Unique Profile of Inflammation and Immune Activation in Pregnant People With HIV in the United States
Bibliographic record
Abstract
BACKGROUND: Little is known about inflammation/immune activation during pregnancy in people with HIV (PWH) and growth in their children who are HIV-exposed and uninfected (CHEU). METHODS: Using data from the Pediatric HIV/AIDS Cohort Study and an HIV-seronegative comparison group, we assessed associations of (1) HIV status, mode of HIV acquisition (perinatally vs nonperinatally acquired), and type of antiretroviral therapy (ART) with inflammation/immune activation in pregnancy; and (2) inflammation/immune activation in pregnancy with growth of CHEU at 12 months. Interleukin 6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), soluble(s) TNF-α receptor 1 and 2 (sTNFR1, sTNFR2), sCD14, and sCD163 were measured between 13 and 27 weeks' gestation. Linear regression models were fit to estimate differences between groups for each log-transformed biomarker, adjusted for confounders. RESULTS: Pregnant PWH (188 total, 39 perinatally acquired, 149 nonperinatally acquired) and 76 HIV-seronegative persons were included. PWH had higher IL-6, sTNFR1, sCD14, and sCD163 and lower sTNFR2 compared to HIV-seronegative persons in adjusted models. Among PWH, sCD163 was higher in those with perinatally versus nonperinatally acquired HIV and on PI-based versus INSTI-based ART. Higher maternal concentrations of IL-6, sTNFR2, and hs-CRP were associated with poorer growth at 12 months. CONCLUSIONS: Maternal HIV status is associated with a distinct profile of inflammation/immune activation during pregnancy, which may influence child growth.
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 | 0.001 |
| 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".