Identification of the molecular mechanism of insulin-like growth factor-1 (IGF-1): a promising therapeutic target for neurodegenerative diseases associated with metabolic syndrome
Bibliographic record
Abstract
Neurodegenerative disorders are accompanied by neuronal degeneration and glial dysfunction, resulting in cognitive, psychomotor, and behavioral impairment. Multiple factors including genetic, environmental, metabolic, and oxidant overload contribute to disease progression. Recent evidences suggest that metabolic syndrome is linked to various neurodegenerative diseases. Metabolic syndrome (MetS) is known to be accompanied by symptoms such as hyperglycemia, abdominal obesity, hypertriglyceridemia, and hypertension. Despite advances in knowledge about the pathogenesis of neurodegenerative disorders, effective treatments to combat neurodegenerative disorders caused by MetS have not been developed to date. Insulin growth factor-1 (IGF-1) deficiency has been associated with MetS-related pathologies both in-vivo and in-vitro. IGF-1 is essential for embryonic and adult neurogenesis, neuronal plasticity, neurotropism, angiogenesis, metabolic function, and protein clearance in the brain. Here, we review the evidence for the potential therapeutic effects of IGF-1 in the neurodegeneration related to metabolic syndrome. We elucidate how IGF-1 may be involved in molecular signaling defects that occurs in MetS-related neurodegenerative disorders and highlight the importance of IGF-1 as a potential therapeutic target in MetS-related neurological diseases.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".