An overview of the gut side of the SARS-CoV-2 infection
Bibliographic record
Abstract
In late 2019, an outbreak of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) initiated in Wuhan, Hubei province, China. The major clinical symptoms described for coronavirus disease (COVID-19) include respiratory distress and pneumonia in severe cases, and some patients may experience gastrointestinal impairments. In accordance, viral RNA or live infectious virus have been detected in feces of patients with COVID-19. Binding of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) is a vital pathway for the virus entry into human cells, including those of the respiratory mucosa, esophageal epithelium as well as the absorptive enterocytes from ileum and colon. The interaction between SARS-CoV-2 and ACE2 receptor may decrease the receptor expression and disrupt the function of B0AT1 transporter influencing the diarrhea observed in COVID-19 patients. In this context, a fecal-oral transmission route has been considered and points out a role for the digestive tract in disease transmission and severity. Here, in order to further understand the impact of COVID-19 in human physiology, the cellular and molecular mechanisms of SARS-CoV-2 infection and disease severity are discussed in the context of gastrointestinal disturbances.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".