Experimental models of neuroprotection relevant to multiple sclerosis
Bibliographic record
Abstract
Activated T cells, particularly those of the T-helper (Th) 1 subset, have the capacity to kill neurons. Strategies for preventing such damage may include deviation of activated T cells into the Th2 subset (e.g., via use of glatiramer acetate), alteration of functional properties of Th1 cells (e.g., through use of interferon [INF]-beta or IV immunoglobulin), and inhibition of activated cell migration into the CNS (e.g., by employing INF-beta or natalizumab). Matrix metalloproteinase-9 (MMP-9) also causes neuron death in neurotoxicity models, and examination of medications with MMP inhibitory activity indicates that minocycline is capable of preventing such damage. Minocycline also has other properties relevant to conferring neuroprotection, such as inhibition of microglial activity and apoptosis pathways. In a small pilot study in patients with relapsing-remitting multiple sclerosis, minocycline treatment produced favorable outcomes in terms of gadolinium-enhancing lesions and clinical course. Further studies are needed to establish whether experimental neuroprotection strategies involving these mechanisms may be translated into preventing neurodegeneration in multiple sclerosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".