Additional file 1 of Human endometrium-derived stem cell improves cardiac function after myocardial ischemic injury by enhancing angiogenesis and myocardial metabolism
Bibliographic record
Abstract
Additional file 1: Fig. S1. Results from RNA-seq and Protein array. a Heatmap from RNA-seq representing expression levels for differentially expressed genes (DEGs) between hBMSCs and hEMSCs. Red: upregulation; Blue: downregulation. Expression intensity was based on R software analysis of gene expression levels. b Twenty categories with significant enrichment were screened for functional enrichment analysis of gene ontology (GO). Yellow line: Number of genes. Bars: Significance level. c Heatmap showing up- and down-regulation for DEGs in hBMSCs and hEMSCs related to angiogenesis. d Venn diagram and protein array analysis between hBMSCs and hEMSCs for DEGs. Black circle in Venn diagram represents DEGs related to angiogenesis between hEMSCs and hBMSCs by RNA-seq, while gray circle represents DEGs identified through protein arrays. The intersection of the two circles represents overlapping DEGs between RNA-seq and protein array for angiogenesis, where five angiogenesis-related factors were found, of which PGF and ANG4 were up-regulated in hBMSCs, while FLT-1, TEK (also known as TIE2), and FGF9 were upregulated in hEMSCs. FGF9: Fibroblast growth factor 9; FLT-1: Fms-like tyrosine kinase 1; PGF: Placental growth factor; ANG4: Angiopoietin 4. Table S1. Formulation of Differentiation Media. Table S2. Real-time qPCR primer sets.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.850 | 0.155 |
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".