CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection.
COVID-19
Cytokine
Inflammation
Interstitial lung disease
SARS-CoV-2
Journal
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
ISSN: 1420-908X
Titre abrégé: Inflamm Res
Pays: Switzerland
ID NLM: 9508160
Informations de publication
Date de publication:
03 Feb 2024
03 Feb 2024
Historique:
received:
22
09
2023
accepted:
21
01
2024
revised:
17
01
2024
medline:
3
2
2024
pubmed:
3
2
2024
entrez:
3
2
2024
Statut:
aheadofprint
Résumé
We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients. Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled. CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses. CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1. Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.
Identifiants
pubmed: 38308760
doi: 10.1007/s00011-024-01852-1
pii: 10.1007/s00011-024-01852-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministero della Salute
ID : COVID-2020-12371808
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Ochani RK, Asad A, Yasmin F, Shaikh S, Khalid H, Batra S, et al. COVID-19 pandemic: from origins to outcomes. A comprehensive review of viral pathogenesis, clinical manifestations, diagnostic evaluation, and management. Infez Med. 2021;29:20–36.
pubmed: 33664170
Alsharif W, Qurashi A. Effectiveness of COVID-19 diagnosis and management tools: a review. Radiography. 2021. https://doi.org/10.1016/j.radi.2020.09.010 .
doi: 10.1016/j.radi.2020.09.010
pubmed: 33008761
Sharif PM, Nematizadeh M, Saghazadeh M, Saghazadeh A, Rezaei N. Computed tomography scan in COVID-19: a systematic review and meta-analysis. Pol J Radiol. 2022. https://doi.org/10.5114/pjr.2022.112613 .
doi: 10.5114/pjr.2022.112613
pubmed: 35140824
pmcid: 8814899
Verzellesi L, Botti A, Bertolini M, Trojani V, Carlini G, Nitrosi A, et al. Machine and deep learning algorithms for COVID-19 mortality prediction using clinical and radiomic features. Electronics. 2023. https://doi.org/10.3390/electronics12183878 .
doi: 10.3390/electronics12183878
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973. https://doi.org/10.1109/TSMC.1973.4309314 .
doi: 10.1109/TSMC.1973.4309314
Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004. https://doi.org/10.1016/j.crad.2004.07.008 .
doi: 10.1016/j.crad.2004.07.008
pubmed: 15556588
Tourassi GD. Journey toward computer-aided diagnosis: role of image texture analysis. Radiology. 1999. https://doi.org/10.1148/radiology.213.2.r99nv49317 .
doi: 10.1148/radiology.213.2.r99nv49317
pubmed: 10551208
van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging. 2020. https://doi.org/10.1186/s13244-020-00887-2 .
doi: 10.1186/s13244-020-00887-2
pubmed: 32785796
pmcid: 7423816
Liu X, Li Y, Qian Z, Sun Z, Xu K, Wang K, et al. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin. 2018. https://doi.org/10.1016/j.nicl.2018.10.014 .
doi: 10.1016/j.nicl.2018.10.014
pubmed: 30573410
pmcid: 6411915
Iori M, Di Castelnuovo C, Verzellesi L, Meglioli G, Lippolis DG, Nitrosi A, et al. Mortality prediction of COVID-19 patients using radiomic and neural network features extracted from a wide chest X-ray sample size: a robust approach for different medical imbalanced scenarios. Appl Sci. 2022. https://doi.org/10.3390/app12083903 .
doi: 10.3390/app12083903
Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol. 2023. https://doi.org/10.1016/j.semcancer.2023.05.004 .
doi: 10.1016/j.semcancer.2023.05.004
pubmed: 37302519
Castello A, Castellani M, Florimonte L, Urso L, Mansi L, Lopci E. The role of radiomics in the era of immune checkpoint inhibitors: a new protagonist in the jungle of response criteria. J Clin Med. 2022. https://doi.org/10.3390/jcm11061740 .
