Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system.

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive and carry risks of complications. Herein, we introduce a plasmonic gold nanopolyhedron (AuNH)-coated needle-based surface-enhanced Raman scattering (SERS) sensor, integrated with endoscopy, for direct mucus sampling and label-free detection of CRC. The thin and flexible stainless-steel needle is coated with polymerized dopamine, which serves as an adhesive layer and simultaneously initiates the nucleation of gold nanoparticle (AuNP) seeds on the needle surface. The AuNP seeds are further grown through a surface-directed reduction using Au ions-hydroxylamine hydrochloride solution, resulting in the formation of dense AuNHs. The formation mechanism of AuNHs and the layered structure of the plasmonic needle-based SERS (PNS) sensor are thoroughly analyzed. Furthermore, a strong field enhancement of the PNS sensor is observed, amplified around the edges of the polyhedral shapes and at nanogap sites between AuNHs. The feasibility of the PNS sensor combined with endoscopy system is further investigated using mouse models for direct colonic mucus sampling and verifying noninvasive label-free classification of CRC from normal controls. A logistic regression-based machine learning method is employed and successfully differentiates CRC and normal mice, achieving 100% sensitivity, 93.33% specificity, and 96.67% accuracy. Moreover, Raman profiling of metabolites and their correlations with Raman signals of mucus samples are analyzed using the Pearson correlation coefficient, offering insights for identifying potential cancer biomarkers. The developed PNS-assisted endoscopy technology is expected to advance the early screening and diagnosis approach of CRC in the future.

Cancer screening through surface-enhanced Raman spectroscopy fingerprinting analysis of urinary metabolites using surface-carbonized silver nanowires on a filter membrane.

BACKGROUND: Label-free surface-enhanced Raman spectroscopy (SERS)-based metabolic profiling has great potential for early cancer diagnosis, but further advancements in analytical methods and clinical evidence studies are required for clinical applications. To improve the cancer diagnostic accuracy of label-free SERS spectral analysis of complex biological fluids, it is necessary to obtain specifically enhanced SERS signals of cancer-related metabolites present at low concentrations. RESULTS: This study presents a novel 3D SERS sensor, comprising a surface-carbonized silver nanowire (AgNW)-stacked filter membrane, alongside an optimized urine/methanol/chloroform extraction technique, which specifically changes the molecular adsorption and orientation of aromatic metabolites onto SERS substrates. By analyzing the pretreated urine samples on the surface-carbonized AgNW 3D SERS sensor, distinct and highly enhanced SERS peaks derived from semi-polar aromatic metabolites were observed for pancreatic cancer and prostate cancer samples compared with normal controls. Urine metabolite analysis using SERS fingerprinting successfully differentiated pancreatic cancer and prostate cancer groups from normal control group: normal control (n = 56), pancreatic cancer (n = 40), and prostate cancer (n = 39). SIGNIFICANCE AND NOVELTY: We confirmed the clinical feasibility of performing fingerprint analysis of urinary metabolites based on the surface-carbonized AgNW 3D SERS sensor and methanol/chloroform extraction for noninvasive cancer screening. This technology holds potential for large-scale screening owing to its high accuracy, and cost effective, simple and rapid detection method.

3D plasmonic coral nanoarchitecture paper for label-free human urine sensing and deep learning-assisted cancer screening.

Practical human biofluid sensing requires a sensor device to differentiate patients from the normal group with high sensitivity and specificity. Label-free molecular identification from human biofluids allows direct classification of abnormal samples, providing insights for disease diagnosis and finding of new biomarkers. Here, we introduce a label-free surface-enhanced Raman scattering sensor based on a three-dimensional plasmonic coral nanoarchitecture (3D-PCN), which has strong electromagnetic field enhancement through multiple hot spots. The 3D-PCN was synthesized on a paper substrate via direct one-step gold reduction, forming a coral-like nanoarchitecture with high absorption property for biofluids. This was fabricated as a urine test strip and then integrated with a handheld Raman system to develop an on-site urine diagnostic platform. The developed platform successfully classified the human prostate and pancreatic cancer urines in a label-free method supported by two types of deep learning networks, with high clinical sensitivity and specificity. Our technology has the potential to be utilized not only for urinary cancer diagnosis but also for various human biofluid sensing systems as a future point-of-care testing platform.