RETRACTED: Paclitaxel and docetaxel resistance in prostate cancer: Molecular mechanisms and possible therapeutic strategies
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Prostate cancer is among most malignant tumors around the world and this urological tumor can be developed as result of genomic mutations and their accumulation during progression towards advanced stage. Due to lack of specific symptoms in early stages of prostate cancer, most cancer patients are diagnosed in advanced stages that tumor cells display low response to chemotherapy. Furthermore, genomic mutations in prostate cancer enhance the aggressiveness of tumor cells. Docetaxel and paclitaxel are suggested as well-known compounds for chemotherapy of prostate tumor and they possess a similar function in cancer therapy that is based on inhibiting depolymerization of microtubules, impairing balance of microtubules and subsequent delay in cell cycle progression. The aim of current review is to highlight mechanisms of paclitaxel and docetaxel resistance in prostate cancer. When oncogenic factors such as CD133 display upregulation and PTEN as tumor-suppressor shows decrease in expression, malignancy of prostate tumor cells enhances and they can induce drug resistance. Furthermore, phytochemicals as anti-tumor compounds have been utilized in suppressing chemoresistance in prostate cancer. Naringenin and lovastatin are among the anti-tumor compounds that have been used for impairing progression of prostate tumor and enhancing drug sensitivity. Moreover, nanostructures such as polymeric micelles and nanobubbles have been utilized in delivery of anti-tumor compounds and decreasing risk of chemoresistance development. These subjects are highlighted in current review to provide new insight for reversing drug resistance in prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".