Lithium and its Neuroprotective and Neurotrophic Effects: Potential Treatment for Post-Ischemic Stroke Sequelae
Bibliographic record
Abstract
Post-stroke cognitive impairment has a high prevalence in stroke patients and is associated with poor short and long term outcomes, including a negative impact on functional recovery. There is evidence that post-stroke impairment is the direct result of stroke induced neurological injury. Gray matter atrophy has been implicated in the development of post-stroke cognitive impairment and is the result of a series of neurochemical processes that are activated by ischemia. Lithium, traditionally used as a mood stabilizer, has been recognized in the last 10 years for its robust neuroprotective and neurotrophic effects against diverse insults, such as ischemia, both in vitro and in vivo. This has generated several preclinical and clinical studies of lithium treatment for managing neurodegenerative diseases and cerebral ischemia. Evidence suggests that lithium may protect against the cerebral atrophy and neuronal degeneration induced by the neurochemical processes and pathways known to regulate cell death and atrophy after an ischemic event. Lithiummediated neurotroprotective and neurotrophic effects involve mechanisms highly relevant to the post-stroke population including the increased expression of brain-derived neurotrophic factor (BDNF) and Bcl-2, and inhibition of GSK-3β. Lithium-induced increases in human gray matter have been reported and occur within a time frame consistent with the known effects of lithium through increased expression of BDNF, Bcl-2 and GSK-3β inhibition. This article reviews the evidence to support the use of lithium to reduce neuronal damage post-stroke through 1) mechanisms of excitotoxicity and post-ischemic inflammation; and 2) neurotrophic signaling cascades. Lithiums relevant actions in preclinical and clinical studies will be reviewed and presented to support the neuroprotective and neurotrophic effects of lithium as well as other clinical considerations in using lithium in the post-ischemic stroke population. Keywords: Stroke, ischemia, atrophy, neuroprotection, neurotrophic factors, brain-derived neurotrophic factor (BDNF), glycogen synthase kinase 3 beta (GSK-3β), lithium, brain gray matter volume, cognitive impairment, neurological injury, post-ischemic inflammation, functional outcome, rehabilitation, neuronal degeneration, neurotrophic drugs, depression, anxiety, apathy, Current Treatments for Ischemic Stroke, heparin, acetylsalicylic acid (ASA), clopidogrel, dipyridamole, ticlopidine, oral anticoagulants, POST-ISCHEMIC STROKE, excitotoxicity, neurotransmitters, glutamate receptors, Neurotrophic Signaling Cascades Post-Stroke, Lithium Increases BDNF, Lithium Increases the Expression of Bcl-2, Lithium Inhibits GSK-3, Pro-Apoptotic Signaling Molecule, Post-Stroke Atrophy, Polymorphisms, Hemorrhagic Strokes
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".