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Record W3195595778 · doi:10.11159/icbb21.167

Microarchitectonic of Decellularized Bovine Pericardium Matrix

2021· article· en· W3195595778 on OpenAlexvenueno aff
Nataliia V. Shchotkina, A. A. Sokol, G. I. Yemets, Liudmyla V. Dolinchuk, Iryna M. Skorokhod, Olena V. Shepeleva, Nadiia M. Rudenko, Iliia M. Yemets

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2021
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsDecellularizationPericardiumMatrix (chemical analysis)Materials scienceBiomedical engineeringMedicineCardiologyComposite materialTissue engineering

Abstract

fetched live from OpenAlex

The use of xenotissue for the needs of regenerative and cardiac medicine is a promising area of tissue engineering.The decellularization process provides complete purification of the elastin-collagen matrix of the bovine pericardium from cells and their components.The use of high concentrations of sodium dodecyl sulfate and glutaraldehyde can lead to a damage of the matrix architectonics.Therefore, the purpose of this study was to study the microarchitectonics of the decellularized matrix using a low concentration of sodium dodecyl sulfate (0.1% solution) and avoiding glutaraldehyde.Further stabilization and fixation of the matrix was carried out using 10 mM 1-Ethyl-3 (3-dimethylaminopropyl) carbodiimide hydrochloride and 10 mM N-Hydroxysuccinimide. The effect of decellularization was assessed by staining the samples with hematoxylin-eosin and by scanning electron microscopy.The research results confirmed the absence of structural changes in the collagen-elastin fibers of the matrix.Thus, the efficiency of using the proposed technology for obtaining a decellularized matrix based on the bovine pericardium was confirmed.

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 score0.001
Threshold uncertainty score0.001

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 integrity0.0000.000
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 head0.011
GPT teacher head0.260
Teacher spread0.248 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicTissue Engineering and Regenerative MedicineFrench-language works237,207