Diagnosis Of Several Diseases Research Articles

Clinical utility of chest radiography for severe COVID-19.

Chest radiography (CXR) is performed more widely and readily than CT for the management of coronavirus disease (COVID-19), but there remains little data on its clinical utility. This study aims to assess the diagnostic performance of CXR, with emphasis on its predictive value, for severe COVID-19 disease. A retrospective cohort study was conducted, 358 chest radiographs were performed on 109 COVID-19 patients (median age 44.4 years, 58 males and 30 with comorbidities) admitted between 22 January 2020 and 15 March 2020. Each CXR was reviewed and scored by three radiologists in consensus using a 72-point COVID-19 Radiographic Score (CRS). Disease severity was determined by the need for supplemental oxygen and mechanical ventilation. Patients who needed supplemental oxygen (n=19, 17.4%) were significantly older (P<0.001) and significantly more of them had co-morbidities (P=0.011). They also had higher C-reactive protein (CRP) (P<0.001), higher lactate dehydrogenase (LDH) (P<0.001), lower lymphocyte count (P<0.001) and lower hemoglobin (Hb) (P=0.001). Their initial (CRSinitial) and maximal CRS (CRSmax) were higher (P<0.001). Adjusting for age and baseline hemoglobin, the AUROC of CRSmax (0.983) was as high as CRPmax (0.987) and higher than the AUROC for lymphocyte countmin (0.897), and LDHmax (0.900). The AUROC for CRSinitial was slightly lower (0.930). CRSinitial ≥5 had a sensitivity of 63% and specificity of 92% in predicting the need for oxygen, and 73% sensitivity and 88% specificity in predicting the need for mechanical ventilation. CRS between the 6th and 10th day from the onset of symptoms (CRSD6-10) ≥5 had a sensitivity of 89% and specificity of 95% in predicting the need for oxygen, and 100% sensitivity and 86% specificity in predicting the need for mechanical ventilation. Adjusting for key confounders of age and baseline Hb, CRSmax performed comparable to or better than laboratory markers in the diagnosis of severe disease. CXR performed between the 6th and 10th days from symptom onset was a better predictor of severe disease than CXR performed earlier at presentation. A benign clinical course was seen in CXR that were normal or had very mild abnormalities.

(Invited) Microfluidic Sensors based on Molecularly Imprinted Polymers for the Selective Detection of Volatile Organic Compounds

