Vitamin D as Radiosensitizer: A Review in Cell Line
Bibliographic record
Abstract
Introduction: Vitamin D has been shown to have anti-cancer properties such as antioxidants, anti-proliferative, and cell differentiation. The property of vitamin D as an anticancer agent triggers researchers to find out whether vitamin D is useful as a radiosensitizer. Multiple studies have been carried out on cell lines in various types of cancer, but the benefits of vitamin D as a radiosensitizer still controversial. This paperwork aims to investigate the utilization of Vitamin D3 (Calcitriol) as radiosensitizer in various cell line through literature review.Methods: A systematic search of available medical literature databases was performed on in-vitro studies with Vitamin D as a radiosensitizer in all types of cell lines. A total of 11 in-vitro studies were evaluated.Results: Nine studies in this review showed a significant effect of Vitamin D as a radiosensitizer agent by promoting cytotoxic autophagy, increasing apoptosis, inhibiting of cell survival and proliferation, promoting gene in ReIB inhibition, inducing senescene and necrosis. The two remaining studies showed no significant effect in the radiosensitizing mechanism of Vitamin D due to lack of evidence in-vitro settings.Conclusion: Vitamin D have anticancer property and can be used as a radiosensitizer by imploring various mechanism pathways in various cell lines. Further research especially in-vivo settings need to be evaluated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".