Bibliographic record
Abstract
Ligand-gated ion channels are an important class of signalling protein that depend on small chemical neurotransmitters such as acetylcholine, l-glutamate, glycine and gamma-aminobutyrate for activation. Although numerous in number, neurotransmitter substances have always been thought to drive the receptor complex into the open state in much the same way and not rely substantially on other factors. However, recent work on kainate-type (KAR) ionotropic glutamate receptors (iGluRs) has identified an exception to this rule. Here, the activation process fails to occur unless external monovalent anions and cations are present. This absolute requirement of ions singles out KARs from all other ligand-gated ion channels, including closely related AMPA- and NMDA-type iGluR family members. The uniqueness of ion-dependent gating has earmarked this feature of KARs as a putative target for the development of selective ligands; a prospect all the more compelling with the recent elucidation of distinct anion and cation binding pockets. Despite these advances, much remains to be resolved. For example, it is still not clear how ion effects on KARs impacts glutamatergic transmission. I conclude by speculating that further analysis of ion-dependent gating may provide clues into how functionally diverse iGluRs families emerged by evolution. Consequently, ion-dependent gating of KARs looks set to continue to be a subject of topical inquiry well into the future.
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 | 0.001 | 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) | 0.002 | 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".