Bitter Taste Receptors in Inflammatory and Infectious Diseases
Bibliographic record
Abstract
Bitter taste receptors (TAS2Rs), initially identified for their role in detecting bitter compounds in the oral cavity, have emerged as multifunctional receptors with critical roles beyond taste perception. These G protein-coupled receptors are expressed in various extra-oral tissues, where they influence immune responses, inflammation, and cellular processes associated with both infectious and chronic diseases. TAS2Rs play a key role in pathogen detection, immune modulation, and physiological regulation, contributing to defense mechanisms and homeostasis across multiple systems, including the respiratory, cardiovascular, metabolic, and central nervous systems. Their unique signaling pathways, broad ligand specificity, and genetic polymorphisms highlight their complex roles in health and disease. As our understanding of TAS2Rs deepens, these receptors are gaining recognition as potential therapeutic targets for managing a wide range of conditions. However, significant challenges remain, including interspecies variability and the limited in vivo characterization of their functions. Advances in technologies such as cryo-electron microscopy and transgenic models are providing valuable insights into TAS2R structure and function, paving the way for the development of novel therapeutic strategies. This review explores the expanding landscape of TAS2R research, emphasizing their emerging importance in addressing pressing global health challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".