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Record W3194351110 · doi:10.3390/ma14174785

Assessment of the Impact of Decellularization Methods on Mechanical Properties of Biocomposites Used as Skin Substitute

2021· article· en· W3194351110 on OpenAlexaff
Bożena Gzik‐Zroska, Kamil Joszko, W. Wolański, Sławomir Suchoń, Michał Burkacki, Marek Ples, Jerzy Małąchowski, Michał Tomaszewski, Arkadiusz Szarek, G. Stradomski, Diana Kitala, Mohsen Akbari, Marek Gzik

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDispaseSterilization (economics)DecellularizationSodium dodecyl sulfateMaterials scienceGlycerolBiomedical engineeringChromatographyPulp and paper industryChemistryMedicineBiochemistryCollagenaseTissue engineering

Abstract

fetched live from OpenAlex

This work aimed to assess the impact of acellularization and sterilization methods on the mechanical properties of biocomposites used as a skin substitute. On the basis of the statistical analysis, it was ascertained that the values of the Young modulus for the samples before the sterilization process-only in the cases of substances such as: trypsin, 15% glycerol and dispase-changed in a statistically significant way. In the case of dispase, the Young modulus value before the sterilization process amounted to 66.6 MPa, for trypsin this value equalled 33.9 MPa, whereas for 15% glycerol it was 11 MPa. In the case of samples after the completion of the sterilization process, the analysis did not show any statistically significant differences between the obtained results of Young's modulus depending on the respective reagents applied. It was confirmed that different methods of acellularization and the process of sterilization effect the alteration of mechanical properties of allogeneic skins. In the case of the decellularization method using SDS (Sodium Dodecyl Sulfate), liquid nitrogen and 85% glycerol the highest values of strain were observed. In the authors' opinion, it is the above-mentioned methods that should be recommended in the process of preparation of skin substitutes.

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)0.001
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 score0.052
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.025
GPT teacher head0.358
Teacher spread0.334 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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