Cholecalciferol induces apoptosis via autocrine metabolism in epidermoid cervical cancer cells
Bibliographic record
Abstract
The anti-cancer effects of vitamin D are of fundamental interest. Cholecalciferol is sequentially hydroxylated endogenously to calcidiol and calcitriol. Here, SiHa epidermoid cervical cancer cells were treated with cholecalciferol (10–2600 nmol/L). Cell count and viability were assayed using Crystal Violet and Trypan Blue, respectively. Apoptosis was assessed using flow cytometry for early and late biomarkers along with brightfield microscopy and transmission electron microscopy. Autocrine vitamin D metabolism was analysed by reverse transcription-quantitative PCR and immunoblotting for activating enzymes: 25-hydroxylases (CYP2R1 and CYP27A1) and 1α-hydroxylase (CYP27B1), the catabolic 24-hydroxylase (CYP24A1), and the vitamin D receptor (VDR). Data were analysed using one-way ANOVA and Bonferroni post-hoc test, and p < 0.05 was considered significant. After cholecalciferol, cell count ( p = 0.011) and viability ( p < 0.0001) decreased, apoptotic biomarkers were positive, mitochondrial membrane potential decreased ( p = 0.0145), and phosphatidylserine externalisation ( p = 0.0439), terminal caspase activity ( p = 0.0025), and nuclear damage ( p = 0.004) increased. Microscopy showed classical features of apoptosis. Gene and protein expression were concordant. Immunoblots revealed increased CYP2R1 ( p = 0.021), VDR ( p = 0.04), and CYP24A1 ( p = 0.0274) and decreased CYP27B1 ( p = 0.031). The authors conclude that autocrine activation of cholecalciferol to calcidiol may mediate VDR signalling of growth inhibition and apoptosis in SiHa cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".