Apoptotic and cytostatic actions of maslinic acid in colorectal cancer cells through possible IKK-β inhibition
Bibliographic record
Abstract
Objective: To explore the anti-cancer activity of maslinic acid against colorectal cancer (CRC) cell lines and its possible mechanism. Methods: The inhibitory effect of maslinic acid was screened against five CRC cell lines (HT-29, HCT 116, SW480, SW48, and LS 174T) via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Apoptosis and cell cycle analyses were carried out using annexin V-FITC/propidium iodide staining and propidium iodide staining, respectively and subjected to fluorescence-activated cell sorting analysis. Protein expression studies of inhibitor of κB kinase-β (IKK-β), checkpoint kinase 1 (Chk1) and cyclin D1 were conducted using the JESS system. Results: Maslinic acid exhibited growth inhibitory effect in a dose- and time-dependent manner in HT-29 and HCT 116 cell lines. A more prominent apoptosis induced by maslinic acid was observed in HCT 116 cell line. However, in HT-29 cell line, maslinic acid induced cell cycle arrest by inhibiting the G 1 S transition, which was accompanied by the downregulation of cyclin D1. The expression of unphosphorylated IKK-β protein was increased in both (HT-29 and HCT 116) cell lines after maslinic acid treatment. Conclusions: Maslinic acid inhibits the growth of HT-29 and HCT 116 cells in a different manner, induces cell cycle arrest in HT-29 cells and causes apoptosis in HCT 116 cells partially via NF-κB pathway inhibition.
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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".