Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer

Non-invasive detection of systemic lupus erythematosus using SERS serum detection technology and deep learning algorithms

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques.

Surface-enhanced Raman scattering-based strategies for tumor markers detection: A review

The presence of malignant tumors poses a significant threat to people's life and well-being. As biochemical parameters indicate the occurrence and development of tumors, tumor markers play a pivotal role in early cancer detection, treatment, prognosis, efficient monitoring, and other aspects. Surface-enhanced Raman scattering (SERS) is considered a potent tool for the detection of tumor markers owing to its exceptional advantages encompassing high sensitivity, superior selectivity, rapid analysis speed, and photobleaching resistance nature. This review aims to provide a comprehensive understanding of SERS applications in the detection of tumor markers. Firstly, we introduce the SERS enhancement mechanism, classification of active substrates, and SERS detection techniques. Secondly, the latest research progress of in vitro SERS detection of different types of tumor markers in body fluids and the application of SERS imaging in biomedical imaging are highlighted in sections of the review. Finally, according to the current status of SERS detection of tumor markers, the challenges and problems of SERS in biomedical detection are discussed, and insights into future developments in SERS are offered.

Biogenic nanoparticles: pioneering a new era in breast cancer therapeutics-a comprehensive review

Breast cancer, a widespread malignancy affecting women globally, often arises from mutations in estrogen/progesterone receptors. Conventional treatments like surgery, radiotherapy, and chemotherapy face limitations such as low efficacy and adverse effects. However, nanotechnology offers promise with its unique attributes like targeted delivery and controlled drug release. Yet, challenges like poor size distribution and environmental concerns exist. Biogenic nanotechnology, using natural materials or living cells, is gaining traction for its safety and efficacy in cancer treatment. Biogenic nanoparticles synthesized from plant extracts offer a sustainable and eco-friendly approach, demonstrating significant toxicity against breast cancer cells while sparing healthy ones. They surpass traditional drugs, providing benefits like biocompatibility and targeted delivery. Thus, this current review summarizes the available knowledge on breast cancer (its types, stages, histopathology, symptoms, etiology and epidemiology) with the importance of using biogenic nanomaterials as a new and improved therapy. The novelty of this work lies in its comprehensive examination of the challenges and strategies for advancing the industrial utilization of biogenic metal and metal oxide NPs. Additionally; it underscores the potential of plant-mediated synthesis of biogenic NPs as effective therapies for breast cancer, detailing their mechanisms of action, advantages, and areas for further research.

SLE diagnosis research based on SERS combined with a multi-modal fusion method

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Determination of latent tuberculosis infection from plasma samples via label-free SERS sensors and machine learning

Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS). We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 × 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database. We demonstrated that SERS analysis with machine learning is a potential diagnostic tool for LTBI screening.

Unlocking Catalytic Potential: Exploring the Impact of Thermal Treatment on Enhanced Electrocatalysis of Nanomaterials

In the evolving field of electrocatalysis, thermal treatment of nano-electrocatalysts has become an essential strategy for performance enhancement. This review systematically investigates the impact of various thermal treatments on the catalytic potential of nano-electrocatalysts. The focus encompasses an in-depth analysis of the changes induced in structural, morphological, and compositional properties, as well as alterations in electro-active surface area, surface chemistry, and crystal defects. By providing a comprehensive comparison of commonly used thermal techniques, such as annealing, calcination, sintering, pyrolysis, hydrothermal, and solvothermal methods, this review serves as a scientific guide for selecting the right thermal technique and favorable temperature to tailor the nano-electrocatalysts for optimal electrocatalysis. The resultant modifications in catalytic activity are explored across key electrochemical reactions such as electrochemical (bio)sensing, catalytic degradation, oxygen reduction reaction, hydrogen evolution reaction, overall water splitting, fuel cells, and carbon dioxide reduction reaction. Through a detailed examination of the underlying mechanisms and synergistic effects, this review contributes to a fundamental understanding of the role of thermal treatments in enhancing electrocatalytic properties. The insights provided offer a roadmap for future research aimed at optimizing the electrocatalytic performance of nanomaterials, fostering the development of next-generation sensors and energy conversion technologies.

An integrative machine learning framework for classifying SEER breast cancer

Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.

The Role of Silver Nanoparticles in the Diagnosis and Treatment of Cancer: Are There Any Perspectives for the Future?

Cancer is a fatal disease with a complex pathophysiology. Lack of specificity and cytotoxicity, as well as the multidrug resistance of traditional cancer chemotherapy, are the most common limitations that often cause treatment failure. Thus, in recent years, significant efforts have concentrated on the development of a modernistic field called nano-oncology, which provides the possibility of using nanoparticles (NPs) with the aim to detect, target, and treat cancer diseases. In comparison with conventional anticancer strategies, NPs provide a targeted approach, preventing undesirable side effects. What is more, nanoparticle-based drug delivery systems have shown good pharmacokinetics and precise targeting, as well as reduced multidrug resistance. It has been documented that, in cancer cells, NPs promote reactive oxygen species (ROS) production, induce cell cycle arrest and apoptosis, activate ER (endoplasmic reticulum) stress, modulate various signaling pathways, etc. Furthermore, their ability to inhibit tumor growth in vivo has also been documented. In this paper, we have reviewed the role of silver NPs (AgNPs) in cancer nanomedicine, discussing numerous mechanisms by which they render anticancer properties under both in vitro and in vivo conditions, as well as their potential in the diagnosis of cancer.