(Nano)platforms in bladder cancer therapy: Challenges and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it