The differentiation of mesenchymal bone marrow stem cells into nerve cells induced by Chromolaena odorata extracts
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> Mesenchymal stem cells (MSCs) can differentiate into nerve cells with an induction from chemical compounds in medium culture. <ns3:italic>Chromolaena odorata</ns3:italic> contains active compounds, such as alkaloids and flavonoids, that can initiate the transformation of MSCs into nerve cells. The aim of this study was to determine the potential of methanol extracted <ns3:italic>C. odorata</ns3:italic> leaf to induce the differentiation of bone marrow MSCs into nerve cells. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> A serial concentration of <ns3:italic>C. odorata</ns3:italic> leaf extract (0.7–1.0 mg/mL) with two replications was used. The parameters measured were the number of differentiated MSCs into nerve cells (statistically analyzed using ANOVA) and cell confirmation using reverse transcription polymerase chain reaction (RT-PCR). </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> The results showed that the <ns3:italic>C. odorata</ns3:italic> extract had a significant effect on the number MSCs differentiating into nerve cells ( <ns3:italic>p</ns3:italic> < 0.05) on the doses of 0.8 mg/ml with 22.6%. Molecular assay with RT-PCR confirmed the presence of the nerve cell gene in all of the samples. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> In conclusion, this study showed the potential application of <ns3:italic>C. odorata</ns3:italic> leaf extract in stem cell therapy for patients experiencing neurodegeneration by inducing the differentiation of MSCs into nerve cells. </ns3:p>
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".