Inhibition of GSK3 by lithium, from single molecules to signaling networks
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.
Bibliographic record
Abstract
For more than 60 years, the mood stabilizer lithium has been used alone or in combination for the treatment of bipolar disorder, schizophrenia, depression, and other mental illnesses. Despite this long history, the molecular mechanisms trough which lithium regulates behavior are still poorly understood. Among several targets, lithium has been shown to directly inhibit glycogen synthase kinase 3 alpha and beta (GSK3α and GSK3β). However in vivo, lithium also inhibits GSK3 by regulating other mechanisms like the formation of a signaling complex comprised of beta-arrestin 2 (βArr2) and Akt. Here, we provide an overview of in vivo evidence supporting a role for inhibition of GSK3 in some behavioral effects of lithium. We also explore how regulation of GSK3 by lithium within a signaling network involving several molecular targets and cell surface receptors [e.g., G protein coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs)] may provide cues to its relative pharmacological selectivity and its effects on disease mechanisms. A better understanding of these intricate actions of lithium at a systems level may allow the rational development of better mood stabilizer drugs with enhanced selectivity, efficacy, and lesser side effects.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it