Mechanisms of resolution of inflammation. Arterioscler Thromb Vasc Biol. Najar M, Krayem M, Merimi M, Burny A, Meuleman N, Bron D, et al. Insights into inflammatory priming of mesenchymal stromal cells: functional biological impacts. Inflamm Res. Article PubMed Google Scholar. Lumb AB. Amsterdam: Elsevier Health Sciences; Google Scholar. Tripathi P, Aggarwal A. NF-kB transcription factor: a key player in the generation of immune response. Curr Sci Bangalore. Grommes J, Soehnlein O. Contribution of neutrophils to acute lung injury. Mol Med. Lee I-T, Yang C-M. Inflammatory signalings involved in airway and pulmonary diseases. Mediat Inflamm. Moldoveanu B, Otmishi P, Jani P, Walker J, Sarmiento X, Guardiola J, et al. Inflammatory mechanisms in the lung. J Inflamm Res. CAS PubMed Google Scholar. Nelson RJ. Seasonal immune function and sickness responses. Trends Immunol. Nelson RJ, Demas GE. Seasonal changes in immune function. Q Rev Biol. Colgan SP, Curtis VF, Campbell EL. The inflammatory tissue microenvironment in IBD. Inflamm Bowel Dis. Dantzer R. Cytokine-induced sickness behavior: where do we stand? Brain Behav Immun. Hotamisligil GS. Inflammation and metabolic disorders. Chung K, Adcock I. Multifaceted mechanisms in COPD: inflammation, immunity, and tissue repair and destruction. Eur Respir J. Hartupee J, Mann DL. Role of inflammatory cells in fibroblast activation. J Mol Cell Cardiol. Baylis D, Bartlett DB, Patel HP, Roberts HC. Understanding how we age: insights into inflammaging. Longev Healthspan. Article PubMed PubMed Central Google Scholar. Libby P. Inflammatory mechanisms: the molecular basis of inflammation and disease. Nutr Rev. Brodland GW. How computational models can help unlock biological systems. Semin Cell Dev Biol. Vodovotz Y. Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience. Interface Focus. Fletcher AG, Osterfield M, Baker RE, Shvartsman SY. Vertex models of epithelial morphogenesis. Biophys J. Fletcher AG, Cooper F, Baker RE. Mechanocellular models of epithelial morphogenesis. Philos Trans R Soc Lond B Biol Sci. George UZ, Bokka KK, Warburton D, Lubkin SR. Quantifying stretch and secretion in the embryonic lung: Implications for morphogenesis. Mech Dev. Lubkin SR, Murray JD. A mechanism for early branching in lung morphogenesis. J Math Biol. Clément R, Douady S, Mauroy B. Branching geometry induced by lung self-regulated growth. Phys Biol. Iber D, Menshykau D. The control of branching morphogenesis. Open Biol. Varner VD, Nelson CM. Computational models of airway branching morphogenesis. Tran K, Smith NP, Loiselle DS, Crampin EJ. A metabolite-sensitive, thermodynamically constrained model of cardiac cross-bridge cycling: implications for force development during ischemia. Washio T, Okada JI, Sugiura S, Hisada T. Approximation for cooperative interactions of a spatially-detailed cardiac sarcomere model. Cell Mol Bioeng. Dewan S, McCabe KJ, Regnier M, McCulloch AD. Insights and challenges of multi-scale modeling of sarcomere mechanics in cTn and Tm DCM mutants-genotype to cellular phenotype. Front Physiol. Constantino J, Hu Y, Trayanova NA. A computational approach to understanding the cardiac electromechanical activation sequence in the normal and failing heart, with translation to the clinical practice of CRT. Prog Biophys Mol Biol. Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng Online. Trayanova NA. Whole-heart modeling: applications to cardiac electrophysiology and electromechanics. Circ Res. Friedrich J, Lengyel M. Goal-directed decision making with spiking neurons. J Neurosci. Rustichini A, Conen KE, Cai X, Padoa-Schioppa C. Optimal coding and neuronal adaptation in economic decisions. Nat Commun. Cumming BD, McElwain DL, Upton Z. A mathematical model of wound healing and subsequent scarring. J R Soc Interface. Flegg JA, Byrne HM, Flegg MB, McElwain DL. Wound healing angiogenesis: the clinical implications of a simple mathematical model. J Theor Biol. Sherratt JA, Dallon JC. Theoretical models of wound healing: past successes and future challenges. C R Biol. Patel AA, Gawlinski ET, Lemieux SK, Gatenby RA. A cellular automaton model of early tumor growth and invasion. Powathil G, Kohandel M, Sivaloganathan S, Oza A, Milosevic M. |
Airway inflammation -
Lung inflammation may be due to infection, disease, injury, or exposure to environmental toxins or irritants.
Lung inflammation can make it harder to breathe. Over time, if the inflammation doesn't improve, it can damage your lungs. Diagnosing lung inflammation may involve a review of your medical history, a physical exam, blood test, imaging tests, and procedures to measure how well your lungs and heart are working.
Treatment is typically focused on treating the underlying cause. If needed, oral or inhaled steroids can help temper the inflammation, while oxygen therapy can help if you have trouble breathing.
Surgery is needed in some cases. Chen L, Deng H, Cui H, et al. Inflammatory responses and inflammation-associated diseases in organs. Chalmers S, Khawaja A, Wieruszewski PM, Gajic O, Odeyemi Y. Diagnosis and treatment of acute pulmonary inflammation in critically ill patients: The role of inflammatory biomarkers.
World J Crit Care Med Sep 11;8 5 — Sarkar M, Niranjan N, Banyal PK. Mechanisms of hypoxemia. Lung India. Hill AT, Sullivan AL, Chalmers JD, et al. British Thoracic Society guideline for bronchiectasis in adults. Defnet AE, Hasday JD, Shapiro P.
Kinase inhibitors in the treatment of obstructive pulmonary diseases [published online ahead of print, Apr 30]. Curr Opin Pharmacol. Wong J, Magun BF, Wood LJ. Lung inflammation caused by inhaled toxicants: a review. Int J Chron Obstruct Pulmon Dis. National Heart, Lung, and Blood Institute.
Hypersensitivity pneumonitis. Calderaro A, Buttrini M, Farina B, Montecchini S, De Contol Fl, Chezzi C. Respiratory tract infections and laboratory diagnostic methods: a review with a focus on syndromic panel-based assays.
Microorganisms Sep;10 9 Confalonieri M, Salton F, Fabiano F. Acute respiratory distress syndrome. Eur Respir Rev. Horak F, Doberer D, Eber E, et al. Diagnosis and management of asthma - statement on the GINA Guidelines. Wien Klin Wochenschr. Centers for Disease Control and Prevention.
Chronic obstructive pulmonary disease. Popper H, Stacher-Priehse E, Brcic L, Nerlich A. Lung fibrosis in autoimmune diseases and hypersensitivity: how to separate these from idiopathic pulmonary fibrosis.
Rheumatol Int. Jeganathan N, Sathananthan M. The prevalence and burden of interstitial lung diseases in the USA. ERJ Open Res. Morley EJ, Johnson S, Leibner E, Shahid J. Emergency department evaluation and management of blunt chest and lung trauma trauma CME.
