Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment

Assessment of blood serum stability with Raman spectroscopy and explanatory AI

This study explores the potential of conventional Raman spectroscopy and commonly used spectral analysis pipelines for rapid and straightforward assessment of degradation in serum samples resulting from storage delays. Serum samples from 18 volunteers were processed within 2 h of extraction, which later on were analyzed via Raman spectroscopy over 4 days, while the corresponding serum vials were kept at room temperature. The resulting spectra were processed, including silicon normalization and a newly proposed outlier detection ensemble method. Next, baseline correction was performed, and spectral unmixing along with Principal Component Analysis (PCA) were applied. Several classification models (KNN, RF, and SVM) were trained and evaluated on three distinct balanced datasets: one including all data, one excluding low signal-to-noise ratio (SNR) data, and one excluding low-SNR data with baseline correction. Feature importance, assessed through random permutations, was used for explainability. Spectral unmixing and PCA indicated limited spectral changes directly attributable to analyte degradation, with inter- and intra-sample variability dominating. Classification results showed that while removing the baseline led to inconclusive results, models trained on datasets retaining the baseline effectively identified non-degraded samples. These findings suggest that while conventional Raman spectroscopy may not be optimally sensitive to subtle analyte variations in serum stored at room temperature, the auto-fluorescence background holds promise as a potential biomarker for monitoring serum storage quality.

Emerging biomedical applications of surface-enhanced Raman spectroscopy integrated with artificial intelligence and microfluidic technologies

The integration of surface-enhanced Raman spectroscopy (SERS), artificial intelligence (AI), and microfluidics represent a transformative approach for biomedical applications. By combining the molecular sensitivity of SERS, AI-driven spectral analysis, and the precise sample handling of microfluidics, these novel integrated systems enable ultrasensitive, label-free diagnostics with minimal sample processing. The development of portable, cost-effective platforms could democratize advanced diagnostics for resource-limited settings. However, challenges such as reproducibility, clinical validation, and system integration hinder widespread adoption. This review explores these new integrated platforms, beginning with a discussion of SERS principles, their biomedical applications, and the critical roles of AI and microfluidics in enhancing analytical performance. We evaluate recent advances in the application of these integrated systems, while addressing key challenges such as substrate scalability, biocompatibility, and point-of-care translation, with a focus on nanomaterials, AI models, and lab-on-chip designs. Finally, we outline future directions, including multimodal sensing, sustainable materials, and embedded AI for real-time diagnostics, to bridge the gap between technological innovation and clinical implementation.

Nasopharyngeal cancer screening and immunotherapy efficacy evaluation based on plasma separation combined with label-free SERS technology

BACKGROUND

In recent years, significant progress has been made in the treatment of nasopharyngeal carcinoma (NPC). The application of immunotherapy, especially the use of Programmed Cell Death Protein 1 inhibitors, has demonstrated excellent therapeutic effects. However, inconsistent immunotherapy outcomes due to individual differences in patients remain a major challenge, making the search for specific biomarkers to screen for the population who may benefit from immunotherapy a priority. Surface-enhanced Raman spectroscopy (SERS) has shown great potential as a highly sensitive and specific optical analytical tool for identifying and monitoring tumor-related markers in NPC.

RESULTS

In this study, plasma samples from Nasopharyngeal cancer patients before and after immunotherapy, along with those from healthy volunteers, were tested using label-free SERS combined with plasmapheresis. Especially, the components with varying molecular weight sizes were analyzed via the separation process, thereby preventing the potential loss of diagnostic information that could result from competitive adsorption. Subsequently, a robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was used to extract features from plasma SERS data and establish an effective discriminant model. The results showed that in the upper plasma layer, the most optimal discrimination was found between normal patients and those post-treatment, with sensitivity and specificity of 88.5 % and 92.3 %, respectively. In the lower plasma layer, the most optimal discrimination was found between the pre-treatment and post-treatment patients, with sensitivity and specificity of 84.6 % and 80.8 %, respectively.

SIGNIFICANCE AND NOVELTY

Plasmapheresis can reveal latent diagnostic information in the plasma and holds promise for treating NPC, the screening of the population who may benefit from immunotherapy, and the postoperative evaluation of immunotherapy. Further exploration of the feasibility of using SERS to detect tumor markers in the population who may benefit from immunotherapy for NPC will help improve the therapeutic efficacy, optimize clinical practice, and promote the development of individualized treatment strategies.

Computational studies for the development of extracellular vesicle-based biosensors

Cancer affects millions of people, and early detection and efficient treatment are two strong levers to hurdle this disease. Recent studies on exosomes, a subset of extracellular vesicles, have deliberately shown the potential to function as a biomarker or treatment tool, thereby attracting the attention of researchers who work on developing biosensors. Due to the ability of computational methods to predict of the behavior of biomolecules, the combination of experimental and computational methods would enhance the analytical performance of the biosensor, including sensitivity, accuracy, and specificity, even detecting such vesicles from bodily fluids. In this regard, the role of computational methods such as molecular docking, molecular dynamics simulation, and density functional theory is overviewed in the development of biosensors. This review highlights the investigations and studies that have been reported using these methods to design exosome-based biosensors. This review concludes with the role of the quantum mechanics/molecular mechanics method in the investigation of chemical processes of biomolecular systems and the deficiencies in using this approach to develop exosome-based biosensors. In addition, the artificial intelligence theory is explained briefly to show its importance in the study of these biosensors.

The emerging role of artificial intelligence applied to exosome analysis: from cancer biology to other biomedical fields

In recent years, exosomes versatility has prompted their study in the biomedical field for diagnostic, prognostic, and therapeutic applications. Exosomes are bi-lipid small extracellular vesicles (30-150 nm) secreted by various cell types, containing proteins, lipids, and DNA/RNA. They mediate intercellular communication and can influence multiple human physiological and pathological processes. So far, exosome analysis has revealed their role as promising diagnostic tools for human pathologies. Concurrently, artificial intelligence (AI) has revolutionised multiple sectors, including medicine, owing to its ability to analyse large datasets and identify complex patterns. The combination of exosome analysis with AI processing has displayed a novel diagnostic approach for cancer and other diseases. This review explores the current applications and prospects of the combined use of exosomes and AI in medicine. Firstly, we provide a biological overview of exosomes and their relevance in cancer biology. Then we explored exosome isolation techniques and Raman spectroscopy/SERS analysis. Finally, we present a summarised essential guide of AI methods for non-experts, emphasising the advancements made in AI applications for exosome characterisation and profiling in oncology research, as well as in other human diseases.

