Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis

Serum Raman spectroscopy: Unearthing the snapshot of distinct metabolic profile in patients with congenital heart defects (CHDs)

In the present study, efficacy of minimally-invasive serum Raman spectroscopy (SRS) in stratification of congenital heart diseases was explored. Blood was collected from 62 subjects [42 congenital heart defect (CHD) patients (19 with atrial septal defect, 13 with ventricular septal defect and 10 with tetralogy of fallot) and 20 controls], and serum separated. Raman spectra of sera were recorded, pre-processed and subjected to spectral and multivariate analyses. Multivariate curve resolution-alternating least squares (MCR-ALS) analyses indicated alterations in lipid and protein levels between the study groups. Principal Component Analysis (PCA) and Principal Component based Linear Discriminant Analysis (PC-LDA), cross-validated with Leave-one-out cross validation (LOOCV), were employed to study stratification between the different groups. CHD could be classified from controls with 76 % efficiency. The different CHD subtypes could be distinguished with efficiencies as high as ∼90 %. To the best of our knowledge, differentiation between controls and CHDs as well as the stratification between controls and CHDs subtypes was for the first time successfully accomplished by serum-based Raman spectroscopy.

Gold nanostructures/quantum dots for the enhanced efficiency of organic solar cells

Incorporating gold nanoparticles (AuNPs) into organic solar cell (OSC) structures provides an effective means to manipulate light-matter interactions. AuNPs have been used as plasmonic-enhancement and light-trapping materials in OSCs and exhibit diverse single and mixed morphologies. Substantial near-field enhancement from metal periodic structures has consistently demonstrated high enhancement in solar cell efficiency. Additionally, coupling with atomic gold clusters in the form of gold quantum dots holds promise for light harvesting through fluorescence phenomena. The configured devices optimize light utilization in OSCs by considering factors such as the morphology, position, and hybridization of localized surface plasmon resonance, propagating surface plasmon resonance, and fluorescence phenomena. This optimization enhances light absorption, scattering, and efficient trapping facilitated by gold nanostructures/quantum dots. The configured setup exhibits multiple effects, concurrently improving plasmonic and fluorescence responses under solar irradiation, thereby enhancing energy conversion performance. Integrating plasmonic nanostructures with OSCs can address fundamental issues, providing opportunities to enhance the light-absorption intensity and charge transfer efficiency at intra and intermolecular levels. This comprehensive review demonstrates that the greatest improvement in solar cell efficiency exceeded 30% compared with the reference cells.

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as "fingerprint", in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

Machine learning-assisted serum SERS strategy for rapid and non-invasive screening of early cystic echinococcosis

Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label-free serum surface-enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non-invasive diagnosis of early-stage CE. Specifically, by establishing early- and middle-stage mouse models, the corresponding CE-infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early- and middle-stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein-related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label-free serum SERS analysis is a potential early-stage CE detection method that is promising for clinical translation.

Label-free SERS detection of prostate cancer based on multi-layer perceptron surrogate model method

Prior surface-enhanced Raman spectroscopy (SERS) research has shown that pre-processing is necessary before analysis. Pre-processing also typically serves the dual purposes of removing the auto-fluorescence background and minimizing data volatility. This method allows for a more accurate comparison of spectral traits and relative SERS peak strength. However, because there are so many different kinds of samples, it can take a long time, and there is no assurance that the approach chosen will work well with a particular kind of sample. Therefore, this study employed a deep learning technique called multi-layer perceptron (MLP) to simplify the pre-processing of blood plasma SERS samples in patients with prostate cancer (PC), as well as to enhance the sensitivity and specificity of diagnosis using SERS technology. First of all, significant variations in peak intensity can be observed in the difference spectra, facilitating differentiation between PC and normal groups. Second, the data analysis was carried out in three different stages (raw data, defluorescenced data, and normalized data) using principal component analysis and linear discriminant analysis (PCA-LDA), as well as PCA-multi-layer perceptron (PCA-MLP). Finally, when SERS data was analyzed using PCA-LDA, there were significant differences in classification accuracy across each stage (The classification accuracy of three different stages were 76.90%, 85.60%, 95.20%, respectively). However, when PCA-MLP was utilized for SERS data analysis, the classification accuracy remained consistently high and stable (The classification accuracy of three different stages were 92.00%, 92.40%, 96.70%, respectively). The experimental results of PCA-MLP for classifying specific SERS data indicate that analyzing raw data directly can simplify the experimental process and enhance the efficacy of SERS analysis.

