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Record W3213791209 · doi:10.3390/bioengineering8110184

Heat-Inactivation of Fetal and Newborn Sera Did Not Impair the Expansion and Scaffold Engineering Potentials of Fibroblasts

2021· article· en· W3213791209 on OpenAlexafffund
Félix-Antoine Pellerin, Christophe Caneparo, Ève Pellerin, Stéphane Chabaud, Martin Pelletier, Stéphane Bolduc

Bibliographic record

VenueBioengineering · 2021
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsFetal bovine serumTissue engineeringScaffoldFetusCell biologyHormoneCell cultureStromaCell growthBiologyAndrologyCellChemistryImmunologyBiomedical engineeringEndocrinologyBiochemistryMedicinePregnancyImmunohistochemistry

Abstract

fetched live from OpenAlex

Heat inactivation of bovine sera is routinely performed in cell culture laboratories. Nevertheless, it remains debatable whether it is still necessary due to the improvement of the production process of bovine sera. Do the benefits balance the loss of many proteins, such as hormones and growth factors, that are very useful for cell culture? This is even truer in the case of tissue engineering, the processes of which is often very demanding. This balance is examined here, from nine populations of fibroblasts originating from three different organs, by comparing the capacity of adhesion and proliferation of cells, their metabolism, and the capacity to produce the stroma; their histological appearance, thickness, and mechanical properties were also evaluated. Overall, serum inactivation does not appear to provide a significant benefit.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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