Genomic expression profiles of blood and placenta reveal significant immune‐related pathways and categories in Chinese women with gestational diabetes mellitus
Bibliographic record
Abstract
AIMS: We used microarray as well as quantitative real-time PCR (Q-RT-PCR) validation to define the genes and/or pathways that are involved in gestational diabetes mellitus (GDM) in patients of Chinese ethnicity. METHODS: We used the Illumina microarray platform to obtain comprehensive gene expression profiles of blood and placenta taken from GDM-positive and GDM-negative women. RESULTS: We found 5197 genes in blood and 243 genes in placenta, which had significantly altered expression profiles attributable to GDM. Genes previously known to have altered expressions as a result of GDM (such as TNF, IL1B, LEP, IFNG and HLA-G) were also validated. In addition, we identified a number of previously unreported genes: VAV3, PTPN6, CD48 and IL15, which had expression patterns that were significantly different from our GDM and control samples, as determined by both microarray and Q-RT-PCR assays. Two significant pathways were identified as GDM-associated pathways through integrated functional annotation. These pathways were: 'Natural killer cell mediated cytotoxicity' in blood and 'Cytokine-cytokine receptor interaction' in placenta. Furthermore, despite differences between blood and placenta in terms of the quantity of gene expression, we nonetheless observed similar functional distributions in both tissues in terms of immune-related genes. CONCLUSIONS: These newly identified key genes and pathways may provide valuable information about the pathogenesis of GDM and can be used to improve early diagnosis, prevention, medication design and clinical treatment.
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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".