Bibliographic record
Abstract
Abnormalities in molecular signalling have been implicated in neurodegeneration. It is emerging that glycogen synthase kinase-3 (GSK-3) is a key signalling molecule that induces neurodegeneration and deficits in memory formation related to Alzheimer's disease (AD). Early stages of AD are associated with deficits in memory formation before neuronal cell death is detectable. Recent studies in rodents have suggested that these impairments in memory formation might result from increased GSK-3 signalling, because enhanced GSK-3 activity impairs hippocampal memory formation. GSK-3 activity blocks synaptic long-term potentiation and induces long-term depression. Furthermore, increased GSK-3 signalling is likely to be a key contributor to the formation of the pathological hallmarks in AD, neurofibrillary tangles (NFTs) and amyloid plaques. Recent studies with mouse models have indicated that GSK-3, but not cyclin-dependent kinase 5, is critical for hyperphosphorylation of the cytoskeletal protein tau, which is the prerequisite for NFT formation in AD. Furthermore, increased GSK-3 signalling in AD mice causes abnormal processing of the amyloid precursor protein so that amyloid peptide production augments and neurotoxicity is induced. Taken together, the current evidences suggest that increased GSK-3 signalling may be responsible for the deficits in memory formation in early stages of AD and neurodegeneration in later stages of the disease.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".