Refinement and validation of infrared thermal imaging (IRT): a non-invasive technique to measure disease activity in a mouse model of rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: The discovery and development of new medicines requires high-throughput screening of possible therapeutics in a specific model of the disease. Infrared thermal imaging (IRT) is a modern assessment method with extensive clinical and preclinical applications. Employing IRT in longitudinal preclinical setting to monitor arthritis onset, disease activity and therapeutic efficacies requires a standardized framework to provide reproducible quantitative data as a precondition for clinical studies. METHODS: Here, we established the accuracy and reliability of an inexpensive smartphone connected infrared (IR) camera against known temperature objects as well as certified blackbody calibration equipment. An easy to use protocol incorporating contactless image acquisition and computer-assisted data analysis was developed to detect disease-related temperature changes in a collagen-induced arthritis (CIA) mouse model and validated by comparison with two conventional methods, clinical arthritis scoring and paw thickness measurement. We implemented IRT to demonstrate the beneficial therapeutic effect of nanoparticle drug delivery versus free methotrexate (MTX) in vivo. RESULTS: The calibrations revealed high accuracy and reliability of the IR camera for detecting temperature changes in the rheumatoid arthritis animal model. Significant positive correlation was found between temperature changes and paw thickness measurements as the disease progressed. IRT was found to be superior over the conventional techniques specially at early arthritis onset, when it is difficult to observe subclinical signs and measure structural changes. CONCLUSION: IRT proved to be a valid and unbiased method to detect temperature changes and quantify the degree of inflammation in a rapid and reproducible manner in longitudinal preclinical drug efficacy 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".