Plasma lipopolysaccharide levels predict mortality in acutely ill children in Low- and Middle-Income Countries
Bibliographic record
Abstract
Childhood mortality remains high in low-resource settings, where environmental enteric dysfunction (EED) is prevalent. Peripheral blood bacterial lipopolysaccharides (LPS) are potential biomarkers of intestinal microbial translocation and inflammation; however, the effects of LPS translocation on mortality in this context remains unexplored. We investigate the association between plasma LPS and mortality among 638 acutely ill hospitalised children and compare them to 251 well community peers in a nested case-cohort (NCC) conducted between November 2016 and January 2019 across 9 sites in 6 countries in sub-Saharan Africa and South Asia. Higher levels of plasma LPS and inflammatory biomarkers (fecal calprotectin, plasma myeloperoxidase, and CD14) are associated with elevated 90-day mortality, and those associations are independent of wasting status. Non-survivors with high plasma LPS exhibit elevated gram-negative enteric microbiota, increased fecal biomarkers of EED, systemic inflammatory proteins, and differentially expressed proteins linked to the Insulin-like growth factor (IGF) nutritional axis, Interleukin-1 and collagen regeneration. Cellular interaction network models deconvoluted from a single-cell transcriptomic dataset enable an exploratory investigation of systemic immune responses and epithelial-immune cells crosstalk active in pathways leading to mortality. This knowledge can guide the identification of potential therapeutic signaling pathways in settings with high EED and malnutrition.
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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 | 0.002 |
| 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 | 0.001 | 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".