Rapid Diagnosis of Urinary Tract Cancers on a LEGO-Inspired Detection Platform via Chemiresistive Profiling of Volatile Metabolites.
Linlin SunXueliang LiuSihui LiuXiaofeng ChenZheng LiPublished in: Analytical chemistry (2023)
Rapid and in situ profiling of volatile metabolites from body fluids represents a new trend in cancer diagnosis and classification in the early stages. We report herein an on-chip strategy that combines an array of conductive nanosensors with a chaotic gas micromixer for real-time monitoring of volatiles from urine and for accurate diagnosis and classification of urinary tract cancers. By integrating a class of LEGO-inspired microchambers immobilized with MXene-based sensing nanofilms and zigzag microfluidic gas channels, it enables the intensive intermingling of volatile organic chemicals with sensor elements that tremendously facilitate their ion-dipole interactions for molecular recognition. Aided with an all-in-one, point-of-care platform and an effective machine-learning algorithm, healthy or diseased samples from subpopulations (i.e., tumor subtypes, staging, lymph node metastasis, and distant metastasis) of urinary tract cancers can be reliably fingerprinted in a few minutes with high sensitivity and specificity. The developed detection platform has proven to be a noninvasive supplement to the liquid biopsies available for facile screening of urinary tract cancers, which holds great potential for large-scale personalized healthcare in low-resource areas.
Keyphrases
- urinary tract
- machine learning
- high throughput
- loop mediated isothermal amplification
- lymph node metastasis
- papillary thyroid
- deep learning
- healthcare
- single cell
- squamous cell carcinoma
- lymph node
- artificial intelligence
- ms ms
- label free
- gas chromatography
- room temperature
- reduced graphene oxide
- young adults
- real time pcr
- gold nanoparticles
- health insurance
- highly efficient
- neural network
- gas chromatography mass spectrometry