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Record W2464676154 · doi:10.4103/2045-9912.184713

Hyperbaric oxygen increases tissue-plasminogen activator-induced thrombolysis in vitro, and reduces ischemic brain damage and edema in rats subjected to thromboembolic brain ischemia

2016· article· en· W2464676154 on OpenAlexaff
J.H. Abraini, Laurent Chazalviel, Benoît Haelewyn, Mickaël Degoulet, Jean-Éric Blatteau, Nicolas Vallée, Jean‐Jacques Risso, S. Besnard

Bibliographic record

VenueMedical Gas Research · 2016
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsThrombolysisIschemiaMedicineTissue plasminogen activatorNeuroprotectionAnesthesiaCerebral blood flowBrain damagePlasminogen activatorBrain ischemiaIn vivoPharmacologyCardiologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Recent data have shown that normobaric oxygen (NBO) increases the catalytic and thrombolytic efficiency of recombinant tissue plasminogen activator (rtPA) in vitro, and is as efficient as rtPA at restoring cerebral blood flow in rats subjected to thromboembolic brain ischemia. Therefore, in the present study, we studied the effects of hyperbaric oxygen (HBO) (i) on rtPA-induced thrombolysis in vitro and (ii) in rats subjected to thromboembolic middle cerebral artery occlusion-induced brain ischemia. HBO increases rtPA-induced thrombolysis in vitro to a greater extent than NBO; in addition, HBO treatment of 5-minute duration, but not of 25-minute duration, reduces brain damage and edema in vivo. In line with the facilitating effect of NBO on cerebral blood flow, our findings suggest that 5-minute HBO could have provided neuroprotection by promoting thrombolysis. The lack of effect of HBO exposure of longer duration is discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.366
Teacher spread0.329 · 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 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

Citations20
Published2016
Admission routes1
Has abstractyes

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