Transfer of Maternal Antimicrobial Immunity to HIV-Exposed Uninfected Newborns
Bibliographic record
Abstract
The transfer of maternal immune factors to the newborn is critical for protection from infectious disease in early life. Maternally acquired passive immunity provides protection until the infant is beyond early life's increased susceptibility to severe infections or until active immunity is achieved following infant's primary immunization. However, as reviewed here, human immunodeficiency virus (HIV) infection alters the transfer of immune factors from HIV-infected mothers to the HIV-exposed newborns and young infants. This may relate to the immune activation in HIV-infected pregnant women, associated with the production of inflammatory cytokines at the maternofetal interface associated with inflammatory responses in the newborn. We also summarize mother-targeting interventions to improve the health of infants born to HIV-infected women, such as immunization during pregnancy and reduction of maternal inflammation. Maternal immunization offers the potential to compensate for the decreased transplacentally transferred maternal antibodies observed in HIV-exposed infants. Current data suggest reduced immunogenicity of vaccines in HIV-infected pregnant women, possibly reducing the protective impact of maternal immunization for HIV-exposed infants. Fortunately, levels of antibodies appear preserved in the breast milk of HIV-infected women, which supports the recommendation to breast-feed during antiretroviral treatment to protect HIV-exposed infants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".