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Record W1988272235 · doi:10.1586/14737175.2014.972945

Should losartan be administered following brain injury?

2014· review· en· W1988272235 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsDalhousie University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsEpileptogenesisLosartanNeuroprotectionMedicineEpilepsyTraumatic brain injuryBlood–brain barrierNeurodegenerationNeurosciencePharmacologyAngiotensin IIPsychologyInternal medicineReceptorCentral nervous systemPsychiatry

Abstract

fetched live from OpenAlex

Brain injury is a major health concern and associated with delayed neurological complications, including post-injury epilepsy, cognitive and emotional disabilities. Currently, there is no strategy to prevent post-injury delayed complications. We recently showed that dysfunction of the blood-brain barrier, often reported in brain injuries, can lead to epilepsy and neurodegeneration via activation of inflammatory TGF-β signaling in astrocytes. We further showed that the FDA approved angiotensin II type 1 receptor antagonist, losartan, blocks brain TGF-β signaling and prevents epilepsy in the albumin or blood-brain barrier breakdown models of epileptogenesis. Here we discuss the potential of losartan as an anti-epileptogenic and a neuroprotective drug, the rationale of its use following brain injury and the challenges of designing clinical trials. We highlight the urgent need to develop reliable biomarkers for epileptogenesis (and other complications) after brain injury as a pre-requisite to challenge neuroprotective therapies.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.100
GPT teacher head0.436
Teacher spread0.336 · 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