A Comprehensive Review of Genistein’s Effects in Preclinical Models of Cervical Cancer
Bibliographic record
Abstract
Cervical cancer is associated with persistent Human Papilloma Virus (HPV) infections and is the fourth most common cancer in women worldwide. Current treatment options; surgery, chemotherapy, and radiation, are often associated with severe side effects including possible infertility. Novel treatment options are required to help combat this disease and reduce side effects. Many plant-derived chemicals, including paclitaxel and docetaxel, are already in use as treatments for various cancers. Genistein is a polyphenolic isoflavone found in foods including soybeans and legumes, and studies have shown that it has various biological effects and anti-cancer properties. This review aims to summarize the existing studies examining the effects of genistein on cervical cancer. All relevant in vitro and in vivo studies are summarized, and the key findings are highlighted in the associated tables. Based on the available in vitro/cell culture studies reported here, genistein inhibits cervical cancer cell proliferation and induces apoptosis. Use of genistein in combination with radiation or chemotherapy agents resulted in enhanced response indicating radio- and chemo-sensitization properties. More animal studies are required to examine the effectiveness of genistein in vivo. Such studies will form the basis for future human studies exploring the potential of genistein to be used in the treatment of cervical cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".