NF-κB Inhibitors Attenuate MCAO Induced Neurodegeneration and Oxidative Stress—A Reprofiling Approach
Bibliographic record
Abstract
Stroke is the leading cause of morbidity and mortality worldwide. About 87% of stroke cases are ischemic, which disrupt the physiological activity of the brain, thus leading to a series of complex pathophysiological events. Despite decades of research on neuroprotectants to probe for suitable therapies against ischemic stroke, no successful results have been obtained, and new alternative approaches are urgently required in order to combat this pathological torment. To address these problems, drug repositioning/reprofiling is explored extensively. Drug repurposing aims to identify new uses for already established drugs, and this makes it an attractive commercial strategy. Nuclear factor-kappa beta (NF-κB) is reported to be involved in many physiological and pathological conditions, such as neurodegeneration, neuroinflammation, and ischemia/reperfusion (I/R) injury. In this study, we examined the neuroprotective effects of atorvastatin, cephalexin, and mycophenolate against the NF-κB in ischemic stroke, as compared to the standard NF-κB inhibitor caeffic acid phenethyl ester (CAPE). An in-silico docking analysis was performed and their potential neuroprotective activities in the in vivo transient middle cerebral artery occlusion (t-MCAO) rat model was examined. The percent (%) infarct area and 28-point composite neuro score were examined, and an immunohistochemical analysis (IHC) and enzyme-linked immunosorbent assay (ELISA) were further performed to validate the neuroprotective role of these compounds in stroke as well as their potential as antioxidants. Our results demonstrated that these novels NF-κB inhibitors could attenuate ischemic stroke-induced neuronal toxicity by targeting NF-κB, a potential therapeutic approach in ischemic stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".