Maternal Obesity Modifies the Impact of Active SARS-CoV-2 Infection on Placental Pathology
Bibliographic record
Abstract
BACKGROUND: Obesity during pregnancy is associated with an elevated risk of severe COVID-19, including higher rates of maternal complications, intensive care admission, and adverse neonatal outcomes. The impact of combination of SARS-CoV-2 infection and maternal obesity in placental pathology has not been properly investigated. AIM: To compare the histopathological changes in the placenta induced by active SARS-CoV-2 infection in obese and non-obese patients. METHODS: This retrospective cohort study included human placentas from non-obese women and pre-gestationally obese women with active SARS-CoV-2 infection (SARS and OB+SARS, respectively), and placentas from non-obese women and pre-gestationally obese women without SARS-CoV-2 infection (control and OB, collected in the post- and pre-pandemic periods, respectively). RESULTS: A higher (50%) occurrence of ischemic injury and subchorionic fibrin deposits and a 15× higher risk of occurrence of these lesions were found in the OB+SARS group, in relation to control. In contrast, a 10% lower risk of developing chorangiosis in the OB+SARS group than the OB group was observed. CONCLUSIONS: An increased risk of lesions related to both maternal and fetal malperfusion and ischemic injury and a lower risk for chorangiosis exist in placentas from obese women affected by SARS-CoV-2 infection. Importantly, these differences were not observed in placentas from non-obese women.
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 | 0.001 | 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".