Introduction Selective and sensitive detection of volatile organic compounds (VOCs) is of great importance in applications involving monitoring of hazardous chemicals or non-invasive diagnosis. Diffusion-base microfluidic gas sensors are a new generation of gas detectors. These sensors have increased selectivity; this is primarily due to the effect of diffusion and adsorption/desorption (highly influenced by the coating materials and dimensions of the microfluidic channel) of the analyte molecules along the microfluidic channel (1,2). Therefore, the combination of the diffusion and adsorption/desorption rates of different analytes with different concentrations leads to a unique response (i.e., “smell print”) from the embedded sensor (3). In addition to the coating (or the combination of the layers coated) the surface roughness can affect adsorption and desorption phenomena and hence the performance (mainly selectivity) of the sensors. There are several ways to change the coating and roughness of the channel. One interesting method, which changes the surface material of the microchannel and interaction with the analyte, is the integration of molecularly imprinted polymer nanoparticles (MIP NPs) in to the channel.Column CoatingMIP is a synthetic polymer possessing selective molecular recognition properties (due to its binding sites) with size, shape and functionalities complementary to the target molecules. MIPs, often called artificial antibodies or receptors, are alternatives to antibodies in a variety of applications. The advantages of MIPs over natural antibodies include easy and low-cost preparation, longer life cycles (up to several years), highly tailorable recognition properties, solvents and thermal stability, robustness and resistance to a wide range of pH, and ease of regeneration and sterilization.For the first time, MIP NPs were synthesized with acetone recognition sites due to its importance as a biomarker for the non-invasive diagnosis of several diseases like diabetes. The synthesized MIP NPs were integrated into a 3D-printed dual-channel platform (one channel without and the other with MIP NPs) to develop a highly selective detection device against various VOCs (alcohols, ketones, nitrile, and aromatic compounds). Method The MIP NPs with acetone recognition sites were prepared for the first time using the simple precipitation procedure. After polymerization, the solution was centrifuged and the final polymer was ground into a powder. To remove the template, the MIP powder was washed with acetonitrile, followed by heating the powder at 80ºC for 48 h. The morphology and chemical structure of MIP NPs were investigated by field emission scanning electron microscope (FE-SEM, FEI-Nova NanoSEM 450) and Fourier-transform infrared spectroscopy (FTIR, Shimadzu FTIR Prestige 21).The microfluidic-based gas platform consists of two microfluidic channels and gas sensors (embedded at the channel terminus). The microchannels were fabricated from VeroClear RGD810 using a 3D-printer (Connex 500). Each microchannel has equal dimensions of 500 µm in height, 3 mm in width, and 3 cm in length. The inner surfaces of the microchannels are coated with Parylene C using a chemical vapor deposition (CVD) coating machine (SCS PDS 2010 Labcoater). To investigate the effect of treating the channel with MIP NPs on the selectivity of the sensors, one channel was coated with MIP NPs (referred to as the MIP channel) and another one was left uncoated (referred to as the control channel). For this purpose, a certain amount of synthesized MIP NPs dispersed in acetonitrile was drop-casted on the surface of the MIP channel and dried for a day in a clean air chamber. VOCs flow into the channel inlet, diffuse along the microfluidic channel, and reach gas detectors placed at the end of the microchannels, when exposed to the device.The synthesized MIP NPs were integrated into a 3D-printed dual-channel platform (one channel without and the other with MIP NPs) to develop a highly selective detection device against various VOCs (alcohols, ketones, nitrile, and aromatic compounds). A simple feature extraction method was used to distinguish signals arising from different VOCs. The differences between the feature extraction of the two channels showed that in general this platform was specific to acetone, proving the potential application of the MIP NPs in artificial olfaction platforms. Results and Conclusions Fig. 1 shows the response of the dual-channel detector when exposed to 200, 400, 800, 2000, 4000 ppm concentrations of several VOCs. The responses of both detectors increase with increasing concentrations of the target VOC. Although the response of the two detectors to the same target analyte was significantly different, with the increase in the concentration the magnitude of signals increases; however, the time of the maximum response still remains the same. It was also observed that for the low concentration of acetone, butanone and ethanol, the responses from the nano MIP-coated detector are not distinguishable. This again confirms the interaction between these analytes and the MIP NPs.

Image Quality and Dose Comparison of Single-Energy CT (SECT) and Dual-Energy CT (DECT).

CT and its comprehensive usage have become one of the most indispensable components in medical field especially in the diagnosis of several diseases. SECT and DECT have developed CT diagnostic potentials in several means. In this review article we have discussed the basic principles of single-energy and dual-energy computed tomography and their important physical differences which can cause better diagnostic evaluation. Moreover, different organs diagnostic evaluations through single-energy and dual-energy computed tomography have been discussed. Conventional or single-energy CT (SECT) uses a single polychromatic X-ray beam (ranging from 70 to 140 kVp with a standard of 120 kVp) emitted from a single source and received by a single detector. The concept of dual-energy computed tomography (DECT) is almost as old as the CT technology itself; DECT initially required substantially higher radiation doses (nearly two times higher than those employed in single-energy CT) and presented problems associated with spatial misregistration of the two different kV image datasets between the two separate acquisitions. The basic principles of single-energy and dual-energy computed tomography and their important physical differences can cause better diagnostic evaluation. Moreover, different organs diagnostic evaluations through single-energy and dual-energy computed tomography have been discussed. According to diverse data and statistics it is controversial to definitely indicate the accurate comparison of image quality and dose amount.

Metabolomics in endometrial cancer diagnosis: A systematic review.

Endometrial cancer (EC) is the most common gynecological malignancy in the developed world. The prognosis of EC strongly depends on tumor stage, hence the importance of improving diagnosis. Metabolomics has recently appeared as a promising test for a non-invasive diagnosis of several diseases. Nevertheless, no metabolic marker has been approved for use in the routine practice. We aimed to provide an overview of metabolomics findings in the diagnosis of EC. A systematic review was performed by searching eight electronic databases from their inception to October 2019 for studies assessing metabolomics in EC diagnosis. Extracted data included characteristics of patients and EC, serum concentration of metabolites in women with and without EC and its association with EC diagnosis, tumor behavior and pathological characteristics. Six studies with 732 women (356 cases and 376 controls) were included. Several metabolites were found able to predict the presence of EC, tumor behavior (progression and recurrence) and pathological characteristics (histotype, myometrial invasion and lymph vascular space invasion). Metabolomics might be suitable for a non-invasive diagnosis and screening of EC, offering the possibility to predict tumor behavior and pathological characteristics. Further studies are necessary to validate these results.

Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Machine learning methods have been frequently used for the diagnosis of several diseases recently because of its reliability and convenience. In this paper, a comprehensive overview of the literature related to diabetes and diabetic retinopathy has been done and diagnosis of diabetic retinopathy disease is investigated. Artificial Neural Networks (ANN) method has been applied to the problem using Rapid Miner, a data mining tool. Some other methods have also adapted to the problem, but ANN based detection approach gave the best results. 88.52% sensitivity has been obtained using the features of Messidor dataset. Besides showing the success of ANN in diabetic retinopathy detection, this study also proved that Rapid Miner can be used effectively for the analysis of diabetic retinopathy.

Salivary molecular spectroscopy: A sustainable, rapid and non-invasive monitoring tool for diabetes mellitus during insulin treatment

Monitoring of blood glucose is an invasive, painful and costly practice in diabetes. Consequently, the search for a more cost-effective (reagent-free), non-invasive and specific diabetes monitoring method is of great interest. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been used in diagnosis of several diseases, however, applications in the monitoring of diabetic treatment are just beginning to emerge. Here, we used ATR-FTIR spectroscopy to evaluate saliva of non-diabetic (ND), diabetic (D) and insulin-treated diabetic (D+I) rats to identify potential salivary biomarkers related to glucose monitoring. The spectrum of saliva of ND, D and D+I rats displayed several unique vibrational modes and from these, two vibrational modes were pre-validated as potential diagnostic biomarkers by ROC curve analysis with significant correlation with glycemia. Compared to the ND and D+I rats, classification of D rats was achieved with a sensitivity of 100%, and an average specificity of 93.33% and 100% using bands 1452 cm-1 and 836 cm-1, respectively. Moreover, 1452 cm-1 and 836 cm-1 spectral bands proved to be robust spectral biomarkers and highly correlated with glycemia (R2 of 0.801 and 0.788, P < 0.01, respectively). Both PCA-LDA and HCA classifications achieved an accuracy of 95.2%. Spectral salivary biomarkers discovered using univariate and multivariate analysis may provide a novel robust alternative for diabetes monitoring using a non-invasive and green technology.

Noble Metal-Assisted Surface Plasmon Resonance Immunosensors.

For the early diagnosis of several diseases, various biomarkers have been discovered and utilized through the measurement of concentrations in body fluids such as blood, urine, and saliva. The most representative analytical method for biomarker detection is an immunosensor, which exploits the specific antigen-antibody immunoreaction. Among diverse analytical methods, surface plasmon resonance (SPR)-based immunosensors are emerging as a potential detection platform due to high sensitivity, selectivity, and intuitive features. Particularly, SPR-based immunosensors could detect biomarkers without labeling of a specific detection probe, as typical immunosensors such as enzyme-linked immunosorbent assay (ELISA) use enzymes like horseradish peroxidase (HRP). In this review, SPR-based immunosensors utilizing noble metals such as Au and Ag as SPR-inducing factors for the measurement of different types of protein biomarkers, including viruses, microbes, and extracellular vesicles (EV), are briefly introduced.

Simultaneous piezoelectric noninvasive detection of multiple vital signs

The monitoring of vital signs plays a key role in the diagnosis of several diseases. Piezoelectric sensors have been utilized to collect a corresponding representative signal from the chest surface. The subject typically needs to hold his or her breath to eliminate the respiration effect. This work further contributes to the extraction of the corresponding representative vital signs directly from the measured respiration signal. The contraction and expansion of the heart muscles, as well as the respiration activities, will induce a mechanical vibration across the chest wall. The induced vibration is then captured by the piezoelectric sensor placed at the chest surface, which produces an electrical output voltage signal conformally mapped with the respiration-cardiac activities. During breathing, the measured voltage signal is composed of the cardiac cycle activities modulated along with the respiratory cycle activity. A representative model that incorporates the cardiac and respiratory activities is developed and adopted. The piezoelectric and the convolution theories along with Fourier transformation are applied to extract the corresponding cardiac activity signal from the respiration signal. All the results were validated step by step by a conventional apparatus, with good agreement observed.

