Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Surface-enhanced Raman spectroscopy for the characterization of filtrate portions of hepatitis B blood serum samples using 100 kDa ultra filtration devices

The blood serum of patients infected by the Hepatitis B virus contains high molecular weight fractions and low molecular weight fractions (LMWF) of biomarker proteins of the disease. The LMWF including the associated peptidome and metabolome, is recognized as a critical molecular population with high potential for research on disease-associated biomarkers. This fraction of biomarkers can be suppressed by HMWF, proteins such as albumin, and immunoglobulins hence difficult to be detected. The purpose of this study is to separate HMWF) and LMWF using 100 kDa centrifugal filtration devices resulting in two parts including residue (HMWF) and filtrate parts (LMWF) of blood serum followed by the analysis of the later part employing surface-enhanced Raman spectroscopy (SERS). This strategy can enhance this optical technique's capability to characterize the biochemical changes caused by the infection of HBV and the diagnosis of the disease. The silver nanoparticles (Ag-NPs) were employed as a SERS substrate to distinguish between filtrate parts of the blood serum of HBV patients and healthy individuals based on their specific SERS peaks. The SERS spectral features associated with the filtrate parts of HBV patients' blood serum are well differentiated from the healthy volunteers. Principle component analysis (PCA) was applied on the SERS spectral data sets of HBV patients and healthy individuals and found extremely beneficial for the classification of their SERS spectral groups. Moreover, partial least square regression analysis (PLSR) has shown excellent performance in the quantitative analysis of the viral load values of the HBV patients using their SERS spectral data sets.

Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review

Nanomedicines are nanoscale, biocompatible materials that offer promising alternatives to conventional treatment options for brain disorders. The recent technological developments in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are transforming the nanomedicine field by improving disease diagnosis, biomarker identification, prognostic assessment and disease monitoring, targeted drug delivery, and therapeutic intervention as well as contributing to computational and methodological developments. These advancements can be achieved by analysis of large clinical datasets and facilitating the design and optimization of nanomaterials for in vivo testing. Such advancement offers exciting possibilities for the improvement in the management of brain disorders, including brain cancer, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, where early diagnosis, targeted delivery, and effective treatment strategies remain a great challenge. This review article provides an overview of recent advances in AI-based nanomedicine development to accelerate effective and quick diagnosis, biomarker identification, prognosis, drug delivery, methodological advancement and patient-specific therapies for managing brain disorders.

Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection

Microplastics (MPs) and nanoplastics (NPs) present formidable global environmental challenges with serious risks to human health and ecosystem sustainability. Despite their significance, the accurate assessment of environmental MP and NP pollution remains hindered by limitations in existing detection technologies, such as low resolution, substantial data volumes, and prolonged imaging times. Machine learning (ML) provides a promising pathway to overcome these challenges by enabling efficient data processing and complex pattern recognition. This systematic Review aims to address these gaps by examining the role of ML techniques combined with spectroscopy in improving the detection and characterization of NPs. We focused on the application of ML and key tools in MP and NP detection, categorizing the literature into key aspects: (1) Developing tailored strategies for constructing ML models to optimize plastic detection while expanding monitoring capabilities. Emphasis is placed on harnessing the unique molecular fingerprinting capabilities offered by spectroscopy, including both infrared (IR) and Raman spectra. (2) Providing an in-depth analysis of the challenges and issues encountered by current ML approaches for NP detection. This Review highlights the critical role of ML in advancing environmental monitoring and improving our further, deeper investigation of the widespread presence of NPs. By identifying current key challenges, this Review provides valuable insights for future direction in environmental management and public health protection.

