Offspring affected with <i>in utero</i> Zika virus infection retain molecular footprints in the bone marrow and blood cells
Bibliographic record
Abstract
Congenital virus infections, for example cytomegalovirus and rubella virus infections, commonly affect the central nervous and hematological systems in fetuses and offspring. However, interactions between emerging congenital Zika virus and hematological system—bone marrow and blood—in fetuses and offspring are mainly unknown. Our overall goal was to determine whether silent in utero Zika virus infection can cause functional and molecular footprints in the bone marrow and blood of fetuses and offspring. We specifically focused on silent fetal infection because delayed health complications in initially asymptomatic offspring were previously demonstrated in animal and human studies. Using a well-established porcine model for Zika virus infection and a set of cellular and molecular experimental tools, we showed that silent in utero infection causes multi-organ inflammation in fetuses and local inflammation in the fetal bone marrow. In utero infection also caused footprints in the offspring bone marrow and PBMCs. These findings should be considered in a broader clinical context because of growing concerns about health sequelae in cohorts of children affected with congenital Zika virus infection in the Americas. Understanding virus-induced molecular mechanisms of immune activation and inflammation in fetuses may provide targets for early in utero interventions. Also, identifying early biomarkers of in utero-acquired immunopathology in offspring may help to alleviate long-term sequelae.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".