Hypoxic/Normoxic Preconditioning Increases Endothelial Differentiation Potential of Human Bone Marrow CD133+ Cells
Bibliographic record
Abstract
CD133+ cells are hemangioblasts that have capacity to generate into both hematopoietic and endothelial cells (ECs). Hypoxia/normoxia has shown to be the regulator of the balance between stemness and differentiation. In this study we performed Agilent's whole human genome oligo microarray analysis and examined the differentiation potential of the bone-marrow-derived CD133+ cells after hypoxic/normoxic preconditioning of CD133+ cells. Results showed that there was no significant increase in erythroid colony forming unit (CFU-E) and CFU-granulocyte, erythrocyte, monocyte, and megakaryocyte formation with cells treated under hypoxia/normoxia. However, a significant increment of EC forming unit at 24 h (143.2 +/- 8.0%) compared to 0 h (100 +/- 11.4%) was observed in CFU-EC analysis. Reverse transcription-polymerase chain reaction and immunostaining analysis showed that the differentiated cells diminished hematopoietic stem cell surface markers and acquired the gene markers and functional phenotype of ECs. The transcriptome profile revealed a cluster of 232 downregulated and 498 upregulated genes in cells treated for 24 h under hypoxia. The upregulated genes include angiogenic genes, angiogenic growth factor genes, angiogenic cytokine and chemokine genes, as well as angiogenic-positive regulatory genes, including FGFBP1, PDGFB, CCL15, CXCL12, CXCL6, IL-6, PTN, EREG, ERBB2, EDG5, FGF3, FHF2, GDF15, JUN, L1CAM, NRG1, NGFR, and PDGFB. On the other hand, angiogenesis inhibitors and related genes, including IL12A, MLLT7, STAB1, and TIMP2, are downregulated. Taken together, hypoxic/normoxic preconditioning may lead to the differentiation of CD133+ cells toward endothelial lineage, which may improve the current clinical trial studies.
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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 | 0.000 | 0.000 |
| 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".