Implications of the Neuroprotective Effects of Lithium for the Treatment of Bipolar and Neurodegenerative Disorders
Bibliographic record
Abstract
Bipolar disorder is increasingly recognized as an illness that may progress to impairment in neurocognitive functioning and cell loss in cortical and limbic brain regions. Glutamatergic damage and/or damage due to high glucocorticoid levels that inhibit adult neurogenesis are likely contributing mechanisms. Drug treatments with possible neuroprotective effects are becoming increasingly important both clinically and as research tools. Mood stabilizing drugs and lithium in particular may act to prevent neuronal damage and tissue loss that may occur in the brain of patients with bipolar disorders. Lithium has been shown to exert neuroprotective effects in vitro and to stimulate neurogenesis in the hippocampus. Animal studies have demonstrated pharmacological effects of lithium suggestive of its role in neuroprotection, which range from reducing excitotoxicity through increased glutamate uptake, to regulation of a number of signal transduction intermediates such as myo-inositol, protein kinase C, phosphotidylinositol-3 kinase (PI-3K)/protein kinase B (Akt), ras-mitogen-activated protein kinase (MAPK), glycogen synthase kinase (GSK)-3alpha and -3beta and calcium. It remains to be established whether lithium treatment protects against possible cell damage in the same manner as it protects against recurrences of the illness. We propose to examine the effect of long-term lithium treatment on neurocognitive functioning of bipolar patients and the use of lithium in the treatment of chronic neuropsychiatric disorders.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".