Raman spectroscopy for cancer diagnosis: how far have we come?

A New Approach in the Early Electrochemical Diagnosis of Hepatitis B Virus Infection using Carbon-based Nanomaterials

The importance of early diagnosis of hepatitis B virus infection to treat and follow up this disease has led to many advances in diagnostic techniques and materials. Conventional diagnostic tests are not very useful, especially in the early stages of infection; it is therefore suggested that nanomaterials can enhance them by changing and strengthening their performance for a more accurate and rapid diagnosis. Electrochemical immunosensors with unique features such as miniaturization, low cost, specificity and simplicity have become a suitable and vital tool in the rapid diagnosis of hepatitis B since the patent. Different strategies have been presented, such as graphene oxide and gold nanorods (GO-GNRs), graphene oxide (GO), copper metal-organic framework/ electrochemically reduced graphene oxide (Cu-MOF/ErGO) composite, Label-free graphene oxide/ Fe3O4/Prussian Blue (GO/Fe3O4/PB) immunosensor, and graphene oxide-ferrocene-CS/Au (GOFc- CS/Au) nanoparticle layered electrochemical immunosensor. In this review, we discuss a group of the most widely used nanostructures, such as graphene and carbon nanotubes, which are used to develop electrochemical immunosensors for the early diagnosis of the hepatitis B virus.

Advancing cerumen analysis: exploring innovative vibrational spectroscopy techniques with respect to their potential as new point-of-care diagnostic tools

Cerumen, commonly known as earwax, is a complex mixture composed of secretions from ceruminous glands. These secretions are heterogeneous mixtures mainly composed of lipids and proteins. Despite its prevalence, the potential diagnostic value of cerumen remains largely unexplored. Here, we present an in-depth analysis of cerumen utilizing well-known vibrational approaches such as conventional Raman spectroscopy or surface-enhanced Raman spectroscopy (SERS) together with advanced vibrational spectroscopy techniques such as coherent Raman scattering (CRS), i.e. broadband coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), as well as optical photothermal infrared (OPTIR) spectroscopy. Through the integration of these vibrational spectroscopic methods, lipids and proteins can be identified as the main components of cerumen; however, they contribute to the final spectral information to various extents depending on the vibrational detection scheme applied. The inherently weak Raman signal could be enhanced by linear (SERS) and non-linear (CRS) processes, resulting in efficient acquisition of fingerprint information and allowing for the detection of marker modes, which cannot be addressed by conventional Raman spectroscopy. OPTIR spectroscopy provides complementary information to Raman spectroscopy, however, without the contribution of a fluorescence background. Our findings underscore the utility of these cutting-edge techniques in unveiling the intricate molecular landscape of cerumen, paving the way for novel point-of-care diagnostic methodologies and therapeutic interventions.

Raman spectroscopy and topological machine learning for cancer grading

In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

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.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic "molecular finger-print" of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Highly Sensitive and Cost-Effective Portable Sensor for Early Gastric Carcinoma Diagnosis

Facile and efficient early detection of cancer is a major challenge in healthcare. Herein we developed a novel sensor made from a polycarbonate (PC) membrane with nanopores, followed by sequence-specific Oligo RNA modification for early gastric carcinoma diagnosis. In this design, the gastric cancer antigen CA72-4 is specifically conjugated to the Oligo RNA, thereby inhibiting the electrical current through the PC membrane in a concentration-dependent manner. The device can determine the concentration of cancer antigen CA72-4 in the range from 4 to 14 U/mL, possessing a sensitivity of 7.029 µAU^-1mLcm^-2 with a linear regression (R²) of 0.965 and a lower detection limit of 4 U/mL. This device has integrated advantages including high specificity and sensitivity and being simple, portable, and cost effective, which collectively enables a giant leap for cancer screening technologies towards clinical use. This is the first report to use RNA aptamers to detect CA72-4 for gastric carcinoma diagnosis.

