Generation of Dendritic Cells in a Closed System Using GMP Guidelines.
Bibliographic record
Abstract
Abstract Objective: The application of a dendritic cell (DC) based immunotherapy requires the ability to consistently produce a clinically acceptable product using Good Manufacturing Practice (GMP). We describe our experience using the Nexell SteriCell container to generate monocyte derived DC in a closed system using GMP guidelines. Design/Materials and Methods: Leukapheresis product was used as source material to first isolate monocytes by adherence to culture bag and then generate DC from monocytes using a serum free media containing GM-CSF and IL-4 and culturing for 7 days at 37°C /5% CO2. DC generation was characterized by phenotyping cultured cells for decreased expression of CD14 and increased appearance of CD80, CD86 and DR. In addition to phenotyping, cultures were tested pre and post culturing for viability and microbial contamination. Results: Cultured cells expressed a decreased expression of CD14 (mean % decrease 58.8 /range 11.2–94.9) and increased expression of CD 80/86 (mean fold increase 23.6X /range 5.7 – 36.2), and DR (mean fold increase 9.3X /range 3.2 – 16). All microbial cultures were negative and viability ranged from 54% to 90% post incubation. Conclusions: The Canadian Blood Services cell processing laboratory is a FACT (Foundation for the Accreditation of Cellular Therapy) accredited facility following GMP guidelines. The results obtained from our study demonstrate a system easily adaptable to larger scale production of DC for possible future immunotherapy clinical application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".