MétaCan
Menu
Back to cohort
Record W2800776893 · doi:10.1186/s13041-018-0357-8

Targeting NMDA receptors in stroke: new hope in neuroprotection

2018· review· en· W2800776893 on OpenAlexafffund
Qiu Jing Wu, Michael Tymianski

Bibliographic record

VenueMolecular Brain · 2018
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanada Research Chairs
KeywordsNeuroprotectionNMDA receptorNeuroscienceStroke (engine)NeurologyReceptorMedicineExcitotoxicityPsychopharmacologyPharmacologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

NMDA (N-methyl-d-aspartate) receptors (NMDARs) play a central role in excitotoxic neuronal death caused by ischemic stroke, but NMDAR channel blockers have failed to be translated into clinical stroke treatments. However, recent research on NMDAR-associated signaling complexes has identified important death-signaling pathways linked to NMDARs. This led to the generation of inhibitors that inhibit these pathways downstream from the receptor without necessarily blocking NMDARs. This therapeutic approach may have fewer side effects and/or provide a wider therapeutic window for stroke as compared to the receptor antagonists. In this review, we highlight the key findings in the signaling cascades downstream of NMDARs and the novel promising therapeutics for ischemic stroke.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.071
GPT teacher head0.384
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations323
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueMolecular BrainSame topicNeuroscience and Neuropharmacology ResearchFrench-language works237,207