Raman spectroscopy and its plasmon-enhanced counterparts: A toolbox to probe protein dynamics and aggregation

Protein unfolding and aggregation are often correlated with numerous diseases such as Alzheimer's, Parkinson's, Huntington's, and other debilitating neurological disorders. Such adverse events consist of a plethora of competing mechanisms, particularly interactions that control the stability and cooperativity of the process. However, it remains challenging to probe the molecular mechanism of protein dynamics such as aggregation, and monitor them in real-time under physiological conditions. Recently, Raman spectroscopy and its plasmon-enhanced counterparts, such as surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS), have emerged as sensitive analytical tools that have the potential to perform molecular studies of functional groups and are showing significant promise in probing events related to protein aggregation. We summarize the fundamental working principles of Raman, SERS, and TERS as nondestructive, easy-to-perform, and fast tools for probing protein dynamics and aggregation. Finally, we highlight the utility of these techniques for the analysis of vibrational spectra of aggregation of proteins from various sources such as tissues, pathogens, food, biopharmaceuticals, and lastly, biological fouling to retrieve precise chemical information, which can be potentially translated to practical applications and point-of-care (PoC) devices. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Current advances in detecting genetic and epigenetic biomarkers of colorectal cancer

Colorectal carcinoma (CRC) is the third most common cancer in terms of diagnosis and the second in terms of mortality. Recent studies have shown that various proteins, extracellular vesicles (i.e., exosomes), specific genetic variants, gene transcripts, cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and altered epigenetic patterns, can be used to detect, and assess the prognosis of CRC. Over the last decade, a plethora of conventional methodologies (e.g., polymerase chain reaction [PCR], direct sequencing, enzyme-linked immunosorbent assay [ELISA], microarray, in situ hybridization) as well as advanced analytical methodologies (e.g., microfluidics, electrochemical biosensors, surface-enhanced Raman spectroscopy [SERS]) have been developed for analyzing genetic and epigenetic biomarkers using both optical and non-optical tools. Despite these methodologies, no gold standard detection method has yet been implemented that can analyze CRC with high specificity and sensitivity in an inexpensive, simple, and time-efficient manner. Moreover, until now, no study has critically reviewed the advantages and limitations of these methodologies. Here, an overview of the most used genetic and epigenetic biomarkers for CRC and their detection methods are discussed. Furthermore, a summary of the major biological, technical, and clinical challenges and advantages/limitations of existing techniques is also presented.

Detection of nasopharyngeal cancer cells using the laser tweezer Raman spectroscopy technology

Nasopharyngeal cancer (NPC), which arises from the nasopharyngeal epithelial lining, is one of the common malignant otorhinolaryngological tumors in China. Due to its insidious anatomical location and highly invasive and metastatic features, it is challenging to detect NPC at early stages. In this work, a rapid laser tweezer Raman spectroscopic (LTRS) system was built and used to trap and characterize single NPC cells. Using LTRS, high-quality Raman signals of the normal nasopharyngeal cell line (NP69) and NPC cells could be successfully obtained. By analysing the Raman peaks, some unique changes were found in components, such as DNA, amide I and amide III, in NPC cells compared with normal cells. In addition, we also used a multivariate statistical algorithm to establish a diagnostic model for identifying NPC cells with an accuracy of 90.0%. These results demonstrate that LTRS in combination with the multivariate statistical analysis is a convenient and high-efficiency cell identification technology, providing a novel and rapid methodology for NPC detection at the single cell level.

Application of Nanoparticles in the Diagnosis of Gastrointestinal Diseases: A Complete Future Perspective

The diagnosis of gastrointestinal (GI) diseases currently relies primarily on invasive procedures like digestive endoscopy. However, these procedures can cause discomfort, respiratory issues, and bacterial infections in patients, both during and after the examination. In recent years, nanomedicine has emerged as a promising field, providing significant advancements in diagnostic techniques. Nanoprobes, in particular, offer distinct advantages, such as high specificity and sensitivity in detecting GI diseases. Integration of nanoprobes with advanced imaging techniques, such as nuclear magnetic resonance, optical fluorescence imaging, tomography, and optical correlation tomography, has significantly enhanced the detection capabilities for GI tumors and inflammatory bowel disease (IBD). This synergy enables early diagnosis and precise staging of GI disorders. Among the nanoparticles investigated for clinical applications, superparamagnetic iron oxide, quantum dots, single carbon nanotubes, and nanocages have emerged as extensively studied and utilized agents. This review aimed to provide insights into the potential applications of nanoparticles in modern imaging techniques, with a specific focus on their role in facilitating early and specific diagnosis of a range of GI disorders, including IBD and colorectal cancer (CRC). Additionally, we discussed the challenges associated with the implementation of nanotechnology-based GI diagnostics and explored future prospects for translation in this promising field.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Development of a micro-Raman system for in vivo studying the mechanism of laser biological effects

The laser irradiation on organism will produce a series of biological effects, which can be used for basic medical research, diagnosis and treatments of diseases. However, the mechanism of this biological effects is still unclear. As a sensitive molecular monitoring technique, Raman spectroscopy has became a very popular detection method in biomedical research especially in vivo study. In this paper, we present a compact and flexible micro-Raman system for in vivo studying the mechanism of laser biological effects. The system has the two functions of laser induction and Raman measurement, which can realize the micro-area radiation of laser and simultaneously collect the corresponding Raman spectra in vivo. The detection method provided by this home-built system is able to deepen the understanding of laser biological effects mechanism at molecular level, so it is expected that the system is significant for the treatments and diagnosis of diseases in the near future.

