Putative neuroprotective pharmacotherapies to target the staged progression of mental illness
Bibliographic record
Abstract
AIM: Neuropsychiatric disorders including depression, bipolar and schizophrenia frequently exhibit a neuroprogressive course from prodrome to chronicity. There are a range of agents exhibiting capacity to attenuate biological mechanisms associated with neuroprogression. This review will update the evidence for putative neuroprotective agents including clinical efficacy, mechanisms of action and limitations in current assessment tools, and identify novel agents with neuroprotective potential. METHOD: Data for this review were sourced from online databases PUBMED, Embase and Web of Science. Only data published since 2012 were included in this review, no data were excluded based on language or publication origin. RESULTS: Each of the agents reviewed inhibit one or multiple pathways of neuroprogression including: inflammatory gene expression and cytokine release, oxidative and nitrosative stress, mitochondrial dysfunction, neurotrophin dysregulation and apoptotic signalling. Some demonstrate clinical efficacy in preventing neural damage or loss, relapse or cognitive/functional decline. Agents include: the psychotropic medications lithium, second generation antipsychotics and antidepressants; other pharmacological agents such as minocycline, aspirin, cyclooxygenase-2 inhibitors, statins, ketamine and alpha-2-delta ligands; and others such as erythropoietin, oestrogen, leptin, N-acetylcysteine, curcumin, melatonin and ebselen. CONCLUSIONS: Signals of evidence of clinical neuroprotection are evident for a number of candidate agents. Adjunctive use of multiple agents may present a viable avenue to clinical realization of neuroprotection. Definitive prospective studies of neuroprotection with multimodal assessment tools are required.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".