doi: 10.3390/jcm11061740
pubmed: 35330068
pmcid: 8948743
Wen Q, Zhu J, Meng X, Ma C, Bai T, Sun X, et al. The value of CBCT-based tumor density and volume variations in prediction of early response to chemoradiation therapy in advanced NSCLC. Sci Rep. 2017. https://doi.org/10.1038/s41598-017-14548-w .
doi: 10.1038/s41598-017-14548-w
pubmed: 29263357
pmcid: 5738440
Wen Q, Yang Z, Zhu J, Qiu Q, Dai H, Feng A, et al. Pretreatment CT-based radiomics signature as a potential imaging biomarker for predicting the expression of PD-L1 and CD8+TILs in ESCC. Onco Targets Ther. 2020. https://doi.org/10.2147/OTT.S261068 .
doi: 10.2147/OTT.S261068
pubmed: 33447049
pmcid: 7685373
Wen Q, Yang Z, Dai H, Feng A, Li Q. Radiomics study for predicting the expression of PD-L1 and tumor mutation burden in non-small cell lung cancer based on CT images and clinicopathological features. Front Oncol. 2021. https://doi.org/10.3389/fonc.2021.620246 .
doi: 10.3389/fonc.2021.620246
pubmed: 35155211
pmcid: 8739957
Qiu Q, Xing L, Wang Y, Feng A, Wen Q. Development and validation of a radiomics nomogram using computed tomography for differentiating immune checkpoint inhibitor-related pneumonitis from radiation pneumonitis for patients with non-small cell lung cancer. Front Immunol. 2022. https://doi.org/10.3389/fimmu.2022.870842 .
doi: 10.3389/fimmu.2022.870842
pubmed: 36776400
pmcid: 9802119
Venerito V, Manfredi A, Lopalco G, Lavista M, Cassone G, Scardapane A, et al. Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: a proof-of-concept study. Front Med (Lausanne). 2023. https://doi.org/10.3389/fmed.2022.1069486 .
doi: 10.3389/fmed.2022.1069486
pubmed: 36698825
Yang CC, Chen CY, Kuo YT, Ko CC, Wu WJ, Liang CH, et al. Radiomics for the prediction of response to antifibrotic treatment in patients with idiopathic pulmonary fibrosis: a pilot study. Diagnostics (Basel). 2022. https://doi.org/10.3390/diagnostics12041002 .
doi: 10.3390/diagnostics12041002
pubmed: 36611322
pmcid: 10078567
Schniering J, Maciukiewicz M, Gabrys HS, Brunner M, Blüthgen C, Meier C, et al. Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. Eur Respir J. 2022. https://doi.org/10.1183/13993003.04503-2020 .
doi: 10.1183/13993003.04503-2020
pubmed: 35604818
pmcid: 9386332
Rabaan AA, Al-Ahmed SH, Muhammad J, Khan A, Sule AA, Tirupathi R, et al. Role of inflammatory cytokines in COVID-19 patients: a review on molecular mechanisms, immune functions, immunopathology and immunomodulatory drugs to counter cytokine storm. Vaccines. 2021. https://doi.org/10.3390/vaccines9050436 .
doi: 10.3390/vaccines9050436
pubmed: 33946736
pmcid: 8145892
Zanza C, Romenskaya T, Manetti AC, Franceschi F, La Russa R, Bertozzi G, et al. Cytokine storm in COVID-19: immunopathogenesis and therapy. Medicina (Kaunas). 2022. https://doi.org/10.3390/medicina58020144 .
doi: 10.3390/medicina58020144
pubmed: 36295545
Rizzi M, Costanzo M, Tonello S, Matino E, Casciaro FG, Croce A, et al. Prognostic markers in hospitalized COVID-19 patients: the role of IP-10 and C-reactive protein. Dis Markers. 2022. https://doi.org/10.1155/2022/3528312 .
