New methods for multiple sclerosis drug discovery
Bibliographic record
Abstract
INTRODUCTION: MS is a heterogeneous disorder that requires the development of better diagnostics to identify disease subtypes enabling appropriate therapeutic intervention at an early stage of the disease. Accumulating evidence indicates that members of the inhibitor of apoptosis (IAP) family play an important role in the pathogenesis of MS by reducing the apoptotic elimination of autoreactive immune cells. AREAS COVERED: The authors describe improved animal modeling strategies to identify compounds that have immunomodulatory, neurorestorative and neuroprotective properties. In addition, the authors propose new approaches to better model cognitive dysfunction in MS, which will aid the development of novel therapeutics for this complex disorder. The paper provides the reader with an appreciation for the diagnostic and therapeutic potential of apoptosis-related proteins for MS. EXPERT OPINION: Recent evidence suggests that increased resistance of autoreactive immune cells to apoptotic elimination is a contributing factor to both disease susceptibility and progression in MS. This occurs, at least in part, because of elevated levels of the IAP family of anti-apoptotic genes that display distinct expression profiles associated with different subtypes of MS. The authors believe that the detection and targeting of members of the IAP family can provide better drugs for MS. Particularly, the authors feel that the overexpression of IAPs in animal models can provide novel insights into MS for both its pathogenesis and the discovery of new lead compounds.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".