Different Facets of Aging in Human Mesenchymal Stem Cells
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) have to be culture expanded to gain relevant cell numbers for therapeutic applications. However, within 2-3 months the proliferation rate of MSCs decays until they ultimately reach a senescent state. This is accompanied by enlarged morphology, reduced expression of surface markers, and decreased differentiation potential. So far it is only scarcely understood how long-term culture affects MSC preparations, and five processes seem to be involved: (1) MSCs are composed of different sub-populations, and due to different proliferation rates the heterogeneity changes in the course of in vitro expansion; (2) cells in culture acquire mutations and other stochastic cellular defects; (3) self-renewal of MSCs may be impaired under culture conditions, leading to gradual differentiation; (4) the number of cell divisions might be restricted (e.g., by loss of telomeres), and (5) replicative senescence might be associated with the aging process of the organism. There is a growing perception that long-term culture has to be taken into account--especially for clinical applications. On the other hand, the state of replicative senescence is poorly defined by the number of population doublings or even by the number of passages. Reliable molecular measures for cellular aging are urgently needed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".