Harnessing Photo-Energy Conversion in Nanomaterials for Precision Theranostics

The rapidly advancing field of theranostics aims to integrate therapeutic and diagnostic functionalities into a single platform for precision medicine, enabling the simultaneous treatment and monitoring of diseases. Photo-energy conversion-based nanomaterials have emerged as a versatile platform that utilizes the unique properties of light to activate theranostics with high spatial and temporal precision. This review provides a comprehensive overview of recent developments in photo-energy conversion using nanomaterials, highlighting their applications in disease theranostics. The discussion begins by exploring the fundamental principles of photo-energy conversion in nanomaterials, including the types of materials used and various light-triggered mechanisms, such as photoluminescence, photothermal, photoelectric, photoacoustic, photo-triggered SERS, and photodynamic processes. Following this, the review delves into the broad spectrum of applications of photo-energy conversion in nanomaterials, emphasizing their role in the diagnosis and treatment of major diseases, including cancer, neurodegenerative disorders, retinal degeneration, and osteoarthritis. Finally, the challenges and opportunities of photo-energy conversion-based technologies for precision theranostics are discussed, aiming to advance personalized medicine.

Comprehensive Analysis of Advancement in Optical Biosensing Techniques for Early Detection of Cancerous Cells

This investigation presents an overview of various optical biosensors utilized for the detection of cancer cells. It covers a comprehensive range of technologies, including surface plasmon resonance (SPR) sensors, which exploit changes in refractive index (RI) at the sensor surface to detect biomolecular interactions. Localized surface plasmon resonance (LSPR) sensors offer high sensitivity and versatility in detecting cancer biomarkers. Colorimetric sensors, based on color changes induced via specific biochemical reactions, provide a cost-effective and simple approach to cancer detection. Sensors based on fluorescence work using the light emitted from fluorescent molecules detect cancer-specific targets with specificity and high sensitivity. Photonics and waveguide sensors utilize optical waveguides to detect changes in light propagation, offering real-time and label-free detection of cancer biomarkers. Raman spectroscopy-based sensors utilize surface-enhanced Raman scattering (SERS) to provide molecular fingerprint information for cancer diagnosis. Lastly, fiber optic sensors offer flexibility and miniaturization, making them suitable for in vivo and point-of-care applications in cancer detection. This study provides insights into the principles, applications, and advancements of these optical biosensors in cancer diagnostics, highlighting their potential in improving early detection and patient outcomes.

Precision screening with sequential multi-algorithm reclassification technique (SMART): Saving bladders from unnecessary cystectomy

Bladder cancer, when diagnosed at an advanced stage, often necessitates inevitable invasive intervention. Consequently, non-invasive biosensor-based cancer detection and AI-based precision screening are being actively employed. However, the misclassification of cancer patients as normal-referred to as false negatives-remains a significant concern, as it could lead to fatal outcomes in lifespan. Moreover, while ensemble techniques such as soft voting and other methods can improve model accuracy and reduce misclassification, their effectiveness is limited and not applicable to all diagnostic tasks. Here, we developed a double stage cancer screening system that utilizes a sensitive urinary electrical biosensor implemented with an AI model and XAI interpretation tools. This system is designed for screening bladder cancer, well-known for its notable recurrence and high tendency to advance from non-invasive muscle tumors to muscle-invasive tumors. Four urinary biomarkers (CK8, CK18, PD-1, PD-L1) were measured by a field-effect transistor biosensor, and along with gender and age information, patients underwent initial screening by the CatBoost classification model. Patients initially classified as normal were reclassified using local explanations from neural networks offering a different perspective than CatBoost. After the second-stage screening, all of the false negatives from the initial screening could be correctly reclassified as cancer patients. Furthermore, global explanation guides the improvement of the AI model to be trained on an appropriate set of biomarker features to achieve high accuracy.

Peptide-functionalized gold nanostars-assisted single-cell Raman spectra for molecular analysis of tumor metastasis based on cancer stem cells

Cancer is one of the most fatal diseases threatening public health globally, and tumor metastasis causes greater than 90 % of cancer-associated deaths, presenting huge challenges for detection and efficient treatment of various human cancers. Cancer stem cells (CSCs) are a rare population of cancer cells and increasing evidences indicated CSCs are the driving force of tumor metastasis. In this study, a p-AuNSs-assisted single-cell Raman spectra has been established, to extract and amplify of CSCs fingerprints with single cell sensitivity. The gathered rich molecular information was further assigned according to the characteristic Raman peaks, and the results revealed a huge difference in the expression of molecules between CSCs and cancer cells, such as nucleic acids, proteins, saccharides and lipids. Furthermore, multiple data analysis algorithms including PCA, SVM, RF and KNN, were employed to reveal the fundamental characteristics and classification of CSCs and cancer cells based on the whole p-AuNSs-assisted single-cell Raman spectra. This work is beneficial for not only providing deep insights for molecular behaviors of tumor metastasis based on CSCs, but also could obtain significant information to aid medical diagnosis and to design effective CSCs-based therapeutic intervention clinically.

