Neuroinflammation and Endoplasmic Reticulum Stress Are Coregulated by Crocin To Prevent Demyelination and Neurodegeneration
Bibliographic record
Abstract
Endoplasmic reticulum (ER) stress is a homeostatic mechanism, which is used by cells to adapt to intercellular and intracellular changes. Moreover, ER stress is closely linked to inflammatory pathways. We hypothesized that ER stress is an integral component of neuroinflammation and contributes to the development of neurological diseases. In autopsied brain specimens from multiple sclerosis (MS) and non-MS patients, XBP-1 spliced variant (XBP-1/s) was increased in MS brains (p < 0.05) and was correlated with the expression of the human endogenous retrovirus-W envelope transcript, which encodes the glycoprotein, Syncytin-1 (p < 0.05). In primary human fetal astrocytes transfected with a Syncytin-1-expressing plasmid, XBP-1/s, BiP, and NOS2 were induced, which was suppressed by crocin treatment (p < 0.05). Crocin also protected oligodendrocytes exposed to cytotoxic supernatants derived from Syncytin-1-expressing astrocytes (p < 0.05) and NO-mediated oligodendrocytotoxicity (p < 0.05). During experimental autoimmune encephalomyelitis (EAE), the transcript levels of the ER stress genes XBP-1/s, BiP, PERK, and CHOP were increased in diseased spinal cords compared with healthy littermates (p < 0.05), although CHOP expression was not involved in the EAE disease phenotype. Daily treatment with crocin starting on day 7 post-EAE induction suppressed ER stress and inflammatory gene expression in spinal cords (p < 0.05), which was accompanied by preserved myelination and axonal density, together with reduced T cell infiltration and macrophage activation. EAE-associated neurobehavioral deficits were also ameliorated by crocin treatment (p < 0.05). These findings underscored the convergent roles of pathogenic ER stress and immune pathways in neuroinflammatory disease and point to potential therapeutic applications for crocin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".