Modulation of renal ischemia/reperfusion in rats by a combination of ischemic preconditioning and adipose-derived mesenchymal stem cells (ADMSCs)
Bibliographic record
Abstract
The present study investigated the effects of combination of ischemic preconditioning (Ipre) and adipose-derived mesenchymal stem cells (ADMSCs) on renal ischemia-reperfusion (I-R) injury in rats. 90 male Sprague Dawley rats were divided into 5 equal groups; sham operated, control (45 min left renal ischemia), Ipre group as control group with 3 cycles of Ipre just before renal ischemia, ADMSCs-treated group (as control with ADMSCs 10(6) cells in 0.1 mL via penile vein 60 min before ischemia time), and Ipre + ADMSCs group as ADMCs group with 3 cycles of Ipre. Ipre and ADMSCs groups showed significant decrease in serum creatinine and blood urea nitrogen (BUN) and caspase-3 and CD45 expression in kidney and significant increase in HIF-1α, SDF-1α, CD31, and Ki67 expressions in kidney compared with the control group (p < 0.05). Moreover, the Ipre + ADMSCs group showed significant decrease in serum BUN and caspase-3 and CD45 expression in kidney with significant increase in HIF-1α, SDF-1α, CD31, and Ki67 expression in kidney compared with the Ipre and ADMCs groups (p < 0.05). We concluded that Ipre potentiates the renoprotective effect of ADMSCs against renal I/R injury probably by upregulation of HIF-1α, SDF-1α, CD31, and Ki67 and downregulation of caspase-3 and CD45.
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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) | 0.001 | 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 | 0.001 |
| 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".