Bioorthogonal SERS-bioluminescence dual-modal imaging for real-time tracking of triple-negative breast cancer metastasis

Triple-negative breast cancer (TNBC) represents an aggressive subtype of breast cancer, characterized by early metastasis and a poor prognosis. Traditional imaging modalities often lack the sensitivity and molecular specificity required for the early detection of metastatic lesions. In this study, we developed a dual-modal imaging strategy that integrates surface-enhanced Raman scattering (SERS) and bioluminescence imaging probes, utilizing bioorthogonal labeling to track TNBC organ metastasis. The SERS probes were encapsulated with azide-labeled macrophage membranes to extend circulation time and enhance targeting efficiency. Additionally, bioorthogonal metabolic glycolengineering was employed to modify luciferase-labeled tumor cells (4T1-Luc) with bicyclo[6.1.0]nonyne (BCN) groups, facilitating precise binding between the probes and 4T1-Luc cells through click chemistry reactions. This dual-modal imaging approach enabled real-time monitoring of small metastatic lesions with high sensitivity, providing a non-invasive and accurate method for assessing tumor metastasis and therapeutic response in vivo. Our findings indicate that the dual-modal imaging technique, combining SERS and bioluminescence with bioorthogonal labeling, holds significant potential for advanced applications in oncology. STATEMENT OF SIGNIFICANCE: This study devised a surface-enhanced Raman scattering (SERS) and bioluminescence dual-modal imaging strategy integrated with a bioorthogonal label to address the challenge of tracking the metastasis of aggressive triple-negative breast cancer (TNBC). In contrast to conventional methods, this approach facilitated real-time, whole-body monitoring of tumor dissemination through bioluminescence. Simultaneously, it achieved the detection of micro-metastases in organs using SERS, thereby exceeding the sensitivity limitations of existing imaging techniques. Clinical validation with human samples further demonstrated its potential for non-invasive therapeutic assessment and early intervention. By bridging preclinical innovation and clinical requirements, this research offered a transformative tool for precision oncology. It is expected to attract the interest of researchers in the fields of biomedicine, nanotechnology, and cancer therapeutics.

Microcapillary-Derived Plasmonic-Enhanced Cluster through the Self-Assembly Process for Breast Cancer Diagnosis

Artificial intelligence (AI)-based surface-enhanced Raman scattering (SERS) is a powerful system for cancer diagnosis, leveraging its unique advantages by combining the high sensitivity of the SERS technique with the advanced classification capabilities provided by computing power. While previous studies have yielded significant results through using exosomes, miRNA, and phenotypic biomarkers for detecting breast cancer, these methods frequently entail time-consuming and complex pretreatment steps, demanding highly skilled handling. Here, we present a free-label SERS platform with faster sampling without any pretreat using blood plasma for breast cancer diagnosis. In this study, a cluster structure of gold nanoparticles within a confines space of microcapillary was fabricated to generate close-packing nanoparticles for enhancing electromagnetic field and large number of "hot spot." We demonstrate that our SERS platform can significantly amplify the Raman signal through standard chemical detection of R6G molecules. Consequently, a solution mixed appropriately between blood plasma collected from participants with gold nanoparticles to build the hybrid cluster in the microcapillary for SERS measurement. With the support of a machine learning model, the breast cancer diagnosis has successfully classified between patients and normal participants with a high accuracy of 87.5%.

Surface-Enhanced Raman Scattering (SERS) combined with machine learning enables accurate diagnosis of cervical cancer: from molecule to cell to tissue level

The rising number of cervical cancer cases is placing a heavy economic strain on the country and its people. Improving survival rates hinges on early detection, precise diagnosis, and thorough treatment. Common screening and diagnostic methods like Pap smears, HPV testing, colposcopy, and histopathological exams are used in clinical practice, but they are often costly, time-consuming, invasive, subjective, and may lack the necessary sensitivity and specificity for accurate diagnosis. Developing a quick, non-invasive, and precise method for cervical cancer screening is crucial. Raman spectroscopy offers structural insights without damaging samples, but its weak signals and interference from biological fluorescence limit its clinical use. Surface-Enhanced Raman Scattering (SERS) overcomes these challenges, and recent advances, especially when combined with machine learning, enhance cervical cancer diagnosis by enabling precise detection of tumor. This paper comprehensively reviews and summarizes the application of SERS in cervical cancer diagnosis, ranging from molecular biomarker detection to live cell level and then to tissue level diagnosis. By integrating with machine learning, it facilitates the development of accurate, non-invasive diagnosis of cervical cancer.

Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron

The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spectral analysis of surface-enhanced Raman scattering (SERS). By performing radical SERS scanning through the coffee-ring area and sampling across the coffee ring, complicated chemical information was spatially gathered for further spectra analysis. The corresponding application in classification of serum samples from breast cancer patients was also proposed. A simulated serum environment was constructed by mixing phenylalanine, hypoxanthine, and bovine serum albumin (BSA), yielding the coffee-ring patterns along with gold nanoparticles. Distinct divergence in the distributions between hypoxanthine and phenylalanine within the rings were characterized, which is attributed to the inherent electrostatic properties of the noble metal colloid and the interactions among different solvents. Subsequently, this method was applied to serum samples from patients diagnosed with the four breast cancer subtypes. By preparing serum with SERS substrates and forming the coffee-ring patterns, radial SERS scanning was conducted across the rings. The acquired spectra were spatially segmented and processed by employing a multilayer perceptron for learning and prediction. The classification results demonstrated a predictive accuracy of 85.7% in distinguishing among the four breast cancer subtypes, highlighting the feasibility and effectiveness of the coffee-ring assisted radial SERS analysis.

Kan-AAE-driven synthetic SERS spectra generation method for Precise cancer identification

Surface-Enhanced Raman Spectroscopy (SERS) is gaining popularity in cancer detection studies because it offers a non-invasive and rapid approach. Label-free SERS detection techniques often needs machine learning, which depends on adequate data for training. The scarcity of blood serum samples from cancer patients, due to challenges in collection linked to confidentiality concerns and other restrictions, can result in model overfitting and poor generalization ability. To tackle this challenge, we propose the KAN-AAE method, a new approach for creating synthetic SERS spectra, which lever- ages the power of Kolmogorov-Arnold Networks (KAN) in conjunction with Adversarial Autoencoders (AAE) and has the excellent capability of fitting the distribution of complex data in feature space. We conducted experiments by collecting serum samples from patients with four different types of cancer, two types of other diseases, and healthy individuals, subsequently measuring their SERS spectra. We trained the KAN-AAE model using the SERS spectral data and used it to produce synthetic spectra, which were then combined with actual data for classifier training, enhancing data diversity. Utilizing the combined dataset, there was a notable increase of 1% to 3% in the accuracy of classification models like logistic regression, decision tree, multilayer perceptron, 1D-convolutional neural network, and KAN. The KAN classifier outperformed others, achieving an accuracy rate of 95.62%. The experimental results demonstrate that: (1) our proposed method gener- ates high-quality and reliable SERS spectra data; (2) the method effectively improves the classification accuracy for various types of cancer.

