Enhanced efficiency in isolation and expansion of hAMSCs via dual enzyme digestion and micro-carrier
Bibliographic record
Abstract
Abstract A two-stage method of obtaining viable human amniotic stem cells (hAMSCs) in large-scale is described. First, human amniotic stem cells are isolated via dual enzyme (collagenase II and DNAase I) digestion. Next, relying on a culture of the cells from porous chitosan-based microspheres in vitro, high purity hAMSCs are obtained in large-scale. Dual enzymatic (collagenase II and DNase I) digestion provides a primary cell culture and first subculture with a lower contamination rate, higher purity and a larger number of isolated cells. The obtained hAMSCs were seeded onto chitosan microspheres (CM), gelatin–chitosan microspheres (GCM) and collagen–chitosan microspheres (CCM) to produce large numbers of hAMSCs for clinical trials. Growth activity measurement and differentiation essays of hAMSCs were realized. Within 2 weeks of culturing, GCMs achieved over 1.28 ± 0.06 × 107hAMSCs whereas CCMs and CMs achieved 7.86 ± 0.11 × 106and 1.98 ± 0.86 × 106respectively within this time. In conclusion, hAMSCs showed excellent attachment and viability on GCM-chitosan microspheres, matching the hAMSCs’ normal culture medium. Therefore, dual enzyme (collagenase II and DNAase I) digestion may be a more useful isolation process and culture of hAMSCs on porous GCM in vitro as an ideal environment for the large-scale expansion of highly functional hAMSCs for eventual use in stem cell-based therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".