Mapping the Potential of Microfluidics in Early Diagnosis and Personalized Treatment of Head and Neck Cancers
Bibliographic record
Abstract
Head and neck cancers (HNCs) account for ~4% of all cancers in North America and encompass cancers affecting the oral cavity, pharynx, larynx, sinuses, nasal cavity, and salivary glands. The anatomical complexity of the head and neck region, characterized by highly perfused and innervated structures, presents challenges in the early diagnosis and treatment of these cancers. The utilization of sub-microliter volumes and the unique phenomenon associated with microscale fluid dynamics have facilitated the development of microfluidic platforms for studying complex biological systems. The advent of on-chip microfluidics has significantly impacted the diagnosis and treatment strategies of HNC. Sensor-based microfluidics and point-of-care devices have improved the detection and monitoring of cancer biomarkers using biological specimens like saliva, urine, blood, and serum. Additionally, tumor-on-a-chip platforms have allowed the creation of patient-specific cancer models on a chip, enabling the development of personalized treatments through high-throughput screening of drugs. In this review, we first focus on how microfluidics enable the development of an enhanced, functional drug screening process for targeted treatment in HNCs. We then discuss current advances in microfluidic platforms for biomarker sensing and early detection, followed by on-chip modeling of HNC to evaluate treatment response. Finally, we address the practical challenges that hinder the clinical translation of these microfluidic advances.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".