Bibliographic record
Abstract
Ion channels are membrane proteins that flicker open and shut to regulate the flow of ions down their electrochemical gradient across the membrane and consequently regulate cellular excitability. Every living cell expresses ion channels, as they are critical life-sustaining proteins. Ion channels are generally either activated by voltage or by ligand interaction. For each group of ion channels the channels' molecular biology and biophysics will be introduced and the pharmacology of that group of channels will be reviewed. The in vitro and in vivo literature will be reviewed and, for ion channel groups in which clinical trials have been conducted, the efficacy and therapeutic potential of the neuroprotective compounds will be reviewed. A large part of this article will deal with glutamate receptors, focusing specifically on N-methyl-D-aspartate (NMDA) receptors. Although the outcome of clinical trials for NMDA receptor antagonists as therapeutics for acute stroke is disappointing, the culmination of these failed trials was preceded by a decade of efforts to develop these agents. Sodium and calcium channel antagonists will be reviewed and the newly emerging efforts to develop therapeutics targeting potassium channels will be discussed. The future development of stroke therapeutics targeting ion channels will be discussed in the context of the failures of the last decade in hopes that this decade will yield successful stroke therapeutics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".