Effect of maternal HIV status on the early neonatal microbiome
Bibliographic record
Abstract
Microbiome disruption is a proposed mechanism for the observed differences in child health outcomes by maternal HIV status, but the early neonatal microbiome of HIV-exposed (HE) newborns is not well-studied. We used 16S ribosomal ribonucleic acid sequencing to analyze the microbiome composition of nasal, skin, and rectal samples collected ≤72 h after birth from 57 hospitalized neonates in Botswana, 33% of whom were HE. Beta diversity differed by anatomic compartment (P = .001) and days since birth; however, interindividual differences were greater than those by anatomic site (P = .001). There were not significant differences by maternal HIV status. When timing of maternal HIV diagnosis was accounted for, however, we noted statistically significant differences in beta diversity for nasal and skin swabs. Microbial composition of samples from neonates with mothers diagnosed with HIV prior to pregnancy were more similar to samples from HIV-unexposed than HE neonates with mothers diagnosed with HIV during this pregnancy (P = .03 and P < .01 in skin and nasal, respectively) suggesting that microbiome variations mediated by HIV exposure might only emerge later in infancy. In the entire cohort, we examined differences in relative taxa abundance of neonatal pathogens and other species of clinical interest. We noted differences by anatomic compartment, for example, increased Klebsiella pneumoniae in rectal samples and increased Acinetobacter baumannii in nasal samples, whereas other pathogens expected to differ by body site did not, for example, Enterococcus faecium and Streptococcus agalactiae, highlighting that in the early neonatal microbiome exposures may have a significant impact on microbiome development.
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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 | 0.001 | 0.003 |
| 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".