Trehalose Conjugates of Silybin as Prodrugs for Targeting Toxic Aβ Aggregates
Bibliographic record
Abstract
Alzheimer’s disease (AD) is linked to the abnormal accumulation of amyloid β peptide (Aβ) aggregates in the brain. Silybin B, a natural compound extracted from milk thistle ( Silybum marianum ), has been shown to significantly inhibit Aβ aggregation in vitro and to exert neuroprotective properties in vivo . However, further explorations of silybin B’s clinical potential are currently limited by three main factors: (a) poor solubility, (b) instability in blood serum, and (c) only partial knowledge of silybin’s mechanism of action. Here, we address these three limitations. We demonstrate that conjugation of a trehalose moiety to silybin significantly increases both water solubility and stability in blood serum without significantly compromising its antiaggregation properties. Furthermore, using a combination of biophysical techniques with different spatial resolution, that is, TEM, ThT fluorescence, CD, and NMR spectroscopy, we profile the interactions of the trehalose conjugate with both Aβ monomers and oligomers and evidence that silybin may shield the “toxic” surfaces formed by the N-terminal and central hydrophobic regions of Aβ. Finally, comparative analysis with silybin A, a less active diastereoisomer of silybin B, revealed how even subtle differences in chemical structure may entail different effects on amyloid inhibition. The resulting insight on the mechanism of action of silybins as aggregation inhibitors is anticipated to facilitate the future investigation of silybin’s therapeutic potential.
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) | 0.001 | 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".