Assessment of treatment efficacy using surface-enhanced Raman spectroscopy analysis of urine in rats with kidney transplantation or kidney disease

Application of raman spectroscopy in the non-invasive diagnosis of urological diseases via urine

OBJECTIVES

The objective of this review is to provide a comprehensive overview of the utilization of Raman spectroscopy in urinary system diseases, highlighting its potential in non-invasive diagnostic methodologies for early diagnosis and prognostic assessment of urinary ailments.

METHODS

We searched PubMed, Web of Science, and Google Scholar using 'raman,' 'bladder,' 'kidney,' 'prostate,' 'cancer,' 'infection,' 'stone or urinary calculi,' and 'urine or urinary,' along with 'AND' and 'OR' to refine our search. We excluded irrelevant articles and screened potential ones based on titles and abstracts before assessing the full texts for relevance and quality.

FINDINGS

The findings indicate that RS can furnish data on biomolecules in urine, which is significant for non-invasive diagnostic approaches. It has shown potential within non-invasive diagnostic methodologies and is expected to play a pivotal role in the early diagnosis and prognostic assessment of urinary system diseases, such as malignancies, urinary tract infections, kidney diseases, urolithiasis, and other urinary conditions.

CONCLUSIONS

Raman spectroscopy has demonstrated significant potential in providing precise and rapid diagnostic approaches for clinical use in the context of urinary system diseases. Its ability to analyze biomolecules non-invasively positions it as an increasingly important tool in the early diagnosis and prognostic assessment of these conditions.

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.

The application of nanotechnology in kidney transplantation

Kidney transplantation is a crucial treatment option for end-stage renal disease patients, but challenges related to graft function, rejection and immunosuppressant side effects persist. This review highlights the potential of nanotechnology in addressing these challenges. Nanotechnology offers innovative solutions to enhance organ preservation, evaluate graft function, mitigate ischemia-reperfusion injury and improve drug delivery for immunosuppressants. The integration of nanotechnology holds promise for improving outcomes in kidney transplantation.

Rapid identification of early renal damage in asymptomatic hyperuricemia patients based on urine Raman spectroscopy and bioinformatics analysis

Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease. Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed. Results: The Raman spectra showed significant differences in the levels of uric acid (640 cm^-1), urea, creatinine (1,608, 1,706 cm^-1), proteins/amino acids (642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,615 cm^-1), and ketone body (1,643 cm^-1) (p < 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD (ALB, MYC, IL10, FOS, TOP2A, PLG, REN, FGA, CCNA2, and BUB1) were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD. Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA.

The Potential Applications of Raman Spectroscopy in Kidney Diseases

Raman spectroscopy (RS) is a spectroscopic technique based on the inelastic interaction of incident electromagnetic radiation (from a laser beam) with a polarizable molecule, which, when scattered, carries information from molecular vibrational energy (the Raman effect). RS detects biochemical changes in biological samples at the molecular level, making it an effective analytical technique for disease diagnosis and prognosis. It outperforms conventional sample preservation techniques by requiring no chemical reagents, reducing analysis time even at low concentrations, and working in the presence of interfering agents or solvents. Because routinely utilized biomarkers for kidney disease have limitations, there is considerable interest in the potential use of RS. RS may identify and quantify urinary and blood biochemical components, with results comparable to reference methods in nephrology.

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm^-1, 724 cm^-1, 1094 cm^-1 and 1393 cm^-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm^-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.

The Application of Nanoparticles in Diagnosis and Treatment of Kidney Diseases

Nanomedicine is currently showing great promise for new methods of diagnosing and treating many diseases, particularly in kidney disease and transplantation. The unique properties of nanoparticles arise from the diversity of size effects, used to design targeted nanoparticles for specific cells or tissues, taking renal clearance and tubular secretion mechanisms into account. The design of surface particles on nanoparticles offers a wide range of possibilities, among which antibodies play an important role. Nanoparticles find applications in encapsulated drug delivery systems containing immunosuppressants and other drugs, in imaging, gene therapies and many other branches of medicine. They have the potential to revolutionize kidney transplantation by reducing and preventing ischemia-reperfusion injury, more efficiently delivering drugs to the graft site while avoiding systemic effects, accurately localizing and visualising the diseased site and enabling continuous monitoring of graft function. So far, there are known nanoparticles with no toxic effects on human tissue, although further studies are still needed to confirm their safety.

