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Record W4226324013 · doi:10.1038/s41598-022-10221-z

Vero cells gain renal tubule markers in low-calcium and magnesium chemically defined media

2022· article· en· W4226324013 on OpenAlexafffund
Megan Logan, Karsten Rinas, Brendan J. McConkey, Marc G. Aucoin

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVero cellSuspension (topology)MagnesiumChemically defined mediumCalciumCell cultureGrowth mediumBiologyChemistryMolecular biologyCell biologyBiochemistryIn vitroGenetics

Abstract

fetched live from OpenAlex

In this study, a chemically defined, animal component-free media was developed to promote Vero growth in suspension. Key media compounds were screened using Plackett-Burman styled experiments to create a media formulation to support suspension growth. Vero cells remained viable in suspension, but their growth rate was extremely low, conversely, other cell types such as CHO-K1, MDCK and HEK293T were able to grow in single cell suspension in the same media. To investigate the slow growth of Vero cells, RNA-seq analysis was conducted. Vero cells were cultured in three different conditions: adherently in serum-containing medium, adherently in in-house medium, and in suspension in low calcium and magnesium in-house medium. This study illustrates that adherent cells maintain similar gene expression, while the suspension phenotype tends to overexpress genes related to renal tubules.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.230
Teacher spread0.219 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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