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Record W2015642857 · doi:10.1038/jcbfm.2012.176

Inhaled Nitric Oxide Reduces Secondary Brain Damage after Traumatic Brain Injury in Mice

2012· article· en· W2015642857 on OpenAlexaff
Nicole A. Terpolilli, Seong-Woong Kim, Serge C. Thal, Wolfgang M. Kuebler, Nikolaus Plesnila

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineTraumatic brain injuryInhalationCerebral blood flowAnesthesiaPenumbraIschemiaIntracranial pressureBrain damageNitric oxideBrain ischemiaInternal medicine

Abstract

fetched live from OpenAlex

Ischemia, especially pericontusional ischemia, is one of the leading causes of secondary brain damage after traumatic brain injury (TBI). So far efforts to improve cerebral blood flow (CBF) after TBI were not successful because of various reasons. We previously showed that nitric oxide (NO) applied by inhalation after experimental ischemic stroke is transported to the brain and induces vasodilatation in hypoxic brain regions, thus improving regional ischemia, thereby improving brain damage and neurological outcome. As regional ischemia in the traumatic penumbra is a key mechanism determining secondary posttraumatic brain damage, the aim of the current study was to evaluate the effect of NO inhalation after experimental TBI. NO inhalation significantly improved CBF and reduced intracranial pressure after TBI in male C57 Bl/6 mice. Long-term application (24 hours NO inhalation) resulted in reduced lesion volume, reduced brain edema formation and less blood-brain barrier disruption, as well as improved neurological function. No adverse effects, e.g., on cerebral auto-regulation, systemic blood pressure, or oxidative damage were observed. NO inhalation might therefore be a safe and effective treatment option for TBI patients.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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.

Study designObservational
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

Citations107
Published2012
Admission routes1
Has abstractyes

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