Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer

Liquid biopsies and exosomal ncRNA: Transforming pancreatic cancer diagnostics and therapeutics

Pancreatic cancer is a highly fatal malignancy due to poor early detection rate and resistance to conventional therapies. This review examines the potential for liquid biopsy as a transformative technology to identify diagnostic and therapeutic targets in pancreatic cancer. Specifically, we explore emerging biomarkers such as exosomal non-coding RNAs (ncRNAs), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs). Tumor-derived exosomes contain nucleic acid and protein that reflect the unique molecular landscape of the malignancy and can serve as an alternative diagnostic approach vs traditional biomarkers like CA19-9. Herein we highlight exosomal miRNAs, lncRNAs, and other ncRNAs alongside ctDNA and CTC-based strategies, evaluating their combined ability to improve early detection, disease monitoring and treatment response. Furthermore, the therapeutic implications of ncRNAs such as lncRNA UCA1 and miR-3960 in chemoresistance and progression are also discussed via suppression of EZH2 and PTEN/AKT pathways. Emerging therapeutic strategies that target the immune response, epithelial-mesenchymal transition (EMT) and drug resistance are explored. This review demonstrates a paradigm shift in pancreatic cancer management toward personalized, less invasive and more effective approaches.

Nanotechnology-based biomedical devices in the cancer diagnostics and therapy

Nanotechnology has significantly transformed the field of cancer diagnostics and therapeutics by introducing advanced biomedical devices. These nanotechnology-based devices exhibit remarkable capabilities in detecting and treating various cancers, addressing the limitations of traditional approaches, such as limited specificity and sensitivity. This review aims to explore the advancements in nanotechnology-driven biomedical devices, emphasizing their role in the diagnosis and treatment of cancer. Through a comprehensive analysis, we evaluate various nanotechnology-based devices across different cancer types, detailing their diagnostic and therapeutic effectiveness. The review also discusses FDA-approved nanotechnology products, patents, and regulatory trends, highlighting the innovation and clinical impact in oncology. Nanotechnology-based devices, including nanobots, smart pills, and multifunctional nanoparticles, enable precise targeting and treatment, reducing adverse effects on healthy tissues. Devices such as DNA-based nanorobots, quantum dots, and biodegradable stents offer noninvasive diagnostic and therapeutic options, showing high efficacy in preclinical and clinical settings. FDA-approved products underscore the acceptance of these technologies. Nanotechnology-based biomedical devices offer a promising future for oncology, with the potential to revolutionize cancer care through early detection, targeted treatment, and minimal side effects. Continued research and technological improvements are essential to fully realize their potential in personalized cancer therapy.

Biological Effects of Calceolarioside A as a Natural Compound: Anti-Ovarian Cancer, Anti-Tyrosinase, and Anti-HMG-CoA Reductase Potentials with Molecular Docking and Dynamics Simulation Studies

One kind of hydroxycinnamic acid is calceolarioside A. Plantago coronopus, Cassinopsis madagascariensis, and other organisms for whom data are available are known to have this naturally occurring compound. IC50 values of Calceolarioside A for ovarian cell lines (NIH-OVCAR-3, ES-2, UACC-1598, Hs832.Tc, TOV-21G, UWB1.289) were 24.42, 13.50, 9.31, 14.90, 20.07, and 16.18 µM, respectively. IC50 values were 19.83 and 73.48 µM for tyrosinase and HMG-CoA reductase enzymes. The chemical activities of Calceolarioside A against HMG-CoA reductase and tyrosinase were assessed by conducting the molecular docking study, MM/GBSA calculation, and molecular dynamics (MD) simulation. The anticancer activities of this compound were evaluated against some ovarian cancer cells, such as NIH-OVCAR-3, ES-2, UACC-1598, Hs832.Tc, TOV-21G, and UWB1.289 cell lines. The chemical activities of Calceolarioside A against some of the expressed surface receptor proteins (folate receptor, CD44, EGFR, Formyl Peptide Receptor-Like 1, M2 muscarinic receptor, and estrogen receptors) were investigated using computational methods. The results exhibited the interplay among atoms. The compound formed robust associations with both the enzymes and receptors. Calceolarioside A can hinder the functioning of these enzymes and the proliferation of malignant cells.

An update on selective estrogen receptor modulator: repurposing and formulations

The selective estrogen receptor modulator (SERM) raloxifene hydrochloride (RLH) is used extensively in the management and prevention of breast cancer and osteoporosis. Recent clinical studies show the repurposing of RLH in various diseases based on its structure and some clinical trials studies. Optimizing the clinical effectiveness of this important drug requires a thorough review of the formulation techniques, patent environment, and analytical procedures. The purpose of this study is to give a thorough understanding of dug repurposing with the most recent formulation strategies, patents, and analytical methods related to RLH. Highlighting recent developments, pointing out current issues, and suggesting future lines of inquiry and development are the objectives. A thorough literature analysis was carried out with an emphasis on repurposing of RLH for various diseases and analytical techniques employed in the measurement and quality control of RLH. These techniques included spectroscopic, chromatographic, and electrochemical approaches. Key advancements and trends were found by analyzing patent databases. The evaluation also looked into formulation techniques intended to improve the medicine's therapeutic efficacy and bioavailability, notably cutting-edge drug delivery methods. For the study of RLH, the review identifies several sophisticated analytical techniques that provide increased accuracy and robustness. Significant innovation has been revealed by the patent landscape, particularly in formulations targeted at enhancing solubility and bioavailability. Notable formulation techniques that overcome the drawbacks of conventional techniques include transdermal patches, nanoparticulate systems, and various drug delivery techniques.

