Comparative metagenome-associated analysis of gut microbiota and antibiotic resistance genes in acute gastrointestinal injury patients with the risk of in-hospital mortality
Bibliographic record
Abstract
ABSTRACT Acute gastrointestinal injury (AGI) is known for its poor long-term prognosis and the associated increase in mortality among intensive care unit (ICU) patients. As the role of the gut microbiome and resistome in AGI remains unclear, the present study aimed to explore the possible associations between dysbacteriosis and in-hospital mortality in ICU patients with gastrointestinal dysfunction. Fecal samples were collected from a prospective cohort of 210 ICU patients with AGI, and shotgun metagenomic sequencing was used to determine the taxonomic composition of gut microbiota and the differences of antibiotic resistance genes (ARGs) between the Death and Survival groups. Compared to the Survival group, patients in the Death group shifted from strict anaerobes to facultative anaerobes in the fecal microbial community, with more Klebsiella but less Prevotella . The co-occurrence patterns revealed that more ARG subtypes were enriched in microbial taxa in the Death group, especially for Clostridium and Methanobrevibacter . Furthermore, the ARG type had large area under the curve (AUCs) in receiver operating characteristic for predicting the disease severity, and a combined gut microbiota-ARG subtype classifiers showed better performance than either of them. Thus, comparative metagenome-associated analysis can help to obtain valuable information about gut microbiota and gene coding for antibiotic resistance in AGI patients. IMPORTANCE A metagenomic-related strategy was conducted to obtain a highly valuable resource to improve understanding of intestinal microbiota dysbiosis and antibiotic resistance genes (ARGs) profiles. The results indicate that intestinal microbiota, including Klebsiella and Prevotella , changed dramatically in intensive care unit (ICU) patients with acute gastrointestinal injury (AGI). Due to longer ICU stays and receiving more antibiotic treatment, the types and correlations of ARGs in the Death group were significantly higher than those in the Survival group. The findings of this study are expected to expand our knowledge of gut microbiota and resistome profiles reflecting gastrointestinal status, accelerate the identification of disease biomarkers, and provide new insights into the prevention and treatment of AGI-related diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".