Raman Spectroscopy and Microscopy Applications in Cardiovascular Diseases: From Molecules to Organs

The combined use of serum Raman spectroscopy and D dimer testing for the early diagnosis of acute aortic dissection

Objectives

Acute aortic dissection (AAD) is an extremely life-threatening medical emergency, often misdiagnosed in its early stages, resulting in prolonged wait times for rescue. This study aims to identify potential serum biomarkers that can assist in the accurate diagnosis of AAD and effectively differentiate it from other conditions causing severe chest pain.

Methods

A total of 122 patients with AAD and 129 patients with other severe chest pain disorders were included in the study. Serum samples were analyzed by measuring the peak intensities of Raman spectra. For each measurement, the Raman spectrum was accumulated by two accumulations (3 s per acquisition). Logistic regression and nomogram models were developed using these peak intensities as well as D-dimer levels to predict the occurrence of AAD. The clinical utilities of these models were assessed through receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) in both training and internal test cohorts.

Results

The D-dimer levels of AAD patients were significantly increased, as well as higher intensities at specific Raman peaks, including 505 cm-1, 842 cm-1, 947 cm-1, 1254 cm-1, 1448 cm-1, and 1655 cm-1 when compared to non-AAD patients. Conversely, decreased intensities were observed at Raman peaks such as 750 cm-1, 1004 cm-1, 1153 cm-1, 1208 cm-1, and 1514 cm-1 in AAD patients. Least absolute shrinkage and selection operator regression analysis on the training cohort identified eight potential predictors: D-dimer along with intensity measurements at peaks such as 505 cm-1, 750 cm-1, 1153 cm-1, 1208 cm-1, 1254 cm-1, 1448 cm-1, and 1655 cm-1. The combination of these eight potential predictors demonstrated a good discriminatory performance, with an area under the curve (AUC) value of 0.928 in the training cohort and an AUC of 0.936 in the internal test cohort, outperforming the use of D-dimer alone. Furthermore, DCA curve analysis revealed that leveraging this combination of eight potential predictors would provide substantial net benefits for clinical application. Moreover, this combination significantly augmented discrimination power, as evidenced by a continuous NRI of 39.8 % and IDI of 9.95 % in the training cohort, as well as a continuous NRI of 27.1 % and IDI of 9.95 % in the internal test cohort.

Conclusions

The employment of this combination of eight potential predictors effectively rules out AAD to a greater extent. This study presents a promising diagnostic strategy for early detection using optical diagnostic techniques such as Raman spectroscopy.

Cardiac multiscale bioimaging: from nano- through micro- to mesoscales

Cardiac multiscale bioimaging is an emerging field that aims to provide a comprehensive understanding of the heart and its functions at various levels, from the molecular to the entire organ. It combines both physiologically and clinically relevant dimensions: from nano- and micrometer resolution imaging based on vibrational spectroscopy and high-resolution microscopy to assess molecular processes in cardiac cells and myocardial tissue, to mesoscale structural investigations to improve the understanding of cardiac (patho)physiology. Tailored super-resolution deep microscopy with advanced proteomic methods and hands-on experience are thus strategically combined to improve the quality of cardiovascular research and support future medical decision-making by gaining additional biomolecular information for translational and diagnostic applications.

Exploring novel and fast stability or sameness evaluation tool for different categories of injectable formulations

The establishment of drug product stability and sameness is the heart of generic formulation development. For regulatory filing, various instrumental methods are used on a case basis to establish the generic and innovator product sameness in multiple aspects. Here in the present study, we explored the applicability of the Time-correlated single photon counting (TCS-PC) technique as a fast, reliable, and nondestructive method for establishing the sameness of three different categories of injectable formulations, namely, Amphotericin B liposome for injection, enoxaparin injection, and iron sucrose injection. All three category formulations were evaluated in their native and artificially induced post degradation state to identify the discrimination power of the used instrumental techniques. The degradation of materials were confirmed by high performance liquid chromatography (HPLC). Based on the product category, pre and post-degradation samples were evaluated by selective instrumental methods like differential scanning calorimetry (DSC), nuclear magnetic resonance (NMR), fluorescence spectroscopy, particle size analysis by dynamic light scattering (DLS), small angle X-ray scattering (SAXS), Raman spectroscopy, inductively coupled plasma optical-emission spectrometry (ICP-OES) and circular dichroism study. All pre and post-degradation samples were further analyzed by TCS-PC. We observed that, TCS-PC can identify the differences between the initial and post degradation samples in very less time with promising discrimination power across the different injectable formulation types. Thus TCS-PC can be used as a fast and promising stability or sameness evaluation tool for different injectable drug products.

