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Record W4387675036 · doi:10.1002/jex2.115

Cell culture‐derived extracellular vesicles: Considerations for reporting cell culturing parameters

2023· article· en· W4387675036 on OpenAlexaff
Faezeh Shekari, Faisal J. Alibhai, Hossein Baharvand, Verena Börger, Stefania Bruno, Owen G. Davies, Bernd Giebel, Mario Gimona, Ghasem Hosseini Salekdeh, Lorena Martín‐Jaular, Suresh Mathivanan, Inge Nelissen, Esther N. M. Nolte‐‘t Hoen, Lorraine O’Driscoll, Francesca Perut, Stefano Pluchino, Gabriella Pòcsfalvi, Carlos Salomón, Carolina Soekmadji, Simon Staubach, Ana Cláudia Torrecilhas, Ganesh Vilas Shelke, Tobias Tertel, Dandan Zhu, Clotilde Théry, Kenneth W. Witwer, Rienk Nieuwland

Bibliographic record

VenueJournal of Extracellular Biology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity Health Network
FundersNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Institute on Drug AbuseNational Institute of Mental Health
KeywordsExtracellular vesiclesExtracellular vesicleStandardizationExtracellularVesicleFunction (biology)CellCell biologyChecklistBiotechnologyBiologyBiochemical engineeringComputer scienceBiochemistryMicrovesiclesEngineeringMembrane

Abstract

fetched live from OpenAlex

Cell culture-conditioned medium (CCM) is a valuable source of extracellular vesicles (EVs) for basic scientific, therapeutic and diagnostic applications. Cell culturing parameters affect the biochemical composition, release and possibly the function of CCM-derived EVs (CCM-EV). The CCM-EV task force of the Rigor and Standardization Subcommittee of the International Society for Extracellular Vesicles aims to identify relevant cell culturing parameters, describe their effects based on current knowledge, recommend reporting parameters and identify outstanding questions. While some recommendations are valid for all cell types, cell-specific recommendations may need to be established for non-mammalian sources, such as bacteria, yeast and plant cells. Current progress towards these goals is summarized in this perspective paper, along with a checklist to facilitate transparent reporting of cell culturing parameters to improve the reproducibility of CCM-EV research.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.038
GPT teacher head0.298
Teacher spread0.260 · 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.

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

Citations73
Published2023
Admission routes1
Has abstractyes

Explore more

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