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

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 μL of serum obtained at the same time was added onto the Au-ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy; however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.

Ultrasensitive Hierarchical AuNRs@SiO2@Ag SERS Probes for Enrichment and Detection of Insulin and C-Peptide in Serum

Introduction

Insulin and C-peptide played crucial roles as clinical indicators for diabetes and certain liver diseases. However, there has been limited research on the simultaneous detection of insulin and C-peptide in trace serum. It is necessary to develop a novel method with high sensitivity and specificity for detecting insulin and C-peptide simultaneously.

Methods

A core-shell-satellites hierarchical structured nanocomposite was fabricated as SERS biosensor using a simple wet-chemical method, employing 4-MBA and DTNB for recognition and antibodies for specific capture. Gold nanorods (Au NRs) were modified with Raman reporter molecules and silver nanoparticles (Ag NPs), creating SERS tags with high sensitivity for detecting insulin and C-peptide. Antibody-modified commercial carboxylated magnetic bead@antibody served as the capture probes. Target materials were captured by probes and combined with SERS tags, forming a "sandwich" composite structure for subsequent detection.

Results

Under optimized conditions, the nanocomposite fabricated could be used to detect simultaneously for insulin and C-peptide with the detection limit of 4.29 × 10-5 pM and 1.76 × 10-10 nM in serum. The insulin concentration (4.29 × 10-5-4.29 pM) showed a strong linear correlation with the SERS intensity at 1075 cm-1, with high recoveries (96.4-105.3%) and low RSD (0.8%-10.0%) in detecting human serum samples. Meanwhile, the C-peptide concentration (1.76 × 10-10-1.76 × 10-3 nM) also showed a specific linear correlation with the SERS intensity at 1333 cm-1, with recoveries 85.4%-105.0% and RSD 1.7%-10.8%.

Conclusion

This breakthrough provided a novel, sensitive, convenient and stable approach for clinical diagnosis of diabetes and certain liver diseases. Overall, our findings presented a significant contribution to the field of biomedical research, opening up new possibilities for improved diagnosis and monitoring of diabetes and liver diseases.

Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model

Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine-induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.

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.

Construction of a novel nano-enzyme for ultrasensitive glucose detection with surface-enhanced Raman scattering

Fabricating more sensitive, stable and low-cost nanomaterials for the detection of glucose is important for the disease diagnosis and monitoring. Herein, we established a nanocomposite (polypyrrole bridging GO@Au@MnO₂) as a novel surface-enhanced Raman scattering (SERS) nanoprobe for the quantitative detection of glucose in trace serum. Each component in the nanocomposites played an irreplaceable role in SERS detection of glucose. Polypyrrole (PPy) could act as Raman signal and extra SERS signal molecules didn't need to be introduced; Graphene oxide (GO) and gold nanoparticles (Au NPs) could enhance Raman signal of PPy; Au NPs also acted as glucose oxidase, which can oxidize glucose to produce gluconic acid and hydrogen peroxide(H₂O₂); Manganese oxide (MnO₂) further enhanced Raman signal of PPy and responded to hydrogen peroxide, which will induce the decrease of Raman intensity of PPy. Thus, glucose can be quantified according to Raman signal output of PPy, which displayed a liner range from 1 to 10 μM, with detectable limit of 0.114 μM. Because of the merits in sensitivity, convenience and versatility, the novel method shows large potential space for disease-related substance detection in the future.

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.

Dietary supplementation with nanoparticle CMCS-20a enhances the resistance to GCRV infection in grass carp (Ctenopharyngodon idella)

Combination of antimicrobial proteins and nanomaterials provides a platform for the development of immunopotentiators. Oral administration of immunopotentiators can significantly enhance the immunity of organisms, which provides ideas for disease prevention. In this study, we confirmed that nanoparticles CMCS-20a can efficiently prevent grass carp reovirus (GCRV) infection. Firstly, we verified that CiCXCL20a is involved in the immune responses post GCRV challenge in vivo and alleviates the cell death post GCRV challenge in CIK cells. Then, we prepared nanoparticles CMCS-20a using carboxymethyl chitosan (CMCS) loaded with grass carp (Ctenopharyngodon idella) CXCL20a (CiCXCL20a). Meanwhile, we confirmed nanoparticles CMCS-20a can alleviate the degradation in intestine. Subsequently, we added it to the feed by low temperature vacuum drying method and high temperature spray drying method, respectively. Grass carp were oral administration for 28 days and challenged by GCRV. Low temperature vacuum drying group (LD-CMCS-20a) significantly improve grass carp survival rate, but not high temperature spray drying group (HD-CMCS-20a). To reveal the mechanisms, we investigated the serum biochemical indexes, intestinal mucus barrier, immune gene regulation and tissue damage. The complement component 3 content, lysozyme and total superoxide dismutase activities are highest in LD-CMCS-20a group. LD-CMCS-20a effectively attenuates the damage of GCRV to the number of intestinal villous goblet cells and mucin thickness. LD-CMCS-20a effectively regulates mRNA expressions of immune genes (IFN1, Mx2, Gig1 and IgM) in spleen and head kidney tissues. In addition, LD-CMCS-20a obviously alleviate tissue lesions and viral load in spleen. These results indicated that the nanoparticles CMCS-20a can enhance the disease resistance of fish by improving their immunity, which provides a new perspective for fish to prevent viral infections.