The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer

Artificial intelligence (AI) is revolutionizing scientific research by facilitating a paradigm shift in data analysis and discovery. This transformation is characterized by a fundamental change in scientific methods and concepts due to AI's ability to process vast datasets with unprecedented speed and accuracy. In breast cancer research, AI aids in early detection, prognosis, and personalized treatment strategies. Liquid biopsy, a noninvasive tool for detecting circulating tumor traits, could ideally benefit from AI's analytical capabilities, enhancing the detection of minimal residual disease and improving treatment monitoring. Extracellular vesicles (EVs), which are key elements in cell communication and cancer progression, could be analyzed with AI to identify disease-specific biomarkers. AI combined with EV analysis promises an enhancement in diagnosis precision, aiding in early detection and treatment monitoring. Studies show that AI can differentiate cancer types and predict drug efficacy, exemplifying its potential in personalized medicine. Overall, the integration of AI in biomedical research and clinical practice promises significant changes and advancements in diagnostics, personalized medicine-based approaches, and our understanding of complex diseases like cancer.

Surface-Enhanced Raman Scattering (SERS) for exosome detection

BACKGROUND

Exosomes, nanoscale extracellular vesicles secreted by various cells, are abundantly present in biological fluids. They have been identified as carriers of specific molecules, suggesting their potential role in early disease detection. However, their clinical application is hindered by several challenges, including the need for large sample volumes for enrichment, limitations of traditional detection methods, and the complexity involved in phenotype analysis and separation.

OBJECTIVE

This review aims to explore the application of Surface-Enhanced Raman Scattering (SERS) technology in exosome detection. SERS, known for its unique photonic properties and high sensitivity, offers a promising solution for detecting exosomes without the need for large sample volumes or extensive phenotypic analysis. This review focuses on the real-time and non-invasive assessment capabilities of SERS in exosome detection, providing insights into its potential for early disease diagnosis.

CONCLUSION

The review concludes by emphasizing the potential of SERS-based exosome detection in advancing early disease diagnosis. By overcoming existing challenges, SERS technology offers a promising approach for the development of sensitive and specific diagnostic assays, contributing to better patient outcomes and personalized medicine.

A fluorescence and SERS dual-mode biosensor for quantification and imaging of Mucin1 in living cells

Mucin1 (MUC1) is a cell surface transmembrane protein overexpressed in multiple types of tumor cells, which is generally considered as a tumor-associated biomarker. Thus, quantifying and imaging of MUC1 in tumor cells is of great significance for the diagnosis and biological therapy of tumors. Herein, a fluorescence (FL) and surface-enhanced Raman scattering (SERS) dual-mode biosensor was developed for sensitive detection and imaging of MUC1 in living cells. The FL-SERS biosensor was based on self-assembled satellite structures mediated by the competition of MUC1 and complementary DNA modified on Au nanostars (AuNS-cDNA-PEG) with aptamer modified on quantum dots (QD-PDDA-Apt). This biosensor achieved dual-mode quantification and imaging of MUC1 by quenching the FL signal of QD and significantly enhancing the SERS signal of PDDA through the metal FL quenching effect and hotspot effect of AuNS, respectively. The dual-mode biosensor exhibited high sensitivity to MUC1, with a detection limit of 1.19 fg/mL under FL mode and 1.16 fg/mL under SERS mode. Moreover, this biosensor displayed good selectivity, nice biological stability and low cytotoxicity. Importantly, this biosensor possessed an excellent MUC1 dual-mode imaging capability with high specificity in different tumor cells, providing a new idea for clinical diagnosis of tumors.

Recent advances in SERS assays for detection of multiple extracellular vesicles biomarkers for cancer diagnosis

As the prevalence of cancer is escalating, there is an increased demand for early and sensitive diagnostic tools. A major challenge in early detection is the lack of specific biomarkers, and a readily accessible, sensitive and rapid detection method. To meet these challenges, cancer-derived small extracellular vesicles (sEVs) have been discovered as a new promising cancer biomarker due to the high abundance of sEVs in body fluids and their extensive cargo of biomarkers. Additionally, surface-enhanced Raman scattering (SERS) presents a sensitive, multiplexed, and rapid method that has gained attraction with recent studies showing promising results from patient samples for the multiplex detection of cancer sEVs. Various label-based SERS multiplex assays have been developed in the field of SERS including bead assays, lateral flow immunoassays, microfluidic devices, and artificial intelligence (AI)-based label-free SERS chips, targeting multiple surface proteins to ensure comprehensive multiplex diagnostics. These assays hold promise for enabling early detection, quantification, and subtyping of cancer-derived sEVs for cancer diagnostic applications. This review aims to provide a summary of the recent advances in the field of SERS multiplex assays for detection, quantification, and subtyping of sEVs to facilitate cancer diagnosis. This review further provides unique insights into the use of sEVs as a biomarker and aims to address the issues surrounding their translation from laboratories to clinics.

Precision Fabrication and Optimization of Nanostructures for Exosome Detection via Surface-Enhanced Raman Spectroscopy

Exosome detection is crucial for biomedical research and clinical diagnostics due to their unique characteristics. Surface-enhanced Raman spectroscopy (SERS) based on nanostructure substrates with local field enhancement capability is a promising detection approach. However, the random distribution of nanostructures leads to uneven "hotspots" distribution, which limits their application in SERS detection. Here, we systematically investigated the impact of experimental parameters on nanostructure morphology and analyzed their formation mechanism, achieving controllable nanocone fabrication. Subsequent experiments confirmed the reliability and effectiveness of the fabricated nanocone in exosome SERS detection. This work not only realized flexible control of nanostructures but also expanded their application prospects in the field of exosome analysis.

