Regulation of Inflammatory Response in Neural Cells in Vitro by Thiadiazolidinones Derivatives through Peroxisome Proliferator-activated Receptor γ Activation
Bibliographic record
Abstract
In most neurodegenerative disorders, including multiple sclerosis, Parkinson disease, and Alzheimer disease, a massive neuronal cell death occurs as a consequence of an uncontrolled inflammatory response, where activated astrocytes and microglia and their cytotoxic agents play a crucial pathological role. Current treatments for these diseases are not effective. In the present study we investigate the effect of thiadiazolidinone derivatives, which have been recently suggested to play a role in neurodegenerative disorders. We have found that thiadiazolidinones are potent neuroprotector compounds. Thiadiazolidinones inhibited inflammatory activation of cultured brain astrocytes and microglia by diminishing lipopolysaccharide-induced interleukin 6, tumor necrosis factor alpha, inducible nitric-oxide synthase, and inducible cyclooxygenase type 2 expression. In addition, thiadiazolidinones inhibited tumor necrosis factor-alpha and nitric oxide production and, concomitantly, protected cortical neurons from cell death induced by the cell-free supernatant from activated microglia. The neuroprotective effects of thiadiazolidinones are completely inhibited by the peroxisome proliferator-activated receptor gamma antagonist GW9662. In contrast the glycogen synthase kinase 3beta inhibitor LiCl did not show any effect. These findings suggest that thiadiazolidinones potently attenuate lipopolysaccharide-induced neuroinflammation and reduces neuronal death by a mechanism dependent of peroxisome proliferator-activated receptor gamma activation.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".