Amniotic fluid proteomic signatures of cervical insufficiency and their association with length of latency
Bibliographic record
Abstract
PROBLEM: Cervical insufficiency is a precursor of preterm birth. Treatment with emergency cervical cerclage is contraindicated in the presence of intra-amniotic infection. Detecting infection with Gram stain and culture of amniotic fluid lacks sensitivity. Proteomic profiling of amniotic fluid in cervical insufficiency may help identify pregnancies best suited for emergency cerclage. METHOD OF STUDY: Thirty-two pregnant women underwent amniocentesis for routine genetic testing (n = 22) or after diagnosis of cervical insufficiency (n = 10). The proteomic profiles of the amniotic fluid samples were compared in a cross-sectional fashion, including sub-analyses of women with cervical insufficiency and latency periods of <1 week and >1 week post-diagnosis. RESULTS: Mean gestational age at diagnosis of cervical insufficiency was 21.4 weeks (95% CI 20.6-22.1). Proteomic analysis yielded 40 (7.2%, P < 0.05) differentially expressed proteins between women with delivery <1 week (n = 6) vs. >1 week (n = 4). Women who delivered <1 week had activated inflammatory response (z = 2.3, P = 6.71E-09), chemotaxis of immune cells (z = 2.9, P = 2.01E-08), and inhibited bacterial growth (z = -2.2, P = 5.82E-05). A multivariate model of eight biomarkers positively associated with cases of <1 week latency and distinguished cases from controls (97.8%, cross-validation accuracy 92.7%, P = 0.0009). CONCLUSION: In this pilot study, significant differences in the amniotic fluid proteomic profiles in cases of cervical insufficiency compared to genetic amniocentesis were observed. Proteomic signatures were predictive of achieving latency > 1 week after diagnosis of cervical insufficiency. These preliminary findings suggest that proteomic analysis may be of value in predicting outcome following cervical insufficiency and warrants further validation in larger studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".