Different Facets of Aging in Human Mesenchymal Stem Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it