Machine learning-assisted internal standard calibration label-free SERS strategy for colon cancer detection

Liquid biopsies have significance for early colon cancer screening and improving patient survival. Recently, several researchers have applied surface-enhanced Raman spectroscopy (SERS) for the label-free and non-invasive detection of serum. Most of these studies performed the assay using a mixture of noble metal nanoparticles (NMNPs) with serum. However, SERS analysis of serum remains a challenge in terms of reproducibility and stability, as NMNPs tend to aggregate when mixed with serum, resulting in a non-uniform distribution of hot spots. Here, we report on the non-invasive identification of colon cancer (CC) using an internal standard (IS)-calibrated label-free serum SERS assay in combination with machine learning. Serum SERS spectra of 50 CC patients and 50 health volunteers have been obtained using silver nanoparticle (Ag NP) colloid and mercaptopropionic acid-modified Ag NPs (Ag NPs-MPA) as the SERS substrates. Decision tree (DT), random forest (RF), and principal component and linear discriminant analysis (PCA-LDA) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying. The results show that the RF model provides a high diagnostic accuracy compared to PCA-LDA. Following calibration with IS molecules, high diagnostic accuracy of over 90% and 100% specificity can be achieved with DT, RF, and PCA-LDA algorithms to differentiate between cancer and normal groups. The results from this exploratory work demonstrate that serum SERS detection combined with multivariate statistical methods and IS calibration has great potential for the non-invasive and label-free detection of CC.

Profiling of Tumor Cell-Delivered Exosome by Surface Enhanced Raman Spectroscopy-Based Biosensor for Evaluation of Nasopharyngeal Cancer Radioresistance

Although the advancement of radiotherapy significantly improves the survival of nasopharyngeal cancer (NPC), radioresistance associated with recurrence and poor outcomes still remains a daunting challenge in the clinical scenario. Currently, effective biomarkers and convenient detection methods for predicting radioresistance have not been well established. Here, the surface-enhanced Raman spectroscopy combined with proteomics is used to firstly profile the characteristic spectral patterns of exosomes secreted from self-established NPC radioresistance cells, and reveals specific variations of proteins expression during radioresistance formation, including collagen alpha-2 (I) chain (COL1A2) that is associated with a favorable prognosis in NPC and is negatively associated with DNA repair scores and DNA repair-related genes via bioinformatic analysis. Furthermore, deep learning model-based diagnostic model is generated to accurately identify the exosomes from radioresistance group. This work demonstrates the promising potential of exosomes as a novel biomarker for predicting the radioresistance and develops a rapid and sensitive liquid biopsy method that will provide a personalized and precise strategy for clinical NPC treatment.

Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer

Breast cancer (BC) is the most prevalent cancer worldwide. The prognosis and survival of these patients are directly related to the diagnostic stage. Even so, the gold standard screening method (mammography) has a long waiting period, high rates of false positives, anxiety for patients, and consequently delays the diagnosis by core needle biopsy (invasive method). Alternatively, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a noninvasive, low-cost, rapid, and reagent-free technique that generates the spectral metabolomic profile of biomolecules. This makes it possible to assess systemic repercussions, such as the BC carcinogenesis process. Blood plasma samples (n = 56 BC and n = 18 controls) were analyzed in the spectrophotometer in the ATR-FTIR mode. For the exploratory analysis of the data, interval Principal Component Analysis (iPCA) was used, and for predictive chemometric modeling, the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) algorithm with validation by leave-one-out cross-validation. iPCA in the region of 1118-1052 cm^-1 (predominantly DNA/RNA bands) showed significant clustering of molecular subtypes and control. The OPLS-DA model achieved 100% accuracy with only 1 latent variable and Root Mean Square Error of Cross-Validation (RMSECV) < 0.005 for all molecular subtypes and control. The wavenumbers (cm^-1) with the highest iPCA peaks (loadings: 1117, 1089, 1081, 1075, 1057, and 1052) were used as input to MANOVA (Wilks' Lambda, p < 0.001 between molecular subtypes and control). The rapid and low-cost detection of BC molecular subtypes by ATR-FTIR spectroscopy would plausibly allow initial screening and clinical management, improving prognosis, reducing mortality and costs for the health system.

Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELİSA method. Raman spectra showed higher Raman intensities at 1447 cm^-1 1560 cm^-1, 1665 cm^-1, and 1769 cm^-1, which originated from CH₂ proteins and lipids, amide II and amide I, and CO lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm^-1 and 1306 cm^-1 can be used as spectroscopy markers of colon cancer.

Analysis of urine using electronic tongue towards non-invasive cancer diagnosis

Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.

