<p>Targeting Chemokines and Chemokine Receptors in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis</p>
Bibliographic record
Abstract
Multiple sclerosis (MS) is an immune-mediated and neurodegenerative disorder that results in inflammation and demyelination of the central nervous system (CNS). MS symptoms include walking difficulties, visual weakening, as well as learning and memory impairment, thus affecting the quality of the patient's life. Chemokines and chemokine receptors are expressed on the immune cells as well as the CNS resident cells. Several sets of chemokine receptors and their ligands tend to be pathogenic players in MS, including CCL2, CCL3, CCL4, CCL5, CCL7, CCL8, CCL11, CCL17, CCL19, CCL21, CCL22, CXCL1, CXCL8, CXCL9, CXCL10, CXCL11, and CXCL16. Furthermore, current modulatory drugs that are used in the treatment of MS and its animal model, the experimental autoimmune encephalomyelitis (EAE), affect the expression of several chemokine and chemokine receptors. In this review, we highlight the pathogenic roles of chemokines and their receptors as well as utilizing them as potential therapeutic targets through selective agents, such as specific antibodies and receptor blockers, or indirectly through MS or EAE immunomodulatory drugs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".