Bibliographic record
Abstract
Multiple sclerosis is an autoimmune demyelinating disease that affects more that 2.5 million people worldwide. Biochemical and in vitro data put forward many therapeutic candidates that have to be verified in vivo models of MS. Experimental autoimmune encephalomyelitis (EAE) is a widely utilized model that replicates many aspects of MS. The traditionally used assessment of behavioural outcomes is performed by two independent observers in the blind to experimental setup manner. Such way of assessment is person dependent and allow only evaluate a small number of parameters. Also, the scores often are different among different laboratories. Here we propose to utilize 40 parameters behavioural assessment during development and progression of EAE. We used 24hr/day recording of animals before and three weeks after induction of EAE. The films were analysed using Clever Sys software. We found dramatic differences in behavioural outcomes even within the first week of the EAE induction. First, we noted a significant reduction of total distance walked per day. Mice with EAE walked two-fold less after 1-week post-EAE and four-fold less after two weeks of EAE. Hanging behaviour significantly declined (up to 30 fold) in mice within two weeks of EAE. On the other hand, grooming behaviour significantly increased in the first week of EAE. Detailed analysis of mouse activity revealed that mice are most active between 20h and 24h and least active during 11h and 15hr. In conclusion, we demonstrate that multiple parameter automated analysis of behavioural outcomes may help to validate therapeutic targets and give insights into the mechanisms of the neuroinflammation during MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".