An emerging role for comprehensive proteome analysis in human pregnancy research
Bibliographic record
Abstract
Elucidation of underlying cellular and molecular mechanisms is pivotal to the comprehension of biological systems. The successful progression of processes such as pregnancy and parturition depends on the complex interactions between numerous biological molecules especially within the uterine microenvironment. The tissue- and stage-specific expression of these bio-molecules is intricately linked to and modulated by several endogenous and exogenous factors. Malfunctions may manifest as pregnancy disorders such as preterm labour, pre-eclampsia and fetal growth restriction that are major contributors to maternal and perinatal morbidity and mortality. Despite the immense amount of information available, our understanding of several aspects of these physiological processes remains incomplete. This translates into significant difficulties in the timely diagnosis and effective treatment of pregnancy-related complications. However, the emergence of powerful mass spectrometry-based proteomic techniques capable of identifying and characterizing multiple proteins simultaneously has added a new dimension to the field of biomedical research. Application of these high throughput methodologies with more conventional techniques in pregnancy-related research has begun to provide a novel perspective on the biochemical blueprint of pregnancy and its related disorders. Further, by enabling the identification of proteins specific to a disease process, proteomics is likely to contribute, not only to the comprehension of the underlying pathophysiologies, but also to the clinical diagnosis of multifactorial pregnancy disorders. Although the application of this technology to pregnancy research is in its infancy, characterization of the cellular proteome, unearthing of functional networks and the identification of disease biomarkers can be expected to significantly improve maternal healthcare in the future.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".