Melatonin pretreatment of human adipose tissue-derived mesenchymal stromal cells enhances their prosurvival and protective effects on human kidney cells
Bibliographic record
Abstract
The efficacy of cell therapy for many diseases can be limited by the poor survival of implanted cells in an environment of tissue injury. Melatonin has been reported to have antioxidative and antiapoptotic effects. Adipose tissue-derived mesenchymal stromal cells (ASCs), cells easily obtained in high amounts and with minimal discomfort, have shown great promise in cell therapy applications, such as in acute kidney injury. We hypothesized that melatonin pretreatment of human ASCs (hASCs) would improve their renoprotective and prosurvival effects. We therefore investigated the action of melatonin on hASCs, as well as the effect of the resulting hASCs-conditioned media (CM) on human kidney cells exposed to oxidative and apoptotic injury-provoking doses of cisplatin. Our results demonstrated that pretreatment of hASCs with melatonin, 100 μM for 3 h, significantly increased their proliferation and their expression of prosurvival P-Erk1/2 and P-Akt, and of antioxidative enzymes catalase and heme oxygenase (HO)-1. In addition, the CM from hASCs pretreated with melatonin provoked a significantly higher proliferation and migration of HK-2 human kidney epithelial cells. Furthermore, this CM exerted significantly higher prosurvival and antiapoptotic actions on HK-2 cells exposed to cisplatin in vitro. Western blot analysis showed higher expression of P-Erk1/2, Bcl-2, SOD-1, and HO-1 in the HK-2 cells exposed to cisplatin in the presence of CM from melatonin-pretreated hASCs. In sum, our study revealed that in vitro pretreatment of hASCs with melatonin may significantly enhance their survival and their therapeutic effectiveness on injured tissue.
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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".