Modulation of Experimental Autoimmune Encephalomyelitis by VLA‐2 Blockade
Bibliographic record
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS). Adhesion molecules play important roles in cell-cell and cell-extracellular matrix (ECM) interactions in inflammation. Blocking the interaction between inflammatory cells and vascular endothelia can prevent cell entry into tissues and harmful inflammatory responses, that is, autoimmunity, but could also limit immunosurveillance by anti-viral T cells in sites of infection or latency. Development of progressive multifocal leukoencephalopathy in patients treated with antibody against very late antigen (VLA)-4 prompted us to explore an alternative therapeutic approach. We used an antibody against the integrin alpha2, VLA-2, that interacts with ECM, not vascular endothelium. SJL/J mice were sensitized with myelin proteolipid protein (PLP)(139-151) peptide to induce experimental autoimmune encephalomyelitis (EAE), an animal model for MS. Treatment of mice with VLA-2 antibody suppressed clinical signs and CNS inflammation of EAE, when antibody was given immediately after disease onset. In contrast, VLA-4 or VLA-2 antibody treatment of mice during the priming or remission phase of EAE had minor effects on the disease's clinical course. No differences were found in lymphoproliferative responses to PLP(139-151) among treatment groups. Data suggest that blocking cell-ECM interactions can be an alternative therapy for 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".