SERS calibration substrate with a silent region internal standard for reliable simultaneous detection of multiple antibiotics in water

As the extensive use of antibiotics has led to the rapid spread of antibiotic resistance, there is an urgent need for quantitative assessment of antibiotic residues in the environment. Surface-enhanced Raman spectroscopy (SERS) has emerged as a rapid and cost-effective detection method, but it suffers from the high variability in signal intensities, its quantitative detection remains challenging. Herein, we have developed a SERS calibration substrate with a silent region internal standard, enabling simultaneous and reliable quantitative detection of three commonly antibiotics of penicillin potassium (PP), tetracycline hydrochloride (TCH) and levofloxacin (LEV). The calibration substrate is made by assembling Au @ 4-mercaptobenzonitrile (4-MBN) @ SiO2 on silicon wafer. The chemically-inert silica shell allows the substrate to remain SERS active for more than 24 weeks in the air. The vC ≡ N of 4-MBN in the silent region of 1800-2800 cm-1 provides an effective reference for correcting Raman signal fluctuations. The relative SERS intensity by normalizing to the internal standard (IS) of vC ≡ N shows a linear response against to the logarithmic concentration with a correlation coefficient of 0.997, 0.976, 0.998 and a limit of detection (LOD) of 26.9, 28.2, 2.4 nM for PP, TCH and LEV, respectively. Furthermore, simultaneous detection and principal component analysis (PCA) of these antibiotics in lake water with a concentration range of 1-100 mg/L can achieve a sensitivity and specificity of 100 %. This novel quantitative technique of using this SERS calibration substrate shows promises as a high-throughput platform for multiple trace antibiotics analysis.

Machine learning assisted dual-modal SERS detection for circulating tumor cells

Detecting circulating tumor cells (CTCs) from blood has become a promising approach for cancer diagnosis. Surface-enhanced Raman Spectroscopy (SERS) has rapidly developed as a significant detection technology for CTCs, offering high sensitivity and selectivity. Encoded SERS bioprobes have gained attention due to their excellent specificity and ability to identify tumor cells using Raman signals. Machine learning has also made significant contributions to biomedical applications, especially in medical diagnosis. In this study, we developed a detection strategy combining encoded SERS bioprobes and machine learning models to identify CTCs. Dual-modal SERS bioprobes were designed and co-incubated with tumor cells by the "cocktail" method. An identification model for CTCs was constructed using principal component analysis (PCA) and the Random Forest classification algorithm. This innovative strategy endows SERS bioprobes with both effective magnetic separation and highly sensitive identification of CTCs, even at low concentrations of 2 cells/mL. It achieved a high detection rate of 98% for CTCs and effectively eliminated interference from peripheral WBCs. This simple and efficient strategy provides a new approach for CTCs detection and holds important significance for cancer diagnosis.

Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.

Exosomes in cancer diagnosis based on the Latest Evidence: Where are We?

Exosomes are small extracellular vesicles (EVs) derived from various cellular sources and have emerged as favorable biomarkers for cancer diagnosis and prognosis. These vesicles contain a variety of molecular components, including nucleic acids, proteins, and lipids, which can provide valuable information for cancer detection, classification, and monitoring. However, the clinical application of exosomes faces significant challenges, primarily related to the standardization and scalability of their use. In order to overcome these challenges, sophisticated methods such as liquid biopsy and imaging are being combined to augment the diagnostic capabilities of exosomes. Additionally, a deeper understanding of the interaction between exosomes and immune system components within the tumor microenvironment (TME) is essential. This review discusses the biogenesis and composition of exosomes, addresses the current challenges in their clinical translation, and highlights recent technological advancements and integrative approaches that support the role of exosomes in cancer diagnosis and prognosis.

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.

Integrated SERS-Microfluidic Sensor Based on Nano-Micro Hierarchical Cactus-like Array Substrates for the Early Diagnosis of Prostate Cancer

The detection and analysis of cancer cell exosomes with high sensitivity and precision are pivotal for the early diagnosis and treatment strategies of prostate cancer. To this end, a microfluidic chip, equipped with a cactus-like array substrate (CAS) based on surface-enhanced Raman spectroscopy (SERS) was designed and fabricated for the detection of exosome concentrations in Lymph Node Carcinoma of the Prostate (LNCaP). Double layers of polystyrene (PS) microspheres were self-assembled onto a polyethylene terephthalate (PET) film to form an ordered cactus-like nanoarray for detection and analysis. By combining EpCAM aptamer-labeled SERS nanoprobes and a CD63 aptamer-labeled CAS, a 'sandwich' structure was formed and applied to the microfluidic chips, further enhancing the Raman scattering signal of Raman reporter molecules. The results indicate that the integrated microfluidic sensor exhibits a good linear response within the detection concentration range of 105 particles μL-1 to 1 particle μL-1. The detection limit of exosomes in cancer cells can reach 1 particle μL-1. Therefore, we believed that the CAS integrated microfluidic sensor offers a superior solution for the early diagnosis and therapeutic intervention of prostate cancer.

Electroactivated SERS Nanoplatform for Rapid and Sensitive Detection and Identification of Tumor-Derived Exosome miRNA

Exosomal miRNA expression derived from tumor cells provides a valuable and promising noninvasive modality for the early diagnosis and assessment of the efficacy of cancer treatment. However, accurate detection and identification of miRNA within exosomes have been challenging due to its low abundance and the complexity and tedious extraction with large sample volumes in the separation process. Here, we developed an electrically activated nanoplatform for rapid and sensitive detection and identification of exosome miRNA, through triggering miRNA release by opening exosomes that were captured on the electrode surface using a slightly applied electric field (50 mV), and simultaneously detected them with surface-enhanced Raman spectroscopy (SERS) in situ. The method possessed superior specificity and sensitivity for exosomal miRNA detection, with a low detection concentration of 0.5 nM. The SERS sensor chips also showed a superior sensing performance of exosomal miRNA in complex body fluids such as urine and blood. We found that exosomal miRNA contents derived from tumor cells were significantly higher than those in normal cells, and importantly, the concentrations of exosomes secreted from three different cell lines were distinctly augmented after mild electrical stimulation (ES) treatment. Furthermore, the miRNA expression within exosomes was upregulated after the ES treatment of cells. The developed approach and SERS detection platform for exosomal miRNA are promising for noninvasive and precise screening, classification, and monitoring of cancer.

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.

