Poly-sialylated glycan of cervicovaginal fluid can be a potential marker of preterm birth
Bibliographic record
Abstract
Preterm birth is a global health issue associated with neonatal death and morbidity. However, current methods of predicting preterm birth are insufficient to accurately screen for risk. This study aimed to assess the potential of site-specific N-glycosylation of cervicovaginal fluid (CVF) proteins as predictive biomarkers of preterm birth in a case-control study. Statistical analysis used Student's t-tests, ROC curve and logistic regression adjusted age and BMI. Using N-glycoproteomic analysis of the CVF, we identified 862 N-glycoproteins in CVF samples form 20 pregnancies and 6595 N-linked glycopeptides used a false discovery rate of less than 1%. Of 173 upregulated glycan in preterm group, we found low levels of fucosylation and high levels of sialylation in preterm birth (p < 0.05). Then we found that three poly-sialylated glycans had a high predictive value (AUC = 0.802, p < 0.017), which were expressed in all samples. In addition, the glycan model with clinical markers performed better. The results indicate that poly-sialylated glycans in CVF have potential value as novel clinical markers for predicting preterm birth during pregnancy. This study suggests strategies for developing new predictive biomarkers using cervicovaginal glycans to detect preterm birth in advance.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".