Responses to Herbal Compounds in Brain Cancer Cells: two Cell-Calcium Assays and a Molecular Docking Computation Study
Bibliographic record
Abstract
Herbal plant secondary metabolites such as curcumin, resveratrol, and capsaicin are becoming increasingly popular as natural remedies. These herbal compounds are also known to act on the calcium signaling pathways, and therefore cell calcium assays can be used to study the relative interactions of these compounds with cellular receptors pertaining to natural remedies. To investigate this, Fluo-4, a fluorophore that specifically binds to Ca2+, was used to detect the increase in calcium signaling in human glioblastoma cells treated with curcumin, resveratrol, and capsaicin. The human cells used are U87 MG cells which expresses TRPV1, a pain receptor on the cell membrane. The increases of the fluorescence intensity in the cells treated with the three herbal compounds were measured using a bulk microplate assay, which generates data in a high throughput, and a microfluidic single-cell assay, which allows for the observation of the cell calcium changes in real-time. It was found that all three compounds would increase the intracellular Ca2+ concentrations on the two assays, with curcumin generating the highest increase, which confirms the greatest responses elicited by this herbal compound from the cell. Furthermore, the data obtained by a molecular docking computation study, which has been used to determine the binding affinities of the three compounds with the TRPV1 receptor, corroborate with the experimental finding of the highest cellular response due to curcumin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".