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Advanced Cardiac Imaging in the Assessment of Aortic Stenosis

Aortic stenosis is the most common form of valve disease in the Western world and a major healthcare burden. Although echocardiography remains the central modality for the diagnosis and assessment of aortic stenosis, recently, advanced cardiac imaging with cardiovascular magnetic resonance, computed tomography, and positron emission tomography have provided invaluable pathological insights that may guide the personalized management of the disease. In this review, we discuss applications of these novel non-invasive imaging modalities for establishing the diagnosis, monitoring disease progression, and eventually planning the invasive treatment of aortic stenosis.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Development of multivariate classification models for the diagnosis of dengue virus infection

The dengue virus (DENV) infection is a worldwide cause of serious illness and death. Early and efficient prediction of disease may help in proper medical management to control disease. Keeping this in view, multivariate classification models by combining with Raman spectroscopy have been developed for the diagnosis of DENV infection in human blood sera. For study design, a statistical analysis is performed to select the sample size for training of models. Total 1240 Raman spectra have been acquired from 39 DENV infected and 23 healthy sera samples. Prior to model development, Raman spectra were examined using ANOVA test for significant differences present in the intensities of newly appeared Raman bands at 622, 645, 700, 746, 800, 814, 873, 890, 948, 1002, 1018, 1080, 1235, 1250, 1272, 1386, 1404, 1446, 1609 and 1645 cm^-1. The significant differences and characteristic patterns of Raman bands induced by disease played decisive role and are exploited for development of multivariate model. Classification models are developed by utilizing principal component analysis (PCA) to extract discriminant features from multidimensional Raman spectral dataset and followed by support vector machines (SVM) with Polynomial of 5, RBF, and liner kernels. The proposed model for this study is built using 10-fold cross validation technique and evaluated on independent dataset to demonstrate its robustness. PCA-SVM (poly-5) model successfully yielded high diagnostic accuracy of 99.52%, sensitivity of 99.75%, specificity of 99.09% for classification of unknown suspected samples. For comparison, PCA discriminant analysis (PCA-DA), partial least squares regression (PLSR) are PLS-DA have been compared. It is found that PCA-SVM (poly-5) approach is more effective and robust compared to other state-of-the-art approaches and it can be used for clinical prediction of DENV infection in human blood sera.

Raman spectroscopy biochemical characterisation of bladder cancer cisplatin resistance regulated by FDFT1: a review

Bladder cancer is the fourth most common malignancy in males. It can present across the whole continuum of severity, from mild through well-differentiated disease to extremely malignant tumours with poor survival rates. As with other vital organ malignancies, proper clinical management involves accurate diagnosis and staging. Chemotherapy consisting of a cisplatin-based regimen is the mainstay in the management of muscle-invasive bladder cancers. Control via cisplatin-based chemotherapy is threatened by the development of chemoresistance. Intracellular cholesterol biosynthesis in bladder cancer cells is considered a contributory factor in determining the chemotherapy response. Farnesyl-diphosphate farnesyltransferase 1 (FDFT1), one of the main regulatory components in cholesterol biosynthesis, may play a role in determining sensitivity towards chemotherapy compounds in bladder cancer. FDFT1-associated molecular identification might serve as an alternative or appendage strategy for early prediction of potentially chemoresistant muscle-invasive bladder cancer tissues. This can be accomplished using Raman spectroscopy. Developments in the instrumentation have led to it becoming one of the most convenient forms of analysis, and there is a highly realistic chance that it will become an effective tool in the pathology lab. Chemosensitive bladder cancer tissues tend to have a higher lipid content, more protein genes and more cholesterol metabolites. These are believed to be associated with resistance towards bladder cancer chemotherapy. Herein, Raman peak assignments have been tabulated as an aid to indicating metabolic changes in bladder cancer tissues that are potentially correlated with FDFT1 expression.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Aortic aneurysm evaluation by scanning acoustic microscopy and Raman spectroscopy

Aortic aneurysm is observed as a result of the extensive alteration in the elasticity of the aortic wall due to the breakdown of elastin and collagen. In this study, we studied the feasibility of scanning acoustic microscopy (SAM) and Raman spectroscopy (RS) in characterizing the dilated segments of the aorta from male and female patients with aortic aneurysm. SAM determined the acoustic property variation in the aorta by calculating the acoustic impedance values of aorta samples of 18 patients. RS determined the disease states by analyzing the chemical variation especially in the peaks related to elastin and collagen using the k-means classification method. Consequently, we assume that combining these two techniques in clinics will help to investigate the dilated segment of the aorta with micrometer resolution, which will reduce the possibility of new aneurysm formation due to a segment not excised during the surgery.

Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease characterized by hemolysis of red blood cells (RBC) and venous thrombosis. The gold standard method for the diagnosis of this disease is flow cytometry. Here, we propose a combined optical tweezers and Raman spectral (Raman tweezers) approach to analyze blood samples from volunteers with or without PNH conditions. Raman spectroscopy is a well-known method for investigating a material's chemical structure and is also used in molecular analysis of biological compounds. In this study, we trap individual RBCs found in whole blood samples drawn from PNH patients and the control group. Evaluation of the Raman spectra of these cells by band component analysis and machine learning shows a significant difference between the two groups. The specificity and the sensitivity of the training performed by support vector machine (SVM) analysis were found to be 81.8% and 78.3%, respectively. This study shows that an immediate and high accuracy test result is possible for PNH disease by employing Raman tweezers and machine learning.

Efficiency Boost in Dye-Sensitized Solar Cells by Post- Annealing UV-Ozone Treatment of TiO2 Mesoporous Layer

The organic residues on titanium(IV) oxide may be a significant factor that decreases the efficiency of dye-sensitized solar cells (DSSC). Here, we suggest the UV-ozone cleaning process to remove impurities from the surface of TiO₂ nanoparticles before dye-sensitizing. Data obtained from scanning electron microscopy, Kelvin probe, Fourier-transform infrared spectroscopy, and Raman spectroscopy showed that the amounts of organic contamination were successfully reduced. Additionally, the UV-VIS spectrophotometry, spectrofluorometry, and secondary ion mass spectrometry proved that after ozonization, the dyeing process was relevantly enhanced. Due to the removal of organics, the power conversion efficiency (PCE) of the prepared DSSC devices was boosted from 4.59% to 5.89%, which was mostly caused by the increment of short circuit current (J_sc) and slight improvement of the open circuit voltage (V_oc).

Tyrosine and Tryptophan vibrational bands as markers of kidney injury: a renocardiac syndrome induced by renal ischemia and reperfusion study

Renal injury caused by renal ischemia and reperfusion strongly influences heart morphology, electrophysiology, and redox unbalance. The so-called cardiorenal syndrome is an important class of dysfunction since heart and kidneys are responsible for hemodynamic stability and organ perfusion through a complex network. In the present work we investigate the vibrational spectral features probed by Fourier-Transform Raman (FT-Raman) spectroscopy due to physiological alterations induced by renal ischemic reperfusion aiming to detect molecular markers related to progression of acute to chronic kidney injury and mortality predictors as well. C57BL/6J mice were subjected to unilateral occlusion of the renal pedicle for 60 min and reperfusion for 5, 8, and 15 days. Biopsies of heart and kidney tissues were analyzed. Our findings indicated that cysteine/cystine, fatty acids, methyl groups of Collagen, α-form of proteins, Tyrosine, and Tryptophan were modulated during renal ischemia and reperfusion process. These changes are consistent with fibroblast growth factors and Collagen III contents changes. Interestingly, Tyrosine and Tryptophan, precursor molecules for the formation of uremic toxins such as indoxyl sulfate and p-cresyl sulfate were also modulated. They are markers of kidney injury and their increase is strongly correlated to cardiovascular mortality. Regarding this aspect, we notice that monitoring the Tyrosine and Tryptophan bands at 1558, 1616, and 1625 cm^-1 is a viable and and advantageous way to predict fatality in cardiovascular diseases both "in vivo" or "in vitro", using the real-time, multiplexing, and minimally invasive advantages of FT-Raman spectroscopy.

