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.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".