Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

SERS sensing for cancer biomarker: Approaches and directions

These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

Application of Nanohybrid Substrates with Layer-by-Layer Self-Assembling Properties to High-Sensitivity Surface-Enhanced Raman Scattering Detection

The present study was conducted to prepare and investigate large-area, high-sensitivity surface-enhanced Raman scattering (SERS) substrates. Organic/inorganic nanohybrid dispersants consisting of an amphiphilic triblock copolymer (hereafter referred to simply as "copolymer") and graphene oxide (GO) were used to stabilize the growth and size of gold nanoparticles (AuNPs). Ion-dipole forces were present between the AuNPs and copolymer dispersants, while the hydrogen bonds between GO and the copolymer prevented the aggregation of GO, thereby stabilizing the AuNP/GO nanohybrids. Transmission electron microscopy (TEM) revealed that the AuNPs had particle sizes of 25-35 nm and a relatively uniform size distribution. The AuNP/GO nanohybrids were deposited onto the glass substrate by using the solution drop-casting method and employed for SERS detection. The self-assembling properties of two-dimensional sheet-like GO led to a regular lamellar arrangement of AuNP/GO nanohybrids, which could be used for the preparation of large-area SERS substrates. Following removal of the copolymer by annealing at 300 °C for 2 h, measurements were obtained under scanning electron microscopy. The results confirmed that 2D GO nanosheets were capable of stabilizing AuNPs, with the final size reaching approximately 40 nm. These AuNPs were adsorbed on both sides of the GO nanosheets. Because the GO nanosheets were merely 5 nm-thick, a good three-dimensional hot-junction effect was generated along the z-axis of the AuNPs. Lastly, the prepared material was used for the SERS detection of rhodamine 6G (R6G), a commonly used highly fluorescent dye. An enhancement factor (EF) of up to 3.5 × 106 was achieved, and the limit of detection was approximately 10-10 M. Detection limits of 10-10 M and < 10-10 M were also observed with the detection of Direct Blue 200 and the biological molecule adenine. It is therefore evident that AuNP/copolymer/GO nanohybrids are large-area flexible SERS substrates that hold great potential in environmental monitoring and biological system detection applications.

High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network

BACKGROUND

Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing.

RESULT

Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate，which indicates that it is an extremely promising auxiliary technology for cancer diagnosis.

SIGNIFICANCE

The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.

Advancements and prospects of Raman spectroscopy in urological tumors: a bibliometric analysis

Raman spectroscopy exhibits potential as a tool for identifying the chemical composition of substances and has witnessed a growing application in urological oncology. This study undertook a bibliometric analysis to chart the present state and future prospects of Raman spectroscopy applications in urological tumors. The present study retrieved literature on the utilization of Raman spectroscopy in urological oncology from the Science Citation Index Expanded of Web of Science Core Collection, spanning from its inception to June 2023. Data from included studies were analyzed using CiteSpace, Python, and Excel. Our study examined 897 articles from 65 countries and identified a significant exponential growth in annual publications. The USA and China were prominent contributors to this field, with high publication rates and funding agencies. Shanghai Jiao Tong University emerged as the most influential institution. The journals Analytical Chemistry and Analyst were found to be the most productive. Our keyword analysis revealed an intense interest for "gold nanoparticle" and "pathology," with the most recent bursts occurring for "surface-enhanced Raman scattering (SERS)," "biomarkers," and "prostate specific antigen." As a detection tool, Raman spectroscopy holds the potential to assist in the identification, management, and prognostication of urological tumors. Notably, adjuvant diagnosis and prognosis evaluation based on SERS of tumor markers is a hot research topic. These findings offer valuable insights into the current state of Raman spectroscopy research in urological oncology, which could inform future studies and clinical practice.

Differentiation of Urothelial Carcinoma and Normal Bladder Tissues by Means of Fiber-Based ATR IR Spectroscopy

Surgical treatment is widely applied curative approach for bladder cancer. White light cystoscopy (WLC) is currently used for intraoperative diagnostics of malignant lesions but has relatively high false-negative rate. Here we represent an application of label free fiber-based attenuated total reflection infrared spectroscopy (ATR IR) for freshly resected human bladder tissue examination for 54 patients. Defined molecular spectral markers allow to identify normal and urothelial carcinoma tissues. While methods of statistical analysis (Hierarchical cluster analysis (HCA) and Principal component analysis (PCA)) used for spectral data treatment allow to discriminate tissue types with 91% sensitivity and 96-98% specificity. In the present study the described method was applied for tissue examination under ex vivo conditions. However, after method validation the equipment could be translated from laboratory studies to in situ or even in vivo studies in operating room.

Label-Free Surface-Enhanced Raman Spectroscopic Analysis of Proteins: Advances and Applications

Surface-enhanced Raman spectroscopy (SERS) is powerful for structural characterization of biomolecules under physiological condition. Owing to its high sensitivity and selectivity, SERS is useful for probing intrinsic structural information of proteins and is attracting increasing attention in biophysics, bioanalytical chemistry, and biomedicine. This review starts with a brief introduction of SERS theories and SERS methodology of protein structural characterization. SERS-active materials, related synthetic approaches, and strategies for protein-material assemblies are outlined and discussed, followed by detailed discussion of SERS spectroscopy of proteins with and without cofactors. Recent applications and advances of protein SERS in biomarker detection, cell analysis, and pathogen discrimination are then highlighted, and the spectral reproducibility and limitations are critically discussed. The review ends with a conclusion and a discussion of current challenges and perspectives of promising directions.