Diagnosis in a Preclinical Model of Bladder Pain Syndrome Using a Au/ZnO Nanorod-based SERS Substrate

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Diagnosis of Ischemic Renal Failure Using Surface-Enhanced Raman Spectroscopy and a Machine Learning Algorithm

To diagnose renal function using a biochip capable of detecting SERS and to assess Raman measurements taken from a bilateral renal ischemia model and the feasibility of early diagnosis was done. After generating a bilateral renal ischemia rat model, blood and urine were collected. After confirming the presence of renal injury and function, liquid drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods. SERS biomarkers that diffused into the nanogaps were selectively amplified. Raman signals varied based on the severity of the renal function, and these differences were confirmed statistically. These results confirm that renal ischemia leads to renal dysfunction and that surface-enhanced Raman spectroscopy and a machine learning algorithm can be used to track signals in the urine from the release of SERS biomarkers.

Ultrasensitive SERS Analysis of Liquid and Gaseous Putrescine and Cadaverine by a 3D-Rosettelike Nanostructure-Decorated Flexible Porous Substrate

Putrescine and cadaverine are toxic biogenic amines in spoiled food, which poses a serious threat to food security. In this work, we reported a highly sensitive three-dimensional (3D)-rosettelike surface-enhanced Raman spectroscopy (SERS) substrate functionalized with a p-mercaptobenzoic acid (p-MBA) monolayer to detect liquid and gaseous putrescine and cadaverine in pork samples. The SERS substrate was made by a combination of the merit of the 3D morphology of ZnO nanorod arrays on a flexible porous poly(vinylidene fluoride) (PVDF) membrane and the in situ chemical growth of Au nanoparticle seeds on Au film-coated ZnO nanorods, which produced a 3D-rosettelike BigAuNP/Au/ZnO/P heterostructure with abundant SERS-active hot spots that significantly enhanced the localized surface plasmonic resonance (LSPR) effect and charge-transfer (CT) effect of Raman enhancement. This SERS substrate showed high sensitivity, reproducibility, stability, and uniformity. With the p-MBA molecular monolayer as the sensing interface, our SERS substrate realized the highly sensitive and quantitative detection of liquid putrescine and cadaverine within 10 min, with a limit of detection (LOD) of 3.2 × 10^-16 and 1.6 × 10^-13 M, respectively. Additionally, the sensor showed efficient SERS responses to gaseous amine molecules at low concentrations (putrescine: 1.26 × 10^-9 M, cadaverine: 2.5 × 10^-9 M). Further, the sensor was successfully applied to determine the total content of putrescine and cadaverine. Moreover, the practicability of this SERS sensor was verified by the measurement of liquid and gaseous amines in pork samples, and it showed great potential applications for sensitive detection of food spoilage.

Selective Detection of Nano-Sized Diagnostic Markers Using Au-ZnO Nanorod-Based Surface-Enhanced Raman Spectroscopy (SERS) in Ureteral Obstruction Models

Background

This study investigated the diagnosis of renal diseases using a biochip capable of detecting nano-sized biomarkers. Raman measurements from a kidney injury model were taken, and the feasibility of early diagnosis was assessed.

Materials and Methods

Rat models with mild and severe unilateral ureteral obstructions were created, with the injury to the kidney varying according to the tightness of the stricture. After generating the animal ureteral obstruction models, urine was collected from the kidney and bladder.

Results and Discussion

After confirming the presence of renal injury, urine drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods, allowing nano-sized biomarkers that diffused into the nanogaps to be selectively amplified. The Raman signals varied according to the severity of the renal damage, and these differences were statistically confirmed.

Conclusion

These results confirm that ureteral stricture causes kidney injury and that signals in the urine from the release of nano-biomarkers can be monitored using surface-enhanced Raman spectroscopy.