Bibliographic record
Abstract
The extensive disposition of amyloid-beta (Ab) plaques cemented between the nerve cells have been known to hold decisive clues to an age-related neurodegenerative disorder – Alzheimer’s disease (AD), which is the most prevalent cause of dementia among older people over 65. The progressive memory deficits and other disturbances in Alzheimer’s patients’ daily activities have often been observed and associated with significant social burden, eventually leading to the increase of morbidity and mortality. Because of the modest therapeutic benefit seen with currently available cholinesterase inhibitors and NMDA-receptor antagonist, it is critical to develop novel treatment options for AD. In this regard, our study aims to design, synthesize, and evaluate phenylthiazole based compound libraries with anti-Ab activity as disease-modifying agents in treating AD. A library of these novel small molecules was synthesized and evaluated for their anti-amyloid aggregation activity in the presence and absence of Ab using the thioflavin T (ThT)-based fluorescence spectroscopy. The binding interactions were investigated by the computational studies. These investigations have shown that phenylthiazole based derivatives are capable of preventing Abaggregation. Future studies such as transmission electron microscopy (TEM) experiments, and the in vitro cell-based assays and in vivo studies will provide more insight on the potential of these novel compounds to treat AD.
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) | 0.005 | 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".