Assessment of murine collagen-induced arthritis by longitudinal non-invasive duplexed molecular optical imaging
Bibliographic record
Abstract
OBJECTIVE: In the present study we evaluated the use of four commercially available fluorescent probes to monitor disease activity in murine CIA and its suppression during glucocorticoid therapy. METHODS: Arthritis was induced in male DBA/1 mice by immunization with type II collagen in Complete Freund's Adjuvant, followed by a boost of collagen in PBS. Four fluorescent probes from PerkinElmer in combination [ProSense 750 fluorescent activatable sensor technology (FAST) with Neutrophil Elastase 680 FAST and MMPSense 750 FAST with CatK 680 FAST] were used to monitor disease development from day 5 through to day 40 post-immunization. Fluorescence generated in vivo by the probes was correlated with clinical and histological score and paw measurements. RESULTS: The fluorescence intensity emitted by each probe was shown to correlate with the conventional measurements of disease. The highest degree of correlation was observed with ProSense 750 FAST in combination with Neutrophil Elastase 680 FAST; these probes were then used to successfully assess CIA suppression during dexamethasone treatment. CONCLUSION: We have demonstrated that longitudinal non-invasive duplexed optical fluorescence imaging provides a simple assessment of arthritic disease activity within the joints of mice following the induction of CIA and may represent a powerful tool to monitor the efficacy of drug treatments in preclinical studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".