Raman spectroscopies for cancer research and clinical applications: a focus on cancer stem cells

Over the last 2 decades, research has increasingly focused on cancer stem cells (CSCs), considered responsible for tumor formation, resistance to therapies, and relapse. The traditional "static" CSC model used to describe tumor heterogeneity has been challenged by the evidence of CSC dynamic nature and plasticity. A comprehensive understanding of the mechanisms underlying this plasticity, and the capacity to unambiguously identify cancer markers to precisely target CSCs are crucial aspects for advancing cancer research and introducing more effective treatment strategies. In this context, Raman spectroscopy (RS) and specific Raman schemes, including CARS, SRS, SERS, have emerged as innovative tools for molecular analyses both in vitro and in vivo. In fact, these techniques have demonstrated considerable potential in the field of cancer detection, as well as in intraoperative settings, thanks to their label-free nature and minimal invasiveness. However, the RS integration in pre-clinical and clinical applications, particularly in the CSC field, remains limited. This review provides a concise overview of the historical development of RS and its advantages. Then, after introducing the CSC features and the challenges in targeting them with traditional methods, we review and discuss the current literature about the application of RS for revealing and characterizing CSCs and their inherent plasticity, including a brief paragraph about the integration of artificial intelligence with RS. By providing the possibility to better characterize the cellular diversity in their microenvironment, RS could revolutionize current diagnostic and therapeutic approaches, enabling early identification of CSCs and facilitating the development of personalized treatment strategies.

Preparation, characterisation, and application of gelatin/sodium carboxymethyl cellulose/peach gum ternary composite microcapsules for encapsulating sweet orange essential oil

This study successfully developed a gelatin-sodium carboxymethyl cellulose-peach gum composite microcapsule system using the complex coacervation method. Optimal preparation conditions were determined by turbidity, complex condensate yield and encapsulation efficiency: the ratio of gelatin to sodium carboxymethyl cellulose was 7:1, the ratio of gelatin/sodium carboxymethyl cellulose to peach gum was 4:1, and the pH value was 4.2. The morphology of the system was evaluated using scanning electron microscopy and laser confocal microscopy. Structural properties and viscoelasticity were analyzed through Fourier-transform infrared spectroscopy, Raman spectroscopy, and rheology. Results indicated that adding peach gum enhanced the system's structural stability and solid behavior. Thermogravimetric analysis, differential scanning calorimetry, swelling, release, antioxidant, and antibacterial experiments confirmed that the addition of peach gum increased the thermal decomposition temperature of the microcapsule system to 246 °C, enhanced the sustained release capacity, and improved both the antioxidant effects and antibacterial properties. The highest lipid antioxidant value was 1.1 mg MDA/kg, while the total bacterial count reached a maximum of 5.3 log CFU/g, both of which remained below the threshold of food spoilage, thereby confirming the role of the gelatin-carboxymethyl cellulose sodium-peach gum composite system in meat preservation. The gelatin-sodium carboxymethyl cellulose-peach gum composite system's excellent sustained-release capacity, antioxidant effect, and the antibacterial properties of the gelatin-sodium carboxymethyl cellulose-peach gum composite system could provide a new carrier for the application of active substances in the food sector.

Raman spectroscopy in the detection and diagnosis of lung cancer: a meta-analysis

Lung cancer is the world's biggest cause of death related to cancer, and its dismal prognosis has been greatly exacerbated by late-stage diagnosis. Even with improvements in treatment strategies, current diagnostic techniques are frequently imprecise, especially when it comes to early-stage detection. A prospective substitute is Raman spectroscopy, which provides a non-invasive, real-time, and extremely sensitive study of biological samples. The objective of this study is to assess the diagnostic efficacy of Raman spectroscopy in the identification and diagnosis of lung cancer across a range of sample types. Nine studies that focused on Raman spectroscopy as a stand-alone diagnostic tool and met strict inclusion criteria were found through a systematic review of the literature published between 2014 and 2024. Statistical methods were used to extract, pool, and show diagnostic measures. The remarkable diagnostic accuracy of Raman spectroscopy was highlighted by its pooled sensitivity and specificity which were 98.68% and 91.81%, respectively. Serum-based research showed the strongest findings, with multivariate models such as PCA-LDA supporting specificity and sensitivity values that, in several cases, reached 100%. Diagnostic accuracy was greatly improved by models such as SVM and CNN, particularly when it came to detecting minute spectral alterations associated with cancer. Raman spectroscopy shows great promise as a lung cancer diagnostic method. However, issues including spectral data standardization, sample preparation heterogeneity and the requirement for bigger, multicentre research needs to be addressed. These results will open the door for the incorporation of Raman spectroscopy into standard clinical procedures.