Disentangled global and local features of multi-source data variational autoencoder: An interpretable model for diagnosing IgAN via multi-source Raman spectral fusion techniques

A single Raman spectrum reflects limited molecular information. Effective fusion of the Raman spectra of serum and urine source domains helps to obtain richer feature information. However, most of the current studies on immunoglobulin A nephropathy (IgAN) based on Raman spectroscopy are based on small sample data and low signal-to-noise ratio. If a multi-source data fusion strategy is directly adopted, it may even reduce the accuracy of disease diagnosis. To this end, this paper proposes a data enhancement and spectral optimization method based on variational autoencoders to obtain reconstructed Raman spectra with doubled sample size and improved signal-to-noise ratio. In the diagnosis of IgAN in multi-source domain Raman spectra, this paper builds a global and local feature decoupled variational autoencoder (DMSGL-VAE) model based on multi-source data. First, the statistical features after spectral segmentation are extracted, and the latent variables obtained by the variational encoder are decoupled through the decoupling module. The global representation and local representation obtained represent the global shared information and local unique information of the serum and urine source domains, respectively. Then, the cross-source reconstruction loss and decoupling loss are used to constrain the decoupling, and the effectiveness of the decoupling is proved quantitatively and qualitatively. Finally, the features of different source domains were integrated to diagnose IgAN, and the results were analyzed for important features using the SHapley Additive exPlanations algorithm. The experimental results showed that the AUC value of the DMSGL-VAE model for diagnosing IgAN on the test set was as high as 0.9958. The SHAP algorithm was used to further prove that proteins, hydroxybutyrate, and guanine are likely to be common biological fingerprint substances for the diagnosis of IgAN by serum and urine Raman spectroscopy. In summary, the DMSGL-VAE model designed based on Raman spectroscopy in this paper can achieve rapid, non-invasive, and accurate screening of IgAN in terms of classification performance. And interpretable analysis may help doctors further understand IgAN and make more efficient diagnostic measures in the future.

Investigating the clinical significance of OAS family genes in breast cancer: an in vitro and in silico study

BACKGROUND

Breast cancer is the most common malignancy among women worldwide, characterized by complex molecular and cellular heterogeneity. Despite advances in diagnosis and treatment, there is an urgent need to identify reliable biomarkers and therapeutic targets to improve early detection and personalized therapy. The OAS (2'-5'-oligoadenylate synthetase) family genes, known for their roles in antiviral immunity, have emerged as potential regulators in cancer biology. This study aimed to explore the diagnostic and functional relevance of OAS family genes in breast cancer.

METHODOLOGY

Breast cancer cell lines and controls were cultured under specific conditions, and DNA and RNA were extracted for downstream analyses. RT-qPCR, bisulfite sequencing, and Western blotting were employed to assess gene expression, promoter methylation, and knockdown efficiency of OAS family genes. Functional assays, including CCK-8, colony formation, and wound healing, evaluated cellular behaviors, while bioinformatics tools (UALCAN, GEPIA, HPA, OncoDB, cBioPortal, and others) validated findings and explored correlations with clinical data.

RESULTS

The OAS family genes (OAS1, OAS2, OAS3, and OASL) were found to be significantly upregulated in breast cancer cell lines and tissues compared to normal controls. This overexpression was strongly associated with reduced promoter methylation. Receiver operating characteristic (ROC) analysis demonstrated high diagnostic accuracy, with area under the curve (AUC) values exceeding 0.93 for all four genes. Increased OAS expression correlated with advanced cancer stages and poor overall survival in breast cancer patients. Functional analysis revealed their involvement in critical biological processes, including immune modulation and oncogenic pathways. Silencing OAS genes in breast cancer cells significantly inhibited cell proliferation and colony formation, while unexpectedly enhancing migratory capacity. Additionally, correlations with immune cell infiltration, molecular subtypes, and drug sensitivity highlighted their potential roles in the tumor microenvironment and therapeutic response.

CONCLUSION

The findings of this study established OAS family genes as potential biomarkers and key players in breast cancer progression, offering promise as diagnostic biomarkers and therapeutic targets to address unmet clinical needs.

