Glucocorticoid Receptor Signaling: Intricacies and Therapeutic Opportunities
Bibliographic record
Abstract
The paper "Glucocorticoid receiver signing: intricacies and thermal opportunities" published in the journal Trends in Biochemical Sciences on February 29, 2024, authored by Dorien Clarisse, Laura Van Moortel, Chlo é Van Leene, Kris Gevaert, Karolin De Bosscher, and others from the University of Bridget et al. This study delves into the signaling mechanism of glucocorticoid receptors (GR) and their potential therapeutic applications. GR, as a nuclear receptor (NR), plays a crucial role in regulating the treatment of inflammatory diseases and certain cancers. By reviewing the basic biological characteristics of GR, including its activation and nuclear translocation mechanisms in cells, this study discusses in detail the interaction between GR and chromatin, revealing how it regulates downstream gene expression by directly binding to DNA responsive elements or interacting with other transcription factors. In addition, new concepts in GR signaling were explored, such as the recruitment of co regulatory factors, the role of aggregate formation, and strategies to enhance therapeutic efficacy through interaction with other nuclear receptors. By integrating multiple technologies to study the conformation and modification of chromatin, we can gain a deeper understanding of the cell-specific effects of glucocorticoids (GCs), providing a new perspective for developing GR ligands and therapeutic strategies with fewer side effects. This study not only provides valuable insights into the complex role of GR in cells, but also provides a theoretical basis for the development of new therapeutic methods in the future, especially in research aimed at reducing treatment-related side effects while improving efficacy.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".