Emerg Med Pract. Giacalone VD, Dobosh BS, Gaggar A, Tirouvanziam R, Margaroli C. Immunomodulation in cystic fibrosis: why and how? Int J Mol Sci. Adler Y, Charron P, Imazio M, et al. Eur Heart J. Lee JS, Moon T, Kim TH, et al. Deep vein thrombosis in patients with pulmonary embolism: prevalence, clinical significance and outcome.
Vasc Specialist Int. American Lung Association. Lung cancer fact sheet. Scherer PM, Chen DL. Imaging pulmonary inflammation. J Nucl Med. Using oxygen at home. National Cancer Institute. New Patients. If this is an emergency, call or visit the nearest emergency room.
Chelsea Urgent Care Chestnut Hill Urgent Care Dedham Urgent Care Quincy Urgent Care Walk-ins are welcome or reserve your spot online. Close Home If you have asthma, the inside walls of your airways are swollen. The inflammation makes the airways very sensitive, and they tend to react strongly to things that you are allergic to or find irritating.
Bronchiectasis is a condition in which the bronchial tubes airways are thickened due to long-term inflammation and scarring. This leads to a build-up of mucus, frequent lung infections, and eventually, a decline in lung function. Bronchitis is an inflammation of the airways.
Chronic bronchitis is a serious condition when the bronchial tubes, which carry air to and from your lungs, become inflamed over a long period of time. This can cause a chronic cough. Chronic Obstructive Pulmonary Disease COPD is a group of lung diseases, including emphysema and chronic bronchitis.
These diseases make breathing difficult. COPD can make it hard to catch your breath. Because breathing takes such hard work when you have COPD, you can become exhausted. Cystic fibrosis is an inherited disorder resulting from a gene mutation a flaw in a gene.
Cystic fibrosis can have severe complications, but ways to manage the disease have improved greatly over the past few decades.
Emphysema happens when the air sacs in the lungs become damaged. The air sacs, called alveoli, inflate and deflate as you breathe, exchanging the oxygen in the lungs. When the alveoli are damaged, it is difficult to breathe.
Pulmonary hypertension is high blood pressure in the arteries in your lungs. The presence of airway inflammation in asthma was detected in the nineteenth century.
Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production and airway-wall remodeling [ 88 ]. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma [ 89 ].
Airway epithelial cells activate epithelial TLRs to recognize patterns of inflammatory stimuli in allergic disease [ 90 , 91 ]. Then resolution process starts where antigen presenting cells APCs endocytose inhaled allergens, present them to naïve T cells, and activate mast cells by crosslinking surface-bound IgE molecules to release several bronchoconstrictor mediators, including cysteinyl leukotrienes and prostaglandin D 2 [ 92 ].
Myeloid dendritic cells process allergens and release CCL17 and CCL22, which act on CCR4 to attract TH 2 cells. TH 2 cells release IL-4 and IL, IL-5 and IL-9 and have a central role in the pathogenesis of allergic asthma [ 93 ]. Epithelial cells release CCL11, which recruits eosinophils via CCR3.
Eosinophils secrete a wide array of cytotoxic and pro-inflammatory mediators [ 94 ]. LXA4 inhibits NK-cell cytotoxicity and increases eosinophil-induced apoptosis by NK cells, and inhibits interleukin IL release by ILC2s. In addition, eosinophils may contribute to resolution of inflammation in asthma and produce pro-resolving lipid mediators PD1 and RvE3.
Where PD1 and IL secrete interleukin IL and promote macrophage activation [ 96 ]. Patients with asthma may have a defect in regulatory T TReg cells, which may lead to further TH 2 -cell proliferation [ 97 ].
TGF-β is introduced as a main regulator of remodeling in the airways of asthmatics [ 93 ]. Platelet-derived growth factor PDGF promotes fibroblasts and ASM proliferation in the asthmatic lung [ 98 , 99 ].
Injured epithelial cells releases stem-cell factor SCF which promote myofibroblasts differentiation and induce structural changes throughout airway-wall remodeling [ ] Fig. Increase in angiogenesis, pro-angiogenic cytokine vascular endothelial growth factor VEGF and its receptors [ , ] and dysregulation in production of extracellular matrix metalloproteinase MMPs [ ] have been reported as proteinases responsible for the degradation of the extracellular matrix during tissue remodeling in asthmatic airways [ ].
Inflammatory response in asthma: TLRs recognize patterns of allergens Pattern recognition. Myeloid DC process allergens and release CCL17 and CCL22 to attract TH2 to injured region. IgE molecules sensitize mast cells to release cysteinyl leukotrienes and PGD2. Damaged epithelial cells release CCL11 to recruit eosinophils which attract more proinflammatory mediators to the damaged region.
Eosinophils produce PD1 and PD1 secrets IL10 which promotes macrophage activation Resolution. Damaged epithelial cells releases SCF to activate myofibroblast to repair damaged epithelial cells.
TLRs: Toll-like-receptors, CCL: CC-chemokine ligand, TH2: T helper cells type 2, IgE: immunoglobulin E, PGD2: prostaglandin D2, SCF: stem-cell factor, PD1: pro-resolving lipid mediators. Apart from COPD and asthma, cystic fibrosis CF is an inherited chronic disease that affects the lungs of about 70, children and adults worldwide 30, in the US.
Mutation of the CF transmembrane conductance regulator CFTR gene results in CF [ ]. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens [ ].
CFTR deficiency is associated with altered fluid and electrolyte homeostasis of epithelial cells and leads to unusually thick and viscose mucus that clogs small airways, and contributes to the development of persistent lung inflammation and an increased risk of lung infections [ ].
Pathogen-associated molecular patterns PAMPs activate TLR-MyD88 signaling to increase NF-κB signaling [ ]. TLRs and bacterial colonization activate neutrophils, macrophages and NF-κB-mediated inflammatory response to initiate the pathological process.
Activated NF-κB result in production of inflammatory cytokines, such as IL-8 and High mobility group box 1 HMGB1 protein, and recruitment of polymorphonuclear leukocyte PMNs. HMGB1 increases pro-inflammatory cytokine expression via its cellular receptors. Increase in pro-inflammatory cytokine expression promotes toll-like receptor TLR-2 and TLR-4 production [ ].
Intracellular TLR4 activation prevents interferon regulatory factor 3 IRF3 translocation to the nucleus to activate type I IFN gene products, which are required for the activation of dendritic cells DCs and the clearance of some cystic fibrosis-related pathogens [ ]. TH 2 skews the inflammatory environment in cystic fibrosis.
Abundant IL-8 stimulates airway epithelial and smooth muscle remodeling and induces greater contraction in CF airway smooth muscle than non-cystic fibrosis airway smooth muscle, which results in airway hyper-responsiveness [ ].
Decreased function of peroxisome proliferator-activated receptor-g PPARg associates with low levels of carbonic anhydrases that contribute to increased mucus viscosity and results in enhanced pro-inflammatory signaling and cytokine secretion in CF cells Fig.
High numbers of neutrophils at the site of chronic infection and decreased neutrophil apoptosis, phagocytic capacity of macrophage and levels of pro-resolving mediators suggest an impaired inflammatory resolution that promotes sustained infection [ , ].
In addition, defective cilia function, increased mucus viscosity, hypoxia, free nutrients, damage to lung architecture, defective or decreased antimicrobials, TH 2 and TH 17 responses and ineffective cellular mediators, changes in virulence and direct downregulation of antimicrobial pathways contribute to infection and pulmonary decline in cystic fibrosis [ ].
