Dysbiosis of a microbiota–immune metasystem in critical illness is associated with nosocomial infections
Bibliographic record
Abstract
Critically ill patients in intensive care units experience profound alterations of their gut microbiota that have been linked to a high risk of hospital-acquired (nosocomial) infections and adverse outcomes through unclear mechanisms. Abundant mouse and limited human data suggest that the gut microbiota can contribute to maintenance of systemic immune homeostasis, and that intestinal dysbiosis may lead to defects in immune defense against infections. Here we use integrated systems-level analyses of fecal microbiota dynamics in rectal swabs and single-cell profiling of systemic immune and inflammatory responses in a prospective longitudinal cohort study of critically ill patients to show that the gut microbiota and systemic immunity function as an integrated metasystem, where intestinal dysbiosis is coupled to impaired host defense and increased frequency of nosocomial infections. Longitudinal microbiota analysis by 16s rRNA gene sequencing of rectal swabs and single-cell profiling of blood using mass cytometry revealed that microbiota and immune dynamics during acute critical illness were highly interconnected and dominated by Enterobacteriaceae enrichment, dysregulated myeloid cell responses and amplified systemic inflammation, with a lesser impact on adaptive mechanisms of host defense. Intestinal Enterobacteriaceae enrichment was coupled with impaired innate antimicrobial effector responses, including hypofunctional and immature neutrophils and was associated with an increased risk of infections by various bacterial and fungal pathogens. Collectively, our findings suggest that dysbiosis of an interconnected metasystem between the gut microbiota and systemic immune response may drive impaired host defense and susceptibility to nosocomial infections in critical illness.
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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 | 0.001 | 0.000 |
| 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".