Aryl hydrocarbon receptor: current perspectives on key signaling partners and immunoregulatory role in inflammatory diseases
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AhR) is a versatile environmental sensor and transcription factor found throughout the body, responding to a wide range of small molecules originating from the environment, our diets, host microbiomes, and internal metabolic processes. Increasing evidence highlights AhR's role as a critical regulator of numerous biological functions, such as cellular differentiation, immune response, metabolism, and even tumor formation. Typically located in the cytoplasm, AhR moves to the nucleus upon activation by an agonist where it partners with either the aryl hydrocarbon receptor nuclear translocator (ARNT) or hypoxia-inducible factor 1β (HIF-1β). This complex then interacts with xenobiotic response elements (XREs) to control the expression of key genes. AhR is notably present in various crucial immune cells, and recent research underscores its significant impact on both innate and adaptive immunity. This review delves into the latest insights on AhR's structure, activating ligands, and its multifaceted roles. We explore the sophisticated molecular pathways through which AhR influences immune and lymphoid cells, emphasizing its emerging importance in managing inflammatory diseases. Furthermore, we discuss the exciting potential of developing targeted therapies that modulate AhR activity, opening new avenues for medical intervention in immune-related conditions.
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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 | 0.002 |
| 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) | 0.007 | 0.004 |
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".