Application of Surface-Enhanced Raman Spectroscopy in Head and Neck Cancer Diagnosis

Surface-enhanced Raman spectroscopy (SERS) has emerged as a crucial analytical tool in the field of oncology, particularly presenting significant challenges for the diagnosis and treatment of head and neck cancer. This Review provides an overview of the current status and prospects of SERS applications, highlighting their profound impact on molecular biology-level diagnosis, tissue-level identification, HNC therapeutic monitoring, and integration with emerging technologies. The application of SERS for single-molecule assays such as epidermal growth factor receptors and PD-1/PD-L1, gene expression analysis, and tumor microenvironment characterization is also explored. This Review showcases the innovative applications of SERS in liquid biopsies such as high-throughput lateral flow analysis for ctDNA quantification and salivary diagnostics, which can offer rapid and highly sensitive assays suitable for immediate detection. At the tissue level, SERS enables cancer cell visualization and intraoperative tumor margin identification, enhancing surgical precision and decision-making. The role of SERS in radiotherapy, chemotherapy, and targeted therapy is examined along with its use in real-time pharmacokinetic studies to monitor treatment response. Furthermore, this Review delves into the synergistic relationship between SERS and artificial intelligence, encompassing machine learning and deep learning algorithms, marking the dawn of a new era in precision oncology. The integration of SERS with genomics, metabolomics, transcriptomics, proteomics, and single-cell omics at the multiomics level will revolutionize our comprehension and management of HNC. This Review offers an overview of the transformative impacts of SERS and examines future directions as well as challenges in this dynamic research field.

GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessment

The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue's pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.

Optical Based Methods for Water Monitoring in Biological Tissue

Skin homeostasis is strongly dependent on its hydration levels, making skin water content measurement vital across various fields, including medicine, cosmetology, and sports science. Noninvasive diagnostic techniques are particularly relevant for clinical applications due to their minimal risk of side effects. A range of optical methods have been developed for this purpose, each with unique physical principles, advantages, and limitations. This review provides an in-depth examination of optical techniques such as diffuse reflectance spectroscopy, optoacoustic spectroscopy, optoacoustic tomography, hyperspectral imaging, and Raman spectroscopy. We explore their efficacy in noninvasive monitoring of skin hydration and edema, which is characterized by an increase in interstitial fluid. By comparing the parameters, sensitivity, and clinical applications of these techniques, this review offers a comprehensive understanding of their potential to enhance diagnostic precision and improve patient care.

From patterns to prediction: machine learning and antifungal resistance biomarker discovery

Fungal pathogens significantly impact human health, agriculture, and ecosystems, with infections leading to high morbidity and mortality, especially among immunocompromised individuals. The increasing prevalence of antifungal resistance (AFR) exacerbates these challenges, limiting the effectiveness of current treatments. Identifying robust biomarkers associated AFR could accelerate targeted diagnosis, shorten decision time for treatment strategies, and improve patient health. This paper examines traditional avenues of AFR biomarker detection, contrasting them with the increasingly effective role of machine learning (ML) in advancing diagnostic and therapeutic strategies. The integration of ML with technologies such as mass spectrometry, molecular dynamics, and various omics-based approaches often results in the discovery of diverse and novel resistance biomarkers. ML's capability to analyse complex data patterns enhances the identification of resistance biomarkers and potential drug targets, offering innovative solutions to AFR management. This paper highlights the importance of interdisciplinary approaches and continued innovation in leveraging ML to combat AFR, aiming for more effective and targeted treatments for fungal infections.

Advances and Prospects in Liquid Biopsy Techniques for Malignant Tumor Diagnosis and Surveillance

Liquid biopsy technology provides invaluable support for the early diagnosis of tumors and surveillance of disease course by detecting tumor-related biomarkers in bodily fluids. Currently, liquid biopsy techniques are mainly divided into two categories: biomarker and label-free. Biomarker liquid biopsy techniques utilize specific antibodies or probes to identify and isolate target cells, exosomes, or molecules, and these techniques are widely used in clinical practice. However, they have certain limitations including dependence on tumor markers, alterations in cell biological properties, and high cost. In contrast, label-free liquid biopsy techniques directly utilize physical or chemical properties of cells, exosomes, or molecules for detection and isolation. These techniques have the advantage of not needing labeling, not impacting downstream analysis, and low detection cost. However, most are still in the research stage and not yet mature. This review first discusses recent advances in liquid biopsy techniques for early tumor diagnosis and disease surveillance. Several current techniques are described in detail. These techniques exploit differences in biomarkers, size, density, deformability, electrical properties, and chemical composition in tumor components to achieve highly sensitive tumor component identification and separation. Finally, the current research progress is summarized and the future research directions of the field are discussed.

