VANL-100 Attenuates Beta-Amyloid-Induced Toxicity in SH-SY5Y Cells
Bibliographic record
Abstract
Antioxidants are being explored as novel therapeutics for the treatment of neurodegenerative diseases such as Alzheimer’s disease (AD) through strategies such as chemically linking antioxidants to synthesize novel co-drugs. The main objective of this study was to assess the cytoprotective effects of the novel antioxidant compound VANL-100 in a cellular model of beta-amyloid (Aβ)-induced toxicity. The cytotoxic effects of Aβ in the presence and absence of all antioxidant compounds were measured using the 3-(4,5-dimethylthiazol-2-yl)2-5-diphenyl-2H-tetrazolium bromide (MTT) assay in SH-SY5Y cells in both pre-treatment and co-treatment experiments. In pre-treatment experiments, VANL-100, or one of its parent compounds, naringenin (NAR), alpha-lipoic acid (ALA), or naringenin + alpha-lipoic acid (NAR + ALA), was administrated 24 h prior to an additional 24-h incubation with 20 μM non-fibril or fibril Aβ25–35. Co-treatment experiments consisted of simultaneous treatment with Aβ and antioxidants. Pre-treatment and co-treatment with VANL-100 significantly attenuated Aβ-induced cell death. There were no significant differences between the protective effects of VANL-100, NAR, ALA, and NAR + ALA with either form of Aβ, or in the effect of VANL-100 between 24-h pre-treatment and co-treatment. These results demonstrate that the novel co-drug VANL-100 is capable of eliciting cytoprotective effects against Aβ-induced toxicity.
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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 | 0.000 |
| 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".