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

Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation

Bladder cancer diagnosis is a challenging task because of its intricacy and variation of tumor features. Moreover, morphological similarities of the cancerous cells make manual diagnosis time-consuming. Recently, machine learning and deep learning methods have been utilized to diagnose bladder cancer. However, manual feature requirements for machine learning and the high volume of data for deep learning make them less reliable for real-time application. This study developed a hybrid model using CNN (Convolutional Neural Network) and less attention-based ViT (Vision Transformer) for bladder lesion diagnosis. Our hybrid model contains two blocks of the inceptionV3 to extract spatial features. Furthermore, the global co-relation of the features is achieved using hybrid attention modules incorporated in the ViT encoder. The experimental evaluation of the model on a dataset consisting of 17,540 endoscopic images achieved an average accuracy, precision and F1-score of 97.73%, 97.21% and 96.86%, respectively, using a 5-fold cross-validation strategy. We compared the results of the proposed method with CNN and ViT-based methods under the same experimental condition, and we achieved much better performance than our counterparts.

Evaluation of Hepatic Progenitor and Hepatocyte-Like Cell Differentiation Using Machine Learning Analysis-Assisted Surface-Enhanced Raman Spectroscopy

Technology has been developed to monitor the differentiation process of human mesenchymal stem cells (hMSCs) into hepatocyte-like cells (HLCs) and hepatic progenitor cells (HPCs). These cell lineages, differentiated from MSCs, are ethically unproblematic and are gaining attention as promising cell-based therapies for treating various liver injuries. High-sensitivity, label-free, real-time monitoring technologies integrated with artificial intelligence have been used to evaluate and optimize cell differentiation for enhancing the efficiency of cell therapy delivery. Using an Au-ZnO nanorod array-based surface-enhanced Raman scattering (SERS) sensing chip, cell differentiation from hMSCs to HPCs and HLCs was nondestructively monitored through spectral analysis of cell secretions. Principal component extraction was employed to reduce variables, followed by discriminant analysis (DA). The application of principal component-linear discriminant analysis (PC-LDA), an artificial intelligence algorithm, to spectral data enabled clear grouping of hMSCs, HPCs, and HLCs, with monitoring accuracies of 96.3%, 98.8%, and 98.8%, respectively. Spectral changes observed during the differentiation from hMSCs to HPCs and from HPCs to HLCs over several days demonstrated the effectiveness of SERS combined with machine learning algorithm analysis for differentiation monitoring. This approach enabled real-time, nondestructive observation of cell differentiation with minimal sample labeling and preprocessing, making it useful for sensing differentiation validation and stability. The machine learning- and nanostructure-based SERS evaluation system was applied to the differentiation of ethically sourced MSCs and demonstrated substantial potential for clinical applicability through the use of patient-derived samples.

Novel Urinary Biomarkers for the Detection of Bladder Cancer

Bladder cancer (BCa) is a highly recurrent malignancy that requires sensitive and noninvasive diagnostic and predictive markers. Conventional diagnostic tools, such as cystoscopy and urine cytology, are far from ideal in terms of sensitivity, specificity, and patient compliance. In this narrative review, the development of novel urinary markers for the diagnosis of BCa is highlighted, with a focus on their application in the clinical arena, detection accuracy, and future potential. An extensive analysis of new urinary biomarkers, including proteinuria-based tests, DNA methylation biomarkers, and RNA-based molecular panels, has been conducted. Various molecular tests, such as Cxbladder®, Bladder EpiCheck®, and UroSEEK, are highly sensitive and clinically valid. Urinary biomarkers provide a promising noninvasive alternative for traditional BCa diagnostics with enhanced specificity and the possibility of early diagnosis. Future research should focus on large-scale clinical validation and standardization of biomarkers to facilitate their use in routine clinical practice.

A Novel Approach for Bladder Cancer Treatment: Nanoparticles as a Drug Delivery System

Bladder cancer represents one of the most prevalent malignant neoplasms of the urinary tract. In the Asian context, it represents the eighth most common cancer in males. In 2022, there were approximately 613,791 individuals diagnosed with bladder cancer worldwide. Despite the availability of efficacious treatments for the two principal forms of bladder cancer, namely non-invasive and invasive bladder cancer, the high incidence of recurrence following treatment and the suboptimal outcomes observed in patients with high-grade and advanced disease represent significant concerns in the management of bladder cancer at this juncture. Nanoparticles have gained attention for their excellent properties, including stable physical properties, a porous structure that can be loaded with a variety of substances, and so on. The in-depth research on nanoparticles has led to their emergence as a new class of nanoparticles for combination therapy, due to their advantageous properties. These include the extension of the drug release window, the enhancement of drug bioavailability, the improvement of drug targeting ability, the reduction of local and systemic toxicity, and the simultaneous delivery of multiple drugs for combination therapy. As a result, nanoparticles have become a novel agent of the drug delivery system. The advent of nanoparticles has provided a new impetus for the development of non-surgical treatments for bladder cancer, including chemotherapy, immunotherapy, gene therapy and phototherapy. The unique properties of nanoparticles have facilitated the combination of diverse non-surgical therapeutic modalities, enhancing their overall efficacy. This review examines the recent advancements in the use of nanoparticles in non-surgical bladder cancer treatments, encompassing aspects such as delivery, therapeutic efficacy, and the associated toxicity of nanoparticles, as well as the challenges encountered in clinical applications.