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.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Convenient determination of serum HER-2 status in breast cancer patients using Raman spectroscopy

Given the significant therapeutic efficacy of anti-HER-2 treatment, the HER-2 status is a crucial piece of information that must be obtained in breast cancer patients. Currently, as per guidelines, HER-2 status is typically acquired from breast tissue of patients. However, there is growing interest in obtaining HER-2 status from serum and other samples due to the convenience and potential for dynamic monitoring. In this study, we have developed a serum Raman spectroscopy technique that allows for the rapid acquisition of HER-2 status in a convenient manner. The established HER-2 negative and positive classification model achieved an area under the curve of 0.8334. To further validate the reliability of our method, we replicated the process using immunohistochemistry and in situ hybridization. The results demonstrate that serum Raman spectroscopy, coupled with artificial intelligence algorithms, is an effective technical approach for obtaining HER-2 status.

Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning

OBJECTIVE

Rheumatoid arthritis and Ankylosing spondylitis are two common autoimmune inflammatory rheumatic diseases that negatively affect activities of daily living and can lead to structural and functional disability, reduced quality of life. Here, this study utilized Fourier transform infrared (FTIR) spectroscopy on dried serum samples and achieved early diagnosis of rheumatoid arthritis and ankylosing spondylitis based on deep learning models.

METHOD

A total of 243 dried serum samples were collected in this study, including 81 samples each from ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. The FTIR was then combined with the MSCNN model to achieve a non-invasive, fast, and accurate diagnosis of ankylosing spondylitis, rheumatoid arthritis, and healthy controls.

RESULTS

Spectral analysis shows that the curves and waveforms of the three spectral graphs are similar. The main differences are distributed in the spectral regions of 3300-3250 cm-1, 3000-2800 cm-1, 1750-1500 cm-1, and 1500-1300 cm-1, which represent: Amides, fatty acids, cholesterol, proteins with a carboxyl group, amide II, free amino acids, and polysaccharides. Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. Through comparison, it was found that the diagnostic AUC value of the MSCNN model was 0.99, and the accuracy rate was as high as 0.93, which was much higher than the other three models.

CONCLUSION

The study demonstrated the superiority of MSCNN in distinguishing ankylosing spondylitis from rheumatoid arthritis and healthy controls. FTIR may become a rapid, sensitive, and non-invasive means of diagnosing rheumatism.

DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%-40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis.

Novel SERS biosensor for rapid detection of breast cancer based on Ag2O-Ag-PSi nanochips

Ag2O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag2O-Ag nano core shell with a diameter of 40-60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag2O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer.

Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening

Introduction

This study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals.

Methods

In this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method.

Results

The results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups.

Discussion

In conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.

Diagnoses in multiple types of cancer based on serum Raman spectroscopy combined with a convolutional neural network: Gastric cancer, colon cancer, rectal cancer, lung cancer

Cancer is one of the major diseases that seriously threaten human health. Timely screening is beneficial to the cure of cancer. There are some shortcomings in current diagnosis methods, so it is very important to find a low-cost, fast, and nondestructive cancer screening technology. In this study, we demonstrated that serum Raman spectroscopy combined with a convolutional neural network model can be used for the diagnosis of four types of cancer including gastric cancer, colon cancer, rectal cancer, and lung cancer. Raman spectra database containing four types of cancer and healthy controls was established and a one-dimensional convolutional neural network (1D-CNN) was constructed. The classification accuracy of the Raman spectra combined with the 1D-CNN model was 94.5%. A convolutional neural network (CNN) is regarded as a black box, and the learning mechanism of the model is not clear. Therefore, we tried to visualize the CNN features of each convolutional layer in the diagnosis of rectal cancer. Overall, Raman spectroscopy combined with the CNN model is an effective tool that can be used to distinguish different cancer from healthy controls.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Decoration of sodium carboxymethylcellulose gel microspheres with modified lignin to enhanced methylene blue removal

The introduction of active groups from biomass is currently the most promising alternative method for increasing the adsorption effect of dyes. In this study, modified aminated lignin (MAL) rich in phenolic hydroxyl and amine groups was prepared by amination and catalytic grafting. The factors influencing the modification conditions of the content of amine and phenolic hydroxyl groups were explored. Chemical structural analysis results confirmed that MAL was successfully prepared using a two-step method. The content of phenolic hydroxyl groups in MAL significantly increased to 1.46 mmol/g. MAL/sodium carboxymethylcellulose (NaCMC) gel microspheres (MCGM) with enhanced methylene blue (MB) adsorption capacity owing to the formation of a composite with MAL were synthesized by a sol-gel process followed by freeze-drying and using multivalent cations Al³⁺ as cross-linking agents. In addition, the effects of the MAL to NaCMC mass ratio, time, concentration, and pH on the adsorption of MB were explored. Benefiting from a sufficient number of active sites, MCGM exhibited an ultrahigh adsorption capacity for MB removal, and the maximum adsorption capacity was 118.30 mg/g. These results demonstrated the potential of MCGM for wastewater treatment applications.

Compared between support vector machine (SVM) and deep belief network (DBN) for multi-classification of Raman spectroscopy for cervical diseases

In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.

Review of quantitative and qualitative methods for monitoring photopolymerization reactions

Authomatic in-situ monitoring and characterization of photopolymerization.