Gut microbiota imbalance in colorectal cancer patients, the risk factor of COVID-19 mortality
Bibliographic record
Abstract
BACKGROUND: COVID-19 pandemic is sweeping across the world. Previous studies have shown that gut microbiota is associated with COVID-19, and operational taxonomic unit (OTU) composed of Blautia genus, Lactobacillus genus, and Ruminococcus genus of Firmicutes is correlated with the severity of COVID-19. Gut microbiota imbalance in colorectal cancer patients may lead to the variation of OTU. RESULTS: Based on the GMrepo database, the gut microbiota of 1374 patients with colorectal neoplasms and 27,329 healthy people was analyzed to investigate the differences in the abundance of microbes between colorectal neoplasms patients and healthy people. Furthermore, We collected feces samples from 12 patients with colorectal cancer and 8 healthy people in Xiangya hospital for metabolomic analysis to investigate the potential mechanisms. Our study showed that the abundance of Blautia and Ruminococcus was significantly increased in colorectal neoplasms, which may increase the severity of COVID-19. The gender and age of patients may affect the severity of COVID-19 by shaping the gut microbiota, but the BMI of patients does not. CONCLUSIONS: Our work draws an initial point that gut microbiota imbalance is a risk factor of COVID-19 mortality and gut microbiota may provide a new therapeutic avenue for colorectal cancer patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".