Multiwavelength Surface-Enhanced Raman Scattering Fingerprints of Human Urine for Cancer Diagnosis

Label-free surface-enhanced Raman spectroscopy (SERS) is capable of capturing rich compositional information from complex biosamples by providing vibrational spectra that are crucial for biosample identification. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Herein, we introduce a multiwavelength SERS approach combined with machine learning (ML)-based classification to improve the discrimination accuracy of human urine specimens for bladder cancer (BCa) diagnosis. This strategy leverages the excitation-wavelength-dependent SERS spectral profiles of complex matrices, which are mainly attributed to wavelength-related vibrational changes in individual analytes and differences in the variation ratios of SERS intensity across different wavelengths among various analytes. By capturing SERS fingerprints under multiple excitation wavelengths, we can acquire more comprehensive and unique chemical information on complex samples. Further experimental examinations with clinical urine specimens, supported by ML algorithms, demonstrate the effectiveness of this multiwavelength strategy and improve the diagnostic accuracy of BCa and staging of its invasion with SERS spectra from increasing numbers of wavelengths. The multiwavelength SERS holds promise as a convenient, cost-effective, and broadly applicable technique for the precise identification of complex matrices and diagnosis of diseases based on body fluids.

Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications

Introduction

Colorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist's expertise and laboratory equipment, and patient survival is influenced by the cancer's stage at detection. Non-invasive spectroscopic techniques can aid early diagnosis, monitor disease progression, and assess changes in physiological parameters in both heterogeneous samples and advanced platforms like Organ-on-Chip (OoC).

Methods

In this study, Raman microspectroscopy combined with Machine Learning was used to analyse structural and biochemical changes in a Caco-2 cell-based intestinal epithelial model before and after treatment with a calcium chelating agent.

Results

The Machine Learning (ML) algorithm successfully classified different epithelium damage conditions, achieving an accuracy of 91.9% using only 7 features. Two data-splitting approaches, "sample-based" and "spectra-based," were also compared. Further, Raman microspectroscopy results were confirmed by TEER measurements and immunofluorescence staining.

Discussion

Experimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis.

Precise diagnosis of tumor cells and hemocytes using ultrasensitive, stable, selective cuprous oxide composite SERS bioprobes assisted with high-efficiency separation microfluidic chips

Efficient enrichment and accurate diagnosis of cancer cells from biological samples can guide effective treatment strategies. However, the accessibility and accuracy of rapid identification of tumor cells have been hampered due to the overlap of white blood cells (WBCs) and cancer cells in size. Therefore, a diagnosis system for the identification of tumor cells using reliable surface-enhanced Raman spectroscopy (SERS) bioprobes assisted with high-efficiency microfluidic chips for rapid enrichment of cancer cells was developed. According to this, a homogeneous flower-like Cu2O@Ag composite with high SERS performance was constructed. It showed a favorable spectral stability of 5.81% and can detect trace alizarin red (10-9 mol L-1). Finite-difference time-domain (FDTD) simulation of Cu2O, Ag and Cu2O@Ag, decreased the fluorescence lifetime of methylene blue after adsorption on Cu2O@Ag, and surface defects of Cu2O observed using a spherical aberration-corrected transmission electron microscope (AC-TEM) demonstrated that the combined effects of electromagnetic enhancement and promoted charge transfer endowed the Cu2O@Ag with good SERS activity. In addition, the modulation of the absorption properties of flower-like Cu2O@Ag composites significantly improved electromagnetic enhancement and charge transfer effects at 532 nm, providing a reliable basis for the label-free SERS detection. After the cancer cells in blood were separated by a spiral inertial microfluidic chip (purity >80%), machine learning-assisted linear discriminant analysis (LDA) successfully distinguished three types of cancer cells and WBCs with high accuracy (>90%). In conclusion, this study provides a profound reference for the rational design of SERS probes and the efficient diagnosis of malignant tumors.

Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities

Bladder cancer (BC) is a serious and common malignant tumor of the urinary system. Accurate and convenient diagnosis and treatment of BC is a major challenge for the medical community. Due to the limited medical resources, the existing diagnosis and treatment protocols for BC without the assistance of artificial intelligence (AI) still have certain shortcomings. In recent years, with the development of AI technologies such as deep learning and machine learning, the maturity of AI has made it more and more applied to the medical field, including improving the speed and accuracy of BC diagnosis and providing more powerful treatment options and recommendations related to prognosis. Advances in medical imaging technology and molecular-level research have also contributed to the further development of such AI applications. However, due to differences in the sources of training information and algorithm design issues, there is still room for improvement in terms of accuracy and transparency for the broader use of AI in clinical practice. With the popularization of digitization of clinical information and the proposal of new algorithms, artificial intelligence is expected to learn more effectively and analyze similar cases more accurately and reliably, promoting the development of precision medicine, reducing resource consumption, and speeding up diagnosis and treatment. This review focuses on the application of artificial intelligence in the diagnosis and treatment of BC, points out some of the challenges it faces, and looks forward to its future development.

Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning

Bladder cancer (BC) diagnosis presents a critical challenge in biomedical research, necessitating accurate tumor classification from diverse datasets for effective treatment planning. This paper introduces a novel wrapper feature selection (FS) method that leverages a hybrid optimization algorithm combining Orthogonal Learning (OL) with a rime optimization algorithm (RIME), termed mRIME. The mRIME algorithm is designed to avoid local optima, streamline the search process, and select the most relevant features without compromising classifier performance. It also introduces mRIME-SVM, a novel hybrid model integrating modified mRIME for FS with Support Vector Machine (SVM) for classification. The mRIME algorithm is employed as an FS method and is also utilized to fine-tune the hyperparameters of it the It SVM, enhancing the overall classification accuracy. Specifically, mRIME navigates complex search spaces to optimize FS without compromising classifier performance. Evaluated on eight diverse BC datasets, mRIME-SVM outperforms popular metaheuristic algorithms, ensuring precise and reliable diagnostic outcomes. Moreover, the proposed mRIME was employed for tackling global optimization problems. It has been thoroughly assessed using the IEEE Congress on Evolutionary Computation 2022 (CEC'2022) test suite. Comparative analyzes with Gray wolf optimization (GWO), Whale optimization algorithm (WOA), Harris hawks optimization (HHO), Golden Jackal Optimization (GJO), Hunger Game optimization algorithm (HGS), Sinh Cosh Optimizer (SCHO), and the original RIME highlight mRIME's competitiveness and efficacy across diverse optimization tasks. Leveraging mRIME's success, mRIME-SVM achieves high classification accuracy on nine BC datasets, surpassing existing models. Results underscore mRIME's competitiveness and applicability across diverse optimization tasks, extending its utility to enhance BC classification. This study contributes to advancing BC diagnostics with a robust computational framework, promising broader applications in bioinformatics and AI-driven medical research.

An efficient improved parrot optimizer for bladder cancer classification

Bladder Cancer (BC) is a common disease that comes with a high risk of morbidity, death, and expense. Primary risk factors for BC include exposure to carcinogens in the workplace or the environment, particularly tobacco. There are several difficulties, such as the requirement for a qualified expert in BC classification. The Parrot Optimizer (PO), is an optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots, but the PO algorithm becomes stuck in sub-regions, has less accuracy, and a high error rate. So, an Improved variant of the PO (IPO) algorithm was developed using a combination of two strategies: (1) Mirror Reflection Learning (MRL) and (2) Bernoulli Maps (BMs). Both strategies improve optimization performance by avoiding local optimums and striking a compromise between convergence speed and solution diversity. The performance of the proposed IPO is evaluated against eight other competitor algorithms in terms of statistical convergence and other metrics according to Friedman's test and Bonferroni-Dunn test on the IEEE Congress on Evolutionary Computation conducted in 2022 (CEC 2022) test suite functions and nine BC datasets from official repositories. The IPO algorithm ranked number one in best fitness and is more optimal than the other eight MH algorithms for CEC 2022 functions. The proposed IPO algorithm was integrated with the Support Vector Machine (SVM) classifier termed (IPO-SVM) approach for bladder cancer classification purposes. Nine BC datasets were then used to confirm the effectiveness of the proposed IPO algorithm. The experiments show that the IPO-SVM approach outperforms eight recently proposed MH algorithms. Using the nine BC datasets, IPO-SVM achieved an Accuracy (ACC) of 84.11%, Sensitivity (SE) of 98.10%, Precision (PPV) of 95.59%, Specificity (SP) of 95.98%, and F-score (F1) of 94.15%. This demonstrates how the proposed IPO approach can help to classify BCs effectively. The open-source codes are available at https://www.mathworks.com/matlabcentral/fileexchange/169846-an-efficient-improved-parrot-optimizer.

Single-Molecule Surface-Enhanced Raman Spectroscopy: Challenges, Opportunities, and Future Directions

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is a powerful experimental technique for label-free sensing, imaging, and chemical analysis. Although Raman spectroscopy itself is an extremely "feeble" phenomenon, the intense interaction of optical fields with metallic nanostructures in the form of plasmonic hotspots can generate Raman signals from single molecules. While what constitutes a true single-molecule signal has taken some years for the scientific community to establish, many SERS experiments, even those not specifically attempting single-molecule sensitivity, have observed fluctuation in both the SERS intensity and spectral features. In this Perspective, we discuss the impact that fluctuating SERS signals have had on the continuing advancement of SM-SERS, along with challenges and current and potential future applications.

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.