An efficient algorithm for detection of white blood cell nuclei using adaptive three stage PCA-based fusion

Abstract Morphological analysis of white blood cells under a microscope is a crucial laboratory procedure for the diagnosis of several diseases including leukemia. This process is however, tedious and subjective, hence the need for innovative, efficient, and automatic analytical techniques. From shortcomings of algorithms in the literature such as inability to segment images with poor brightness and low contrast or deterioration of morphological structure of nucleus during segmentation, this paper sets forth an automatic method for segmentation of white blood cell nuclei in peripheral blood smear images in an attempt to cover these defects. In the proposed method, an adaptive fusion based on principal components analysis is used to fuse color components derived from HSV and CMYK color spaces. The proposed method enables the extraction of the nuclei without the application of morphological operations and presents the advantage of accentuated contrast thanks to PCA-based fusion and independence to color and brightness variation in the peripheral images. The developed algorithm was tested on three completely different public image datasets. The comparison of the results of the proposed algorithm with several other existing methods on the same datasets demonstrated its greater efficacy. A dice similarity coefficient of 97.06% was obtained for CellaVision, 94.75% for BloodSeg base, and 90.79% for JTSC databases.

Chapter 10 - Sex and the noradrenergic system

The central noradrenergic system comprises multiple brainstem nuclei whose cells synthesize and release the catecholamine transmitter norepinephrine (NE). The largest of these nuclei is the pontine locus coeruleus (LC), which innervates the vast majority of the forebrain. NE interacts with a number of pre- and postsynaptically expressed G protein-coupled receptors to affect a wide array of functions, including sensory signal processing, waking and arousal, stress responsiveness, mood, attention, and memory. Given the myriad functions ascribed to the locus coeruleus-noradrenergic (LC-NE) system, it is unsurprising that it is implicated in many disease states, including various mood, cognitive, neuropsychiatric, and neurodegenerative diseases. The LC-NE system is also notably sexually dimorphic with regard to its morphologic and anatomical features as well as how it responds to the peptide transmitter corticotropin releasing hormone (CRH), a major mediator of the central stress response. The sex-biased morphology and signaling that is observed in the LC could then be considered a potential contributor to the differential prevalence of various diseases between men and women. This chapter summarizes the primary differences between the male and female LC, based primarily on preclinical observations and how these disparities may relate to differential diagnoses of several diseases between men and women.

Highly sensitive surface-enhanced Raman spectroscopy (SERS) platform using core/double shell (Ag/polymer/Ag) nanohorn for proteolytic biosensor

Sensitive detection platforms have recently gathered more interest due to its ability to make an early diagnosis of several diseases, which increases the chances of successful treatment. Although several highly sensitive platforms have been developed, improvements are needed to reduce their reaction time and cost. In this study, we established a surface-enhanced Raman spectroscopy (SERS)-based ultrasensitive platform by using SERS-active hollow polypyrrole (hPPy) nanohorn and peptide-functionalized gold nanoparticles (AuNPs). To create a SERS-active platform, we deposited silver on the inside and outside of the hPPy nanohorn surface; this resulted in the formation of core/double-shells Ag/polymer/Ag nanostructures. In addition, peptide-functionalized AuNPs functionalized with Raman dye was pre-immobilized on the Ag/hPPy/Ag nanohorn to enhance the signals and skip additional steps for the biomolecular detection. Using this SERS-sensitive novel core/double-shell nanohorn platform, we enhanced the Raman signal and improved the sensitivity of caspase-3, which is associated with several diseases, and achieved a broad detection range from 10 pg/ml to 10 µg/ml. We suggest that the highly sensitive and simple detection of this nanohorn platform can be widely applied for sensitive detection of proteolytic biomarkers and early diagnosis of several diseases.

