The Role of Epigenetic Regulator SIRT1 in Balancing the Homeostasis and Preventing the Formation of Specific “Soil” of Metabolic Disorders and Related Cancers
Why this work is in the frame
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Bibliographic record
Abstract
SIRT1 was discovered in 1979 but growing interest in this protein occurred only 20 years later when its overexpression was reported to prolong the lifespan of yeast. Since then, several studies have shown the benefits of its increased expression in preventing or delaying of many diseases. SIRT1, as a histone deacetylase, is an epigenetic regulator but it has wide range of non-histone targets which are involved in metabolism, energy sensing pathways, circadian machinery and in inflammatory regulation. Disturbances in these interconnected processes cause different diseases, however it seems they have common roots in unbalanced inflammatory processes and lower level or inactivation of SIRT1. SIRT1 inactivation was implicated in coronavirus disease (COVID-19) severity as well and its low level counted as a predictor of uncontrolled COVID-19. Several other diseases such as metabolic disease, obesity, diabetes, Alzheimer's disease, cardiovascular disease or depression are related to chronic inflammation and similarly show decreased SIRT1 level. It has recently been known that SIRT1 is inducible by calorie restriction/proper diet, physical activity and appropriate emotional state. Indeed, a healthier metabolic state belongs to higher level of SIRT1 expression. These suggest that appropriate lifestyle as non-pharmacological treatment may be a beneficial tool in the prevention of inflammation or metabolic disturbance-related diseases as well as could be a part of the complementary therapy in medical practice to reach better therapeutic response and quality of life. We aimed in this review to link the beneficial effect of SIRT1 with those diseases, where its level decreased. Moreover, we aimed to collect evidences of interventions or treatments, which increase SIRT1 expression and thus, open the possibility to use them as preventive or complementary therapies in medical practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it