Amine oxidases and their inhibitors: what can they tell us about neuroprotection and the development of drugs for neuropsychiatric disorders?
Bibliographic record
Abstract
Although monoamine oxidase (MAO) inhibitors are not used as extensively as other antidepressants, they continue to have an important place in the armamentarium of drugs used to treat psychiatric and neurological disorders. Interest in MAO inhibitors has also increased in recent years because of numerous reports of their neuroprotective/neurorescue properties.1–5 Such studies have resulted in a better understanding of possible mechanisms of neuroprotection; stimulated the development of new drugs, such as rasagiline; provided important clues for the development of other drugs for neuropsychiatric disorders; and contributed to the recent surge of interest in possible neuroprotective actions of psychiatric drugs in general.6,7 Intriguingly, they have also demonstrated that the MAO inhibitors currently available are multifaceted, since, in many cases, the neuroprotection seems to be independent of their MAO inhibition. Studies on semicarbazide-sensitive amine oxidase (SSAO) and its inhibition have also provided exciting results that are relevant to neuropsychiatric disorders and associated diabetes and cardiovascular disease.8,9 These findings are outlined briefly below.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".