ROBUST DISCRIMINATION OF LEUKOCYTES PROTUBERANT TYPES FOR EARLY DIAGNOSIS OF LEUKEMIA

Advanced laboratory technology has made blood testing more automated, robust and tests are being implemented comprehensively. Leukocytes type differentiation is a critical hematological images analysis step of blood film as it delivers valuable information in diagnosis of several diseases. At present, the morphological examination of leukocytes is done manually and this process is very tedious, inefficient and slow. Although many white blood cells detection or classification techniques are presented by different researchers, there is still a need of fully automated and an efficient detection system of blood cells with its particular types for an early diagnosis of leukemia. This paper presents a technique for the classification of protuberant types of leukocytes and early diagnosis of leukemia. The work is divided into the following main stages: (a) image augmentation, (b) wavelet composition and decomposition for attaining high and low frequency bands of the cell image, (c) convolutional neural network (CNN) training model for the classification of leukocytes categories and (d) prediction of leukemia. The main intention behind this study is to develop an automated, robust and efficient classification and detection system of leukocytes for microscopic blood images.

Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data

Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host’s immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost–based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.

Raman and quantitative-phase microscope with counter-propagating beams demonstrated on HeLa cells

Raman spectroscopy probes the chemical composition of biological samples with sub-micron resolution, rendering it a powerful tool in the diagnosis of several diseases and the study unstained biological samples. However, the weak Raman signal leads to long acquisition times, unsuited to study dynamical processes or large samples. Quantitative phase microscopy can speed up the diagnosis, provide complementary data, and potentially link the Raman fingerprint of the sample with corresponding refractive indices. Here we demonstrate a 4$\pi$π microscope that records both the Raman and quantitative phase information from the same sample spot.

Leukocyte subgroup distribution and morphology in blood of premature and full-term newborn babies studied by the cell microarray

Both the ratio of different leukocyte subgroup content and the leukocyte morphology in peripheral blood of newborns are important in diagnosis of several diseases including combined immunodeficiency and neonatal septicemia. There is a need for development of screening methods for parallel study of the leukocyte morphology and population structure in the newborn peripheral blood. We aimed to determine the relative abundance of different leukocyte subsets and to study their morphology in full-term and premature newborn babies and healthy adult volunteers using the cell-binding microarray – a transparent support with immobilized antibodies against leukocyte cluster-of-differentiation antigens. The work was supported by the Scientific council and approved by the ethical committee of the Centre. We have studied the peripheral blood of 12 full-term newborns (38–40 weeks gestation), 9 premature newborns (22–32 weeks gestation) and 18 healthy adults. The relative abundance of the leukocyte and their morphology were determined using the cell-binding microarray including antibodies against CD2, СD3, СD4, CD5, СD7, CD8, CD10, СD11b, CD11c, CD13, CD14, CD15, CD16, CD19, CD20, CD22, CD25, CD33, CD38, CD41a, CD45, CD45RA, CD45RO, CD61, CD64, CD117, CD123, HLA-DR. The percentage of leukocytes positive for every of the studied surface CD antigens among the peripheral blood mononuclear cells of full-term and preterm newborn babies and healthy adults determined on the cell–binding microarray are in good agreement with published flow cytometry data. CD11b+ leukocytes both in premature and full-term newborns included up to 21% myelocytes and 27% metamyelocytes. The reported data can be used as reference values in cell-binding microarray application in diagnosis of combined immunodeficiency or neonatal septicemia.

Optical flow based waveform for the assessment of the vocal fold vibrations.

Assessing vocal fold (VF) vibrations is important for the diagnosis of several diseases, and is made possible through the analysis of videoendoscopy recordings. However, the visual analysis of these recordings is hard due to the high acquisition rate. For this reason, it is commonly used to extract the laryngeal activity information from the recordings and represent it in a way suitable to be visually analyzed. Waveforms, images and playbacks are examples of representations reported in the literature. The main limitation of some of them is the lack of precisely locating the pathology within the VFs. Whereas others require the segmentation of the glottis in all the images of the video which is a complex and hard task given the high amount of images in the video and the necessity for the user intervention. To overcome these problems, the present study proposes a new waveform that maps the local vibrations of the VFs without the need for segmenting all the images of the video. Instead, the segmentation is restricted to only one image per vibratory cycle. Then, a new optical flow based technique is proposed to deduce the cycle-to-cycle dynamics of the VFs. The ability of the proposed approach to provide a reliable visual assessment is experimentally evaluated using different types of phonation and different vocal pathologies.

Ten Years of 2D Longitudinal Strain for Early Myocardial Dysfunction Detection: A Clinical Overview.