Quantitative multiplexing of uric acid and creatinine using polydisperse plasmonic nanoparticles enabled by electrochemical-SERS and machine learning

Surface-enhanced Raman spectroscopy (SERS) is a promising technique for the detection of biomarkers, but it can struggle to quantify multiple analytes in complex fluids. This study combines electrochemical SERS (E-SERS) and machine learning for the quantitative multiplexed detection of uric acid (UA) and creatinine (CRN). Using classical polydisperse Ag nanoparticles (NPs) made by scalable synthesis, we achieved quantitative multiplexing with low limits of detection (LoDs) and high prediction accuracy, comparable to those made by sophisticated approaches. The E-SERS LoDs at the optimal applied potentials were 0.127 μM and 0.354 μM for UA and CRN respectively, compared to 0.504 μM and 1.02 μM for conventional SERS (recorded at 0 V). By collecting a multi-dimensional E-SERS dataset and applying a two-step partial least squares regression - multilayer perceptron (PLSR-MLP) machine learning algorithm, we were able to identify the analyte concentrations in unseen spectra with a prediction accuracy of 0.94. This research demonstrates the potential of E-SERS and machine learning for multiplexed detection in clinical settings.

Interpretable Machine Learning-Aided Optical Deciphering of Serum Exosomes for Early Detection, Staging, and Subtyping of Lung Cancer

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, underscoring an urgent need for strategies that enable early detection and phenotypic classification. Here, we conducted a label-free surface-enhanced Raman spectroscopic (SERS) analysis of serum exosomes from 643 participants to elucidate the biochemical deregulation associated with LC progression and the unique phenotypes of different LC subtypes. Iodide-modified silver nanofilms were prepared to rapidly acquire SERS spectra with a high signal-to-noise ratio using 0.5 μL of patient exosomes. We performed interpretable and automated machine learning (ML) analysis of differential SERS features of serum exosomes to build LC diagnostic models, which achieved accuracies of 100% and 81% for stage I lung adenocarcinoma and its preneoplasia, respectively. In addition, the ML-derived exosomal SERS models effectively recognized different LC subtypes and disease stages to guide precision treatment. Our findings demonstrate that spectral fingerprinting of circulating exosomes holds promise for decoding the clinical status of LC, thus aiding in improving the clinical management of patients.

The new advance of exosome-based liquid biopsy for cancer diagnosis

Liquid biopsy is a minimally invasive method that uses biofluid samples instead of tissue samples for cancer diagnosis. Exosomes are small extracellular vesicles secreted by donor cells and act as mediators of intercellular communication in human health and disease. Due to their important roles, exosomes have been considered as promising biomarkers for liquid biopsy. However, traditional methods for exosome isolation and cargo detection methods are time-consuming and inefficient, limiting their practical application. In the past decades, many new strategies, such as microfluidic chips, nanowire arrays and electrochemical biosensors, have been proposed to achieve rapid, accurate and high-throughput detection and analysis of exosomes. In this review, we discussed about the new advance in exosome-based liquid biopsy technology, including isolation, enrichment, cargo detection and analysis approaches. The comparison of currently available methods is also included. Finally, we summarized the advantages and limitations of the present strategies and further gave a perspective to their future translational use.

Impact of Nanomedicine in Women's Metastatic Breast Cancer

Metastatic breast cancer is responsible for 90% of mortalities among women suffering from various types of breast cancers. Traditional cancer treatments such as chemotherapy and radiation therapy can cause significant side effects and may not be effective in many cases. However, recent advances in nanomedicine have shown great promise in the treatment of metastatic breast cancer. For example, nanomedicine demonstrated robust capacity in detection of metastatic cancers at early stages (i.e., before the metastatic cells leave the initial tumor site), which gives clinicians a timely option to change their treatment process (for example, instead of endocrine therapy they may use chemotherapy). Here recent advances in nanomedicine technology in the identification and treatment of metastatic breast cancers are reviewed.

Role of Exosomes in Salivary Gland Tumors and Technological Advances in Their Assessment

Backgroud: Salivary gland tumors (SGTs) are rare and diverse neoplasms, presenting significant challenges in diagnosis and management due to their rarity and complexity. Exosomes, lipid bilayer vesicles secreted by almost all cell types and present in all body fluids, have emerged as crucial intercellular communication agents. They play multifaceted roles in tumor biology, including modulating the tumor microenvironment, promoting metastasis, and influencing immune responses. Results: This review focuses on the role of exosomes in SGT, hypothesizing that novel diagnostic and therapeutic approaches can be developed by exploring the mechanisms through which exosomes influence tumor occurrence and progression. By understanding these mechanisms, we can leverage exosomes as diagnostic and prognostic biomarkers, and target them for therapeutic interventions. The exploration of exosome-mediated pathways contributing to tumor progression and metastasis could lead to more effective treatments, transforming the management of SGT and improving patient outcomes. Ongoing research aims to elucidate the specific cargo and signaling pathways involved in exosome-mediated tumorigenesis and to develop standardized techniques for exosome-based liquid biopsies in clinical settings. Conclusions: Exosome-based liquid biopsies have shown promise as non-invasive, real-time systemic profiling tools for tumor diagnostics and prognosis, offering significant potential for enhancing patient care through precision and personalized medicine. Methods like fluorescence, electrochemical, colorimetric, and surface plasmon resonance (SPR) biosensors, combined with artificial intelligence, improve exosome analysis, providing rapid, precise, and clinically valid cancer diagnostics for difficult-to-diagnose cancers.

Molecular-Sieving Label-Free Surface-Enhanced Raman Spectroscopy for Sensitive Detection of Trace Small-Molecule Biomarkers in Clinical Samples

Small-molecule biomarkers are ubiquitous in biological fluids with pathological implications, but major challenges persist in their quantitative analysis directly in complex clinical samples. Herein, a molecular-sieving label-free surface-enhanced Raman spectroscopy (SERS) biosensor is reported for selective quantitative analysis of trace small-molecule trimetazidine (TMZ) in clinical samples. Our biosensor is fabricated by decorating a superhydrophobic monolayer of microporous metal-organic frameworks (MOF) shell-coated Au nanostar nanoparticles on a silicon substrate. The design strategy principally combines the hydrophobic surface-enabled physical confinement and preconcentration, MOF-assisted molecular enrichment and sieving of small molecules, and sensitive SERS detection. Our biosensor utilizes such a "molecular confinement-and-sieving" strategy to achieve a five orders-of-magnitude dynamic detection range and a limit of detection of ≈0.5 nM for TMZ detection in either urine or whole blood. We further demonstrate the applicability of our biosensing platform for longitudinal label-free SERS detection of the TMZ level directly in clinical samples in a mouse model.

