Surface-Enhanced Raman Analysis of Uric Acid and Hypoxanthine Analysis in Fractionated Bodily Fluids

Diagnosis and classification of intestinal diseases with urine by surface-enhanced Raman spectroscopy

Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.

A facile, low-cost bimetallic iron-nickel MOF nanozyme-propelled ratiometric fluorescent sensor for highly sensitive and selective uric acid detection and its smartphone application

As a kind of well-known disease biomarker, uric acid (UA) is closely associated with normal metabolism and health. Despite versatile nanozymes facilitating the analysis of UA, most previous works could only generate single-signal outputs with unsatisfactory detection performance. Exploring a novel ratiometric fluorescent UA sensor with high sensitivity, reliability and portable sensing ability based on facile, low-cost nanozymes is still challenging. Herein, we report the first metal-organic-framework (MOF) nanozyme-originated ratiometric fluorescent UA sensor based on Fe3Ni-MOF-NH2 propelled UA/uricase/o-phenylenediamine tandem catalytic reaction. Different from previous reports, the peroxidase-like property and fluorescence of Fe3Ni-MOF-NH2 were simultaneously employed. In the absence of UA, only the MOF's fluorescence at 430 nm (FI430) can be observed, while the addition of UA will initiate UA/uricase catalytic reaction, and the generated H2O2 could oxidize o-phenylenediamine into highly fluorescent 2,3-diaminophenazine (DAP) (emission at 565 nm, FI565) under the catalysis of the MOF nanozyme. Coincidently, MOF's fluorescence can be quenched by DAP via the inner filter effect, resulting in a low FI430 value and high FI565 value, respectively. Therefore, H2O2 and UA can be alternatively detected through monitoring the above contrary fluorescence changes. The limit of detection for UA is 24 nM, which is much lower than those in most previous works, and the lowest among nanozyme-based ratiometric fluorescent UA sensors reported to date. Moreover, the portable sensing of UA via smartphone-based RGB analysis was facilely achieved by virtue of the above nanozyme-propelled tandem catalytic system, and MOF nanozyme-based molecular contrary logic pairs were further implemented accordingly.

Developing New Diagnostic Tools Based on SERS Analysis of Filtered Salivary Samples for Oral Cancer Detection

Cancer still represents one of the biggest challenges in current medical practice. Among different types of cancer, oral cancer has a huge impact on patients due to its great visibility, which is more likely to create social stigma and increased anxiety. New early diagnose methods are still needed to improve treatment efficiency and patients' life quality. Raman/SERS (Surface Enhanced Raman Spectroscopy) spectroscopy has a unique and powerful potential for detecting specific molecules that can become priceless biomarkers in different pathologies, such as oral cancer. In this study, a batch of saliva samples obtained from a group of 17 patients with oro-maxillofacial pathologies compared with saliva samples from 18 healthy donors using the aforementioned methods were evaluated. At the same time, opiorphin, potassium thiocyanate and uric acid were evaluated as potential specific biomarkers for oro-maxillofacial pathologies using multivariate analysis. A careful examination of SERS spectra collected on saliva samples showed that the spectra are dominated by the vibrational bands of opiorphin, potassium thiocyanate and uric acid. Given the fact that all these small molecules are found in very small amounts, we filtrated all the samples to get rid of large molecules and to improve our analysis. By using solid plasmonic substrates, we were able to gain information about molecular concentration and geometry of interaction. On the other hand, the multivariate analysis of the salivary spectra contributed to developing a new detection method for oral cancer.