From Computer to Cognition: Synthesis and Preclinical Validation of a Naphthalene Amide as a Novel Anti-Alzheimer Agent
Bibliographic record
Abstract
Alzheimer's disease is a major global health challenge, imposing a significant healthcare burden due to irreversible neuronal degeneration and impaired cognitive function. An extensive in silico screening followed by in vitro testing was performed to identify potential cost-effective AChE inhibitors for AD treatment. The newly identified compound (SF2) was synthesized and investigated for in vitro and in vivo activities and toxicities. In vitro activity was assessed using AChE inhibitory potential, and toxicity was evaluated using MTT assay on mouse-brain capillary endothelial cells. For in vivo studies, mice were divided into seven groups and treated with SF2 at four different doses for 23 days. Visuospatial learning and memory retention was determined with the help of four nootropic models. The protective effect of SF2 against the AD was assessed by the levels of SOD, CAT, GSH, Nitrite, MDA, amyloid-β, tau, TNF-α, and IL-6. Selected organs (brain, kidney, liver, heart and spleen) were investigated for their oxidative stress markers, along-with CBC, liver and renal function tests. Pronounced amelioration in cognitive performance was observed by SF2 treatment. It significantly attenuated changes in brain pathology, acetylcholinesterase activity, and inflammatory markers induced by the streptozotocin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
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".