Serum analysis method combining cellulose acetate membrane purification with surface-enhanced Raman spectroscopy for non-invasive HBV screening

A highly sensitive, non-invasive, and rapid HBV (Hepatitis B virus) screening method combining membrane protein purification with silver nanoparticle-based surface-enhanced Raman scattering (SERS) spectroscopy was developed in this study. Reproducible serum protein SERS spectra were obtained from cellulose acetate membrane-purified human serum from 94 HBV patients and 89 normal groups. Tentative assignments of serum protein SERS spectra showed that the HBV patients primarily led to specific biomedical changes of serum protein. Principal components analysis and linear discriminate analysis were introduced to analyse the obtained spectra, with the diagnostic sensitivity of 92.6% and specificity of 77.5% were achieved for differentiating HBV patients from normal groups.

Raman-Enhanced Spectroscopy (RESpect) Probe for Childhood Non-Hodgkin Lymphoma

Raman-enhanced spectroscopy (RESpect) probe, which enhances Raman spectroscopy technology through a portable fiber-optic device, characterizes tissues and cells by identifying molecular chemical composition showing distinct differences/similarities for potential tumor markers or diagnosis. In a feasibility study with the ultimate objective to translate the technology to the clinic, a panel of pediatric non-Hodgkin lymphoma tissues and non-malignant specimens had RS analyses compared between standard Raman spectroscopy microscope instrument and RESpect probe. Cryopreserved tissues were mounted on front-coated aluminum mirror slides and analyzed by standard Raman spectroscopy and RESpect probe. Principal Component Analysis revealed similarities between non-Hodgkin lymphoma subtypes but not follicular hyperplasia. Standard Raman spectroscopy and RESpect probe fingerprint comparisons demonstrated comparable primary peaks. Raman spectroscopic fingerprints and peaks of pediatric non-Hodgkin lymphoma subtypes and follicular hyperplasia provided novel avenues to pursue diagnostic approaches and identify potential new therapeutic targets. The information could inform new insights into molecular cellular pathogenesis. Translating Raman spectroscopy technology by using the RESpect probe as a potential point-of-care screening instrument has the potential to change the paradigm of screening for cancer as an initial step to determine when a definitive tissue biopsy would be necessary.

Label-free serum detection of Trichinella spiralis using surface-enhanced Raman spectroscopy combined with multivariate analysis

Based on blood serum surface-enhanced Raman spectroscopy (SERS) analysis, this paper proposed a simple and unlabeled non-invasive serum detection for T. spiralis infection. Serum samples were collected and analyzed from 40 rats at 0 days post infection (dpi) (normal rats), 19 uninfected rats, and 16 rats infected with T. spiralis at 28 dpi, using SERS measurements. Multivariate statistical techniques, such as linear discriminant analysis (LDA) and principal components analysis (PCA), were used to analyze and identify the obtained blood serum SERS spectra. The diagnosis algorithms, based on PCA-LDA, achieved a diagnostic sensitivity of 87.5%, a specificity of 94.7%, and an accuracy of 91.4% for separating the samples infected with T. spiralis from the control samples. This exploratory study demonstrated that colloidal Ag NPs-based SERS serum analysis technique combined with PCA-LDA has a great potential in improving the detection of T. spiralis infection and onsite screening.

Advances in Translational Nanotechnology: Challenges and Opportunities

The burgeoning field of nanotechnology aims to create and deploy nanoscale structures, devices, and systems with novel, size-dependent properties and functions. The nanotechnology revolution has sparked radically new technologies and strategies across all scientific disciplines, with nanotechnology now applied to virtually every area of research and development in the US and globally. NanoFlorida was founded to create a forum for scientific exchange, promote networking among nanoscientists, encourage collaborative research efforts across institutions, forge strong industry-academia partnerships in nanoscience, and showcase the contributions of students and trainees in nanotechnology fields. The 2019 NanoFlorida International Conference expanded this vision to emphasize national and international participation, with a focus on advances made in translating nanotechnology. This review highlights notable research in the areas of engineering especially in optics, photonics and plasmonics and electronics; biomedical devices, nano-biotechnology, nanotherapeutics including both experimental nanotherapies and nanovaccines; nano-diagnostics and -theranostics; nano-enabled drug discovery platforms; tissue engineering, bioprinting, and environmental nanotechnology, as well as challenges and directions for future research.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics

Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.

