Janus kinase/signal transducer and activator of transcription pathway in schizophrenia (Review)
Bibliographic record
Abstract
Schizophrenia is a complex mental disorder that affects cognition, emotions and behaviour.Among the signaling pathways that show dysregulation in schizophrenia is the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway.JAK/STAT is a pleiotropic pathway that plays key roles in multiple cellular functions.A brief overview of dysregulated signaling in schizophrenia is provided in the present review, focusing on the JAK/STAT pathway.Depending on the disease stage, dysregulation of signaling pathways causes inflammation, which is a key factor in the onset and maintenance of schizophrenia.The dysregulated JAK/STAT signaling pathway is implicated in schizophrenia through numerous processes, including neuroand gliogenesis, synaptic plasticity and microglia activation.Research on JAK/STAT pathway components can contribute to knowledge that will be translated into efficient therapeutic management strategies to improve the quality of life of patients with schizophrenia.Contents 1. Introduction 2. Methods 3. Inflammation and dysregulated signaling in schizophrenia 4. Dysregulated JAK/STAT signaling in neurological dysfunction and schizophrenia 5. Crosstalk of ncRNAs with the JAK/STAT pathway in schizophrenia 6. JAK/STAT pathway in the therapeutic management of schizophrenia 7. Models and approaches to study signaling pathways in schizophrenia 8.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".