Predicting prognosis in acute myeloid leukemia patients by surface-enhanced Raman spectroscopy

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.

SERS and Machine Learning-Enabled Liquid Biopsy: A Promising Tool for Early Detection and Recurrence Prediction in Acute Leukemia

Acute leukemia (AL), classified as acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL), is a hematologic malignancy caused by the uncontrolled proliferation of leucocytes in the bone marrow. Early detection of AL is crucial for clinical treatment. Detection methods of AL are currently blood tests, bone marrow tests, imaging, and spinal fluid tests. However, these tests have drawbacks, such as high cost and time consumption. Liquid biopsy using biological fluids such as blood or serum is an emerging technique for noninvasive cancer detection and monitoring. Surface-enhanced Raman spectroscopy (SERS), which enhanced Raman signals by the interaction of plasmonic nanostructures with the analyte, is a highly sensitive and specific detection method with simple sample preparation that has been used in combination with machine learning techniques to analyze liquid biopsy. In this study, we developed a SERS-based liquid biopsy approach that enables accurate classification of AML and ALL subtypes and the prediction of disease recurrence. SERS spectra of serum samples from 24 healthy individuals, 43 AML patients, and 18 ALL patients were obtained using an Ag-based SERS substrate and clustered using hierarchical cluster analysis (HCA). The spectra were then classified using three commonly used classifiers, namely, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN). Our findings demonstrate that the RF classifier has the highest accuracy values, with 96.1, 95.5, and 98.5% for classifying three groups and predicting the recurrence of AML and ALL, respectively. The combination of SERS-based serum analysis with machine learning algorithms represents a remarkable advancement in the realm of hematological disease diagnostics, particularly for AML and ALL. This approach not only facilitates the precise differentiation of disease subtypes but also introduces the novel capability of prognosticating disease recurrence.

Advances of New Extracellular Vesicle Isolation and Detection Technologies in Cancer Diagnosis

Cancer is a global health issue threatening people's lives. Currently, cancer detection methods still have a lot of room for improvement in both efficiency and accuracy. The development and application of new technologies are urgently required for early cancer diagnosis and prognosis. Extracellular vesicles (EVs) are a type of phospholipid bilayer vesicle secreted by cells and play an important role in cancer development and metastasis. These small vesicles participate in cancer information transmission, antigen presentation, angiogenesis, immune response, tumor invasion, and mediate signaling pathways in the tumor microenvironment. Liquid biopsy of EV cargo contents is a fast-developing research area, holding promise for early cancer diagnosis and monitoring cancer progression in real-time. However, current EV detection technologies for clinical translation are still facing many challenges. Recent advancements in developing techniques for EV isolation and detection have made significant progress and are paving the way toward clinical application. Here, the advantages and limitations of traditional EV detection and isolation technologies in cancer diagnosis and prognosis are reviewed. The review also focuses on emerging EV detection and isolation technologies in cancer, discusses the challenges faced by current methods, and explores the perspective of new EV detection techniques for future cancer diagnosis.

Early differential diagnosis of pancytopenia related diseases based on serum surface-enhanced Raman spectroscopy

Pancytopenia is a common blood disorder defined as the decrease of red blood cells, white blood cells and platelets in the peripheral blood. Its genesis mechanism is typically complex and a variety of diseases have been found to be capable of causing pancytopenia, some of which are featured by their high mortality rates. Early judgement on the cause of pancytopenia can benefit timely and appropriate treatment to improve patient survival significantly. In this study, a serum surface-enhanced Raman spectroscopy (SERS) method was explored for the early differential diagnosis of three pancytopenia related diseases, i.e., aplastic anemia (AA), myelodysplastic syndrome (MDS) and spontaneous remission of pancytopenia (SRP), in which the patients with those pancytopenia related diseases at initial stage exhibited same pancytopenia symptom but cannot be conclusively diagnosed through conventional clinical examinations. The SERS spectral analysis results suggested that certain amino acids, protein substances and nucleic acids are expected to be potential biomarkers for their early differential diagnosis. In addition, a diagnostic model was established based on the joint use of partial least squares analysis and linear discriminant analysis (PLS-LDA), and an overall accuracy of 86.67 % was achieved to differentiate those pancytopenia related diseases, even at the time that confirmed diagnosis cannot be made by routine clinical examinations. Therefore, the proposed method has demonstrated great potential for the early differential diagnosis of pancytopenia related diseases, thus it has significant clinical importance for the timely and rational guidance on subsequent treatment to improve patient survival.

