Metabotropic glutamate receptor 5 as a potential therapeutic target in Huntington’s disease
Bibliographic record
Abstract
INTRODUCTION: Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by a polyglutamine expansion in the amino-terminal region of the huntingtin (htt) protein, which underlies the loss of striatal and cortical neurons. Glutamate has been implicated in a number of neurodegenerative diseases, and several studies suggest that the metabotropic glutamate receptor 5 (mGluR5) may represent a target for the treatment of HD. AREAS COVERED: The main goal of this review is to discuss the current data in the literature regarding the role of mGluR5 in HD and evaluate the potential of mGluR5 as a therapeutic target for the treatment of HD. mGluR5 is highly expressed in the brain regions affected in HD and is involved in movement control. Moreover, mGluR5 interacts with htt and mutated htt profoundly affects mGluR5 signaling. However, mGluR5 stimulation can activate both neuroprotective and neurotoxic signaling pathways, depending on the context of activation. EXPERT OPINION: Although the data published so far strongly indicate that mGluR5 plays a major role in HD-associated neurodegeneration, htt aggregation and motor symptoms, it is not clear whether mGluR5 stimulation can diminish or intensify neuronal cell loss and HD progression. Thus, future experiments will be necessary to further investigate the outcome of drugs acting on mGluR5 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".