<i>In Vivo</i>Imaging of Cell Proliferation for a Dynamic, Whole Body, Analysis of Undesired Drug Effects
Bibliographic record
Abstract
Noninvasive in vivo imaging offers a novel approach to preclinical studies opening the possibility of investigating biological events in the spatiotemporal dimension (eg, in any district of the body in time). Toxicological analysis may benefit from this novel approach through precise identification of the time and the target organs of toxicity manifestations, and assessment of the reversibility of toxic insults. The current limitation for routine application of this technology is the lack of appropriate surrogate markers for imaging toxicological events. Here, we demonstrate that in vivo imaging of a proliferation marker is capable of measuring the reduction of cell proliferation due to genotoxic/apoptotic agents, γ rays or antineoplastic drugs, or the increased proliferation associated with the inflammatory and regenerative reactions occurring after a toxic insult. A number of tools are currently available for imaging proliferation in preclinical and clinical settings, however our data provide a novel way to translate the evidence of toxic effects obtained in preclinical animal studies, by the direct, noninvasive measure of dividing cells in humans.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".