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Record W2065225501 · doi:10.1002/bit.20900

Scaled‐up production of mammalian neural precursor cell aggregates in computer‐controlled suspension bioreactors

2006· article· en· W2065225501 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiotechnology and Bioengineering · 2006
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBioprocessBioreactorSCALE-UPSuspension (topology)ImpellerProcess engineeringChemistryBiological systemMaterials scienceChemical engineeringChromatographyBiologyMathematicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The clinical use of neural precursor cells (NPCs) for the treatment of neurological diseases, such as Parkinson's disease and Huntington's disease, requires overcoming the scarcity of these cells through controlled expansion. The main objective of the present study was to develop a large-scale computer-controlled bioprocess for the expansion of mammalian NPCs in suspension culture by scaling up existing reactor protocols. In order to support the oxygen demands of the maximum cell densities achieved, the volumetric mass transfer coefficient was kept above 1.10/h while scaling-up from small-scale 125 mL vessels to large-scale 500 mL bioreactors. In addition, the maximum shear stress at the impeller tip was maintained between 0.30 and 0.75 Pa to reduce damage to the cells. The resulting large-scale bioprocess achieved maximum viable cell densities of 1.2 x 10(6) cells/mL and a batch multiplication ratio of 9.1. Moreover, the process successfully maintained the NPC characteristics observed in small-scale studies.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.218
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.205
Teacher spread0.198 · 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