Raman spectroscopy on dried blood plasma allows diagnosis and monitoring of colorectal cancer

Colorectal cancer (CRC) remains challenging to diagnose, necessitating the identification of a noninvasive biomarker that can differentiate it from other conditions such as inflammatory bowel diseases (IBD) and diverticular disease (DD). Raman spectroscopy (RS) stands out as a promising technique for monitoring blood biochemical profiles, with the potential to identify distinct signatures identifying CRC subjects. We performed RS analysis on dried plasma from 120 subjects: 32 CRC patients, 37 IBD patients, 20 DD patients, and 31 healthy controls. We also conducted longitudinal studies of CRC patient's postsurgery to monitor the spectral changes over time. We identified six spectral features that showed significant differences between CRC and non-CRC patients, corresponding to tryptophan, tyrosine, phenylalanine, lipids, carotenoids, and disulfide bridges. These features enabled the classification of CRC patients with an accuracy of 87.5%. Moreover, longitudinal analysis revealed that the spectral differences normalized over 6 months after surgery, indicating their association with the presence of the disease. Our study demonstrates the potential of RS to identify specific biomolecular signatures related to CRC. These results suggest that RS could be a novel screening and monitoring tool, providing valuable insights for the development of noninvasive and accurate diagnostic methods for CRC.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Real-time monitoring of single dendritic cell maturation using deep learning-assisted surface-enhanced Raman spectroscopy

Background: Dynamic real-time detection of dendritic cell (DC) maturation is pivotal for accurately predicting immune system activation, assessing vaccine efficacy, and determining the effectiveness of immunotherapy. The heterogeneity of cells underscores the significance of assessing the maturation status of each individual cell, while achieving real-time monitoring of DC maturation at the single-cell level poses significant challenges. Surface-enhanced Raman spectroscopy (SERS) holds great potential for providing specific fingerprinting information of DCs to detect biochemical alterations and evaluate their maturation status. Methods: We developed Au@CpG@PEG nanoparticle as a self-reporting nanovaccine for DC activation and maturation state assessment, utilizing a label-free SERS strategy. Fingerprint vibrational spectra of the biological components in different states of DCs were collected and analyzed using deep learning Convolutional Neural Networks (CNN) algorithms, aiding in the rapid and efficient identification of DC maturation. Results: This approach enables dynamic real-time detection of DC maturation, maintaining accuracy levels above 98.92%. Conclusion: By employing molecular profiling, we revealed that the signal ratio of tryptophan-to-carbohydrate holds potential as a prospective marker for distinguishing the maturation status of DCs.

Advances in Bio-Optical Imaging Systems for Spatiotemporal Monitoring of Intestinal Bacteria

Vast and complex intestinal communities are regulated and balanced through interactions with their host organisms, and disruption of gut microbial balance can cause a variety of diseases. Studying the mechanisms of pathogenic intestinal flora in the host and early detection of bacterial translocation and colonization can guide clinical diagnosis, provide targeted treatments, and improve patient prognosis. The use of in vivo imaging techniques to track microorganisms in the intestine, and study structural and functional changes of both cells and proteins, may clarify the governing equilibrium between the flora and host. Despite the recent rapid development of in vivo imaging of intestinal microecology, determining the ideal methodology for clinical use remains a challenge. Advances in optics, computer technology, and molecular biology promise to expand the horizons of research and development, thereby providing exciting opportunities to study the spatio-temporal dynamics of gut microbiota and the origins of disease. Here, this study reviews the characteristics and problems associated with optical imaging techniques, including bioluminescence, conventional fluorescence, novel metabolic labeling methods, nanomaterials, intelligently activated imaging agents, and photoacoustic (PA) imaging. It hopes to provide a valuable theoretical basis for future bio-intelligent imaging of intestinal bacteria.