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Record W2036343301 · doi:10.2174/138945011794182764

Lithium and its Neuroprotective and Neurotrophic Effects: Potential Treatment for Post-Ischemic Stroke Sequelae

2011· article· en· W2036343301 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Drug Targets · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsNeuroprotectionNeurochemicalNeurotrophic factorsMedicineStroke (engine)NeuroscienceIschemiaExcitotoxicityAtrophyLithium (medication)PopulationNeurotrophinNeuroinflammationInternal medicineInflammationPsychologyNMDA receptor

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.266
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it