Innate Immune Responses and Gut Microbiomes Distinguish HIV-Exposed from HIV-Unexposed Children in a Population-Specific Manner
Bibliographic record
Abstract
In both high- and low-income countries, HIV-negative children born to HIV-positive mothers (HIV exposed, uninfected [HEU]) are more susceptible to severe infection than HIV-unexposed, uninfected (HUU) children, with altered innate immunity hypothesized to be a cause. Both the gut microbiome and systemic innate immunity differ across biogeographically distinct settings, and the two are known to influence each other. And although the gut microbiome is influenced by HIV infection and may contribute to altered immunity, the biogeography of immune-microbiome correlations among HEU children have not been investigated. To address this, we compared the innate response and the stool microbiome of 2-y-old HEU and HUU children from Belgium, Canada, and South Africa to test the hypothesis that region-specific immune alterations directly correlate to differences in their stool microbiomes. We did not detect a universal immune or microbiome signature underlying differences between HEU versus HUU that was applicable to all children. But as hypothesized, population-specific differences in stool microbiomes were readily detected and included reduced abundances of short-chain fatty acid-producing bacteria in Canadian HEU children. Furthermore, we did not identify innate immune-microbiome associations that distinguished HEU from HUU children in any population. These findings suggest that maternal HIV infection is independently associated with differences in both innate immunity and the stool microbiome in a biogeographical population-specific way.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".