Microfluidics-Based Technologies for the Assessment of Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
Castration-resistant prostate cancer remains a significant clinical challenge, wherein patients display no response to existing hormone therapies. The standard of care often includes aggressive treatment options using chemotherapy, radiation therapy and various drugs to curb the growth of additional metastases. As such, there is a dire need for the development of innovative technologies for both its diagnosis and its management. Traditionally, scientific exploration of prostate cancer and its treatment options has been heavily reliant on animal models and two-dimensional (2D) in vitro technologies. However, both laboratory tools often fail to recapitulate the dynamic tumor microenvironment, which can lead to discrepancies in drug efficacy and side effects in a clinical setting. In light of the limitations of traditional animal models and 2D in vitro technologies, the emergence of microfluidics as a tool for prostate cancer research shows tremendous promise. Namely, microfluidics-based technologies have emerged as powerful tools for assessing prostate cancer cells, isolating circulating tumor cells, and examining their behaviour using tumor-on-a-chip models. As such, this review aims to highlight recent advancements in microfluidics-based technologies for the assessment of castration-resistant prostate cancer and its potential to advance current understanding and to improve therapeutic outcomes.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".