Diagnosis of Ischemic Renal Failure Using Surface-Enhanced Raman Spectroscopy and a Machine Learning Algorithm

To diagnose renal function using a biochip capable of detecting SERS and to assess Raman measurements taken from a bilateral renal ischemia model and the feasibility of early diagnosis was done. After generating a bilateral renal ischemia rat model, blood and urine were collected. After confirming the presence of renal injury and function, liquid drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods. SERS biomarkers that diffused into the nanogaps were selectively amplified. Raman signals varied based on the severity of the renal function, and these differences were confirmed statistically. These results confirm that renal ischemia leads to renal dysfunction and that surface-enhanced Raman spectroscopy and a machine learning algorithm can be used to track signals in the urine from the release of SERS biomarkers.

Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis

This article compares the applications of traditional gold and silver-based SERS substrates and less conventional (Pd/Pt, Cu, Al, Si-based) SERS substrates, focusing on sensing, biosensing, and clinical analysis. In recent decades plethora of new biosensing and clinical SERS applications have fueled the search for more cost-effective, scalable, and stable substrates since traditional gold and silver-based substrates are quite expensive, prone to corrosion, contamination and non-specific binding, particularly by S-containing compounds. Following that, we briefly described our experimental experience with Si and Al-based SERS substrates and systematically analyzed the literature on SERS on substrate materials such as Pd/Pt, Cu, Al, and Si. We tabulated and discussed figures of merit such as enhancement factor (EF) and limit of detection (LOD) from analytical applications of these substrates. The results of the comparison showed that Pd/Pt substrates are not practical due to their high cost; Cu-based substrates are less stable and produce lower signal enhancement. Si and Al-based substrates showed promising results, particularly in combination with gold and silver nanostructures since they could produce comparable EFs and LODs as conventional substrates. In addition, their stability and relatively low cost make them viable alternatives for gold and silver-based substrates. Finally, this review highlighted and compared the clinical performance of non-traditional SERS substrates and traditional gold and silver SERS substrates. We discovered that if we take the average sensitivity, specificity, and accuracy of clinical SERS assays reported in the literature, those parameters, particularly accuracy (93-94%), are similar for SERS bioassays on AgNP@Al, Si-based, Au-based, and Ag-based substrates. We hope that this review will encourage research into SERS biosensing on aluminum, silicon, and some other substrates. These Al and Si based substrates may respond efficiently to the major challenges to the SERS practical application. For instance, they may be not only less expensive, e.g., Al foil, but also in some cases more selective and sometimes more reproducible, when compared to gold-only or silver-only based SERS substrates. Overall, it may result in a greater diversity of applicable SERS substrates, allowing for better optimization and selection of the SERS substrate for a specific sensing/biosensing or clinical application.

Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm

In this study, we mainly aimed to investigate the diagnostic potential of surface-enhanced Raman spectroscopy for bladder cancer and kidney cancer which are the most common cancers of the urinary system, and evaluate the classification ability of three statistical algorithms: principal component analysis-linear discriminate analysis (PCA-LDA), partial least square-random forest (PLS-RF), and partial least square-support vector machine (PLS-SVM). The plasma of 26 bladder cancer patients, 38 kidney cancer patients and 39 normal subjects was mixed with the same volume of silver nanoparticles, respectively, and then high-quality SERS signal was obtained. The SERS spectra in the range of 400-1800 cm^-1 were compared and analyzed. There were some significant differences in SERS peak intensity, which may reflect the changes in the content of some biomacromolecules in the plasma of cancer patients. Based on the three algorithms of PCA-LDA, PLS-RF and PLS-SVM, the classification accuracy of SERS spectra of plasma from cancer patients and normal subjects was 98.1%, 100% and 100%, respectively. In addition, the classification accuracy of the three diagnostic algorithms to classify the SERS spectra of bladder cancer and kidney cancer was 81.3%, 91.7%, and 98.4%, respectively. This exploratory work demonstrates that SERS combined with PLS-SVM algorithm has superior performance for clinical screening of bladder cancer and kidney cancer through peripheral plasma.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

Simple Summary

Blood is considered a rich reservoir of biomarkers for disease diagnosis. Surface-enhanced Raman scattering (SERS) is known for its high sensitivity and has been successfully employed to differentiate blood samples from cancer patients versus healthy individuals. Different from previous reports, this study aims at investigating the reliability of the observed results by varying several parameters influencing the observed spectra. Thus, blood taken from 30 healthy individuals as the control group, 30 patients with different types of cancers, and 15 patients with various types of chronic diseases were used in the study. The results revealed that spectral differences in the cancer group was directly related to the presence of cancer-related biomarkers. Although data were obtained from only small group of patients, the recorded sensitivity and specificity values clearly show the power of the technique to detect cancer.

