Longitudinal gut microbiota composition of South African and Nigerian infants in relation to tetanus vaccine responses
Bibliographic record
Abstract
Abstract Introduction: Infants who are exposed to HIV but uninfected (iHEU) have higher risk of infectious morbidity than infants who are HIV-unexposed and uninfected (iHUU), possibly due to altered immunity. As infant gut microbiota may influence immune development, we evaluated the effects of HIV exposure on infant gut microbiota and its association with tetanus toxoid (TT) vaccine responses. Methods: We evaluated gut microbiota by 16S rRNA gene sequencing in 278 South African and Nigerian infants during the first and at 15 weeks of life and measured antibodies against TT vaccine by enzyme-linked immunosorbent assay (ELISA) at matched time points. Results: Infant gut microbiota and its succession were more strongly influenced by geographical location and age than by HIV exposure. Microbiota of Nigerian infants drastically changed over 15 weeks, becoming dominated by Bifidobacterium longum subspecies infantis. This change was not observed among EBF South African infants. Lasso regression suggested that HIV exposure and gut microbiota were independently associated with TT vaccine responses at week 15, and that high passive antibody levels may mitigate these effects. Conclusion: In two African cohorts, HIV exposure minimally altered the infant gut microbiota compared to age and country, but both specific gut microbes and HIV exposure independently predicted humoral vaccine responses.
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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 | 0.000 | 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".