Not only lithium: regulation of glycogen synthase kinase-3 by antipsychotics and serotonergic drugs
Bibliographic record
Abstract
Multiple recent lines of evidence have underscored the potential role of glycogen synthase kinase-3 (GSK3) as a common molecular signal integrator and therapeutic target for different classes of psychiatric drugs (Figure 1). GSK3α and GSK3β are two closely related serine/threonine kinases originally associated with the regulation of glycogen synthesis in response to insulin (Embi et al., 1980). These constitutively active kinases are regulated negatively through the phosphorylation of single serine residues, Ser21 (GSK3α) and Ser9 (GSK3β), of their regulatory amino-terminal domain (Frame and Cohen, 2001). The protein kinase Akt/PKB has been shown to inhibit both GSK3 isoforms in response to insulin and insulin growth factors (Cross et al., 1995). However, other protein kinases such as PKA and PKC can also regulate GSK3 in different cellular systems (Frame and Cohen, 2001). Apart from its function in glycogenesis, GSK3 plays a role in a host of physiological processes including neurodevelopment, cell proliferation and apoptosis. Moreover, dysregulation of GSK3 function is involved in tumour proliferation as well as in the formation of neurofibrillary tangles in Alzheimer's disease (Woodgett, 2003).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".