The Effects of Maternal HIV and Antiretroviral Use on Human Placental Morphology and Angiogenesis
Bibliographic record
Abstract
The use of antiretrovirals (ARVs) during pregnancy is essential for maternal health and preventing perinatal HIV transmission. However, ARV use has been associated with increased risk for adverse birth outcomes including preterm births (PTB) and small for gestational age (SGA) births. Yet, the mechanisms underlying these outcomes remain unclear. The placenta, critical for fetal development, may be key in these outcomes. We aimed to explore the effects of HIV and ARV exposure on placental morphology and angiogenesis. In Aim I, we compared capillary per villous ratios between HIV-seronegative and HIV-seropositive pregnant people on ARVs in cohorts from Toronto and Uganda, with the latter including HIV-seronegative individuals on PrEP. Differential effects on capillary per villous ratios were observed in the Toronto cohort but not in our Ugandan cohort. In Aim II, we analysed levels of VEGF, Ang-1 and Ang-2 in placental samples from the Toronto cohort and explored associations with morphological and birth outcomes. We observed varied alterations of these factors at the placental level while further observing differential effects by HIV status and ARV regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".