Abstract

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma

Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

Development of a SERS based cancer diagnosis approach employing cryosectioned thyroid tissue samples on PDMS

An efficient SERS based novel analytical approach named Cryosectioned-PDMS was developed systematically and evaluated applying on 64 thyroid biopsy samples. To utilize thyroid biopsy samples, a 20-μl volume of h-AgNPs suspension was dropped on a 5-μm thick cryosectioned biopsy specimen placed on the PDMS coated glass slide. The SERS spectra from a 10 × 10 points array acquired by mapping 22.5 μm × 22.5 μm sized area from suspended dried droplets placed on the tissue surface. The probability of correctly predicted performance for diagnosis of malignant, benign and healthy tissues was resulted in the accuracy of 100 % for the spectral bands at 667, 724, 920, 960, 1052, 1096, 1315 and 1457 cm^-1 using PCA-fed LDA machine learning. The Cryosectioned-PDMS biophotonic approach with PCA-LDA predictive model demonstrated that the vibrational signatures can accurately recognize the fingerprint of cancer pathology from a healthy one with a simple and fast sample preparation methodology.

Nanotechnology in diagnosis and therapy of gastrointestinal cancer

Advances in nanotechnology have opened new frontiers in the diagnosis and treatment of cancer. Nanoparticle-based technology improves the precision of tumor diagnosis when combined with imaging, as well as the accuracy of drug target delivery, with fewer side effects. Optimized nanosystems have demonstrated advantages in many fields, including enhanced specificity of detection, reduced toxicity of drugs, enhanced effect of contrast agents, and advanced diagnosis and therapy of gastrointestinal (GI) cancers. In this review, we summarize the current nanotechnologies in diagnosis and treatment of GI cancers. The development of nanotechnology will lead to personalized approaches for early diagnosis and treatment of GI cancers.

All-fiber surface-enhanced Raman scattering detection system combining an integrated microfluidic chip and micro-lensed fiber

It is a challenge to perform simple and rapid detection of substances due to their complex structure. Biochemical molecules play a vital role in human health and environmental testing. Surface-enhanced Raman scattering (SERS) detection has the characteristics of strong specificity and real-time performance. At present, most SERS systems are expensive and not portable. Here, we demonstrate a SERS detection system with all-fiber connection, combined with a microfluidic chip and micro-lenses. Compared with the conventional SERS system that uses the spatial optical path, the devices in our system are connected by optical fibers, making the system more stable and operable. Besides, the microfluidic chips are introduced to further improve the system integration and stability. Owing to the micro-lensed fiber probe, the detected Raman signal intensity is increased by 2-3 times. We anticipate that the presented work will lead toward a rapid and portable SERS system and corresponding detection system. It also lays the foundation for real-time recognition in various complex environments in the design of a future optical fiber system.

Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine

In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.

Tracking the Evolution of Metastasis with Self-Functionalized 3D Nanoprobes

Despite recent advances in cancer treatment, metastasis is the cause of mortality in 90% of cancer cases. It has now been well-established that dissemination of cancer cells to distant sites occurs very early during tumorigenesis, resulting in the minimal effect of surgical or chemotherapeutic treatments after the detection of metastasis. The underlying reason for this challenge is mostly due to the limited understanding of molecular mechanisms of the metastasis cascade, particularly related to metastatic traits. Therefore, there is an urgent need to investigate this currently invisible evolution of metastasis. The tracking of metastasis evolution has not been addressed yet. Here, we introduce, for the first time, a synchronous approach to unveil the molecular mechanisms of the metastasis cascade. As cancer stem cells (CSCs) demonstrate cancer initiation, drug resistance, metastasis, and tumor relapse and can exist in a quasi-intermediate epithelial-mesenchymal transition state, the tumor-initiating events during a CSCs metamorphosis were monitored with single-cell sensitivity. Because of the invasive and resistive properties of the metastable intermediate CSCs, investigation of the molecular profiles of the quasi-intermediate CSCs was necessary for the detection of metastasis dissemination. For this purpose, the ultrasensitive technique of surface-enhanced Raman scattering (SERS) was adopted. Titanium-based, biocompatible three-dimensional (3D) nanoprobes that were synthesized for multiphoton ionization achieved a substantial SERS enhancement of ∼80-fold due to the oxygen vacancy-enriched composition of the nanoprobes. The 3D interconnected complex nanoarchitecture of the nanoprobes enabled us to entrap the nonadherent CSCs of three metastatic cancer cell lines (triple negative breast adenocarcinoma (MDAMB231), human Caucasian colon adenocarcinoma (COLO 205), and cervical adenocarcinoma (HeLa)─all very aggressive forms of cancer). The nanoprobes not only promoted the CSC proliferation to successfully attain the quasi-intermediate states but also monitored its reprogramming into a cancer cell state. The nanoprobes substantially amplified weak intracellular Raman signals to capture the molecular events during a CSC transformation. The detection of cancer was achieved with 100% accuracy. We experimentally demonstrated that the molecular signatures of CSC reprogramming are cancer-type specific. This observation enabled us to identify the origin of metastasis with 100% accuracy, providing more clarity on the relatively unknown quasi-intermediate states. This first demonstration of CSC-based tracking of metastasis evolution has the potential to provide an insightful perspective of tumorigenesis that could be useful in cancer diagnosis and prognosis as well as in the monitoring of therapeutic interventions.

Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis

Gastric cancer is usually diagnosed at late stage and has a high mortality rate, whereas early detection of gastric cancer could bring a better prognosis. Conventional gastric cancer diagnostic methods suffer from long diagnostic times, severe trauma, and a high rate of misdiagnosis and rely heavily on doctors’ subjective experience. Raman spectroscopy is a label-free molecular vibrational spectroscopy technique that identifies the molecular fingerprint of various samples based on the inelastic scattering of monochromatic light. Because of its advantages of non-destructive, rapid, and accurate detection, Raman spectroscopy has been widely studied for benign and malignant tumor differentiation, tumor subtype classification, and section pathology diagnosis. This paper reviews the applications of Raman spectroscopy for the in vivo and in vitro diagnosis of gastric cancer, methodology related to the spectroscopy data analysis, and presents the limitations of the technique.

Characterisation of breast cancer molecular signature and treatment assessment with vibrational spectroscopy and chemometric approach

Triple negative breast cancer (TNBC) is regarded as the most aggressive breast cancer subtype with poor overall survival and lack of targeted therapies, resulting in many patients with recurrent. The insight into the detailed biochemical composition of TNBC would help develop dedicated treatments. Thus, in this study Fourier Transform Infrared microspectroscopy combined with chemometrics and absorbance ratios investigation was employed to compare healthy controls with TNBC tissue before and after chemotherapy within the same patient. The primary spectral differences between control and cancer tissues were found in proteins, polysaccharides, and nucleic acids. Amide I/Amide II ratio decrease before and increase after chemotherapy, whereas DNA, RNA, and glycogen contents increase before and decrease after the treatment. The chemometric results revealed discriminatory features reflecting a clinical response scheme and proved the chemotherapy efficacy assessment with infrared spectroscopy is possible.

Quantum tunneling effect on the surface enhanced Raman process in molecular systems

In this paper, we theoretically study the effect of quantum tunneling on the surface enhanced Raman scattering (SERS) of a generic molecule confined in sub-nanometer nanocavities formed by metallic dimers. The tunneling effect was described by the quantum corrected model in combination with finite element simulations. The SERS spectra were calculated by a density matrix method. Simulation results demonstrate that both the field enhancement and the molecular SERS spectra are very sensitive to the size of the cavity. By decreasing the gap size, the local field enhancement first increases then starts to be significantly suppressed as a result of the tunneling effect which neutralizes the positive and negative induced charges in the nanocavity. Consequently, the SERS intensity also experienced dramatic decrease in the short gap distance region. We also show that both the plasmonic enhancement to the local field and the enhanced molecular decay rates have to be taken into account to understand the SERS properties of the molecule in such sub-nanometer nanocavities. These results could be helpful for the understanding of the surface enhanced spectral properties of molecular systems at sub-nanometer nanocavities.

A microsphere nanoparticle based-serum albumin targeted adsorption coupled with surface-enhanced Raman scattering for breast cancer detection

The serum albumin level is inseparable associated with survival in patients with breast cancer, and simultaneously serve as a good indicator of prognosis of cancer. Here, we proposed a novel extraction-isolation analysis method of albumin for breast cancer detection utilizing hydroxyapatite particles (HAp) to targeted adsorb albumin from serum relying on its specific adsorption capacity. An ideal protein-release reagent was used for isolating albumin from the surface of HAp, and meanwhile ensuring that the structure and property of albumin was not suffered damage. The surface-enhanced Raman scattering (SERS) signal of extracted albumin was obtained, and partial least squares (PLS) and linear discriminant analysis (LDA) analysis approach were employed to analyze SERS spectra data, with the aim to assess the capability of HAp method for identifying breast cancer, yielding an ideal diagnostic accuracy of 98.6%, demonstrating promising potential as a non-invasive and sensitive nanotechnology for breast cancer screening.

Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring

Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.

Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches

Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.

SERS liquid biopsy: An emerging tool for medical diagnosis

Surface-enhanced Raman scattering (SERS) is emerging as a novel strategy for biofluid analysis. In this review, we delineate four experimental SERS protocols that are frequently used for the profiling of biofluids: 1) liquid SERS for the detection of purine metabolites; 2) iodide-modified liquid SERS for the detection of proteins; 3) dried SERS for the detection of both purine metabolites and proteins; 4) resonant Raman for the detection of carotenoids. To explain the selectivity of each experimental SERS protocol, we introduce a heuristic model for the chemisorption of analytes mediated by adsorbed ions (adions) onto the SERS substrate. Next, we show that the promising results of SERS liquid biopsy stem from the fact that the concentration levels of purine metabolites, proteins and carotenoids are informative of the cellular turnover rate, inflammation, and oxidative stress, respectively. These processes are perturbed in virtually every disease, from cancer to autoimmune maladies. Finally, we review recent SERS liquid biopsy studies and discuss future steps that are required for translating SERS in the clinical setting.

