Unlocking the secrets of aging: Epigenetic reader BRD4 as the target to combatting aging-related diseases
Bibliographic record
Abstract
BACKGROUND: Aging, a complex and profound journey, leads us through a labyrinth of physiological and pathological transformations, rendering us increasingly susceptible to aging-related diseases. Emerging investigations have unveiled the function of bromodomain containing protein 4 (BRD4) in manipulating the aging process and driving the emergence and progression of aging-related diseases. AIM OF REVIEW: This review aims to offer a comprehensive outline of BRD4's functions involved in the aging process, and potential mechanisms through which BRD4 governs the initiation and progression of various aging-related diseases. KEY SCIENTIFIC CONCEPTS OF REVIEW: BRD4 has a fundamental role in regulating the cell cycle, apoptosis, cellular senescence, the senescence-associated secretory phenotype (SASP), senolysis, autophagy, and mitochondrial function, which are involved in the aging process. Several studies have indicated that BRD4 governs the initiation and progression of various aging-related diseases, including Alzheimer's disease, ischemic cerebrovascular diseases, hypertension, atherosclerosis, heart failure, aging-related pulmonary fibrosis, and intervertebral disc degeneration (IVDD). Thus, the evidence from this review supports that BRD4 could be a promising target for managing various aging-related diseases, while further investigation is warranted to gain a thorough understanding of BRD4's role in these 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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".