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Record W4307244450 · doi:10.1002/jex2.63

Large‐scale production of extracellular vesicles: Report on the “massivEVs” ISEV workshop

2022· article· en· W4307244450 on OpenAlexaff
Lucia Paolini, Marta Tortajada, Marta Costa, Fabio Antenucci, Mario Barilani, Marta Clos‐Sansalvador, André Cronemberger Andrade, Tom A. P. Driedonks, Sara Giancaterino, Stephanie M. Kronstadt, Rachel R. Mizenko, Muhammad Nawaz, Xabier Osteikoetxea, Carla Pereira, Surya Shrivastava, Anders T. Boysen, Simonides Immanuel van de Wakker, Martijn J. C. van Herwijnen, Xiaoqin Wang, Dionysios C. Watson, Mario Gimona, Maria Kaparakis‐Liaskos, Konstantin Konstantinov, Sai Kiang Lim, Nicole Meisner‐Kober, Michiel Stork, Peter Nejsum, Annalisa Radeghieri, Eva Rohde, Nicolas Touzet, Marca H. M. Wauben, Kenneth W. Witwer, Antonella Bongiovanni, Paolo Bergese

Bibliographic record

VenueJournal of Extracellular Biology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsExtracellular vesiclesExtracellular vesicleScale (ratio)NanotechnologyProduction (economics)Computer scienceChemistryBiologyMaterials scienceCell biologyMicrovesiclesGeographyBiochemistryCartography

Abstract

fetched live from OpenAlex

Extracellular vesicles (EVs) large-scale production is a crucial point for the translation of EVs from discovery to application of EV-based products. In October 2021, the International Society for Extracellular Vesicles (ISEV), along with support by the FET-OPEN projects, "The Extracellular Vesicle Foundry" (evFOUNDRY) and "Extracellular vesicles from a natural source for tailor-made nanomaterials" (VES4US), organized a workshop entitled "massivEVs" to discuss the potential challenges for translation of EV-based products. This report gives an overview of the topics discussed during "massivEVs", the most important points raised, and the points of consensus reached after discussion among academia and industry representatives. Overall, the review of the existing EV manufacturing, upscaling challenges and directions for their resolution highlighted in the workshop painted an optimistic future for the expanding EV field.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations89
Published2022
Admission routes1
Has abstractyes

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