Evaluation of the gut microbiome in association with biological signatures of inflammation in murine polytrauma and shock
Bibliographic record
Abstract
Severe injuries are frequently accompanied by hemorrhagic shock and harbor an increased risk for complications. Local or systemic inflammation after trauma/hemorrhage may lead to a leaky intestinal epithelial barrier and subsequent translocation of gut microbiota, potentially worsening outcomes. To evaluate the extent with which trauma affects the gut microbiota composition, we performed a post hoc analysis of a murine model of polytrauma and hemorrhage. Four hours after injury, organs and plasma samples were collected, and the diversity and composition of the cecal microbiome were evaluated using 16S rRNA gene sequencing. Although cecal microbial alpha diversity and microbial community composition were not found to be different between experimental groups, norepinephrine support in shock animals resulted in increased alpha diversity, as indicated by higher numbers of distinct microbial features. We observed that the concentrations of proinflammatory mediators in plasma and intestinal tissue were associated with measures of microbial alpha and beta diversity and the presence of specific microbial drivers of inflammation, suggesting that the composition of the gut microbiome at the time of trauma, or shortly after trauma exposure, may play an important role in determining physiological outcomes. In conclusion, we found associations between measures of gut microbial alpha and beta diversity and the severity of systemic and local gut inflammation. Furthermore, our data suggest that four hours following injury is too early for development of global changes in the alpha diversity or community composition of the intestinal microbiome. Future investigations with increased temporal-spatial resolution are needed in order to fully elucidate the effects of trauma and shock on the gut microbiome, biological signatures of inflammation, and proximal and distal outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".