doi: 10.1155/2022/3528312
pubmed: 35531477
pmcid: 9070408
Potere N, Batticciotto A, Vecchié A, Porreca E, Cappelli A, Abbate A, et al. The role of IL-6 and IL-6 blockade in COVID-19. Expert Rev Clin Immunol. 2021. https://doi.org/10.1080/1744666X.2021.1919086 .
doi: 10.1080/1744666X.2021.1919086
pubmed: 33874829
Gao YD, Ding M, Dong X, Zhang J, Kursat Azkur A, Azkur D, et al. Risk factors for severe and critically ill COVID-19 patients: a review. Allergy. 2021. https://doi.org/10.1111/all.14657 .
doi: 10.1111/all.14657
pubmed: 34570913
Dorgham K, Quentric P, Gökkaya M, Marot S, Parizot C, Sauce D, et al. Distinct cytokine profiles associated with COVID-19 severity and mortality. J Allergy Clin Immunol. 2021. https://doi.org/10.1016/j.jaci.2021.03.047 .
doi: 10.1016/j.jaci.2021.03.047
pubmed: 33894209
pmcid: 8061091
Kassianidis G, Siampanos A, Poulakou G, Adamis G, Rapti A, Milionis H, et al. Calprotectin and imbalances between acute-phase mediators are associated with critical illness in COVID-19. Int J Mol Sci. 2022. https://doi.org/10.3390/ijms23094894 .
doi: 10.3390/ijms23094894
pubmed: 35563282
pmcid: 9099708
Yuan Y, Wang Q, Sun D, Wu Z, Peng H, Liu X, et al. Differences in immune responses between children and adults with COVID-19. Curr Med Sci. 2021. https://doi.org/10.1007/s11596-021-2318-1 .
doi: 10.1007/s11596-021-2318-1
pubmed: 34741251
pmcid: 8571008
Wu Z, Liu X, Liu J, Zhu F, Liu Y, Liu Y, et al. Correlation between ground-glass opacity on pulmonary CT and the levels of inflammatory cytokines in patients with moderate-to-severe COVID-19 pneumonia. Int J Med Sci. 2021. https://doi.org/10.7150/ijms.56683 .
doi: 10.7150/ijms.56683
pubmed: 34790062
pmcid: 8579303
Liu Y, Zhang C, Huang F, Yang Y, Wang F, Yuan J, et al. Elevated plasma levels of selective cytokines in COVID-19 patients reflect viral load and lung injury. Natl Sci Rev. 2020. https://doi.org/10.1093/nsr/nwaa037 .
doi: 10.1093/nsr/nwaa037
pubmed: 34691731
pmcid: 8310770
Antoniou KM, Margaritopoulos GA, Tomassetti S, Bonella F, Costabel U, Poletti V. Interstitial lung disease. Eur Respir Rev. 2014. https://doi.org/10.1183/09059180.00009113 .
doi: 10.1183/09059180.00009113
pubmed: 24591661
pmcid: 9487254
Bonella F, Ulrich C. Biomarkers in connective tissue disease-associated interstitial lung disease. Semin Respir Crit Care Med. 2014. https://doi.org/10.1055/s-0034-1371527 .
doi: 10.1055/s-0034-1371527
pubmed: 24668534
Jee A, Sahhar J, Youssef P, Bleasel J, Adelstein S, Nguyen M, et al. Review: Serum biomarkers in idiopathic pulmonary fibrosis and systemic sclerosis associated interstitial lung disease—frontiers and horizons. Pharmacol Ther. 2019. https://doi.org/10.1016/j.pharmthera.2019.05.014 .
doi: 10.1016/j.pharmthera.2019.05.014
pubmed: 31153954
Maher TM, Oballa E, Simpson JK, Porte J, Habgood A, Fahy WA, et al. An epithelial biomarker signature for idiopathic pulmonary fibrosis: an analysis from the multicentre PROFILE cohort study. Lancet Respir Med. 2017. https://doi.org/10.1016/S2213-2600(17)30430-7 .