Raman microspectroscopy and Raman imaging reveal biomarkers specific for thoracic aortic aneurysms

Aortic rupture and dissection are life-threatening complications of ascending thoracic aortic aneurysms (aTAAs), and risk assessment has been largely based on the monitoring of lumen size enlargement. Temporal changes in the extracellular matrix (ECM), which has a critical impact on aortic remodeling, are not routinely evaluated, and cardiovascular biomarkers do not exist to predict aTAA formation. Here, Raman microspectroscopy and Raman imaging are used to identify spectral biomarkers specific for aTAAs in mice and humans by multivariate data analysis (MVA). Multivariate curve resolution-alternating least-squares (MCR-ALS) combined with Lasso regression reveals elastic fiber-derived (Ce1) and collagen fiber-derived (Cc6) components that are significantly increased in aTAA lesions of murine and human aortic tissues. In particular, Cc6 detects changes in amino acid residues, including phenylalanine, tyrosine, tryptophan, cysteine, aspartate, and glutamate. Ce1 and Cc6 may serve as diagnostic Raman biomarkers that detect alterations of amino acids derived from aneurysm lesions.

Label-free Raman imaging of human/murine ascending thoracic aortic aneurysm (aTAA)

Multivariate analysis of Raman spectra allows detection of aTAA molecular features

Identification of spectral biomarkers for aTAA in elastic and collagen fibers

Alterations in amino acid spectra correlate with aTAA formation

Raman Spectroscopic Assessment of Myocardial Viability in Langendorff-Perfused Ischemic Rat Hearts

Spontaneous Raman spectroscopy, which senses changes in cellular contents of reduced cytochrome c, could be a powerful tool for label-free evaluation of ischemic hearts. However, undetermined is whether it is applicable to evaluation of myocardial viability in ischemic hearts. To address this issue, we investigated sequential changes in Raman spectra of the subepicardial myocardium in the Langendorff-perfused rat heart before and during ligation of the left coronary artery and its subsequent release and re-ligation. Under 532-nm wavelength excitation, the Raman peak intensity of reduced cytochrome c at 747 cm^-1 increased quickly after the coronary ligation, and reached a quasi-steady state within 30 min. Subsequent reperfusion of the heart after a short-term (30-min) ligation that simulates reversible conditions resulted in quick recovery of the peak intensity to the baseline. Further re-ligation resulted in resurgence of the peak intensity to nearly the identical value to the first ischemia value. In contrast, reperfusion after prolonged (120-min) ligation that assumes irreversible states resulted in incomplete recovery of the peak intensity, and re-ligation resulted in inadequate resurgence. Electron microscopic observations confirmed the spectral findings. Together, the Raman spectroscopic measurement for cytochrome c could be applicable to evaluation of viability of the ischemic myocardium without labeling.

Morpho-molecular ex vivo detection and grading of non-muscle-invasive bladder cancer using forward imaging probe based multimodal optical coherence tomography and Raman spectroscopy

Non-muscle-invasive bladder cancer affects millions of people worldwide, resulting in significant discomfort to the patient and potential death. Today, cystoscopy is the gold standard for bladder cancer assessment, using white light endoscopy to detect tumor suspected lesion areas, followed by resection of these areas and subsequent histopathological evaluation. Not only does the pathological examination take days, but due to the invasive nature, the performed biopsy can result in significant harm to the patient. Nowadays, optical modalities, such as optical coherence tomography (OCT) and Raman spectroscopy (RS), have proven to detect cancer in real time and can provide more detailed clinical information of a lesion, e.g. its penetration depth (stage) and the differentiation of the cells (grade). In this paper, we present an ex vivo study performed with a combined piezoelectric tube-based OCT-probe and fiber optic RS-probe imaging system that allows large field-of-view imaging of bladder biopsies, using both modalities and co-registered visualization, detection and grading of cancerous bladder lesions. In the present study, 119 examined biopsies were characterized, showing that fiber-optic based OCT provides a sensitivity of 78% and a specificity of 69% for the detection of non-muscle-invasive bladder cancer, while RS, on the other hand, provides a sensitivity of 81% and a specificity of 61% for the grading of low- and high-grade tissues. Moreover, the study shows that a piezoelectric tube-based OCT probe can have significant endurance, suitable for future long-lasting in vivo applications. These results also indicate that combined OCT and RS fiber probe-based characterization offers an exciting possibility for label-free and morpho-chemical optical biopsies for bladder cancer diagnostics.

Raman Spectroscopy Methods to Characterize the Mechanical Response of Soft Biomaterials

Raman spectroscopy has been used extensively to characterize the influence of mechanical deformation on microstructure changes in biomaterials. While traditional piezo-spectroscopy has been successful in assessing internal stresses of hard biomaterials by tracking prominent peak shifts, peak shifts due to applied loads are near or below the resolution limit of the spectrometer for soft biomaterials with moduli in the kilo- to mega-Pascal range. In this Review, in addition to peak shifts, other spectral features (e.g., polarized intensity and intensity ratio) that provide quantitative assessments of microstructural orientation and secondary structure in soft biomaterials and their strain dependence are discussed. We provide specific examples for each method and classify sensitive Raman characteristic bands common across natural (e.g., soft tissue) and synthetic (e.g., polymeric scaffolds) soft biomaterials upon mechanical deformation. This Review can provide guidance for researchers aiming to analyze micromechanics of soft tissues and engineered tissue constructs by Raman spectroscopy.

