Сточные пары натуральных переменных и их приложения в анализе и в теории чисел
Bibliographic record
Abstract
Structural insight into the conformational changes associated with aggregation and assembly of fibrils has provided a number of targets for therapeutic intervention. Solid-state NMR, hydrogen/deuterium exchange and mutagenesis strategies have been used to probe the secondary and tertiary structure of amyloid fibrils and key intermediates. Rational design of peptide inhibitors directed against key residues important for aggregation and stabilization of fibrils has demonstrated effectiveness at inhibiting fibrillogenesis. Studies on the interaction between Abeta and cell membranes led to the discovery that inositol, the head group of phosphatidylinositol, inhibits fibrillogenesis. As a result, scyllo-inositol is currently in clinical trials for the treatment of AD. Additional small-molecule inhibitors, including polyphenolic compounds such as curcumin, (-)-epigallocatechin gallate (EGCG), and grape seed extract have been shown to attenuate Abeta aggregation through distinct mechanisms, and have shown effectiveness at reducing amyloid levels when administered to transgenic mouse models of AD. Although the results of ongoing clinical trials remain to be seen, these compounds represent the first generation of amyloid-based therapeutics, with the potential to alter the progression of AD and, when used prophylactically, alleviate the deposition of Abeta.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".