Impaired systemic antibody response against gut microbiota pathobionts in critical illness and susceptibility to nosocomial infections
Bibliographic record
Abstract
ABSTRACT Critically ill patients in intensive care units (ICUs) experience high rates of nosocomial infections, commonly caused by translocation and dissemination of pathogenic microorganisms that colonize the intestinal tract (pathobionts). Multiple immune barriers protect the host against commensal and pathogenic colonizers, including a repertoire of circulating anti-commensal antibodies. The integrity of this systemic antibody-mediated defense system, its relationship with gut microbiota dysbiosis, and its impact on nosocomial infections in the ICU have not been explored. We observed markedly impaired plasma IgM and IgG reactivity against intestinal pathobionts such as Escherichia coli , Klebsiella pneumoniae , and Enterococcus faecalis in ICU patients compared to healthy volunteers. Reduced gut pathobiont antibody responses in ICU patients was associated with B cell lymphopenia, and patients with gut microbiota dysbiosis had reduced levels of natural antibody producing B1-like B cells. Reduced IgG against gut Gram-negative pathobionts was associated with an increased risk of nosocomial infection or death. These findings indicate that the systemic antibody barrier against microbiota pathobionts is compromised in critical illness and associated with increased risk of nosocomial infections, identifying a potential role for therapeutic antibody supplementation to prevent infections in the ICU.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".