Inhibition of human LDL oxidation by the neuroprotective drug l-deprenyl
Bibliographic record
Abstract
L-deprenyl (Selegiline) used in the treatment of Parkinson's and Alzheimer's disease also enhances longevity. Oxidized low density lipoprotein promotes atherosclerosis and is toxic to both vascular and neural tissue. The reported association between vascular dysfunction and neurodegenerative diseases prompted us to investigate the effect of l-deprenyl, a MAO-B inhibitor, on low density lipoprotein (LDL) oxidation. LDL was isolated from freshly collected blood and the kinetics of copper induced oxidation of LDL was monitored continuously by spectrophotometry. Oral administration (10 mg) or in vitro (2.8 to 84 microM) addition of l-deprenyl inhibited oxidation of LDL isolated from healthy men and post-menopausal women. This is the first report demonstrating that the antioxidant action of l-deprenyl may be antiatherogenic and cardioprotective. Such an action could contribute to reported extension of life span associated with long-term administration of the drug. In conjunction with inhibition of LDL oxidation, l-deprenyl is unique in that it demonstrates protective effects on both vascular and neuronal tissue. Prophylactic use of low doses of l-deprenyl may accord protection against vascular and neurodegenerative diseases associated with aging.
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 | 0.001 |
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".