Targeting histone deacetylases for the treatment of disease
Bibliographic record
Abstract
Introduction HATs/HDACs and the pro‐inflammatory environment HATs/HDACs and endoplasmic reticulum (ER) stress The potential role of histone deacetylase inhibitors in the treatment of cancer Potential role of histone deacetylase inhibitors in the treatment of diabetes The potential role of histone deacetylase inhibitors in the treatment of neurodegenerative conditions Histone deacetylase inhibitors Caveats Final comments Abstract The ‘histone code’ is a well‐established hypothesis describing the idea that specific patterns of post‐translational modifications to histones act like a molecular ‘code’ recognized and used by non‐histone proteins to regulate specific chromatin functions. One modification, which has received significant attention, is that of histone acetylation. The enzymes that regulate this modification are described as lysine acetyltransferases or KATs, and histone deacetylases or HDACs. Due to their conserved catalytic domain HDACs have been actively targeted as a therapeutic target. The pro‐inflammatory environment is increasingly being recognized as a critical element for both degenerative diseases and cancer. The present review will discuss the current knowledge surrounding the clinical potential and current development of histone deacetylases for the treatment of diseases for which a pro‐inflammatory environment plays important roles, and the molecular mechanisms by which such inhibitors may play important functions in modulating the pro‐inflammatory environment.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".