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Record W4389697195 · doi:10.1126/sciadv.adj2417

Memantine inhibits cortical spreading depolarization and improves neurovascular function following repetitive traumatic brain injury

2023· article· en· W4389697195 on OpenAlexafffund
Mark A. MacLean, Jamil H. Muradov, Ryan Greene, Gerben van Hameren, David B. Clarke, Jens P. Dreier, David O. Okonkwo, Alon Friedman

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchBundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft
KeywordsMemantineMedicineAnesthesiaNeuroprotectionTraumatic brain injuryCortical spreading depressionNMDA receptorKetaminePharmacologyMigraineInternal medicineReceptor

Abstract

fetched live from OpenAlex

Cortical spreading depolarization (CSD) is a promising target for neuroprotective therapy in traumatic brain injury (TBI). We explored the effect of NMDA receptor antagonism on electrically triggered CSDs in healthy and brain-injured animals. Rats received either one moderate or four daily repetitive mild closed head impacts (rmTBI). Ninety-three animals underwent craniectomy with electrocorticographic (ECoG) and local blood flow monitoring. In brain-injured animals, ketamine or memantine inhibited CSDs in 44 to 88% and 50 to 67% of cases, respectively. Near-DC/AC-ECoG amplitude was reduced by 44 to 75% and 52 to 67%, and duration by 39 to 87% and 61 to 78%, respectively. Daily memantine significantly reduced spreading depression and oligemia following CSD. Animals ( N = 31) were randomized to either memantine (10 mg/kg) or saline with daily neurobehavioral testing. Memantine-treated animals had higher neurological scores. We demonstrate that memantine improved neurovascular function following CSD in sham and brain-injured animals. Memantine also prevented neurological decline in a blinded, preclinical randomized rmTBI trial.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.295
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations31
Published2023
Admission routes2
Has abstractyes

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