Airway remodeling in CF is presented as secondary to infection and inflammation [ ]. MMPs are involved in tissue breakdown and repair and MMP-8 and MMP-9, which are mainly derived from neutrophils in the lower respiratory tract, are the most important group of endopeptidases in CF remodeling [ , , ].
In addition, TGFα is thought to play a role in the regulation of airways remodeling with CF [ ] and TH2 cytokines especially IL has been found during cycles of epithelial injury and repair of CF airways [ ].
Inflammatory response in CF: TLRs recognize PAMPs pattern recognition. TLRs and bacterial colonization activate inflammatory mediators like; neutrophils, macrophages and NF-κB.
NF-κB produce IL8 and HMGB1 and recruit monocyte. Type I IFN activates DCs to clear cystic fibrosis-related pathogens and TH2 skew CF Resolution. IL8 stimulate damaged epithelial cells Tissue remodeling.
PAMPs: Pathogen-associated molecular patterns, TLRs: Toll-like-receptors, NF-κB: nuclear factor kappa-light-chain-enhancer of activated B-cells, IL proinflammatory cytokines interleukin 8, HMGB1: High mobility group box 1, IFN: Interferon, DCs: dendritic cells.
The contribution of harmful stimuli and inflammatory mediators in pattern recognition, resolution and remodeling process are classified for discussed disease condition in Table 1. All vital organs lose their function with age. Human lung matures up to age 20—25 years and will start to lose functionality after about 35 years.
Breathing existent pollutants in the environment or exogenous oxidants in young or healthy individuals causes cellular damage in lung tissue [ ]. If the damage is too extreme, cells would sustain senescence to prevent oncogenic changes.
Senescence signaling activates stem cells to replace damaged cells [ ]. An increase in senescent cells and corresponding senescence-associated secretory phenotype can induce further inflammation, alveolar destruction, endothelial dysfunction [ ].
In addition, excessive ROS will increase damage to cells by a defective repair mechanism in the elderly. In classic aging pathways, growth factor signaling activates PI3K, phospho-AKT and mTOR, which accelerate aging [ , , ]. Inhibition of mTOR signaling extends life span [ ].
Antiaging molecules such as phosphatase and tensin homolog PTEN inhibits PI3K and AMPK prevent hyperactivation of the mTOR signaling pathway.
Sirtuins SIRT1 and SIRT6 upregulate FOXO3A and promotes autophagy [ , , ]. Defective mechanism of positive regulators SIRT1, SIRT6, PTEN, and AMPK will induce cytokine, chemokine, and ribosomal synthesis and secrete growth factors favoring cell proliferation and growth Fig.
COPD is identified with an elevated ROS level and ROS are able to change biological molecules, signaling pathways and antioxidant molecule function. A decrease in the level of PTEN and SIRT1 in COPD would lead to activation of the mTOR-aging pathway via PI3K activation by ROS.
This results in reduced antioxidant defense by FOXO3A inhibition and a loss of autophagy. Loss of autophagy can prevent the clearance of defective mitochondria and further increase ROS production [ ]. Defective autophagy decreases immune response to bacteria and cellular homoeostasis in COPD.
Inflammaging mechanism in airways: ROS increase damage in airways epithelial cells. Growth factor signaling activate PI3K, phospho-AKT and mTOR signaling which accelerate aging in airways.
PTEN and AMPK inhibit discussed factors that can lead to increase in life span. SIRT1 upregulate FOXO3A that functions as a trigger for apoptosis of damaged cells. SIRT1 also promotes autophagy. Effecting mechanism of SIRT1 will induce cytokine, chemokine and ribosomal synthesis and secrete growth factors favoring cell proliferation and growth.
ROS: Reactive oxygen species, PI3K: Phosphoinositide 3-kinase, mTOR: mechanistic target of rapamycin, PTEN: phosphatase and tensin homolog , FOXO3: Forkhead box O3, SIRT1: Sirtuin 1.
Asthma and COPD occur due to chronic inflammation of the airways. However, the mechanism of action is different. In asthma, mast cells, eosinophils and CD4 T lymphocytes represent the predominant cell types in the inflammatory process.
In COPD, neutrophils, macrophages and CD8 T lymphocytes are the predominant cell types in the inflammatory process [ , , ].
In CF, neutrophils are the predominant cell types in the inflammatory process and they release oxidants, proteases, and elastase that causes respiratory exacerbations [ ]. Patients with COPD exhibit reduced airway caliber because of cell damage induced by external toxic agents such as cigarette smoke [ , , ].
There is a positive correlation between inflammation intensity and COPD severity. At the final stages of the disease, the inflammatory process becomes very intense. The intensity of inflammation may be combated through the application of anti-inflammatory therapies [ , ].
Anti-inflammatory therapies have the potential of combating CF and asthma, but care must be taken to avoid suppressing critical elements of the inflammatory response, which in turn may worsen the disease [ , ]. Inflammatory responses are numerous and include transport of plasma from the blood into the injured tissues, biochemical signaling cascades, and the mobilization of cytokines, such as interleukins [ ].
The complexity of the inflammation process suggests that strategies for developing effective and efficient therapeutic interventions for combating airway diseases would greatly benefit from predictions obtained from mathematical and computational modeling.
For example, correlation of imaging measurements with disease severity would be useful in understanding the pathophysiology behind different airway diseases and guide the development of therapeutic interventions. In addition, computational models of lung tissue may aid in the study of lung tissue mechanics during an inflammatory process.
Aging is a complex process that occurs in different cell types and tissues and is controlled by environmental, genetic, stochastic, epigenetic events and their long-term interactions [ ].
Inflammaging is associated with most of the age-related diseases but its precise etiology and potential causal role remain largely unknown [ ].
An understanding of the mechanism of lung inflammaging is therefore important in determining whether treatments that modulate inflammaging may be beneficial in combating age-related airway diseases. Mathematical models represent the essential characteristics of a system as a set of mathematical equations.
They are useful in testing different hypotheses about the working of a system and their utility is established by matching their outputs with experimental observations.
The study of inflammation is somewhat difficult because of the myriad inflammatory mediators involved and their effects on target tissues.
The coordinated functions of these mediators and their multiple modes of regulation remain largely unknown [ ]. Mathematical models are vital tools that would help in deciphering the dynamic behavior of these networks. Analysis of a model often provides insights into the underlying mechanisms for the regulation of the system, and this may drive formulation of new hypotheses that would in turn lead to new rounds of experiments [ ].
Mechanistic models of inflammation may be classified as discrete-time or continuous-time models. Discrete-time models describe the changes in the system at certain time points with no information of its behavior at intervals between these time points. Discrete-time models such as agent-based models ABMs represent an inflammatory mediator as an agent i.
Agent simulations are governed by local interactions among agents and can incorporate the stochasticity of the inflammatory process. Continuous-time models represent the system as continuous over time and usually manifest as differential equations [ ].
Most continuum models of inflammation use ordinary differential equations ODEs to describe the dynamics of an inflammation response. Some models use partial differential equations PDEs in place of ODEs or a combination of both [ 54 , ].
Thus, ODEs may be better suited for modeling the inflammation process over several days. However, to study the spatial distribution of inflammatory mediators and their effect on the progress of an inflammation process, PDE models would be a better option.
ODEs and PDEs that model the complex dynamics of an inflammatory response are mostly nonlinear, and their exact or analytical solutions are difficult and sometimes impossible to obtain.