Raman spectromics method for fast and label-free genotype screening

It is now understood that genes and their various mutations are associated with the onset and progression of diseases. However, routine genetic testing techniques are limited by their high cost, time consumption, susceptibility to contamination, complex operation, and data analysis difficulties, rendering them unsuitable for genotype screening in many cases. Therefore, there is an urgent need to develop a rapid, sensitive, user-friendly, and cost-effective method for genotype screening and analysis. In this study, we propose and investigate a Raman spectroscopic method for achieving fast and label-free genotype screening. The method was validated using spontaneous Raman measurements of wild-type Cryptococcus neoformans and its six mutants. An accurate identification of different genotypes was achieved by employing a one-dimensional convolutional neural network (1D-CNN), and significant correlations between metabolic changes and genotypic variations were revealed. Genotype-specific regions of interest were also localized and visualized using a gradient-weighted class activation mapping (Grad-CAM)-based spectral interpretable analysis method. Furthermore, the contribution of each metabolite to the final genotypic decision-making was quantified. The proposed Raman spectroscopic method demonstrated huge potential for fast and label-free genotype screening and analysis of conditioned pathogens.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Advances in the application of Raman spectroscopy in haematological tumours

Hematologic malignancies are a diverse collection of cancers that affect the blood, bone marrow, and organs. They have a very unpredictable prognosis and recur after treatment. Leukemia, lymphoma, and myeloma are the most prevalent symptoms. Despite advancements in chemotherapy and supportive care, the incidence rate and mortality of patients with hematological malignancies remain high. Additionally, there are issues with the clinical diagnosis because several hematological malignancies lack defined, systematic diagnostic criteria. This work provided an overview of the fundamentals, benefits, and limitations of Raman spectroscopy and its use in hematological cancers. The alterations of trace substances can be recognized using Raman spectroscopy. High sensitivity, non-destructive, quick, real-time, and other attributes define it. Clinicians must promptly identify disorders and keep track of analytes in biological fluids. For instance, surface-enhanced Raman spectroscopy is employed in diagnosing gene mutations in myelodysplastic syndromes due to its high sensitivity and multiple detection benefits. Serum indicators for multiple myeloma have been routinely used for detection. The simultaneous observation of DNA strand modifications and the production of new molecular bonds by tip-enhanced Raman spectroscopy is of tremendous significance for diagnosing lymphoma and multiple myeloma with unidentified diagnostic criteria.

Early assessment of chemotherapeutic response in hepatocellular carcinoma based on serum surface-enhanced Raman spectroscopy

In clinical practice, the transcatheter arterial chemoembolization (TACE) has been widely accepted as the first option for non-surgical hepatocellular carcinoma (HCC) treatment. However, patients with HCC often suffer from poor response to TACE therapy. This can be prevented if the chemotherapeutic response can be early and accurately assessed, which is essential to guide timely and rational management. In this study, the serum SERS technique was for the first time investigated as a potential prognostic tool for early assessment of HCC chemotherapeutic response. According to the SERS spectral analysis results, it is newly found that not only the absolute circulating nucleic acids and collagen levels in pre-therapeutic serum but also the changes in circulating nucleic acids and amino acids between pre-therapeutic and post-therapeutic serum are expected to be potential serum markers for HCC prognosis. By further applying chemometrics methods to establish prognostic models, excellent prognostic accuracies were achieved within only 3 days after TACE therapy. Thus, the proposed method is expected to provide guidance on timely and rational management of HCC to improve its survival rate.