Dexamethasone ameliorates Alzheimer’s pathological condition via inhibiting Nf-κB and mTOR signaling pathways
Bibliographic record
Abstract
Alzheimer’s disease (AD) is one of the severe neurodegenerative disorders among the elderly population, so early interventions play an important role in AD progression. The deposition of amyloid-beta(Aβ) plaques and the accumulation of tau tangles within microglia and astrocytes which leads to inflammatory response due to the production of inflammatory mediators such as nitric oxide (NO), reactive oxygen species (ROS), tumor necrosis factor (TNF)-alpha that trigger the neuronal death. In the present study, we performed that dexamethasone as a synthetic anti-inflammatory agent can affect the progression of Alzheimer’s dementia via nuclear factor-κB(Nf-κB) and its downstream pathways. Literature review concentrated on Nf-κB, and the mammalian target of rapamycin(mTOR) pathway was executed in addition to looking for the molecular biology aspects in the Kyoto Encyclopedia of Genes and Genomes (KEGG). According to our hypothesis, it could be suggested that dexamethasone participates in reducing oxidative stress, inflammation, insulin resistance, and deposition of Aβ through inhibiting several pathways such as Nf-κB and mTOR signaling pathways. The present hypothesis proposes that dexamethasone could be a probable candidate to improve the pathological condition of AD. It should, however, be noted that due to little evidence for dexamethasone administration in AD patients, further investigations are required.
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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 | 0.001 | 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) | 0.003 | 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".