Group I Metabotropic Glutamate Receptor Signalling and its Implication in Neurological Disease
Bibliographic record
Abstract
Stimulation of Group I metabotropic glutamate receptors (mGluR1 and mGluR5) leads to activation of a wide variety of signalling pathways. mGluRs couple to Gα(q/)₁₁ proteins, activating phospholipase Cβ1 resulting in both diacylglycerol and inositol-1,4,5-triphosphate formation followed by the activation of protein kinase C. In addition, mGluR activation can lead to modulation of a number of ion channels, such as different types of calcium and potassium channels. Group I mGluRs can also activate other downstream protein kinases, such as ERK1/2 and AKT, which are implicated in cellular growth, differentiation, and survival. Moreover, Group I mGluRs interact with a variety of different proteins that are important for the regulation of synaptic signalling, such as Homer and PDZ domain containing proteins, such as Tamalin. A role for mGluR1/5 in a number of disease states has also been proposed. As mGluR1/5 signal transduction is complex and involves multiple partners, a better understanding of alterations in mGluR signalling in brain disorders will be required in order to discern the molecular and cellular basis of these pathologies. This review will highlight recent findings concerning mGluR signaling alterations in brain pathologies, such as stroke, fragile X syndrome, Alzheimer's disease, Parkinson's disease, Huntington's disease, epilepsy, and drug addiction.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".