Characterization of natural fluorescence in mice
Bibliographic record
Abstract
One important challenge for in-vivo imaging fluorescence in cancer research and related pharmaceutical studies is to discriminate the exogenous fluorescence signal of the specific tagged agents from the natural fluorescence. For mice, natural fluorescence is composed of endogenous fluorescence from organs like the skin, the bladder, etc. and from ingested food. The discrimination between the two kinds of fluorescence makes easy monitoring the targeted tissues. Generally, the amplitude of the fluorescence signal depends on the location and on the amount of injected fluorophore, which is limited in in-vivo experiments. This paper exposes some results of natural fluorescence analysis from in-vivo mice experiments using a time domain small animal fluorescence imaging system: eXplore OptixTM. Fluorescence signals are expressed by a Time Point Spread Function (TPSF) at each scan point. The study uses measures of similarity applied purposely to the TPSF to evaluate the discrepancy and/or the homogeneity of scanned regions of a mouse. These measures allow a classification scheme to be performed on the TPSF's based on their temporal shapes. The work ends by showing how the exogenous fluorescence can be distinguished from natural fluorescence by using the TPSF temporal shape.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".