Dark-field hyperspectral imaging for label free detection of nano-bio-materials

Nanomaterials are playing an increasingly important role in cancer diagnosis and treatment. Nanoparticle (NP)-based technologies have been utilized for targeted drug delivery during chemotherapies, photodynamic therapy, and immunotherapy. Another active area of research is the toxicity studies of these nanomaterials to understand the cellular uptake and transport of these materials in cells, tissues, and environment. Traditional techniques such as transmission electron microscopy, and mass spectrometry to analyze NP-based cellular transport or toxicity effect are expensive, require extensive sample preparation, and are low-throughput. Dark-field hyperspectral imaging (DF-HSI), an integration of spectroscopy and microscopy/imaging, provides the ability to investigate cellular transport of these NPs and to quantify the distribution of them within bio-materials. DF-HSI also offers versatility in non-invasively monitoring microorganisms, single cell, and proteins. DF-HSI is a low-cost, label-free technique that is minimally invasive and is a viable choice for obtaining high-throughput quantitative molecular analyses. Multimodal imaging modalities such as Fourier transform infrared and Raman spectroscopy are also being integrated with HSI systems to enable chemical imaging of the samples. HSI technology is being applied in surgeries to obtain molecular information about the tissues in real-time. This article provides brief overview of fundamental principles of DF-HSI and its application for nanomaterials, protein-detection, single-cell analysis, microbiology, surgical procedures along with technical challenges and future integrative approach with other imaging and measurement modalities. This article is categorized under: Diagnostic Tools > in vitro Nanoparticle-Based Sensing Diagnostic Tools > in vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.

Efficacy of raman spectroscopy in the diagnosis of kidney cancer: A systematic review and meta-analysis

OBJECTIVE

To comprehensively analyze the relative effectiveness of Raman spectroscopy (RS) in the diagnosis of suspected kidney cancer.

PATIENTS AND METHODS

We performed a complete systematic review based on studies from PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library and China National Knowledge Infrastructure. We identified 2413 spectra with strict criteria in 6 individual studies published between January 2008 and November 2018 in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We summarized the test performance using random effects models.

RESULTS

General pooled diagnostic sensitivity and specificity of RS to kidney cancer were 0.96 (95% confidence interval [CI] 0.95-0.97) and 0.91 (95% CI 0.89-0.92). The pooled positive likelihood ratio (LR) was 9.57 (95% CI 5.73-15.46) while the negative LR was 0.04 (95% CI 0.02-0.11). The pooled diagnostic odds ratio was 238.06 (95% CI 77.79-728.54). The area under curve of summary receiver operator characteristics was 0.9466.

CONCLUSION

Through this meta-analysis, we found a promisingly high sensitivity and specificity of RS in the diagnosis of suspected kidney masses and tumors. Other parameters like positive LR, negative LR, diagnostic odds ratio and area under curve of the summary receiver operator characteristics curve all helped to illustrate the high efficacy of RS in the diagnosis of kidney cancer.

Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics

INTRODUCTION

Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These techniques can be used not only for the early diagnosis of cancer but also for monitoring the progression of the disease. Furthermore, chemical pathways to the progression of the disease process can be understood and followed.

AREAS COVERED

More recently, Artificial Intelligence (AI), Neural Network (NN), and Machine Learning are being combined with spectroscopy, which is making it easier to understand the chemical structural details of cancers and biological molecules more precisely and accurately. In this report, these aspects are being outlined by using breast cancer as a specific example.

EXPERT OPINION

A pathway showing to combine vibrational spectroscopy with AI and ML has immense potential in predicting various stages of different disease processes, in particular, in cancer diagnosis, staging, and designing treatment. This will result in improved patient care pathways.

