Effect of Maternal HIV Infection on Infant Development and Outcomes
Bibliographic record
Abstract
Early life represents a period of profound immunological development and heightened susceptibility to infectious disease. The developmental trajectory over this period is influenced by a number of factors, including gestational age, mode of delivery, mode of feeding, microbiome development, and environmental exposures. There are also several maternal factors that have been shown to have a negative effect on both immune development and clinical outcomes, including maternal infection and inflammation. Studies have associated maternal HIV infections with an increase in infectious morbidity and mortality and decreased growth measures among their HIV-exposed uninfected (HEU) offspring. Among HEU infants, socioeconomic factors, maternal nutrition, maternal viral load, and maternal inflammation have also all been associated with impaired infant immune status and clinical outcomes. However, the mechanisms underlying these observations have not been elucidated and, apart from measures of disease severity, few studies thus far have undertaken in-depth assessments of maternal health status or immune function during gestation and how these influence developmental outcomes in their infants. The lack of a mechanistic understanding of how these gestational influences affect infant outcomes inhibits the ability to design and implement effective interventions. This review describes the current state of research into these mechanisms and highlights areas for future study include; how HIV infection causes the inflammatory trajectory to deviate from normal gestation, the mechanism(s) by which in utero exposure to maternal inflammation influences infant immune development and clinical outcomes, the role of socioeconomic factors as an inducer of maternal stress and inflammation, and maternal nutrition during gestation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".