Development of bioreactor protocols for stem cell‐based therapies
Bibliographic record
Abstract
Abstract Stem cell‐based therapies offer a new treatment alternative for individuals suffering from devastating disorders such as Parkinson's disease (PD) and Huntington's disease (HD). Derivation and transplantation of stem cells or their derivatives have shown significant therapeutic outcomes in animal model studies and clinical trials. However, commercialization of stem cell‐based therapies and widespread access to these treatments are largely hampered by access to large quantities of high‐quality products generated under standardized, robust, and controlled conditions. Over the last three decades, the Pharmaceutical Production Research Facility (PPRF) of the University of Calgary, founded by the late Professor Leo A. Behie, has carried out groundbreaking research focusing on the fundamentals of bioprocessing for various stem cells including human neural precursor cells (NPCs) and mesenchymal stem/stromal cells (MSCs). These efforts led to the development of serum‐free growth media and expansion protocols for large‐scale production of therapeutically‐relevant NPCs for the treatment of neurodegenerative disorders. Similarly, high‐performance, defined growth media and reliable, large‐scale computer‐controlled bioreactor protocols were developed for the expansion of MSCs derived from human bone marrow and other sources and the generation of a therapeutically‐relevant secretome. Important bioprocessing considerations such as the hydrodynamics of the bioreactor, mass transfer, cellular microenvironment and synergistic impact of media components were evaluated in these studies. Undoubtedly, the diverse topics of research carried out at PPRF significantly contributed to the translation of the laboratory‐based stem cell culture studies into production of clinical‐grade materials and eventually commercialization of stem cell‐based therapies through applying principles of bioengineering and bioprocessing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
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".