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Record W2895694485 · doi:10.1002/adhm.201701338

Preservation of Blood Vessels with an Oxygen Generating Composite

2018· article· en· W2895694485 on OpenAlexafffund
Huaifa Zhang, Benjamin Dalisson, Simon D. Tran, Jake E. Barralet

Bibliographic record

VenueAdvanced Healthcare Materials · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsOxygenEx vivoHypothermiaHypoxia (environmental)TransplantationViability assayLimitingAortaIn vivoBlood vesselChemistryOxygen metabolismOxygen transportMetabolismAndrologyCellMedicineBiologyInternal medicineBiochemistryIn vitroBiotechnology

Abstract

fetched live from OpenAlex

Damage caused by oxygen deficiency (hypoxia) is one of the major factors limiting tissue and organ preservation time. Cooling tissues slows down metabolic rate of cells thereby prolonging tissue and organ survival sufficiently to allow transport and transplantation within a few hours. Although metabolism is slowed, cells and some enzymes continue to consume oxygen that can render cold stored tissues hypoxic. Here, an oxygen-generating composite (OGC) with sustained oxygen release is reported for ex vivo blood vessel preservation. Aorta segments are cultured under hypothermia for 25 days in vascular preservation media. The presence of OGC increases cell viability from 9 ± 6% to 96 ± 3% and retains 65 ± 8% of original KCl stimulated contractile force after 25 days compared with 25 ± 4% in controls. Culture for 7 days in nitrogen demonstrates proof-of-concept for normothermic blood vessel preservation, OGC increases the cell viability from 45 ± 15% to 78 ± 2%, and KCl stimulates contractile force from 49 ± 7% to 95 ± 8%, respectively. Oxygen release materials then may have a role in augmenting current preservation techniques.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.019
GPT teacher head0.313
Teacher spread0.294 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

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