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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 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)0.001
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
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 teacher head, not a consensus.

Study designBench or experimental
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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