Label free hepatitis B detection based on serum derivative surface enhanced Raman spectroscopy combined with multivariate analysis

Surface-enhanced Raman spectroscopy (SERS) was developed here for the non-invasive detection of the hepatitis B virus (HBV). Chronic hepatitis B virus (HBV) infection is a primary health problem in the world and may further develop into cirrhosis and hepatocellular carcinoma (HCC). SERS measurement was applied to two groups of serum samples. One group included 93 HBV patients and the other group included 94 healthy volunteers as control subjects. Tentative assignments of the Raman bands in the measured SERS spectra have shown the difference of the serum SERS spectra between HBV patients and healthy volunteers. The differences indicated an increase in the relative amounts of L-arginine, Saccharide band (overlaps with acyl band), phenylalanine and tyrosine, together with a decrease in the percentage of nucleic acid, valine and hypoxanthine in the serum of HBV patients compared with those of healthy volunteers. For better analysis of the spectral data, the first-order derivation was applied to the SERS data. Furthermore, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were employed to distinguish HBV patients from healthy volunteers and to realize the diagnostic sensitivity of 78.5% and 91.4%, and specificity of 75% and 83% for SERS and the first order derivative SERS spectrum, respectively. These results suggest that derivative analysis could be an effective method to improve the classification of SERS spectra belonging to different groups. This exploratory work demonstrated that first-order derivative serum SERS spectrum combined with PCA-LDA has great potential for improving the detection of HBV.

Proof-of-concept Raman spectroscopy study aimed to differentiate thyroid follicular patterned lesions

Inter-observer variability and cancer over-diagnosis are emerging clinical problems, especially for follicular patterned thyroid lesions. This challenge strongly calls for a new clinical tool to reliably identify neoplastic lesions and to improve the efficiency of differentiation between benign and malignant neoplasms, especially considering the increased diagnosis of small carcinomas and the growing number of thyroid nodules. In this study, we employed a Raman spectroscopy (RS) microscope to investigate frozen thyroid tissues from fourteen patients with thyroid nodules. To generate tissue classification models, a supervised statistical analysis of the Raman spectra was performed. The results obtained demonstrate an accuracy of 78% for RS based diagnosis to discriminate between normal parenchyma and follicular patterned thyroid nodules, and 89% accuracy - for very challenging follicular lesions (carcinoma versus adenoma). RS translation into intraoperative diagnosis of frozen sections and in preoperative analysis of biopsies can be very helpful to reduce unnecessary surgery in patients with indeterminate cytological reports.

RAMAN spectroscopy imaging improves the diagnosis of papillary thyroid carcinoma

Recent investigations strongly suggest that Raman spectroscopy (RS) can be used as a clinical tool in cancer diagnosis to improve diagnostic accuracy. In this study, we evaluated the efficiency of Raman imaging microscopy to discriminate between healthy and neoplastic thyroid tissue, by analyzing main variants of Papillary Thyroid Carcinoma (PTC), the most common type of thyroid cancer. We performed Raman imaging of large tissue areas (from 100 × 100 μm² up to 1 × 1 mm²), collecting 38 maps containing about 9000 Raman spectra. Multivariate statistical methods, including Linear Discriminant Analysis (LDA), were applied to translate Raman spectra differences between healthy and PTC tissues into diagnostically useful information for a reliable tissue classification. Our study is the first demonstration of specific biochemical features of the PTC profile, characterized by significant presence of carotenoids with respect to the healthy tissue. Moreover, this is the first evidence of Raman spectra differentiation between classical and follicular variant of PTC, discriminated by LDA with high efficiency. The combined histological and Raman microscopy analyses allow clear-cut integration of morphological and biochemical observations, with dramatic improvement of efficiency and reliability in the differential diagnosis of neoplastic thyroid nodules, paving the way to integrative findings for tumorigenesis and novel therapeutic strategies.