Biomedical SERS - the current state and future trends

Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.

Shedding Light on Cellular Secrets: A Review of Advanced Optical Biosensing Techniques for Detecting Extracellular Vesicles with a Special Focus on Cancer Diagnosis

In the relentless pursuit of innovative diagnostic tools for cancer, this review illuminates the cutting-edge realm of extracellular vesicles (EVs) and their biomolecular cargo detection through advanced optical biosensing techniques with a primary emphasis on their significance in cancer diagnosis. From the sophisticated domain of nanomaterials to the precision of surface plasmon resonance, we herein examine the diverse universe of optical biosensors, emphasizing their specified applications in cancer diagnosis. Exploring and understanding the details of EVs, we present innovative applications of enhancing and blending signals, going beyond the limits to sharpen our ability to sense and distinguish with greater sensitivity and specificity. Our special focus on cancer diagnosis underscores the transformative potential of optical biosensors in early detection and personalized medicine. This review aims to help guide researchers, clinicians, and enthusiasts into the captivating domain where light meets cellular secrets, creating innovative opportunities in cancer diagnostics.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

Deep learning-assisted monitoring of trastuzumab efficacy in HER2-Overexpressing breast cancer via SERS immunoassays of tumor-derived urinary exosomal biomarkers

Monitoring drug efficacy is significant in the current concept of companion diagnostics in metastatic breast cancer. Trastuzumab, a drug targeting human epidermal growth factor receptor 2 (HER2), is an effective treatment for metastatic breast cancer. However, some patients develop resistance to this therapy; therefore, monitoring its efficacy is essential. Here, we describe a deep learning-assisted monitoring of trastuzumab efficacy based on a surface-enhanced Raman spectroscopy (SERS) immunoassay against HER2-overexpressing mouse urinary exosomes. Individual Raman reporters bearing the desired SERS tag and exosome capture substrate were prepared for the SERS immunoassay; SERS tag signals were collected to prepare deep learning training data. Using this deep learning algorithm, various complicated mixtures of SERS tags were successfully quantified and classified. Exosomal antigen levels of five types of cell-derived exosomes were determined using SERS-deep learning analysis and compared with those obtained via quantitative reverse transcription polymerase chain reaction and western blot analysis. Finally, drug efficacy was monitored via SERS-deep learning analysis using urinary exosomes from trastuzumab-treated mice. Use of this monitoring system should allow proactive responses to any treatment-resistant issues.

Lectin microarray based glycan profiling of exosomes for dynamic monitoring of colorectal cancer progression

BACKGROUND

Exosomes, as emerging biomarkers in liquid biopsies in recent years, offer profound insights into cancer diagnostics due to their unique molecular signatures. The glycosylation profiles of exosomes have emerged as potential biomarkers, offering a novel and less invasive method for cancer diagnosis and monitoring. Colorectal cancer (CRC) represents a substantial global health challenge and burden. Thus there is a great need for the aberrant glycosylation patterns on the surface of CRC cell-derived exosomes, proposing them as potential biomarkers for tumor characterization.

RESULTS

The interactions of 27 lectins with exosomes from three CRC cell lines (SW480, SW620, HCT116) and one normal colon epithelial cell line (NCM460) have been analyzed by the lectin microarray. The result indicates that Ulex Europaeus Agglutinin I (UEA-I) exhibits high affinity and specificity towards exosomes derived from SW480 cells. The expression of glycosylation related genes within cells has been analyzed by high-throughput quantitative polymerase chain reaction (HT-qPCR). The experimental result of HT-qPCR is consistent with that of lectin microarray. Moreover, the limit of detection (LOD) of UEA-I microarray is calculated to be as low as 2.7 × 105 extracellular vehicles (EVs) mL-1 (three times standard deviation (3σ) of blank sample). The UEA-I microarray has been successfully utilized to dynamically monitor the progression of tumors in mice-bearing SW480 CRC subtype, applicable in tumor sizes ranging from 2 mm to 20 mm in diameter.

SIGNIFICANCE

The results reveal that glycan expression pattern of exosome is linked to specific CRC subtypes, and regulated by glycosyltransferase and glycosidase genes of mother cells. Our findings illuminate the potential of glycosylation molecules on the surface of exosomes as reliable biomarkers for diagnosis of tumor at early stage and monitoring of cancer progression.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

Targeted SERS Imaging and Intraoperative Real-Time Elimination of Microscopic Tumors for Improved Breast-Conserving Surgery

Breast-conserving surgery is the favorable option for breast cancer patients owing to its advantages of less aggressiveness and better cosmetic outcomes over mastectomy. However, it often suffers from postsurgical lethal recurrence due to the incomplete removal of microscopic tumors. Here, a surface-enhanced Raman scattering (SERS) surgical strategy is reported for precise delineation of tumor margins and intraoperative real-time elimination of microscopic tumor foci, which is capable of complete surgical removal of breast tumors and significantly improve the outcomes of breast-conserving surgery without local tumor recurrence. The technique is chiefly based on the human epidermal growth factor receptor 2 (HER2)-targeting SERS probes with integrated multifunctionalities of ultrahigh sensitive detection, significant HER2 expression suppression, cell proliferation inhibition, and superior photothermal ablation. In a HER2+ breast tumor mouse model, the remarkable capability of the SERS surgical strategy for complete removal of HER2+ breast tumors through SERS-guided surgical resection and intraoperative real-time photothermal elimination is demonstrated. The results show complete eradiation of HER2+ breast tumors without local recurrence, consequently delivering a 100% tumor-free survival. Expectedly, this SERS surgical strategy holds great promise for clinical treatment of HER2+ breast cancer with improved patients' survival.

