Microfluidic Liquid Biopsy Minimally Invasive Cancer Diagnosis by Nano-Plasmonic Label-Free Detection of Extracellular Vesicles: Review
Bibliographic record
Abstract
Cancer diagnosis requires alternative techniques that allow for early, non-invasive, or minimally invasive identification. Traditional methods, like tissue biopsies, are highly invasive and can be traumatic for patients. Liquid biopsy, a less invasive option, detects cancer biomarkers in body fluids such as blood and urine. However, early-stage cancer often presents low biomarker levels, making sensitivity a challenge for integrating liquid biopsy into early diagnosis. Recent studies revealed that extracellular vesicles (EVs) secreted by cells are apt markers for liquid biopsy. Detecting extracellular vesicles (EVs) for liquid biopsy faces challenges like low sensitivity, EV subtype heterogeneity, and difficulty isolating pure populations. Label-free methods, such as plasmonic biosensors and Raman spectroscopy, offer potential solutions by enabling direct analysis without markers, improving accuracy, and reducing complexity. This review paper discusses current challenges in EV-based liquid biopsy for cancer diagnosis and prognosis. It addresses the effective use of microfluidics and nano-plasmonic approaches to address these challenges. Enhancing label-free EV detection in liquid biopsy could revolutionize early cancer diagnosis by offering non-invasive, cost-effective, and rapid testing. This could improve patient outcomes through personalized treatment and ease the burden on healthcare systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".