GSK3 Networks in Schizophrenia
Bibliographic record
Abstract
Glycogen synthase kinase (GSK)-3, a ubiquitous serine/threonine kinase, was first identified in the late 1970s as a key enzyme in glucose metabolism. Its association with a multitude of neuronal events and signaling processes has emerged ever since and ample evidence now converges on a prominent role of this conserved kinase in neuropsychiatric disorders such as schizophrenia. First evidence came from the observations that many schizophrenia risk genes directly interact with or are the members of cascades signaling through GSK-3. The fact that both antipsychotics and psychosis-inducing agents influence GSK-3 activity either directly or indirectly position this regulatory enzyme at the crossroads of the pathways that lead to behavioral outcomes and cognitive functions. In this chapter, we describe the major signal transduction cascades regulating GSK-3 activity and the findings of human and animal studies on alteration or deregulation of the GSK-3 signaling partners and networks in schizophrenia. We elaborate on how GSK-3 interaction with its established and putative partners might culminate in behavioral phenotypes. We further speculate how these findings could be exploited to develop novel diagnostics and therapeutic strategies for schizophrenia that target GSK-3 or its interacting molecules.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".