Lipopolysaccharide-pretreated mesenchymal stem cell-conditioned medium optimized with 10 kDa filter attenuates the injury of H9c2 cardiomyocytes in a model of hypoxia/reoxygenation
Bibliographic record
Abstract
Mesenchymal stem cell-derived conditioned medium (MSC-CM) improves cardiac function, which is partly attributed to the released paracrine factors. Since such cardioprotection is moderate and transient, it is essential that MSC-CM's effective components are optimized to alleviate myocardial injury. To optimize MSC-CM, MSCs were treated with or without lipopolysaccharides (LPSs) for 48 h (serum-free), and the supernatant was collected. Then, LPS-CM (MSC stimulated by LPS) was further treated with LPS remover (LPS Re-CM) or was concentrated with a 10 kDa cutoff filter (10 kDa-CM). Enzyme-linked immunosorbent assay showed that all the pretreatments increased the levels of vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), and insulin growth factor (IGF) except LPS Re-CM; 10 kDa-CM was superior to the other CMs. Cell Counting Kit-8 displayed that the viability of injured H9c2 cells was enhanced with the increase in the MSC-CM concentration. We also found that the 10 kDa-CM significantly alleviated H9c2 hypoxia/reoxygenation (H/R) injury, as evidenced by the increased Bcl-2/Bax ratio, and decreased the levels of lactate dehydrogenase and cardiac troponin. Transmission electron microscopy (TEM), TdT-mediated dUTP nick-end labelling (TUNEL), and hematoxylin and eosin staining (H&E) confirmed that 10 kDa-CM inhibited H/R-induced H9c2 morphological changes. Proteomic analysis identified 41 differentially expressed proteins in 10 kDa-CM, among which anti-inflammation, proangiogenesis, and antiapoptosis were related to cardiac protection. This study indicates that 10 kDa-CM protects H9c2 cardiomyocytes from H/R injury by preserving most of the protective factors, such as VEGF, HGF, and IGF, in MSC-CM.
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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) | 0.001 | 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 | 0.001 |
| 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".