An Initial Proteomic Analysis of Human Preterm Labor: Placental Membranes
Bibliographic record
Abstract
Human preterm labor (PL) is the single most significant problem in modern Obstetrics and Gynecology, affecting approximately 10% of pregnancies worldwide, constituting the leading cause of perinatal mortality and morbidity, and contributing significantly to chronic childhood disease. Currently, our molecular understanding of PL remains staggeringly inadequate to reliably diagnose or rationally intervene in PL events; several molecular alterations have been implicated in PL, but these have proven of limited value as diagnostic/prognostic markers. The majority of PL events remain spontaneous and unpredictable: critical care emergencies. Here, we apply functional proteomics to dissect molecular mechanisms of human PL. Human placental tissue was collected in clearly differentiated cases of preterm and term labor. Highly refined two-dimensional gel electrophoresis (2DE) was used for protein separation, coupled with automated differential gel image analysis to compare the resulting proteomic maps. For this initial study, only the most important protein differences were selected for further analysis, that is, proteins that were unique to one sample, and absent from the other, with 100% reproducibility across the sample population. In total, 11 such proteins were identified by tandem mass spectrometry, falling into three distinct functional classes: structural/cytoskeletal components, ER lumenal proteins with enzymatic or chaperone functions, and proteins with anticoagulant properties. These expression changes form the groundwork for further molecular investigation of this devastating medical condition. This approach therefore holds the potential not only to define the underlying molecular components, but also to identify novel diagnostic tools and targets for rational drug intervention.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".