Diaphragm Satellite Cells Isolation by Optimized MACS and the Effect of Mechanical Ventilation on their Proliferation and Differentiation Characteristics through MyoD and Myogenin Pathways
Bibliographic record
Abstract
Abstract Objective: In the present study, we aimed to establish a feasible method to isolate single diaphragm satellite cells from C57 mice, and clarify the effect of mechanical ventilation (MV) on the proliferation and differentiation of diaphragm satellite cells. Moreover, the underlying molecular mechanism was also explored.Methods: After the dissection of the diaphragm, enzymolysis, and specific antibody selection, single diaphragm satellite cells were harvested from C57 mice receiving 6 h of MV or not with optimized magnetic-activated cell sorting (MACS) approach. The cells were stained with BrdU or labeled with the differentiation antibody MYH3, followed by observation using fluorescence microscopy. The cells were counted from randomly selected visual fields, and the proliferation or differentiation characteristics of the control and MV groups were compared by IMAGE software. Besides, the expressions of MyoD and myogenin were detected by quantitative real-time PCR (qRT-PCR). Results: The single diaphragm satellite cells were successfully purified through MACS using a set of optimized parameters. Generally speaking, 1.5×105 cells could be harvested from a single diaphragm. Upon MV, the proliferation rate of diaphragm satellite cells was decreased from 88.74% to 81.92%, while the differentiation rate was increased from 17.94% to 27.58%. Moreover, the expressions of MyoD and myogenin were significantly up-regulated upon MV. Conclusions: In our current work, an efficient method was successfully established to isolate single diaphragm satellite cells. After MV, the differentiation rate of diaphragm satellite cells tended to increase, and the expressions of MyoD and myogenin were up-regulated. Collectively, our findings provided valuable insights into further research and clinical target treatment.
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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 | 0.000 |
| 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 | 0.000 |
| 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".