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The infection of COVID-19 has caused serious threat to the life and health of all mankind and increased huge economic burden. According to the current statistics, the incidence of pulmonary fibrosis after COVID-19 infection is about 27.7% -87%, 81% of severe patients and 37% of moderate patients have residual lung lesions, and 53% of patients still have residual lung abnormalities one year after infection, resulting in restrictive pulmonary dysfunction and affecting the health and life of patients. Therefore, it is very important to study the diagnostic and prognostic markers of pulmonary fibrosis after infection of COVID-19. At present, relevant studies have been carried out on imagomics and serum proteomics of pulmonary fibrosis after COVID-19 infection, and serum biomarkers and imagomics marker models for diagnosing pulmonary fibrosis after COVID-19 pneumonia have been developed. However, there are few studies combining imageomics and serum proteomics, and the mechanism of pulmonary fibrosis after COVID-19 has not been fully clarified. In this study, it is planned to recruit patients with moderate, severe and critical COVID-19 pneumonia infection, collect venous blood from subjects, and perform chest HRCT follow-up. Blood samples were screened by proteomics and verified by expanded samples to screen diagnostic and prognostic markers of pulmonary fibrosis after COVID-19 infection. At the same time, based on deep learning technology, a model was developed to predict the occurrence and prognosis of pulmonary fibrosis after infection of COVID-19 combined with clinical characteristics, serum markers and AI imagomics, so as to provide ideas for further elucidating the mechanism of occurrence and development of pulmonary fibrosis after infection of COVID-19.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy control group |
| ||
| Pulmonary fibrosis after COVID-19 Pneumonia |
| ||
| No pulmonary fibrosis after COVID-19 Pneumonia |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| observational study | Diagnostic Test | observational study |
|
| Measure | Description | Time Frame |
|---|---|---|
| change of pulmonary fibrosis | The change of pulmonary fibrosis were evaluated | At the time of enrollment, The first month, the third month, the sixth month, the twelfth month |
| Measure | Description | Time Frame |
|---|---|---|
| change of protein in serum | Changes in plasma proteins over time | At the time of enrollment, the third month |
| changes of Lung function | Lung function over time |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with newly diagnosed COVID-19 pneumonia were classified as moderate, severe and critical according to clinical classification, and underwent chest CT imaging follow-up and blood protein measurement. In the later stage, patients were divided into groups with pulmonary fibrosis and without pulmonary fibrosis according to whether pulmonary fibrosis occurred; Healthy people as healthy control groups.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yuqi Cheng, PhD | Contact | (86) 087165324888-2471 | yuqicheng@126.com | |
| Jianqing Zhang, PhD | Contact | (86) 18988272502 | ydyyzjq@163.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| First Affiliated Hospital of Kunming Medical University | Kunming | Yunnan | 650000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33907520 | Result | Xue M, Zhang T, Chen H, Zeng Y, Lin R, Zhen Y, Li N, Huang Z, Hu H, Zhou L, Wang H, Zhang XD, Sun B. Krebs Von den Lungen-6 as a predictive indicator for the risk of secondary pulmonary fibrosis and its reversibility in COVID-19 patients. Int J Biol Sci. 2021 Apr 10;17(6):1565-1573. doi: 10.7150/ijbs.58825. eCollection 2021. | |
| 34243776 |
| Label | URL |
|---|---|
| Related Info | View source |
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| ID | Term |
|---|---|
| D011658 | Pulmonary Fibrosis |
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D017563 | Lung Diseases, Interstitial |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D005355 | Fibrosis |
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| At the time of enrollment, the third month, the sixth month, the twelfth month |
| The First Affiliated Hospital of Kunming Medical University | Kunming | Yunnan | 650032 | China |
|
| Li X, Shen C, Wang L, Majumder S, Zhang D, Deen MJ, Li Y, Qing L, Zhang Y, Chen C, Zou R, Lan J, Huang L, Peng C, Zeng L, Liang Y, Cao M, Yang Y, Yang M, Tan G, Tang S, Liu L, Yuan J, Liu Y. Pulmonary fibrosis and its related factors in discharged patients with new corona virus pneumonia: a cohort study. Respir Res. 2021 Jul 9;22(1):203. doi: 10.1186/s12931-021-01798-6. |
| 34272434 | Result | Yang J, Chen C, Chen W, Huang L, Fu Z, Ye K, Lv L, Nong Z, Zhou X, Lu W, Zhong M. Proteomics and metabonomics analyses of Covid-19 complications in patients with pulmonary fibrosis. Sci Rep. 2021 Jul 16;11(1):14601. doi: 10.1038/s41598-021-94256-8. |
| 34093642 | Result | Sardar R, Sharma A, Gupta D. Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data. Front Genet. 2021 May 20;12:636441. doi: 10.3389/fgene.2021.636441. eCollection 2021. |
| 34451904 | Result | Bazdyrev E, Rusina P, Panova M, Novikov F, Grishagin I, Nebolsin V. Lung Fibrosis after COVID-19: Treatment Prospects. Pharmaceuticals (Basel). 2021 Aug 17;14(8):807. doi: 10.3390/ph14080807. |
| Related Info | View source |
| Related Info | View source |
| Related Info | View source |
| D010335 |
| Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |