Targeting mGluR group III for the treatment of neurodegenerative diseases
Bibliographic record
Abstract
Glutamate, an excitatory neurotransmitter, is essential for neuronal function, and it acts on ionotropic or metabotropic glutamate receptors (mGluRs). A disturbance in glutamatergic signaling is a hallmark of many neurodegenerative diseases. Developing disease-modifying treatments for neurodegenerative diseases targeting glutamate receptors is a promising avenue. The understudied group III mGluR 4, 6-8 are commonly found in the presynaptic membrane, and their activation inhibits glutamate release. Thus, targeted mGluRs therapies could aid in treating neurodegenerative diseases. This review describes group III mGluRs and their pharmacological ligands in the context of amyotrophic lateral sclerosis, Parkinson's, Alzheimer's, and Huntington's diseases. Attempts to evaluate the efficacy of these drugs in clinical trials are also discussed. Despite a growing list of group III mGluR-specific pharmacological ligands, research on the use of these drugs in neurodegenerative diseases is limited, except for Parkinson's disease. Future efforts should focus on delineating the contribution of group III mGluR to neurodegeneration and developing novel ligands with superior efficacy and a favorable side effect profile for the treatment of neurodegenerative 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".