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Record W3095515975 · doi:10.3390/ma13214807

3D Cell Culture of Human Salivary Glands Using Nature-Inspired Functional Biomaterials: The Egg Yolk Plasma and Egg White

2020· article· en· W3095515975 on OpenAlexafffund
André M. Charbonneau, Simon D. Tran

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsYolkEgg whiteEgg cellCell biologyChemistryMaterials scienceBiologyEmbryoFood science

Abstract

fetched live from OpenAlex

The egg yolk plasma (EYP)—a translucent fraction of the egg yolk (EY) obtained by centrifugation—was tested as a developmentally encouraging, cost-effective, biomaterial for salivary gland (SG) tissue engineering. To find optimal incubating conditions for both the human NS-SV-AC SG acinar cell line and SG fibroblasts, cells were stained with Live/Dead®. The cellular contents of 96-well plates were analyzed by high content screening image analysis. Characteristically, the EYP biomaterial had lipid and protein content resembling the EY. On its own, the EYP was non-conducive to cell survival. EYP’s pH of 6 mainly contributed to cell death. This was demonstrated by titrating EYP’s pH with different concentrations of either commercial cell culture media, NaOH, or egg white (EW). These additives improved SG mesenchymal and epithelial cell survival. The best combinations were EYP diluted with (1) 70% commercial medium, (2) 0.02 M NaOH, or (3) 50% EW. Importantly, commercial medium-free growth was obtained with EYP + NaOH or EYP + EW. Furthermore, 3D cultures were obtained as a result of EW’s gelatinous properties. Here, the isolation, characterization, and optimization of three EYP-based biomaterial combinations are shown; two were free of commercial medium or supplements and supported both SG cells’ survival.

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 score0.011
Threshold uncertainty score0.818

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)0.0010.000

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 head0.031
GPT teacher head0.272
Teacher spread0.241 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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