Comparison of Surface-Enhanced Raman Scattering Properties of Serum and Urine for the Detection of Chronic Kidney Disease in Patients

Chronic kidney disease (CKD) affects more than 10% of the global population and is associated with significant morbidity and mortality. In most cases, this disease is developed silently, and it can progress to the end-stage renal failure. Therefore, early detection becomes critical for initiating effective interventions. Routine diagnosis of CKD requires both blood test and urinalyses in a clinical laboratory, which are time-consuming and have low sensitivity and specificity. Surface-enhanced Raman scattering (SERS) is an emerging method for rapidly assessing kidney function or injury. This study was designed to compare the differences between the SERS properties of the serum and urine for easy and simple detection of CKD. Enrolled for this study were 126 CKD patients (Stages 2-5) and 97 healthy individuals. SERS spectra of both the serum and urine samples were acquired using a Raman spectrometer (785 nm excitation). The correlation of chemical parameters of kidney function with the spectra was examined using prinicpal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squares (PLS) analysis. Here, we showed that CKD was discriminated from non-CKD controls using PCA-LDA with a sensitivity of 74.6% and a specificity of 93.8% for the serum spectra, and 78.0% and 86.0 % for the urine spectra. The integration area under the receiver operating characteristic curve was 0.937 ± 0.015 (p < 0.0001) for the serum and 0.886 ± 0.025 (p < 0.0001) for the urine. The different stages of CKD were separated with the accuracy of 78.0% and 75.4% by the serum and urine spectra, respectively. PLS prediction (R²) of the serum spectra was 0.8540 for the serum urea (p < 0.001), 0.8536 for the serum creatinine (p < 0.001), 0.7500 for the estimated glomerular filtration rate (eGFR) (p < 0.001), whereas the prediction (R²) of urine spectra was 0.7335 for the urine urea (p < 0.001), 0.7901 for the urine creatinine (p < 0.001), 0.4644 for the eGFR (p < 0.001) and 0.6579 for the urine microalbumin (p < 0.001). In conclusion, the accuracy of associations between SERS findings of the serum and urine samples with clinical conclusions of CKD diagnosis in this limited number of patients is similar, suggesting that SERS may be used as a rapid and easy-to-use method for early screening of CKD, which however needs further evaluation in a large cohort study.

Correlation of surface-enhanced Raman spectroscopic fingerprints of kidney transplant recipient urine with kidney function parameters

Routine monitoring of kidney transplant function is required for the standard care in post-transplantation management, including frequent measurements of serum creatinine with or without kidney biopsy. However, the invasiveness of these methods with potential for clinically significant complications makes them less than ideal. The objective of this study was to develop a non-invasive tool to monitor the kidney transplant function by using Surface-Enhanced Raman Spectroscopy (SERS). Urine and blood samples were collected from kidney transplant recipients after surgery. Silver nanoparticle-based SERS spectra of the urine were measured and evaluated using partial least squires (PLS) analysis. The SERS spectra were compared with conventional chemical markers of kidney transplant function to assess its predictive ability. A total of 110 kidney transplant recipients were included in this study. PLS results showed significant correlation with urine protein (R² = 0.4660, p < 0.01), creatinine (R² = 0.8106, p < 0.01), and urea (R² = 0.7808, p < 0.01). Furthermore, the prediction of the blood markers of kidney transplant function using the urine SERS spectra was indicated by R² = 0.7628 (p < 0.01) for serum creatinine and R² = 0.6539 (p < 0.01) for blood urea nitrogen. This preliminary study suggested that the urine SERS spectral analysis could be used as a convenient method for rapid assessment of kidney transplant function.