Cellular and molecular atlas of the placenta from a COVID‐19 pregnant woman infected at midgestation highlights the defective impacts on foetal health
Bibliographic record
Abstract
OBJECTIVES: The impacts of the current COVID-19 pandemic on maternal and foetal health are enormous and of serious concern. However, the influence of SARS-CoV-2 infection at early-to-mid gestation on maternal and foetal health remains unclear. MATERIALS AND METHODS: Here, we report the follow-up study of a pregnant woman of her whole infective course of SARS-CoV-2, from asymptomatic infection at gestational week 20 to mild and then severe illness state, and finally cured at Week 24. Following caesarean section due to incomplete uterine rupture at Week 28, histological examinations on the placenta and foetal tissues as well as single-cell RNA sequencing (scRNA-seq) for the placenta were performed. RESULTS: Compared with the gestational age-matched control placentas, the placenta from this COVID-19 case exhibited more syncytial knots and lowered expression of syncytiotrophoblast-related genes. The scRNA-seq analysis demonstrated impaired trophoblast differentiation, activation of antiviral and inflammatory CD8 T cells, as well as the tight association of increased inflammatory responses in the placenta with complement over-activation in macrophages. In addition, levels of several inflammatory factors increased in the placenta and foetal blood. CONCLUSION: These findings illustrate a systematic cellular and molecular signature of placental insufficiency and immune activation at the maternal-foetal interface that may be attributed to SARS-CoV-2 infection at the midgestation stage, which highly suggests the extensive care for maternal and foetal outcomes in pregnant women suffering from COVID-19.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".