Locomotor activity as an effective measure of the severity of inflammatory arthritis in a mouse model
Bibliographic record
Abstract
OBJECTIVE: Mouse models are valuable in preclinical studies of inflammatory arthritis. However, current methods for measuring disease severity or responses to treatment are not optimal. In this study a smart cage system using multiple sensors to measure locomotor activity was evaluated in the K/BxN serum transfer model of inflammatory arthritis. METHODS: Arthritis was induced in C57BL/6 mice with injections of K/BxN serum. Clinical index and ankle thickness were measured for 14 days. Locomotor activity was measured in smart cages for 23 h periods on Days 0, 7, and 13. The same measurements were taken in mice consuming diets supplemented or not with fish oil to evaluate a preventative treatment. RESULTS: Initiation, peak and resolution phases of disease could be measured with the smart cages. Locomotor activity including speed, travel distance, number of active movements and rear movements were all significantly lower on Days 7-8 of illness (peak) compared to Days 0 and 13-14 (resolution) (one-way repeated measures analyses, p<0.05). The clinical index and ankle thickness measurements did not capture differences between dietary groups. Significantly increased activity was measured in most of the locomotor parameters in the fish oil group compared to the control mice at both Days 8 and 14 (2-way repeated measures ANOVA, p<0.05). CONCLUSION: The measurement of locomotor activity provided a more detailed evaluation of the impact of inflammatory arthritis on animal well-being and mobility than that provided by measuring clinical index and ankle thickness, and could be a valuable tool in preclinical studies of inflammatory arthritis.
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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) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".