(Nano)platforms in bladder cancer therapy: Challenges and opportunities
Bibliographic record
Abstract
Urological cancers are among the most common malignancies around the world. In particular, bladder cancer severely threatens human health due to its aggressive and heterogeneous nature. Various therapeutic modalities have been considered for the treatment of bladder cancer although its prognosis remains unfavorable. It is perceived that treatment of bladder cancer depends on an interdisciplinary approach combining biology and engineering. The nanotechnological approaches have been introduced in the treatment of various cancers, especially bladder cancer. The current review aims to emphasize and highlight possible applications of nanomedicine in eradication of bladder tumor. Nanoparticles can improve efficacy of drugs in bladder cancer therapy through elevating their bioavailability. The potential of genetic tools such as siRNA and miRNA in gene expression regulation can be boosted using nanostructures by facilitating their internalization and accumulation at tumor sites and cells. Nanoparticles can provide photodynamic and photothermal therapy for ROS overgeneration and hyperthermia, respectively, in the suppression of bladder cancer. Furthermore, remodeling of tumor microenvironment and infiltration of immune cells for the purpose of immunotherapy are achieved through cargo-loaded nanocarriers. Nanocarriers are mainly internalized in bladder tumor cells by endocytosis, and proper design of smart nanoparticles such as pH-, redox-, and light-responsive nanocarriers is of importance for targeted tumor therapy. Bladder cancer biomarkers can be detected using nanoparticles for timely diagnosis of patients. Based on their accumulation at the tumor site, they can be employed for tumor imaging. The clinical translation and challenges are also covered in current review.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".