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

A new perspective on antimicrobial therapeutic drug monitoring: Surface-enhanced Raman spectroscopy

Therapeutic drug monitoring (TDM) enables the personalization of treatment regimens, enhancing efficacy in combating infectious diseases while minimizing toxicity risks and reducing the potential for pathogenic resistance. However, existing TDM techniques still present certain limitations. Chromatographic analysis involves a prolonged detection period, which hampers its capacity for rapid multi-sample analysis. Immunoassay is constrained by poor specificity and stability, as well as a restricted range of detectable drugs. Surface-enhanced Raman spectroscopy (SERS) amplifies the Raman signals of target molecules via the local electromagnetic field and charge transfer effects on the surface of plasmonic materials, offering many significant advantages including high sensitivity, rapid detection, minimal sample requirements, and the ability to provide molecular fingerprints. SERS biosensing has demonstrated considerable potential in the field of blood drug concentration monitoring. This paper comprehensively reviews the research on the application of SERS in the TDM of antimicrobial agents. Beginning with the clinical practice of antimicrobial TDM, this review systematically introduces the principles of SERS techniques, the enhancement substrates, and the commonly used data processing methods including machine learning. It then provides a detailed discussion of the application of SERS in the TDM of various types of antimicrobials. Finally, it summarizes four major challenges currently faced by SERS techniques in antimicrobial TDM-namely protein corona effects, matrix interferences, substrate heterogeneity, and quantification reproducibility-and proposes potential future directions. This paper aims to offer new strategies and perspectives for the TDM and personalized dosage of antimicrobial agents.

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis

Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better clinical practices for monitoring and managing pregnancy and childbirth. Fourier-transform infrared (FT-IR) spectroscopy as a non-invasive optical technique, it has the potential sensibility to detect biochemical components. This technology operates by meansuring the vibrational energy of molecular composition and structural changes occurring in the tissue. A total of 30 pregnant participants undergoing either spontaneous or induced labor were recruited. We detected several biochemical changes during labor, including a significant decrease in FT-IR spectral features associated with collagen and other extracellular matrix (ECM) proteins, attributed to collagen dispersion. Specifically, the amide I and amide II bands, which are indicative of protein secondary structure, showed marked reductions. Our results have demonstrated that FT-IR spectroscopy is sensitive to multiple biochemical remodeling changes in the cervix during labor. Traditional methods have limitations, either due to their invasiveness or insufficient sensitivity to detect subtle biochemical alterations, therefore, FT-IR spectroscopy may be a valuable noninvasive tool for objective cervical assessment to potentially guide clinical labor management.

Factors that Affect Quantification in Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) is an analytical technique capable of detecting trace amounts of specific species. The uniqueness of vibrational signatures is a major advantage of SERS. This combination of sensitivity and specificity has motivated researchers to develop diverse analytical methodologies leveraging SERS. However, even 50 years after its first observation, SERS is still perceived as an unreliable technique for quantification. This perception has precluded the application of SERS in laboratories that rely on consistent quantification (for regulatory purposes, for instance). In this review, we describe some of the aspects that lead to SERS intensity variations and how those challenges were addressed in the 50 years of the technique. The goal is to identify the sources of variations in SERS intensities and then demonstrate that, even with these pitfalls, the technique can be used for quantification when factors such as nature of the substrate, experimental conditions, sample preparation, surface chemistry, and data analysis are carefully considered and tailored for a particular application.

Advancements in Nanomedicine for the Diagnosis and Treatment of Kidney Stones

Kidney stones constitute a common condition impacting the urinary system. In clinical diagnosis and management, traditional surgical interventions and pharmacological treatments are primarily utilized; however, these methods possess inherent limitations. Presently, the field of nanomedicine is undergoing significant advancements. The application of nanomaterials in biosensors enables the accurate assessment of urinary ion composition. Furthermore, contrast agents developed from these materials can improve the signal-to-noise ratio and enhance image clarity. By mitigating oxidative stress-induced cellular damage, nanomaterials can inhibit the formation of kidney stones and enhance the efficacy of drug delivery as effective carriers. Additionally, by modifying the physical and chemical properties of bacteria, nanomaterials can effectively eliminate bacterial presence, thereby preventing severe complications. This review explores the advancements in nanomaterials technology related to the early detection of risk factors, clinical diagnosis, and treatment of kidney stones and their associated complications.

Raman Spectroscopic Analysis of Urinary Creatine and Phosphate in Athletes: Pre- and Post-Training Assessment

The aim of this study was to detect biochemical components in the urine of bodybuilders who ingested creatine pretraining compared to individuals who did not ingest creatine after physical exercise using Raman spectroscopy. Twenty volunteers practicing bodybuilding were selected to collect pre- and post-training urine samples, where 10 volunteers ingested creatine 30 min before pretraining urine collection (creatine group), and 10 did not (control group). The samples were subjected to Raman spectroscopy, and the spectra of both creatine and control groups and the difference (post-pre) for both groups were analyzed. Principal component analysis (PCA) technique was applied to the samples. The results showed peaks of creatine and phosphate in urine after training (creatine post-training group), suggesting that part of the creatine was absorbed and metabolized, and part was excreted. Raman spectroscopy could be applied to detect biocompounds in urine, such as unmetabolized creatine and phosphate.

A gold nanomaterial-integrated distance-based analytical device for uric acid quantification in human urine samples

In this article, we present the first demonstration of a distance-based paper analytical device (dPAD) for uric acid quantification in human urine samples with instrument-free readout and user-friendliness for the rapid diagnosis and prognosis of various related diseases. By employing gold nanoparticles (AuNPs) as a peroxidase-like nanozyme, our proposed technique eliminates the utilization of horseradish peroxidase (HRP), making the device cost-effective and stable. In our dPAD, uric acid in the sample is oxidized by the uricase enzyme and subsequently catalysed with AuNPs in the sample zone, generating hydroxyl radicals (˙OH). Then, the produced ˙OH reacts with 3,3'-diaminobenzidine (DAB) to form poly DAB (oxDAB), resulting in a coloured distance signal in the detection zone of the dPAD. The variation of the distance of the observed red-brown colour is directly proportional to the uric acid concentration. Our sensor exhibited a linear range from 0.50 to 6.0 mmol L-1 (R2 = 0.9922) with a detection limit (LOD) of 0.25 mmol L-1, covering the clinical range of uric acid in urine. Hence, there is no need for additional sample preparation or dilution. Additionally, this assay is highly selective, with no interferences. We also found that this approach could accurately and precisely determine uric acid in human control samples with the recovery ranging from 99.37 to 100.35 with the highest RSD of 4.05%. Our method is comparable with the use of a commercially available uric acid sensor at a 95% confidence interval. Consequently, the developed dPAD offers numerous advantages such as cost-effectiveness, simplicity, and ease of operation with unskilled individuals. Furthermore, this concept can be applied for extensive biosensing applications in monitoring other biomarkers as an alternative analytical point-of-care (POC) device.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

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.