A Dual-Function AgNW@COF SERS Membrane for Organic Pollutant Removal and Simultaneous Concentration Determination

Surface enhanced Raman spectroscopy (SERS) is a highly sensitive analytical detection method commonly employed in biochemical and environmental analysis. Nevertheless, the rapid movement of analytes and interfering components in flow systems can impact the real-time, online detection capability of Raman spectroscopy. To address this issue, we developed an innovative approach utilizing covalent organic framework (COF), a robust porous material with excellent water stability, to coat the surface of Ag nanowire (AgNW) for synthesizing AgNW@COF hybrid. The regular pores of the COF serve to effectively eliminate large interfering molecules while facilitating the efficient transport of specific analytes to SERS hot spots. Additionally, the fluid flow-induced scouring effect aids in excluding interfering molecules from the surface of AgNW. By incorporating AgNW@COF into a bifunctional filter membrane with simultaneous filtration and sensing capabilities, we had achieved efficient purification of organic pollutants as well as real-time identification of pollutant species and concentration.

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

Ultrasensitive and Wash-Free Detection of Tumor Extracellular Vesicles by Aptamer-Proximity-Ligation-Activated Rolling Circle Amplification Coupled to Single Particle ICP-MS

Tumor-derived extracellular vesicles (TEVs) are rich in cellular information and hold great promise as a biomarker for noninvasive cancer diagnosis. However, accurate measurement of TEVs presents challenges due to their low abundance and potential interference from a high number of EVs derived from normal cells. Herein, an aptamer-proximity-ligation-activated rolling circle amplification (RCA) method for EV membrane recognition, coupled with single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) for the quantification of TEVs, is developed. When DNA-labeled ultrasmall gold nanoparticle (AuNP) probes bind to the long chains formed by RCA, they aggregate to form large particles. Notably, small AuNPs scarcely produce pulse signals in sp-ICP-MS, thereby detecting TEVs in a wash-free manner. By leveraging the strong binding affinity of aptamers, dual aptamers for EpCAM and PD-L1 recognition, and the sp-ICP-MS technique, this method offers remarkable sensitivity and selectivity in tracing TEVs. Under optimized conditions, the present method shows a favorable linear relationship between the pulse signal frequency of sp-ICP-MS and TEV concentration within the range of 105-107 particles/mL, along with a detection limit of 1.1 × 104 particles/mL. The pulse signals from sp-ICP-MS combined with machine learning algorithms are used to discriminate cancer patients from healthy donors with 100% accuracy. Due to its simple and fast operation and excellent sensitivity and accuracy, this approach holds significant potential for diverse applications in life sciences and personalized medicine.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives

Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Exosome, a Rising Biomarkers in Liquid Biopsy: Advances of Label-Free and Label Strategy for Diagnosis of Cancer

Cancer is commonly considered as one of the most severe diseases, posing a significant threat to human health and society due to various serious challenges. These challenges include difficulties in accurate diagnosis and a high propensity to form metastasis. Tissue biopsy remains the gold standard for diagnosing and subtyping cancer. However, concerns arise from its invasive nature and the potential risk of metastasis during these complex diagnostic procedures. Meanwhile, liquid biopsy has recently witnessed the rapid advancements with the emergence of three prominent detection biomarkers: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Whereas, the very low abundance of CTCs combined with the instability of ctDNA intensify the challenges and decrease the accuracy of these two biomarkers for cancer diagnosis. While exosomes have gained widespread recognition as a promising biomarker in liquid biopsy due to their relatively low-invasive detection method, excellent biostability, rich resources, high abundance, and ability to provide valuable information about cancer. Therefore, it is crucial to systematically summarize recent advancements mainly in exosome-based detection methods for early cancer diagnosis. Specifically, this review will primarily focus on label-based and label-free strategies for detecting cancer using exosomes. We anticipate that this comprehensive analysis will enhance readers' understanding of the significance and value of exosomes in the fields of cancer diagnosis and therapy.

Weak Value Amplification-Based Biochip for Highly Sensitive Detection and Identification of Breast Cancer Exosomes

Exosomes constitute an emerging biomarker for cancer diagnosis because they carry multiple proteins that reflect the origins of the parent cell. The highly sensitive detection of exosomes is a crucial prerequisite for the diagnosis of cancer. In this study, we report an exosome detection system based on quantum weak value amplification (WVA). The WVA detection system consists of a reflection detection light path and a Zr-ionized biochip. Zr-ionized biochips effectively capture exosomes through the specific interaction between zirconium dioxide and the phosphate groups on the lipid bilayer of exosomes. Aptamer-modified gold nanoparticles (Au NPs) are then used to specifically recognize proteins on exosomes to enhance the detection signal. The sensitivity and resolution of the detection system are 2944.07 nm/RIU and 1.22 × 10-5 RIU, respectively. The concentration of exosomes can be directly quantified by the WVA system, ranging from 105-107 particles/mL with the detection limit of 3 × 104 particles/mL. The use of Au NPs-EpCAM for the specific enhancement of breast cancer MDA-MB-231 exosomes is demonstrated. The results indicate that the WVA detection system can be a promising candidate for the detection of exosomes as tumor markers.

Machine learning-based exosome profiling of multi-receptor SERS sensors for differentiating adenocarcinoma in situ from early-stage invasive adenocarcinoma

Exosomes, extracellular vesicles released by cells, hold potential as diagnostic markers for the early detection of lung cancer. Despite their clinical promise, current technologies lack rapid and effective means to discriminate between exosomes derived from adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC). This challenge arises from the intrinsic structural heterogeneity of exosomes, necessitating the development of advanced methodologies for precise differentiation. Here, we demonstrate a novel approach for plasma exosome detection utilizing multi-receptor surface-enhanced Raman spectroscopy (SERS) technology to differentiate between AIS and early-stage IAC. To accomplish this, we synthesized a stable and uniform two-dimensional SERS substrate (BC/Au NPs film) by fabricating gold nanoparticles onto bacterial cellulose. We then enhanced its capabilities by introducing multi-receptor SERS functionality via modifying the substrate with both low-specificity and physicochemical-selective molecules. Furthermore, by strategically combining all capturer-exosome SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of the exosome. Combining these features with partial least squares regression-discriminant analysis (PLS-DA) modeling significantly improved discriminatory accuracy, achieving 90% sensitivity and 95% specificity in distinguishing AIS from early-stage IAC. Our developed SERS sensor provides an effective method for early detection of lung cancer, thereby paving a new way for innovative advancements in diagnosing lung cancer.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.