doi: 10.1016/S2213-2600(17)30430-7
pubmed: 29150411
pmcid: 5666208
MacKinnon AC, Gibbons MA, Farnworth SL, Leffler H, Nilsson UJ, Delaine T, et al. Regulation of transforming growth factor-β1-driven lung fibrosis by galectin-3. Am J Respir Crit Care Med. 2012. https://doi.org/10.1164/rccm.201106-0965OC .
doi: 10.1164/rccm.201106-0965OC
pubmed: 22822022
pmcid: 3480527
Kameda M, Otsuka M, Chiba H, Kuronuma K, Hasegawa T, Takahashi H, et al. CXCL9, CXCL10, and CXCL11; biomarkers of pulmonary inflammation associated with autoimmunity in patients with collagen vascular diseases-associated interstitial lung disease and interstitial pneumonia with autoimmune features. PLoS ONE. 2020. https://doi.org/10.1371/journal.pone.0241719 .
doi: 10.1371/journal.pone.0241719
pubmed: 33137121
pmcid: 7605704
Wynn TA. Integrating mechanisms of pulmonary fibrosis. J Exp Med. 2011. https://doi.org/10.1084/jem.20110551 .
doi: 10.1084/jem.20110551
pubmed: 21746813
pmcid: 3149216
Wollin L, Wex E, Pautsch A, Schnapp G, Hostettler K, Stowasser S, et al. Mode of action of nintedanib in the treatment of idiopathic pulmonary fibrosis. Eur Respir J. 2015. https://doi.org/10.1183/09031936.00174914 .
doi: 10.1183/09031936.00174914
pubmed: 25745043
pmcid: 4416110
Fiedler U, Augustin HG. Angiopoietins: a link between angiogenesis and inflammation. Trends Immunol. 2006. https://doi.org/10.1016/j.it.2006.10.004 .
doi: 10.1016/j.it.2006.10.004
pubmed: 17045842
Besutti G, Giorgi Rossi P, Iotti V, Spaggiari L, Bonacini R, Nitrosi A, et al. Accuracy of CT in a cohort of symptomatic patients with suspected COVID-19 pneumonia during the outbreak peak in Italy. Eur Radiol. 2020. https://doi.org/10.1007/s00330-020-07050-x .
doi: 10.1007/s00330-020-07050-x
pubmed: 32666316
pmcid: 7358325
Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008. https://doi.org/10.1148/radiol.2462070712 .
doi: 10.1148/radiol.2462070712
pubmed: 19011195
Ho T, Park J, Kim T, Park B, Lee J, Kim J, et al. Deep learning models for predicting severe progression in covid-19-infected patients: retrospective study. JMIR Med Inform. 2021. https://doi.org/10.2196/24973 .
doi: 10.2196/24973
pubmed: 33455900
pmcid: 7850779
VanderWeele T. Explanation in Causal Inference. Methods for Mediation and Interaction. New York: Oxford University Press; 2015.
Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013. https://doi.org/10.1093/ije/dyt127 .
doi: 10.1093/ije/dyt127
pubmed: 24062290
Tsicopoulos A, Chang Y, Ait Yahia S, de Nadai P, Chenivesse C. Role of CCL18 in asthma and lung immunity. Clin Exp Allergy. 2013. https://doi.org/10.1111/cea.12065 .
doi: 10.1111/cea.12065
pubmed: 23786278
Mothes R, Pascual-Reguant A, Koehler R, Liebeskind J, Liebheit A, Bauherr S, et al. Distinct tissue niches direct lung immunopathology via CCL18 and CCL21 in severe COVID-19. Nat Commun. 2023. https://doi.org/10.1038/s41467-023-36333-2 .
doi: 10.1038/s41467-023-36333-2
pubmed: 37857603
pmcid: 10587059
Luzina IG, Tsymbalyuk N, Choi J, Hasday JD, Atamas SP. CCL18-stimulated upregulation of collagen production in lung fibroblasts requires Sp1 signaling and basal Smad3 activity. J Cell Physiol. 2006. https://doi.org/10.1002/jcp.20452 .
