A Preliminary Study on Anti-Colorectal Cancer Effect and Molecular Mechanism of Aegiceras Corniculatum Extract
Bibliographic record
Abstract
Objective: To study the inhibitory effects on colorectal cancer (CRC) and the underlying mechanism of the petroleum ether extract of Aegiceras corniculatum leaves (PACL). Materials and Methods: The effect of PACL on the proliferation of CRC cell lines DLD-1, HT-29, and SW480 was measured by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium assay and colony-forming assay. And then, a wound-healing assay was used to measure the migration ability of three CRC cells. The cell cycle and apoptosis of three CRC cells were measured by PI/RNase staining and annexin V-FITC/double staining, respectively, and the intrinsic apoptosis pathway was studied by the Western blot. The anti-CRC effect of PACL in vivo was evaluated by HT-29 xenograft zebrafish embryos. Results: PACL inhibited cell viability and proliferation in DLD-1, HT-29, and SW480 cells in a dose- and time-dependent manner. PACL can inhibit cell migration in DLD-1 and SW480 cells but not in the less mobile phenotype cell HT-29. PACL treatment resulted in cell cycle arrest of DLD-1 and HT-29 cells in the G2/M phase. Moreover, PACL can induce apoptosis in all three CRC cells, which may be achieved by regulating the intrinsic apoptosis pathway mediated by mitochondria and the endoplasmic reticulum. Interestingly, the tumor sizes were decreased after treatment with PACL and PACL combined with fluorouracil in HT-29 xenograft zebrafish embryos. Conclusions: These findings suggested that PACL may exert its anti-CRC effect by inducing apoptosis through the intrinsic apoptosis pathway and show a significant anti-CRC effect in vitro and in vivo , so it might be potentially developed as an anti-CRC agent.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".