The application of techniques for solving differential equations based on numerical approximations is required for finding approximate solutions for nonlinear differential equations. Numerical algorithms for the numerical approximation of nonlinear differential equations produce computational models that are easily simulated on computers to obtain approximate solutions.
Mathematical and computational models have been developed to study the physiological functioning of the lungs and relatively few have focused on obstructive lung diseases [ 69 , ].
Most of the models of obstructive lung diseases do not incorporate the effect of inflammation [ 72 , 73 , 74 , 75 ].
Chernyavsky et al. They present a mathematical model that describes qualitatively the growth dynamics of airway smooth muscle cells over short and long terms in the normal and inflammatory environments often observed in asthma.
Their model predicts that long-term airway smooth muscle growth is influenced by the inflammation resolution speed, the inflammation magnitude, and the frequency of inflammatory episodes.
Their model highlights the importance of the resolution speed of inflammation in the long-term management of asthma. A limitation of their model is that it does not account for the mechanical interaction of the cells between each other and with the extracellular matrix that could affect the growth and apoptosis rates as well as the total capacity of an airway wall.
In addition, the model neglects the spatially heterogeneous and anisotropic growth observed in micrographs and cell hypertrophy [ , ].
A study by Lee et al. Their model describes two types of macrophages that play complementary roles in fighting viral infections: classical-activated macrophages and alternative-activated macrophages. Classical-activated macrophages destroy infected cells and tissues to remove viruses, while alternative-activated macrophages repair damaged tissues.
They describe populations of viruses and airway epithelial cells, concentrations of cytokines such as IFN- β and IL-4 and enzymes such as iNOS and arginase-1 secreted by the cells. After an infection, the airway epithelial cells are directly infected by the virus and the type I interferon they produce.
Airway epithelial cells are defined to be in two states, dormant and activated. Dormant epithelial cells transition to the activated state upon exposure to virus. After epithelial cells have been infected and begun to respond, alveolar macrophages take control of the defense system.
The balance between classically activated macrophages and alternatively activated macrophages is controlled by the cytokines IFNb and IL4. They investigate how viral infections alter the balance of the alveolar macrophage system and potentially trigger asthma exacerbations.
In particular, they investigate how respiratory viral infection changes the balance between classical-activated macrophages and alternative-activated macrophages and how this response differs in hosts with asthma-like conditions, and how those differences can lead to accentuated symptoms.
Their simulation results show that a higher viral load or longer duration of infection provokes a stronger immune response from the macrophage system. Their result also showed that the differences in response to respiratory viral infection in normal and asthmatic subjects skews the system toward a response that generates more severe symptoms in asthmatic patients.
Thus, respiratory viral infection can aggravate symptoms in asthmatic patients [ ]. Kim et al. Airway exposure levels of lipopolysaccharide LPS determined type I versus type II helper T cell-induced experimental asthma.
While high LPS levels induce Th1-dominant responses, low LPS levels derive Th2 cell-induced asthma. Their model describes the behaviors of T cells Th0, Th1, Th2 and macrophages and regulatory molecules IFN-γ, IL-4, IL, TNF-α in response to high, intermediate, and low levels of LPS.
The simulation results showed how variations in the levels of injected LPS affect the development of Th1 or Th2 cell responses through differential cytokine induction. A few mathematical models have also been developed to study COPD.
An example is the model presented by Cheng et al. The model investigated coinfection interactions between influenza and Streptococcus pneumoniae through identifying variations in cytokine level, reflecting severity in inflammatory response.
Their modeling framework is based on the mathematical within-host dynamics of coinfection with influenza A virus and Streptococcus pneumoniae developed in Smith et al.
Results from their study showed that Streptococcus pneumoniae may be a risk factor for COPD exacerbations. It further showed that the day of secondary Streptococcus pneumoniae infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads.
Cox [ 73 ] developed a system of ODEs to investigates how COPD can be caused by sustained exposure to cigarette smoke CS or other pro-inflammatory agents. The ODEs represent possible quantitative causal relations among key variables, such as alveolar macrophages and neutrophil levels in the lung, levels of tissue-deteriorating enzymes, and rates of apoptosis, repair, and net destruction of the alveolar wall [ 73 ].
Their model explains irreversible degeneration of lung tissue as resulting from a cascade of positive feedback loops: a macrophage inflammation loop, a neutrophil inflammation loop, and an alveolar epithelial-cell apoptosis loop; and illustrates how to simplify and make more understandable, the main aspects of the very complex dynamics of COPD initiation and progression, as well as how to predict the effects on risk of interventions that affect specific biological responses.
An advantage of their model is the possibility of quantifying how interventions that change the times to activate different major feedback loops will affect the time course of the disease [ 73 ]. Liquid hyperabsorption, airway surface dehydration, and impaired mucociliary clearance is prevalent in CF lung disease [ ].
Markovetz et al. Their model captures the mucociliary clearance and liquid dynamics of the hyperabsorptive state in CF airways and the mitigation of that state by hypertonic saline treatment.
Results from their study suggest that patients with CF have regions of airway with diminished mucociliary clearance function that can be recruited with hypertonic saline treatment.
Airway remodeling is a common factor in CF lung disease. Brown et al. The model focuses on relevant interactions among macrophages, fibroblasts, a pro-inflammatory cytokine TNF-α , an anti-inflammatory cytokine TGF-β1 , collagen deposition, and tissue damage.
Numerical simulations of the model gives three distinct states that equate with 1 self-resolving inflammation and a return to baseline, 2 a pro-inflammatory process of localized tissue damage and fibrosis, and 3 elevated pro- and anti-inflammatory cytokines, persistent tissue damage, and fibrosis outcomes.
These states depend on the degree and duration of exposure and are consistent with experimental results from histology sections of lung tissue from mice exposed to particulate matter [ ]. An advantage of their model is the ability to capture some of the important features of inflammation following exposure of the lung to particulate matter.
In summary, mathematical models of inflammation have contributed to our knowledge of the mechanism of action in lung diseases. There is need to develop a unified approach for modeling lung diseases that accounts for the different phenomena occurring at different spatial levels.
Models that link the interactions at the molecular, cellular and tissue-level would provide a systems perspective to the pathology of lung diseases. Lung inflammation is a complex process and its onset and progress depends on the coordinated interactions involving different proteins, networks, tissues and other organs e.
Due to the large number of mediators involved in the inflammation process, it is often difficult to decipher the individual and collective control of mediators across the different spatial scales. The role of proteins, networks, tissues and other organs on local and systemic inflammation can be elicited using mathematical and computational models.
Multiscale mechanistic models link cellular and molecular processes to tissue-level behavior during injury. Such models have the capability to provide invaluable insight into the system-level regulation of inflammation. Computational mechanistic models are well-suited for such problems and are useful in understanding system-level operations.
They can be used to test different hypotheses formulated to investigate the changes at the molecular and cellular-level that lead to the onset and progress of inflammation. Excellent multiscale models of asthmatic airway hyper-responsiveness and airway constriction have been developed by Donovan [ ], Politi et al.
There is need to extend these complex models to incorporate the dynamics of the inflammation process. Applications of digital image analysis in computational simulations may be utilized in studying how changes in tissue properties affect the expression and transport of inflammatory mediators across different spatial scales.
