Effect of Plasmodium Infection during Pregnancy on Passive Neonatal Immunity against Tetanus Toxoid and Rotavirus
Bibliographic record
Abstract
Passive immunity acquired through transplacental IgG transport is essential to protect infants against pathogens as childhood vaccination programs begins. Diarrhea caused by rotavirus and neonatal tetanus are common and potentially fatal childhood infections that can be prevented by transplacental IgG. However, it is not known whether maternal infections in pregnancy can reduce the transfer of these antibodies to the fetus. This study evaluated the effect of submicroscopic Plasmodium infection during pregnancy on the transfer of maternal IgG antibodies against rotavirus (anti-RV) and tetanus toxoid (anti-TT) to newborns of pregnant women residing in Puerto Libertador and Tierralta, Colombia. Expression of different immune mediators and levels of IgG against rotavirus and tetanus toxoid were quantified in pregnant women with and without Plasmodium infection during pregnancy. Submicroscopic infection at the time of delivery was associated with a cord-to-maternal ratio (CMR) > 1 for anti-RV and < 1 for anti-TT IgG, as well as with an increase in the expression of immune mediators of inflammation (IFN-γ), anti-inflammation (IL-10, TGF-β), and regulation (FoxP3, CTLA-4). When compared by species, these findings (CMR > 1 for anti-RV and < 1 for anti-TT IgG) were conserved in submicroscopic Plasmodium vivax infections at delivery. The impact of Plasmodium infections on neonatal susceptibility to other infections warrants further exploration.
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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 | 0.000 | 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 | 0.000 | 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".