Optical Identification of Middle Ear Infection

Ear infection is one of the most commonly occurring inflammation diseases in the world, especially for children. Almost every child encounters at least one episode of ear infection before he/she reaches the age of seven. The typical treatment currently followed by physicians is visual inspection and antibiotic prescription. In most cases, a lack of improper treatment results in severe bacterial infection. Therefore, it is necessary to design and explore advanced practices for effective diagnosis. In this review paper, we present the various types of ear infection and the related pathogens responsible for middle ear infection. We outline the conventional techniques along with clinical trials using those techniques to detect ear infections. Further, we highlight the need for emerging techniques to reduce ear infection complications. Finally, we emphasize the utility of Raman spectroscopy as a prospective non-invasive technique for the identification of middle ear infection.

Chemical imaging of human teeth by a time-resolved Raman spectrometer based on a CMOS single-photon avalanche diode line sensor

Raman spectroscopy is a powerful analytical tool to be used in many biomedical applications and could be potentially translated into clinical work. The challenge of Raman spectroscopy in biomedical applications is the high inherent fluorescence of biological samples. One promising method to suppress the fluorescence background is to use pulsed lasers and time-gated detectors but the complexity of time-gated systems has hindered their widespread usage. We present here chemical imaging of human teeth by means of a new kind of compact and practical fluorescence-suppressed Raman spectrometer based on a time-resolved 16 × 256 CMOS single-photon avalanche diode (SPAD) line sensor with an integrated 256-channel 3-bit on-chip time-to-digital converter. The chemical images were constructed by utilizing a simple unsupervised machine learning algorithm (k-means clustering). The high quality of Raman spectra measured with the time-resolved CMOS SPAD-based Raman spectrometer was verified by comparing the spectra to those collected with a commercial conventional continuous wave (CW) Raman spectrometer. The spectra measured by using the time-resolved CMOS SPAD-based Raman spectrometer had 4.4-8.8 times higher signal to peak-to-peak noise ratio values than the spectra from the CW Raman spectrometer when the same radiant exposure (∼300 J mm^-2) was used with both spectrometers. This paper shows in practice the potential of time-resolved CMOS SPAD-based Raman spectroscopy in the field of biomedicine and we expect that the presented technology could pave the way for the development of new kind of compact and practical fluorescence-suppressed Raman spectrometers to be used both in biomedical research and clinical settings.

Current and future functional imaging techniques for post-traumatic stress disorder

Posttraumatic stress disorder (PTSD) is a trauma and stressor related psychiatric disorder associated with structural, metabolic, and molecular alternations in several brain regions including diverse cortical areas, neuroendocrine regions, the striatum, dopaminergic, adrenergic and serotonergic pathways, and the limbic system. We are in critical need of novel therapeutics and biomarkers for PTSD and a deep understanding of cutting edge imaging and spectroscopy methods is necessary for the development of promising new approaches to better diagnose and treat the disorder. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criterion, all forms of traumatic stress-induced disorder are considered acute stress disorder for the first month following the stressor. Only after symptoms do not remit for one month can the disorder be deemed PTSD. It would be particularly useful to differentiate between acute stress disorder and PTSD during the one month waiting period so that more intensive treatments can be applied early on to patients with a high likelihood of developing PTSD. This would potentially enhance treatment outcomes and/or prevent the development of PTSD. Comprehension of the qualities and limitations of currently applied methods as well as the novel emerging techniques provide invaluable knowledge for fast paced development. Conventional methods of studying PTSD have proven to be insufficient for diagnosis, measurement of treatment efficacy, and monitoring disease progression. As the field currently stands, there is no diagnostic biomarker available for any psychiatric disease, PTSD included. Currently, emerging and available technologies are not utilized to their full capacity and in appropriate experimental designs for the most fruitful possible studies in this area. Therefore, there is an apparent need for improved methods in PTSD research. This review demonstrates the current state of the literature in PTSD, including molecular, cellular, and behavioral indicators, possible biomarkers and clinical and pre-clinical imaging techniques relevant to PTSD, and through this, elucidate the void of current practical imaging and spectroscopy methods that provide true biomarkers for the disorder and the significance of devising new techniques for future investigations. We are unlikely to develop a single biomarker for any psychiatric disorder however. As psychiatric disorders are incomparably complex compared to other medical diagnoses, its most likely that transcriptomic, metabolomic and structural and connectomic imaging data will have to be analyzed in concert in order to produce a dependable non-behavioral marker of PTSD. This can explain the necessity of bridging conventional approaches to novel technologies in order to create a framework for further discoveries in the treatment of PTSD.

Conventional methods of studying posttraumatic stress disorder (PTSD) have proven to be insufficient for diagnosis. We have reviewed clinical and preclinical imaging techniques as well as molecular, cellular, and behavioral indicators for PTSD.