A systematic review of gut microbiota profile in <scp>COVID</scp>‐19 patients and among those who have recovered from <scp>COVID</scp>‐19
Bibliographic record
Abstract
OBJECTIVES: Given the scale and persistence of coronavirus disease 2019 (COVID-19), significant attention has been devoted to understanding the relationship between human gut microbiota and COVID-19. In this systematic review we aimed to comprehensively assess the gut microbiota composition in patients infected with COVID-19 and those recovered from COVID-19 in comparison to healthy controls (HCs). METHODS: Peer-reviewed articles and preprints published up to September 1, 2022, were searched in Ovid MEDLINE, Ovid EMBASE, and SCOPUS. Observational studies reporting the gut microbiota profile in adult (≥18 years) COVID-19 patients or those recovered from COVID-19 compared to HCs were eligible for inclusion in this systematic review. The quality assessment of studies was performed using the Newcastle-Ottawa scale. RESULTS: We identified 27 studies comprising 18 studies that compared COVID-19 patients and six that compared recovered COVID-19 patients to HCs, while the other three studies compared both COVID-19 and recovered COVID-19 patients to HCs. Compared to HCs, decreased gut microbial diversity and richness and a distinctive microbial composition were reported in COVID-19 patients and recovered COVID-19 patients. In COVID-19 patients, Bacteroidetes were found to be enriched, and Firmicutes depleted. Decreased short-chain fatty acid (SCFA)-producing bacteria, such as Faecalibacterium, Ruminococcus, and Bifidobacterium, among others, were also observed in COVID-19 patients, which were not restored to normal levels in those who recovered. CONCLUSION: Gut dysbiosis was evident in COVID-19, and available data suggested that dysbiosis persisted even in recovered COVID-19 patients, with decreased Firmicutes and SCFA-producing bacteria.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".