doi: 10.1002/jcp.20452
pubmed: 16021625
Tizaoui K, Yang JW, Lee KH, Kim JH, Kim M, Yoon S, et al. The role of YKL-40 in the pathogenesis of autoimmune diseases: a comprehensive review. Int J Biol Sci. 2022. https://doi.org/10.7150/ijbs.67587 .
doi: 10.7150/ijbs.67587
pubmed: 35813465
pmcid: 9254466
Zhou Y, Peng H, Sun H, Peng X, Tang C, Gan Y, et al. Chitinase 3-like 1 suppresses injury and promotes fibroproliferative responses in Mammalian lung fibrosis. Sci Transl Med. 2014. https://doi.org/10.1126/scitranslmed.3007096 .
doi: 10.1126/scitranslmed.3007096
pubmed: 25411474
pmcid: 4326221
Lee SY, Lee CM, Ma B, Kamle S, Elias JA, Zhou Y, et al. Targeting chitinase 1 and chitinase 3-Like 1 as novel therapeutic strategy of pulmonary fibrosis. Front Pharmacol. 2022. https://doi.org/10.3389/fphar.2022.826471 .
doi: 10.3389/fphar.2022.826471
pubmed: 36703754
pmcid: 9808094
Sun X, Nakajima E, Norbrun C, Sorkhdini P, Yang AX, Yang D, et al. Chitinase 3 like 1 contributes to the development of pulmonary vascular remodeling in pulmonary hypertension. JCI Insight. 2022. https://doi.org/10.1172/jci.insight.159578 .
doi: 10.1172/jci.insight.159578
pubmed: 36546482
pmcid: 9906965
Schoneveld L, Ladang A, Henket M, Frix AN, Cavalier E, Guiot J, et al. YKL-40 as a new promising prognostic marker of severity in COVID infection. Crit Care. 2021. https://doi.org/10.1186/s13054-020-03383-7 .
doi: 10.1186/s13054-020-03383-7
pubmed: 33593373
pmcid: 7886185
Kamle S, Ma B, He CH, Akosman B, Zhou Y, Lee CM, et al. Chitinase 3-like-1 is a therapeutic target that mediates the effects of aging in COVID-19. JCI Insight. 2021. https://doi.org/10.1172/jci.insight.148749 .
doi: 10.1172/jci.insight.148749
pubmed: 34747367
pmcid: 8663553
De Lorenzo R, Sciorati C, Lorè NI, Capobianco A, Tresoldi C, Cirillo DM, et al. Chitinase-3-like protein-1 at hospital admission predicts COVID-19 outcome: a prospective cohort study. Sci Rep. 2022. https://doi.org/10.1038/s41598-022-11532-x .
doi: 10.1038/s41598-022-11532-x
pubmed: 36289260
pmcid: 9605946
Ebihara T, Matsubara T, Togami Y, Matsumoto H, Tachino J, Matsuura H, et al. Combination of WFDC2, CHI3L1, and KRT19 in plasma defines a clinically useful molecular phenotype associated with prognosis in critically Ill COVID-19 patients. J Clin Immunol. 2023. https://doi.org/10.1007/s10875-022-01386-3 .
doi: 10.1007/s10875-022-01386-3
pubmed: 36331721
Kimura Y, Nakai Y, Shin J, Hara M, Takeda Y, Kubo S, et al. Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach. Sci Rep. 2021. https://doi.org/10.1038/s41598-021-98253-9 .
doi: 10.1038/s41598-021-98253-9
pubmed: 34912016
pmcid: 8674328
Rosenberger CM, Wick KD, Zhuo H, Wu N, Chen Y, Kapadia SB, et al. Early plasma angiopoietin-2 is prognostic for ARDS and mortality amongcritically ill patients with sepsis. Crit Care. 2023. https://doi.org/10.1186/s13054-023-04525-3 .
