Targeting the Chondroitin Sulfate Proteoglycans: Evaluating Fluorinated Glucosamines and Xylosides in Screens Pertinent to Multiple Sclerosis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Chondroitin sulfate proteoglycans (CSPGs) are upregulated in insults to the central nervous system, including multiple sclerosis (MS), an inflammatory demyelinating condition of the central nervous system. CSPGs appear to be detrimental in MS, as they enhance immune responses and act as barriers to oligodendrocyte differentiation and thus remyelination. Despite their deleterious roles, strategies to selectively reduce CSPG production are lacking. The purpose of this study was to develop, screen, and describe a series of glucosamine derivatives and xylosides for their capacity to overcome detrimental CSPGs and inflammatory processes. Specifically, we assess the ability of analogues to interfere with CSPG biosynthesis, promote the outgrowth of oligodendrocyte precursor cells in an inhibitory environment, and lower inflammation by attenuating the proliferation of T lymphocytes. We highlight the beneficial activities of a novel compound, per-O-acetylated 4,4-difluoro- N -acetylglucosamine (Ac-4,4-diF-GlcNAc) in vitro, and report that it reduced inflammation and clinical severity in a mouse model of MS. Thus, this study represents an important advance, as we uncover that targeting CSPG biosynthesis with a potent inhibitor is an effective avenue to ameliorate inflammatory cascades and promote repair processes in MS and other neurological conditions.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".