A highly sensitive strategy for monitoring real-time proliferation of targeted cell types in vivo
Bibliographic record
Abstract
Cell proliferation processes play pivotal roles in timely adaptation to many biological situations. Herein, we establish a highly sensitive and simple strategy by which time-series showing the proliferation of a targeted cell type can be quantitatively monitored in vivo in the same individuals. We generate mice expressing a secreted type of luciferase only in cells producing Cre under the control of the Ki67 promoter. Crossing these with tissue-specific Cre-expressing mice allows us to monitor the proliferation time course of pancreatic β-cells, which are few in number and weakly proliferative, by measuring plasma luciferase activity. Physiological time courses, during obesity development, pregnancy and juvenile growth, as well as diurnal variation, of β-cell proliferation, are clearly detected. Moreover, this strategy can be utilized for highly sensitive ex vivo screening for proliferative factors for targeted cells. Thus, these technologies may contribute to advancements in broad areas of biological and medical research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".