In recent years, the role of left ventricular ejection fraction (EF) as the gold standard parameter for the evaluation of systolic function has been questioned, and many efforts have been concentrated in the clinical validation of new noninvasive tools for the study of myocardial contractility. Improvement in the accuracy of speckle-tracking echocardiography has resulted in a large amount of research showing the ability of two-dimensional strain to overcome EF limitations in the majority of primary and secondary heart diseases. Currently, global longitudinal strain (GLS) is considered the most accurate and sensitive parameter for the assessment of early left ventricular dysfunction. This review summarizes the advantages that this measurement can provide in several clinical settings. Moreover, the important cautions that should be considered in making the choice to use GLS also are addressed. Finally, a special focus on bull's-eye polar maps for the assessment of regional changes of longitudinal function and the usefulness of these maps in the differential diagnosis of several diseases is provided.

The diagnostic accuracy of contrast-enhanced ultrasound for the differentiation of benign and malignant thyroid nodules: A PRISMA compliant meta-analysis.

Background:Contrast-enhanced ultrasound (CEUS) is a non-invasive method that has been used in the diagnosis of several diseases. Recently, CEUS has been used in the differentiation of benign and malignant thyroid nodules. However, the performance of CEUS in thyroid nodules has not been studied clearly.Methods:The databases of Pubmed, Embase, Cochrane library and the unpublished studies were systematically searched for candidate inclusions, with the use of CEUS in differentiating the benign and malignant thyroid nodules. The quality of included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) questionnaire. The pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratio (NLR) were calculated using STATA software version 14.0.Results:Totally 33 diagnostic studies were included for further analysis. The quality of included studies was relatively high using QUADAS method. The pooled estimates of sensitivity and specificity were 0.88 (95% CI 0.85, 0.91) and 0.88 (95% CI 0.83, 0.91), respectively. In addition, the DOR, the positive and NLRs were pooled positive LR and the negative LR were 54 (95% CI 33, 89), 7.1% (5.2%, 9.8%), and 0.13% (0.10%, 0.18%). No significant publication bias was observed.Conclusions:Our meta-analysis further indicated that CEUS is a useful tool in differentiating benign and malignant thyroid nodules, with high sensitivity and specificity.

Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature.

Positron emission tomography (PET) provides images of metabolic activity in the body, and it is used in the research, monitoring, and diagnosis of several diseases. However, the raw data produced by the scanner are severely corrupted by noise, causing a degraded quality in the reconstructed images. In this paper, we proposed a reconstruction algorithm to improve the image reconstruction process, addressing the problem from a variational geometric perspective. We proposed using the weighted Gaussian curvature (WGC) as a regularization term to better deal with noise and preserve the original geometry of the image, such as the lesion structure. In other contexts, the WGC term has been found to have excellent capabilities for preserving borders and structures of low gradient magnitude, such as ramp-like structures; at the same time, it effectively removes noise in the image. We presented several experiments aimed at evaluating contrast and lesion detectability in the reconstructed images. The results for simulated images and real data showed that our proposed algorithm effectively preserves lesions and removes noise.

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

Noninvasive and label-free vibrational spectroscopy and microscopy methods have shown great potential for clinical diagnosis applications. Raman spectroscopy is based on inelastic light scattering due to rotational and vibrational modes of molecular bonds. It has been shown that Raman spectra provide chemical signatures of changes in biological tissues in different diseases, and this technique can be employed in label-free monitoring and clinical diagnosis of several diseases, including cardiovascular studies. However, there are very few literature reviews available to summarize the state of art and future applications of Raman spectroscopy in cardiovascular diseases, particularly cardiac hypertrophy. In addition to conventional clinical approaches such as electrocardiography (ECG), echocardiogram (cardiac ultrasound), positron emission tomography (PET), cardiac computed tomography (CT), and single photon emission computed tomography (SPECT), applications of vibrational spectroscopy and microscopy will provide invaluable information useful for the prevention, diagnosis, and treatment of cardiovascular diseases. Various in vivo and ex vivo investigations can potentially be performed using Raman imaging to study and distinguish pathological and physiological cardiac hypertrophies and understand the mechanisms of other cardiac diseases. Here, we have reviewed the recent literature on Raman spectroscopy to study cardiovascular diseases covering investigations on the molecular, cellular, tissue, and organ level.