The characteristics of Raman spectroscopy of fenbendazole-gold nanoparticles based on the chemical adsorption effect

Fenbendazole, a benzimidazole derivative with anti-tubulin polymerization properties, has been widely used in the treatment of parasitic infections. Because of its anticancer activity similar to that of many anticancer drugs, low cost and few side effects, fenbendazole has attracted wide research attention. The chemical adsorption of fenbendazole and gold nanoparticles are studied by the UV-Vis spectrophotometry, density functional method, Raman spectroscopy and surface-enhanced Raman spectroscopy. By comparing and analyzing the theoretical and experimental Raman spectra, this paper explains the reasons for the difference between the theoretical and experimental Raman spectra. Meanwhile, it is also found that the frequencies at 851 cm^-1, 1222 cm^-1, 1425 cm^-1 and 1566 cm^-1 are greatly enhanced. It is found that imidazole is adsorbed vertically to the surface of the substrate. It is concluded that Fenbendazole is vertically adsorbed on the surface of AuNPs through imidazole.

An efficient primary screening of COVID-19 by serum Raman spectroscopy

The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.

Non-invasive cell classification using the Paint Raman Express Spectroscopy System (PRESS)

Raman scattering represents the distribution and abundance of intracellular molecules, including proteins and lipids, facilitating distinction between cellular states non-invasively and without staining. However, the scattered light obtained from cells is faint and cells have complex structures, making it difficult to obtain a Raman spectrum covering the entire cell in a short time using conventional methods. This also prevents efficient label-free cell classification. In the present study, we developed the Paint Raman Express Spectroscopy System, which uses two fast-rotating galvano mirrors to obtain spectra from a wide area of a cell. By using this system and applying machine learning, we were able to acquire broad spectra of a variety of human and mouse cell types, including pluripotent stem cells and confirmed that each cell type can be classified with high accuracy. Moreover, we classified different activation states of human T cells, despite their similar morphology. This system could be used for rapid and low-cost drug evaluation and quality management for drug screening in cell-based assays.

Triumph against cancer: invading colorectal cancer with nanotechnology

INTRODUCTION

Recent statistics have reported colorectal cancer (CRC) as the second leading cause of cancer-associated deaths in the world. Early diagnosis of CRC may help to reduce the mortality and associated complications. However, the conventional diagnostic techniques often lead to misdiagnosis, fail to differentiate benign from malignant tissue or diagnose only at an advanced stage. For the treatment of CRC, surgery, chemotherapy, immunotherapy, and radiotherapy have been employed. However, the quality of living of the CRC patients is highly compromised after employing current therapeutic approaches owing to the toxicity issues and relapse.

AREA COVERED

This review accentuates the molecular mechanisms involved in the pathogenesis, stages of CRC, conventional approaches for diagnosis and therapy of CRC and the issues confronted thereby. It provides an outlook on the advantages of employing nanotechnology-based approaches for prevention, early diagnosis, and treatment of CRC.

EXPERT OPINION

Employing nanotechnology-based approaches has demonstrated promising outcomes in the prevention, diagnosis, and treatment of CRC. Nanotechnology-based approaches can surmount the major drawbacks of traditional diagnostic and therapeutic approaches. Nanotechnology bestows the advantage of early detection of CRC which helps to undertake instant steps for offering efficient therapy and reducing the mortality rates. For the treatment of CRC, nanocarriers offer the benefit of achieving controlled drug release, improved drug bioavailability, enhanced tumor targetability and reduced adverse effects.

Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm

Extracellular vesicles and particles (EVPs), which contain the same surface proteins as their mother cells, are promising biomarkers for cancer liquid biopsy. However, most of the isolation methods of EVPs are time-consuming and complicated, and hence, sensitive detection and classification methods are required for EVPs. Here, we report a facile polyethylene glycol (PEG)-based method for isolating and classifying EVPs with label-free surface-enhanced Raman scattering (SERS) and pattern recognition algorithm. There are only three steps in the PEG-based isolation method, and it does not require ultracentrifugation, which makes it a low-cost and easy-to-use method. Three types of common male cancer cell lines, namely leukemia (THP-1), prostate cancer (DU-145), and colorectal cancer (COLO-205), and one healthy male blood sample, were utilized to isolate EVPs. To collect the SERS spectra of EVPs, a novel planar nanomaterial, namely amino molybdenum oxide (AMO) nanoflakes, was applied, with the enhancement factor being obtained as 3.2 × 10². Based on the principal component analysis and support vector machine (PCA-SVM) algorithm, cancer and normal EVPs were classified with 97.4% accuracy. However, among the cancer EVPs, the accuracy, precision, and sensitivity were found to be 90.0%, 90.9%, and 83.3% for THP-1; 86.7%, 80.0%, and 92.3% for DU-145; 96.7%, 83.3%, and 100% for COLO-205, respectively. Thus, this work will improve the isolation, detection, and classification of EVPs and promote the development of cancer liquid biopsies.