doi: 10.1186/s13054-023-04525-3
pubmed: 37312169
pmcid: 10261831
Dawson RE, Jenkins BJ, Saad MI. IL-6 family cytokines in respiratory health and disease. Cytokine. 2021. https://doi.org/10.1016/j.cyto.2021.155520 .
doi: 10.1016/j.cyto.2021.155520
pubmed: 33875334
Bivona G, Agnello L, Ciaccio M. Biomarkers for prognosis and treatment response in COVID-19 patients. Ann Lab Med. 2021. https://doi.org/10.3343/alm.2021.41.6.540 .
doi: 10.3343/alm.2021.41.6.540
pubmed: 34108281
pmcid: 8203437
Bhaskaran K, Bacon S, Evans SJ, Bates CJ, Rentsch CT, MacKenna B, et al. Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform. Lancet Reg Health Eur. 2021. https://doi.org/10.1016/j.lanepe.2021.100109 .
doi: 10.1016/j.lanepe.2021.100109
pubmed: 33997835
pmcid: 8106239
Besutti G, Pellegrini M, Ottone M, Cantini M, Milic J, Bonelli E, et al. The impact of chest CT body composition parameters on clinical outcomes in COVID-19 patients. PLoS ONE. 2021. https://doi.org/10.1371/journal.pone.0251768 .
doi: 10.1371/journal.pone.0251768
pubmed: 33989341
pmcid: 8121324
Michaud M, Balardy L, Moulis G, Gaudin C, Peyrot C, Vellas B, et al. Proinflammatory cytokines, aging, and age-related diseases. J Am Med Dir Assoc. 2013. https://doi.org/10.1016/j.jamda.2013.05.009 .
doi: 10.1016/j.jamda.2013.05.009
pubmed: 23792036
Zanatta E, Martini A, Depascale R, Gamba A, Tonello M, Gatto M, et al. CCL18 as a biomarker of interstitial lung disease (ILD) and progressive fibrosing ILD in Patients with idiopathic inflammatory myopathies. Diagnostics. 2023. https://doi.org/10.3390/diagnostics13101715 .
doi: 10.3390/diagnostics13101715
pubmed: 37835823
pmcid: 10572214
Utsunomiya A, Oyama N, Hasegawa M. Potential biomarkers in systemic sclerosis: a literature review and update. J Clin Med. 2020. https://doi.org/10.3390/jcm9113388 .
doi: 10.3390/jcm9113388
pubmed: 33105647
pmcid: 7690387
Arron JR. Biomarkers in systemic sclerosis: mechanistic insights into pathogenesis and treatment. Curr Opin Rheumatol. 2021. https://doi.org/10.1097/BOR.0000000000000827 .
doi: 10.1097/BOR.0000000000000827
pubmed: 34420004
Tong X, Ma Y, Liu T, Zhenzhen L, Sitong L, Guihui W, et al. Can YKL-40 be used as a biomarker for interstitial lung disease?: a systematic review and meta-analysis. Medicine. 2021. https://doi.org/10.1097/MD.0000000000025631 .
doi: 10.1097/MD.0000000000025631
pubmed: 34941156
pmcid: 10545097
Montero P, Milara J, Roger I, Cortijo J. Role of JAK/STAT in interstitial lung diseases; molecular and cellular mechanisms. Int J Mol Sci. 2021. https://doi.org/10.3390/ijms22126211 .
doi: 10.3390/ijms22126211
pubmed: 34948022
pmcid: 8706391
Uehara M, Enomoto N, Mikamo M, Oyama Y, Kono M, Fujisawa T, et al. Impact of angiopoietin-1 and -2 on clinical course of idiopathic pulmonary fibrosis. Respir Med. 2016. https://doi.org/10.1016/j.rmed.2016.03.001 .
doi: 10.1016/j.rmed.2016.03.001
pubmed: 27109807