A Mathematical Model Investigating the Effect of Moringo Oleifera Lam. Leaves Extract on Porcine Mesenchymal Stem Cells Proliferation
Bibliographic record
Abstract
Mesenchymal stem cells have the ability to differentiate into various types of organic cell; therefore, the methods to grow the cells efficiently are widely investigated.In this work, we propose a mathematical model based population dynamics for investigating the effect of Moringa oleifera Lam.Leaves extract on porcine mesenchymal stem cells proliferation according to the experimental data.The results benefit to understand how the number of cells grows under the conditions of different type of extraction.Furthermore, it is possible to obtain the predicted data from the model instead of the experiment.Keywords-Mathematical model, mesenchymal stem cell, cell proliferation and Moringo oleifera Lam.similar to those of humans [3].Porcine bone marrow mesenchymal stem cells (pBM-MSCs) have shown regenerative capacity similar to those of human BMSCs and can therefore be preclinically applied in settings corresponding to autologous transplantation in patients.Especially, in preclinical testing of tissue-engineering approaches in bone defects, large animal models such as the pig provide physical forces similar to those of humans [2].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".