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Record W4411850041 · doi:10.1039/d5bm00245a

Impact of culture vessel materials on biomanufacturing of dendritic cell-based immunotherapies in closed systems

2025· review· en· W4411850041 on OpenAlexafffund
Hamid Ebrahimi Orimi, Natalie Fekete, Nicolas Drolet, Katie Campbell, Michel L. Tremblay, Linda Peltier, Pierre Laneuville, Pierre‐Luc Girard‐Lauriault, Corinne A. Hoesli

Bibliographic record

VenueBiomaterials Science · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsBiomanufacturingCell cultureChemistryDendritic cellCell biologyComputational biologyImmune systemBiologyImmunologyBiotechnology

Abstract

fetched live from OpenAlex

manufacturing of dendritic cells conventionally relies on culturing primary monocytes in dishes or vessels made from tissue culture treated polystyrene. For clinical applications, the implementation of closed culture vessels such as cell culture bags is highly desirable to minimize the risks of contamination and allow automated fluid handling. However, this transition typically represents a significant change in substrate surface properties which can impact cell-surface interactions. This review provides an overview of closed culture systems for dendritic cell therapy product biomanufacturing and describes how material selection can impact cell-surface interactions and thereby the resulting cell fate decisions. Gaining a fundamental understanding of cell culture vessel material surface properties, how proteins adsorb to these materials, and how monocyte-derived dendritic cells may adhere or interact with these surfaces can help guide closed cell culture vessel selection.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.324
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2025
Admission routes2
Has abstractyes

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