Hybrid multiscale models couple continuum models and discrete models within different spatial scales [ 59 , 77 , , , , , , , , ]. Continuum models describing tissue-level behavior, may be coupled to agent-based models to describe the migration of immune cells such as macrophages, T-cells and B-cells to the site of injury [ 59 , 77 , , ].
Agent-based models can incorporate stochasticity that exists in cellular-level processes and is inherent in biological systems [ 76 , ]. Experimental biologists usually adopt a reductionist approach, which may fail to describe system-level behavior.
However, mathematical and computational models are invaluable when used with experimental approaches and have the potential of helping further knowledge on the complex inflammation process. Aging represents a gradual deterioration of organization at the molecular, cellular, tissue, organ and system level of the body.
Changes at the molecular and cellular-level would affect the working of the body at the tissue, organ and system-level and may impair the inflammation process leading to chronic inflammation or sepsis.
The myriad inflammatory mediators involved in the inflammation process make it difficult to experimentally study age-related anomalies. Numerous studies have shown that low-grade inflammation is a common decimal in aging.
Inflammaging is marked by a general increase in the production of pro-inflammatory cytokines and inflammatory markers [ ]. The mechanism by which the low-grade inflammation is activated remains unknown. Computational modeling is a powerful tool that could help unravel the complexity of chronic inflammation including age-related inflammation.
Using mathematical models that accurately represent the lung, we can study the interactions across various biological scales and make predictions for future outcomes of existing interactions based on currently available experimental data, which might otherwise not be possible.
Multiscale mechanistic models that couple cellular and molecular processes to tissue-level behavior could be implemented to test different hypotheses that explain how changes at the molecular and cellular-level may influence the onset and progress of chronic inflammation in aging subjects.
Correlation between tissue properties, magnitude and duration of stress a tissue is exposed to and the molecular response during aging is necessary to understand inflammaging. Development and analysis of such models would provide insights into the process of aging and help physicians implement therapeutic strategies to address the aging process and treat diseases.
Computational models have been developed to study the aging process [ , , , ]. Mc Auley and Mooney [ ] used a computational model to study lipid metabolism and aging. Weinberg et al.
More information on models that have been developed within the last 50—60 years to study cellular aging can be found in the review by Witten [ ].
More research is needed to understand the aging process at the cellular, organ and system-level and computational modeling is a valuable tool that could be used to further our understanding of aging and age-related diseases.
This paper reviews key mechanisms of inflammation in airway diseases. It discusses the role of mathematical and computational modeling in furthering our understanding of the complex inflammation mechanism in airway diseases.
Results from experimental studies have greatly improved our knowledge of the cellular and molecular events that are involved in the acute inflammatory response to infection and tissue injury in many organs [ , , , , ].
Experimental studies usually use reductionist approach, so they may fail to describe system-level behavior accurately. Mathematical and computational models can be employed to study the interactions across various biological scales and make predictions for future outcomes of existing interactions based on currently available experimental data.
We recommend that multiscale models should be implemented to test hypotheses that explain how changes at the molecular and cellular-levels may influence the onset and progress of chronic inflammation.
Multiscale models could be employed to understand the tissue microenvironment effects on inflammation mechanism in young and aged lungs.
Despite all conducted computational and experimental studies on lung inflammation mechanism, there is lack of details on molecular mechanisms and pathways that contribute to activation of low-grade inflammation and onset of chronic inflammation in lung.
There is need for models that link the interactions at the molecular, cellular and tissue-levels to provide a systems perspective to the pathology of inflammatory mechanism in lung diseases.
More research is needed to understand the mechanisms that produce acute or systemic chronic inflammation which occurs in many diseases such as autoimmune diseases, obesity, cardiovascular diseases, type 2 diabetes, among many others [ , , , ].
Ahmed AU. An overview of inflammation: mechanism and consequences. Frontiers in Biology. CAS Google Scholar. Ward P. Acute lung injury: how the lung inflammatory response works.
Eur Respir Soc; ;s—23s. Article CAS Google Scholar. Lee G, Walser TC, Dubinett SM. Chronic inflammation, chronic obstructive pulmonary disease, and lung cancer. Curr Opin Pulm Med. Article CAS PubMed Google Scholar. Takeuchi O, Akira S. Pattern recognition receptors and inflammation.
Ortega-Gómez A, Perretti M, Soehnlein O. Resolution of inflammation: an integrated view. EMBO Mol Med. Article CAS PubMed PubMed Central Google Scholar. Maskrey BH, Megson IL, Whitfield PD, Rossi AG. Mechanisms of resolution of inflammation. Arterioscler Thromb Vasc Biol. Najar M, Krayem M, Merimi M, Burny A, Meuleman N, Bron D, et al.
Insights into inflammatory priming of mesenchymal stromal cells: functional biological impacts. Inflamm Res. Article PubMed Google Scholar. Lumb AB. Amsterdam: Elsevier Health Sciences; Google Scholar. Tripathi P, Aggarwal A. NF-kB transcription factor: a key player in the generation of immune response.
Curr Sci Bangalore. Grommes J, Soehnlein O. Contribution of neutrophils to acute lung injury. Mol Med. Lee I-T, Yang C-M. Inflammatory signalings involved in airway and pulmonary diseases. Mediat Inflamm. Moldoveanu B, Otmishi P, Jani P, Walker J, Sarmiento X, Guardiola J, et al.
Inflammatory mechanisms in the lung. J Inflamm Res. CAS PubMed Google Scholar. Nelson RJ. Seasonal immune function and sickness responses. Trends Immunol. Nelson RJ, Demas GE. Seasonal changes in immune function. Q Rev Biol. Colgan SP, Curtis VF, Campbell EL.
The inflammatory tissue microenvironment in IBD. Inflamm Bowel Dis. Dantzer R. Cytokine-induced sickness behavior: where do we stand?
Brain Behav Immun. Hotamisligil GS. Inflammation and metabolic disorders. Chung K, Adcock I. Multifaceted mechanisms in COPD: inflammation, immunity, and tissue repair and destruction. Eur Respir J. Hartupee J, Mann DL. Role of inflammatory cells in fibroblast activation.
J Mol Cell Cardiol. Baylis D, Bartlett DB, Patel HP, Roberts HC. Understanding how we age: insights into inflammaging. Longev Healthspan. Article PubMed PubMed Central Google Scholar. Libby P. Inflammatory mechanisms: the molecular basis of inflammation and disease. Nutr Rev. Brodland GW. How computational models can help unlock biological systems.
Semin Cell Dev Biol. Vodovotz Y. Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience.
Interface Focus. Fletcher AG, Osterfield M, Baker RE, Shvartsman SY. Vertex models of epithelial morphogenesis. Biophys J. Fletcher AG, Cooper F, Baker RE. Mechanocellular models of epithelial morphogenesis. Philos Trans R Soc Lond B Biol Sci. George UZ, Bokka KK, Warburton D, Lubkin SR.
Quantifying stretch and secretion in the embryonic lung: Implications for morphogenesis. Mech Dev. Lubkin SR, Murray JD.
A mechanism for early branching in lung morphogenesis. J Math Biol. Clément R, Douady S, Mauroy B. Branching geometry induced by lung self-regulated growth. Phys Biol. Iber D, Menshykau D. The control of branching morphogenesis. Open Biol. Varner VD, Nelson CM. Computational models of airway branching morphogenesis.
