A factorial design to identify process parameters affecting whole mechanically disrupted rat pancreata in a perfusion bioreactor
Bibliographic record
Abstract
Few studies report whole pancreatic tissue culture, as it is a difficult task using traditional culture methods. Here, a factorial design was used to investigate the singular and combinational effects of flow, dissolved oxygen concentration (D.O.) and pulsation on whole mechanically disrupted rat pancreata in a perfusion bioreactor. Whole rat pancreata were cultured for 72 h under defined bioreactor process conditions. Secreted insulin was measured and histological (haematoxylin and eosin (H&E)) as well as immunofluorescent insulin staining were performed and quantified. The combination of flow and D.O. had the most significant effect on secreted insulin at 5 h and 24 h. The D.O. had the biggest effect on tissue histological quality, and pulsation had the biggest effect on the number of insulin-positive structures. Based on the factorial design analysis, bioreactor conditions using high flow, low D.O., and pulsation were selected to further study glucose-stimulated insulin secretion. Here, mechanically disrupted rat pancreata were cultured for 24 h under these bioreactor conditions and were then challenged with high glucose concentration for 6 h and high glucose + IBMX (an insulin secretagogue) for a further 6 h. These cultures secreted insulin in response to high glucose concentration in the first 6 h, however stimulated-insulin secretion was markedly weaker in response to high glucose concentration + IBMX thereafter. After this bioreactor culture period, higher tissue metabolic activity was found compared to that of non-bioreacted static controls. More insulin- and glucagon-positive structures, and extensive intact endothelial structures were observed compared to non-bioreacted static cultures. H&E staining revealed more intact tissue compared to static cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:432-444, 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".