Bibliographic record
Abstract
Disorders of the central nervous system (CNS) are complex disease states that represent a major challenge for modern medicine. Although aetilogy is often unknown, it is established that multiple factors such as defects in genetics and/or epigenetics, the environment as well as imbalance in neurotransmitter receptor systems are all at play in determining an individual's susceptibility to disease. Gene therapy is currently not available and therefore, most conditions are treated with pharmacological agents that modify neurotransmitter receptor signaling. Here, I provide a review of ionotropic glutamate receptors (iGluRs) and the roles they fulfill in numerous CNS disorders. Specifically, I argue that our understanding of iGluRs has reached a critical turning point to permit, for the first time, a comprehensive re-evaluation of their role in the cause of disease. I illustrate this by highlighting how defects in AMPA receptor (AMPAR) trafficking are important to fragile X mental retardation and ectopic expression of kainate receptor (KAR) synapses contributes to the pathology of temporal lobe epilepsy. Finally, I discuss how parallel advances in studies of other neurotransmitter systems may allow pharmacologists to work towards a cure for many CNS disorders rather than developing drugs to treat their symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".