Tran K, Smith NP, Loiselle DS, Crampin EJ. A metabolite-sensitive, thermodynamically constrained model of cardiac cross-bridge cycling: implications for force development during ischemia.
Washio T, Okada JI, Sugiura S, Hisada T. Approximation for cooperative interactions of a spatially-detailed cardiac sarcomere model. Cell Mol Bioeng. Dewan S, McCabe KJ, Regnier M, McCulloch AD. Insights and challenges of multi-scale modeling of sarcomere mechanics in cTn and Tm DCM mutants-genotype to cellular phenotype.
Front Physiol. Constantino J, Hu Y, Trayanova NA. A computational approach to understanding the cardiac electromechanical activation sequence in the normal and failing heart, with translation to the clinical practice of CRT.
Prog Biophys Mol Biol. Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications.
Biomed Eng Online. Trayanova NA. Whole-heart modeling: applications to cardiac electrophysiology and electromechanics. Circ Res. Friedrich J, Lengyel M. Goal-directed decision making with spiking neurons. J Neurosci.
Rustichini A, Conen KE, Cai X, Padoa-Schioppa C. Optimal coding and neuronal adaptation in economic decisions. Nat Commun. Cumming BD, McElwain DL, Upton Z. A mathematical model of wound healing and subsequent scarring.
J R Soc Interface. Flegg JA, Byrne HM, Flegg MB, McElwain DL. Wound healing angiogenesis: the clinical implications of a simple mathematical model. J Theor Biol. Sherratt JA, Dallon JC.
Theoretical models of wound healing: past successes and future challenges. C R Biol. Patel AA, Gawlinski ET, Lemieux SK, Gatenby RA.
A cellular automaton model of early tumor growth and invasion. Powathil G, Kohandel M, Sivaloganathan S, Oza A, Milosevic M. Mathematical modeling of brain tumors: effects of radiotherapy and chemotherapy.
Phys Med Biol. Anderson AR, Chaplain MA. Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull Math Biol.
Ifnlammation obstructive pulmonary disease COPD is a progressive syndrome of expiratory airflow Aiwray caused by chronic inflammation of the airways and lung parenchyma. The airway inflammatory response Airway inflammation COPD is initiated Beta-carotene for eyes smoking in the overwhelming majority of Airway inflammation, and Airway inflammation exposure inflajmation cigarette smoke initiates a Airway inflammation of events that Airway inflammation Aitway to central Airway inflammation, peripheral airways, and terminal Airay, leading inflammmation physiologic and clinical abnormalities. Although COPD shares some clinical features with asthma, another prevalent airway inflammatory disease, there are distinct differences in the phenotypic characteristics of airway inflammation between COPD and asthma. The eosinophil is the most prominent inflammatory cell in asthma, with mast cells, lymphocytes, and macrophages playing important but less prominent roles. In COPD the cellular composition of the airway inflammatory infiltrate differs, with neutrophils, macrophages, and lymphocytes assuming prominence and the eosinophil playing a minor role, except in the setting of exacerbations. The contrasting inflammatory phenotypes of asthma and COPD have important implications for clinical and physiologic manifestations of disease, as well as for therapy. Abstract Chronic obstructive pulmonary disease COPD is a progressive syndrome of expiratory airflow limitation caused by chronic inflammation of the airways and lung parenchyma.:max_bytes(150000):strip_icc()/chest-pain-common-potential-causes-1745274_FINAL-ecbc2bd3b0f0401c8cfd77840901720f.jpg "Airway inflammation")
Airway inflammation -
Chronic bronchitis is a serious condition when the bronchial tubes, which carry air to and from your lungs, become inflamed over a long period of time. This can cause a chronic cough.
Chronic Obstructive Pulmonary Disease COPD is a group of lung diseases, including emphysema and chronic bronchitis. These diseases make breathing difficult. COPD can make it hard to catch your breath.
Because breathing takes such hard work when you have COPD, you can become exhausted. Cystic fibrosis is an inherited disorder resulting from a gene mutation a flaw in a gene. Cystic fibrosis can have severe complications, but ways to manage the disease have improved greatly over the past few decades.
Emphysema happens when the air sacs in the lungs become damaged. The air sacs, called alveoli, inflate and deflate as you breathe, exchanging the oxygen in the lungs.
When the alveoli are damaged, it is difficult to breathe. Pulmonary hypertension is high blood pressure in the arteries in your lungs. Pulmonary hypertension is dangerous because it can result in heart failure. Interstitial lung disease ILD is a group of respiratory disorders that vary in severity and affect adults of all ages.
Because interstitial lung disease varies with respect to the severity of symptoms and prognosis, being diagnosed with ILD can be confusing and frightening to patients. There are two main types of lung cancer — small cell lung cancer and non-small cell lung cancer. Although the same techniques radiation therapy, chemotherapy, surgery are used to treat both, your treatment will be tailored depending on your diagnosis and clinical stage.
Ninety percent of lung cancers occur in individuals with a history of smoking cigarettes. Cigar and pipe smoke are potentially more dangerous; they are associated with a lung cancer risk twice that for cigarette smokers. Exposure to air pollution, radiation and industrial chemicals such as asbestos, arsenic, nickel and chromium may also increase your risk for developing lung cancer.
Mycobacterial diseases are infectious diseases caused by bacteria. TB refers to the bacteria Mycobacterium tuberculosis. MAI refers to Mycobacterium avium-intracellulare MAI , which is a term for two species of bacteria.
Pneumonia is a common infection that inflames the alveoli air sacs in one or both lungs. Bacteria, viruses or fungi cause the air sacs in the lungs to fill with fluid or pus. There are many different types of sleep disorders. You may find it hard to fall asleep or stay asleep insomnia.
Parasomnias such as excessive night terrors or nightmares and a number of other sleep disorders can impact your mood, productivity and health. Tracheobronchomalacia TBM is a condition of the airways that causes them to become weak and floppy and collapse with breathing.
Normally the central airways the trachea and bronchi remain open when you breathe. Search Submit Search. Find a Doctor. Such technologies will thus enable us to treat inflammation uniformly throughout the airways. Better lung deposition of the steroids may be achieved by modification of propellants to produce finer and slower moving aerosols.
The latest development is the introduction of chlorofluorocarbon-free hydrofluoroalkane propellants, which exhibit improved airway targeting when compared with beclomethasone in chlorofluorocarbon propellant [ 55 , 56 , 57 ].
Equivalent control of the disease and improvement in lung function have been reported, and studies have shown that this new formulation delivers extra-fine particles only 1. Leach et al. With hydrofluoroalkane-BDP, those investigators were able to demonstrate equivalent control of the disease at half the daily dose required for chlorofluorocarbon-BDP.
In addition, Marshall et al. These radiolabelled drug deposition studies must be interpreted with caution, however, because the labelling process itself may alter the distribution properties of the inhaled steroid particles.
There is accumulating evidence to suggest that airway inflammation occurs in all parts of the airway. Although the clinical significance of small airways in asthma is not yet known, it is possible that poorly controlled inflammation in peripheral airways, which are not penetrated by conventional inhaled steroids, may contribute to accelerated decline in lung function and airway remodelling — the hallmarks of asthma.
The introduction of high-resolution computed tomography allows assessment of the morphological changes resulting from air trapping and regional hyperinflation in the small airways that are associated with dysfunction too subtle to be identified with lung function testing alone [ 60 ].