Engineered nanoparticles for imaging and drug delivery in colorectal cancer

Colorectal cancer (CRC) is one of the deadliest diseases worldwide due to a lack of early detection methods and appropriate drug delivery strategies. Conventional imaging techniques cannot accurately distinguish benign from malignant tissue, leading to frequent misdiagnosis or diagnosis at late stages of the disease. Novel screening tools with improved accuracy and diagnostic precision are thus required to reduce the mortality burden of this malignancy. Additionally, current therapeutic strategies, including radio- and chemotherapies carry adverse side effects and are limited by the development of drug resistance. Recent advances in nanotechnology have rendered it an attractive approach for designing novel clinical solutions for CRC. Nanoparticle-based formulations could assist early tumor detection and help to overcome the limitations of conventional therapies including poor aqueous solubility, nonspecific biodistribution and limited bioavailability. In this review, we shed light on various types of nanoparticles used for diagnosis and drug delivery in CRC. In addition, we will explore how these nanoparticles can improve diagnostic accuracy and promote selective drug targeting to tumor sites with increased efficiency and reduced cytotoxicity against healthy colon tissue.

Rapid and label-free urine test based on surface-enhanced Raman spectroscopy for the non-invasive detection of colorectal cancer at different stages

The concept of being able to urinate in a cup and screen for colorectal cancer (CRC) is fascinating to the public at large. Here, a simple and label-free urine test based on surface-enhanced Raman spectroscopy (SERS) was employed for CRC detection. Significant spectral differences among normal, stages I-II, and stages III-IV CRC urines were observed. Using discriminant function analysis, the diagnostic sensitivities of 95.8%, 80.9%, and 84.3% for classification of normal, stages I-II, and stages III-IV CRC were achieved in training model, indicating the great promise of urine SERS as a rapid, convenient and noninvasive method for CRC staging detection.

Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum

BACKGROUND

Colorectal cancer (CRC) is an important disease worldwide, accounting for the second highest number of cancer-related deaths and the third highest number of new cancer cases. The blood test is a simple and minimally invasive diagnostic test. However, there is currently no blood test that can accurately diagnose CRC.

AIM

To develop a comprehensive, spontaneous, minimally invasive, label-free, blood-based CRC screening technique based on Raman spectroscopy.

METHODS

We used Raman spectra recorded using 184 serum samples obtained from patients undergoing colonoscopies. Patients with malignant tumor histories as well as those with cancers in organs other than the large intestine were excluded. Consequently, the specific diseases of 184 patients were CRC (12), rectal neuroendocrine tumor (2), colorectal adenoma (68), colorectal hyperplastic polyp (18), and others (84). We used the 1064-nm wavelength laser for excitation. The power of the laser was set to 200 mW.

RESULTS

Use of the recorded Raman spectra as training data allowed the construction of a boosted tree CRC prediction model based on machine learning. Therefore, the generalized R² values for CRC, adenomas, hyperplastic polyps, and neuroendocrine tumors were 0.9982, 0.9630, 0.9962, and 0.9986, respectively.

CONCLUSION

For machine learning using Raman spectral data, a highly accurate CRC prediction model with a high R² value was constructed. We are currently planning studies to demonstrate the accuracy of this model with a large amount of additional data.

Recent advances in surface-enhanced Raman spectroscopy based liquid biopsy for colorectal cancer (Review)

As colorectal cancer (CRC) is one of the forms of cancer with the highest prevalence globally and with a high mortality, screening and early detection remains a major issue. Colonoscopy is still the gold standard for detecting premalignant lesions, but it is burdened by some complications. For instance, it is laborious, with some difficulties of acceptance for some patients, and is ultimately an imperfect standard, given that some premalignant lesions or incipient malignancies can be missed by colonoscopic evaluation. In this context, new non-invasive approaches such as surface-enhanced Raman spectroscopy (SERS) based liquid biopsy have gained ground in recent years, showing promising results in oncological pathology diagnosis. These new methods have enabled the detection of subtle molecular profile alterations prior to any macroscopic morphological changes, thus providing a useful tool for early CRC detection. In the present review, we provide a summary of published studies applying SERS in CRC detection, along with our personal experience in using SERS in the diagnosis of different oncological pathologies, including CRC.

Rapid, non-invasive screening of keratitis based on Raman spectroscopy combined with multivariate statistical analysis

This study proposes a multivariate statistical analysis method based on Raman spectroscopy and different dimensionality reduction methods combined with the support vector machine (SVM) algorithm for rapid, non-invasive, high-accuracy classification of keratitis screenings. In this experiment, tear samples from 19 subjects with keratitis and 27 healthy subjects were detected, Raman spectra of the two groups of subjects were compared and analysed, and we found that their spectral intensities were different at 1005 cm^-1 and 1155 cm^-1 Principal component analysis (PCA) and partial least squares (PLS) were used for feature extraction, which greatly reduced the dimensionality of the high-dimensional spectral data. Then, the above two feature extraction methods were used as input to an SVM to build the discriminant diagnosis model. The average accuracy obtained from the PCA-SVM and PLS-SVM models was 77.86 % and 100 %, respectively. Our results suggest that tear Raman spectroscopy combined with multivariate statistical analysis has great potential in screening for keratitis. We expect this technology to could lead to the development of a portable, non-invasive and highly accurate keratitis screening device.