Liver dysfunction during COVID-19 pandemic: Contributing role of associated factors in disease progression and severity
Bibliographic record
Abstract
In December 2019, a new strain of coronavirus was discovered in China, and the World Health Organization declared it a pandemic in March 2020. The majority of people with coronavirus disease 19 (COVID-19) exhibit no or only mild symptoms such as fever, cough, anosmia, and headache. Meanwhile, approximately 15% develop a severe lung infection over the course of 10 d, resulting in respiratory failure, which can lead to multi-organ failure, coagulopathy, and death. Since the beginning of the pandemic, it appears that there has been consideration that pre-existing chronic liver disease may predispose to deprived consequences in conjunction with COVID-19. Furthermore, extensive liver damage has been linked to immune dysfunction and coagulopathy, which leads to a more severe COVID-19 outcome. Besides that, people with COVID-19 frequently have abnormal liver function, with more significant elevations in alanine aminotransferase and aspartate aminotransferase in patients with severe COVID-19 compared to those with mild/moderate disease. This review focuses on the pathogenesis of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the liver, as well as the use of liver chemistry as a prognostic tool during COVID-19. We also evaluate the findings for viral infection of hepatocytes, and look into the potential mechanisms behind SARS-CoV-2-related liver damage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".