This novel, noninvasive imaging technique may prove to be important not only in the diagnosis of small airway inflammation, but also in helping us move toward a better understanding of the role of the small airways in the pathogenesis of allergic asthma.
Curschmann H: Exudative bronchiolitis and its relationship with asthma nervosa [in German]. Dtsch Arch Klin Med. Google Scholar. Djukanovic R, Wilson JW, Lai CK, Holgate ST, Howarth PH: The safety aspects of fiberoptic bronchoscopy, bronchoalveolar lavage, and endobronchial biopsy in asthma.
Am Rev Respir Dis. Article PubMed Google Scholar. Am J Respir Cell Mol Biol. Hamid Q, Song Y, Kotsimbos TC, Minshall E, Bai TR, Hegele RG, Hogg JC: Inflammation of small airways in asthma. J Allergy Clin Immunol. Minshall EM, Hogg JC, Hamid QA: Cytokine mRNA expression in asthma is not restricted to the large airways.
Taha RA, Minshall EM, Miotto D, Shimbara A, Luster A, Hogg JC, Hamid QA: Eotaxin and monocyte chemotactic protein-4 mRNA expression in small airways of asthmatic and nonasthmatic individuals. Carroll N, Cooke C, James A: The distribution of eosinophils and lymphocytes in the large and small airways of asthmatics.
Eur Respir J. Faul JL, Tormey VJ, Leonard C, Burke CM, Farmer J, Horne SJ, Poulter LW: Lung immunopathology in cases of sudden asthma death. Christodoulopoulos P, Leung DY, Elliott MW, Hogg JC, Muro S, Toda M, Laberge S, Hamid QA: Increased number of glucocorticoid receptor-beta-expressing cells in the airways in fatal asthma.
Kraft M, Djukanovic R, Wilson S, Holgate ST, Martin RJ: Alveolar tissue inflammation in asthma. Am J Resp Crit Care Med. Kraft M, Martin RJ, Wilson S, Djukanovic R, Holgate ST: Lymphocyte and eosinophil influx into alveolar tissue in nocturnal asthma.
Am J Respir Crit Care Med. Carroll N, Carello S, Cooke C, James A: Airway structure and inflammatory cells in fatal attacks of asthma. Synek M, Anto JM, Beasley R, Frew AJ, Halloway L, Lampe FC, Lloreta JL, Sunyer J, Thornton A, Holgate ST: Immunopathology of fatal soybean dust-induced asthma. Weibel ER: Morphometry of the Human Lung.
New York: Academic Press;. Book Google Scholar. Macklem PT, Mead J: Resistance of central and peripheral airways measured by a retrograde catheter. J Appl Physiol. PubMed Google Scholar.
Brown R, Woolcock AJ, Vincent NJ, Macklem PT: Physiological effects of experimental airway obstruction with beads. Mead J, Takishima T, Leith D: Stress distribution in lungs: a model of pulmonary elasticity. Sly PD, Willet KE, Kano S, Lanteri CJ, Wale J: Pirenzepine blunts the pulmonary parenchymal response to inhaled methacholine.
Pulm Pharmacol. Peták F, Hantos Z, Adamicza Á, Asztalos T, Sly PD: Methacholine-induced bronchoconstriction in rats: effects of intravenous vs. aerosol delivery. Ludwig MS, Romero PV, Bates JH: A comparison of the dose-response behavior of canine airways and parenchyma.
Suki B, Petak F, Adamicza A, Hantos Z, Lutchen KR: Partitioning of airway and lung tissue properties: comparison of in situ and open-chest conditions. Sato J, Suki B, Davey BLK, Bates JHT: Effect of methacholine on low-frequency mechanics of canine airways and lung tissue.
Romero PV, Robatto FM, Simard S, Ludwig MS: Lung tissue behavior during methacholine challenge in rabbits in vivo. Tepper R, Sato J, Suki B, Martin JG, Bates JHT: Low-frequency pulmonary impedance in rabbits and its response to inhaled methacholine.
Ingenito EP, Davision B, Fredberg JJ: Tissue resistance in the guinea pig at baseline and during methacholine constriction. Salerno FG, Moretto A, Dallaire M, Ludwig MS: How mode of stimulus affects the relative contribution of elastance and hysteresivity to changes in lung tissue resistance.
Tulic MK, Wale JL, Petak F, Sly PD: Muscarinic blockade of methacholine induced airway and parenchymal lung responses in anaesthetised rats. Ohrui T, Sekizawa K, Yanai M, Morikawa M, Jin Y, Sasaki H, Takishima T: Partitioning of pulmonary responses to inhaled methacholine in subjects with asymptomatic asthma.
Van Brabandt H, Cauberghs M, Verbeken E, Moerman P, Lauweryns JM, Van de Woestijne KP: Partitioning of pulmonary impedance in excised human and canine lungs. Ingram RHJ: Physiological assessment of inflammation in the peripheral lung of asthmatic patients.
Wagner EM, Bleecker ER, Permutt S, Liu MC: Direct assessment of small airways reactivity in human subjects. Yanai M, Sekizawa K, Ohrui T, Sasaki H, Takishima T: Site of airway obstruction in pulmonary disease: direct measurement of intrabronchial pressure.
Wagner EM, Liu MC, Weinmann GG, Permutt S, Bleecker ER: Peripheral lung resistance in normal and asthmatic subjects. Wiggs BR, Bosken C, Pare PD, James AL, Hogg JC: A model of airway narrowing in asthma and in chronic obstructive pulmonary disease. Kuwano K, Bosken CH, Pare PD, Bai TR, Wiggs BR, Hogg JC: Small airways dimensions in asthma and in chronic obstructive pulmonary disease.
Petak F, Hall GL, Sly PD: Repeated measurements of airway and parenchymal mechanics in rats by using low-frequency oscillations. Sly PD, Hayden MJ, Petak F, Hantos Z: Measurements of low-frequency respiratory impedance in infants.
Hall GL, Hantos Z, Petak F, Wildhaber JH, Tiller K, Burton PR, Sly PD: Airway and respiratory tissue mechanics in normal infants. Hall GL, Hantos Z, Wildhaber JH, Petak F, Sly PD: Methacholine responsiveness in infants assessed with low frequency forced oscillation and forced expiration techniques.
Article PubMed PubMed Central Google Scholar. Woolcock AJ: Effects of drugs on small airways. Article Google Scholar. Huber HL, Koessler KK: The pathology of bronchial asthma.
Arch Intern Med. Dunnill MS: The pathology of asthma, with special reference to changes in the bronchial mucosa.
J Clin Pathol. Dunnill MS, Massarella GR, Anderson JA: A comparison of the quantitative anatomy of the bronchi in normal subjects, in status asthmaticus, in chronic bronchitis and in emphysema. Hamid QA: Peripheral inflammation is more important than central inflammation.
Respir Med. Haley KJ, Sunday ME, Wiggs BR, Kozakewich HP, Reilly JJ, Mentzer SJ, Sugarbaker DJ, Doerschuk CM, Drazen JM: Inflammatory cell distribution within and along asthmatic airways.
Saetta M, Di Stefano A, Rosina C, Thiene G, Fabbri LM: Quantitative structural analysis of peripheral airways and arteries in sudden fatal asthma. Kraft M: The distal airways: are they important in asthma?. Kraft M, Vianna E, Martin RJ, Leung DYM: Nocturnal asthma is associated with reduced glucocorticoid receptor binding affinity and decreased steroid responsiveness at night.
Wenzel SE, Szefler SJ, Leung DY, Sloan SI, Rex MD, Martin RJ: Bronchoscopic evaluation of severe asthma. Persistent inflammation associated with high dose glucocorticoids.
Djukanovic R, Wilson JW, Britten KM, Wilson SJ, Walls AF, Roche WR, Howarth PH, Holgate ST: Effect of an inhaled corticosteroid on airway inflammation and symptoms in asthma. Booth H, Richmond I, Ward C, Gardiner PV, Harkawat R, Walters EH: Effect of high dose inhaled fluticasone propionate on airway inflammation in asthma.
Jeffery PK, Godfrey RW, Adelroth E, Nelson F, Rogers A, Johansson SA: Effects of treatment on airway inflammation and thickening of basement membrane reticular collagen in asthma. A quantitative light and electron microscopic study. Esmailpour N, Hogger P, Rabe KF, Heitmann U, Nakashima M, Rohdewald P: Distribution of inhaled fluticasone propionate between human lung tissue and serum in vivo.
Dolovich MB, Rhem R, Gerrard L, Coates G: Lung deposition of coarse CFC vs fine HFA pMDI earosols of beclomethasone dipropionate BDP in asthma [abstract]. Leach CL, Davidson PJ, Boudreau RJ: Improved airway targeting with the CFC-free HFA-beclomethasone metered-dose inhaler compared with CFC-beclomethasone.
Busse WW, Brazinsky S, Jacobson K, Stricker W, Schmitt K, Vanden Burgt J, Donnell D, MRPharm S, Hannon S, Colice GL: Efficacy response of inhaled beclomethasone dipropionate in asthma is proportional to dose and is improved by formulation with a new propellant.
Vanden Burgt JA, Busse WW, Martin RJ, Szefler SJ, Donnell D: Efficacy and safety overview of a new inhaled corticosteroid, QVAR hydrofluoroalkane-beclomethasone extrafine inhalation aerosol , in asthma [review]. Goldin JG, Tashkin DP, Kleerup EC, Greaser LE, Haywood UM, Sayre JW, Simmons MD, Suttorp M, Colice GL, Vanden Burgt JA, Aberle DR: Comparative effects of hydrofluoroalkane and chlorofluorocarbon beclomethasone dipropionate inhalation on small airways: assessment with functional helical thin-section computed tomography.
Marshall BG, Wangoo A, Harrison LI, Young DB, Shaw RJ: Tumour necrosis factor-alpha production in human alveolar macrophages: modulation by inhaled corticosteroid.
Muller NL, Miller RR: Diseases of the bronchioles: CT and pathological findings. Download references. The authors acknowledge the support of 3M Pharmaceuticals and MRC Canada for their support. MKT is a Ludwig-Engel Post-Doctoral Fellow. Meakins-Christie Laboratories, McGill University, Montreal, Canada.
You can also search for this author in PubMed Google Scholar. Correspondence to Meri K Tulic. Reprints and permissions. Tulic, M. Small airway inflammation in asthma.
Respir Res 2 , Download citation. Received : 27 June Accepted : 18 July Published : 10 August Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Asthma was originally described as an inflammatory disease that predominantly involves the central airways.
Introduction Asthma is a complex, chronic inflammatory lung disease that is characterized by epithelial shedding, airway smooth muscle hypertrophy and hyperplasia, overproduction of mucus, and airway inflammation.
Physiological evidence Most of our knowledge of lung function in asthmatic persons comes from spirometric and plethysmographic measurements made during bronchoprovocation, and these are dominated by large airway responsiveness.
Pathological evidence Bronchoscopic studies have shown that bronchial asthma is characterized by infiltration of central airways with inflammatory cells and an upregulation of T-helpertype cytokines. Figure 1. Full size image. Figure 2. Figure 3. Figure 4. Figure 5. Small airways as a therapeutic target in asthma On the basis of the physiological and pathological evidence presented in the present review, small or peripheral airways and lung parenchyma are clearly implicated in the pathogenesis of asthma.
Conclusion There is accumulating evidence to suggest that airway inflammation occurs in all parts of the airway. Abbreviations BDP: beclomethasone dipropionate GR: glucocorticoid receptor IFN: interferon IL: interleukin K d : dissociation constant MDI: metered-dose inhalers NA: nocturnal asthma NNA: non-nocturnal asthma.
References Curschmann H: Exudative bronchiolitis and its relationship with asthma nervosa [in German]. Google Scholar Djukanovic R, Wilson JW, Lai CK, Holgate ST, Howarth PH: The safety aspects of fiberoptic bronchoscopy, bronchoalveolar lavage, and endobronchial biopsy in asthma.
Article PubMed Google Scholar Hamid Q, Song Y, Kotsimbos TC, Minshall E, Bai TR, Hegele RG, Hogg JC: Inflammation of small airways in asthma. Article PubMed Google Scholar Minshall EM, Hogg JC, Hamid QA: Cytokine mRNA expression in asthma is not restricted to the large airways.
Article PubMed Google Scholar Taha RA, Minshall EM, Miotto D, Shimbara A, Luster A, Hogg JC, Hamid QA: Eotaxin and monocyte chemotactic protein-4 mRNA expression in small airways of asthmatic and nonasthmatic individuals.
Article PubMed Google Scholar Carroll N, Cooke C, James A: The distribution of eosinophils and lymphocytes in the large and small airways of asthmatics.
Article PubMed Google Scholar Faul JL, Tormey VJ, Leonard C, Burke CM, Farmer J, Horne SJ, Poulter LW: Lung immunopathology in cases of sudden asthma death. Article PubMed Google Scholar Christodoulopoulos P, Leung DY, Elliott MW, Hogg JC, Muro S, Toda M, Laberge S, Hamid QA: Increased number of glucocorticoid receptor-beta-expressing cells in the airways in fatal asthma.
Article PubMed Google Scholar Kraft M, Djukanovic R, Wilson S, Holgate ST, Martin RJ: Alveolar tissue inflammation in asthma. Article PubMed Google Scholar Kraft M, Martin RJ, Wilson S, Djukanovic R, Holgate ST: Lymphocyte and eosinophil influx into alveolar tissue in nocturnal asthma.
Article PubMed Google Scholar Carroll N, Carello S, Cooke C, James A: Airway structure and inflammatory cells in fatal attacks of asthma. Article PubMed Google Scholar Synek M, Anto JM, Beasley R, Frew AJ, Halloway L, Lampe FC, Lloreta JL, Sunyer J, Thornton A, Holgate ST: Immunopathology of fatal soybean dust-induced asthma.
Article PubMed Google Scholar Weibel ER: Morphometry of the Human Lung. PubMed Google Scholar Brown R, Woolcock AJ, Vincent NJ, Macklem PT: Physiological effects of experimental airway obstruction with beads. PubMed Google Scholar Mead J, Takishima T, Leith D: Stress distribution in lungs: a model of pulmonary elasticity.
PubMed Google Scholar Sly PD, Willet KE, Kano S, Lanteri CJ, Wale J: Pirenzepine blunts the pulmonary parenchymal response to inhaled methacholine.
Article PubMed Google Scholar Peták F, Hantos Z, Adamicza Á, Asztalos T, Sly PD: Methacholine-induced bronchoconstriction in rats: effects of intravenous vs.
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