Glycogen synthase kinase-3 in insulin and Wnt signalling: a double-edged sword?
Bibliographic record
Abstract
Glycogen synthase kinase-3 is an unusual protein serine/threonine kinase that, unlike most of its 500-odd relatives in the genome, is active under resting conditions and is inactivated upon cell stimulation. The two mammalian isoforms, GSK-3alpha and beta, play largely overlapping roles and have been implicated in a variety of human pathologies, including Type II diabetes, Alzheimer's disease, bipolar disorder and cancer. Recently, the modes of regulation of this enzyme have been elucidated through a combination of structural and cell biological studies. A series of relatively selective small molecules have facilitated chemical manipulation of the enzyme in intact cells and tissues, and new roles for the protein kinase in embryonic stem cell differentiation and motility have emerged. Despite these advances, the therapeutic value of this enzyme as a drug target remains clouded by uncertainty over the potential of antagonists to promote tumorigenesis. This article describes the state of understanding of this intriguing enzyme, and weighs current evidence regarding whether there is a therapeutic window for amelioration of diseases in which it is implicated, in the absence of inducing new pathologies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".