Comparative proteomic analysis of human placenta derived from assisted reproductive technology
Bibliographic record
Abstract
The aim of this study was to use proteomics-based approach to examine differences in protein expression in placenta derived from assisted reproductive technology (ART) and normal pregnancy. Using 2-DE we found that, compared with the control group, 12 spots in standard in vitro fertilization group and 18 spots in intracytoplasmic sperm injection group were identified as significantly differentially expressed proteins. Among them, six spots were differentially expressed in both standard IVF and ICSI groups with the same change tendency. Totally, 20 proteins were successfully identified by MALDI TOF/TOF MS, including proteins involved in the membrane traffic, metabolism, nucleic acid processing, stress response and cytoskeleton. Notably, five proteins detected to be differentially expressed in both ART groups were identified as annexin A3, hnRNP C1/C2, alpha-SNAP, FTL and ATP5A. Some of the proteins were confirmed by Western blot and immunohistochemistry analysis. Our study allowed for the initial identification of these proteins related to various functions in placentation with significantly altered abundance in ART groups. The present results reveal that abnormal protein profiles are involved in ART placenta and these differentially expressed proteins may be valuable for the evaluation of potential association between ART treatment and offspring outcome.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".