Applications In Cardiology Research Articles

Bioactive Substances in Safflower Flowers and Their Applicability in Medicine and Health-Promoting Foods.

Safflower flowers (Carthamus tinctorius) contain many natural substances with a wide range of economic uses. The most famous dye isolated from flower petals is hydroxysafflor A (HSYA), which has antibacterial, anti-inflammatory, and antioxidant properties. This review is aimed at updating the state of knowledge about their applicability in oncology, pulmonology, cardiology, gynecology, dermatology, gastrology, immunology, and suitability in the treatment of obesity and diabetes and its consequences with information published mainly in 2018-2020. They were also effective in treating obesity and diabetes and its consequences. The issues related to the possibilities of using HSYA in the production of health-promoting food were also analyzed.

Constraints in Clinical Cardiology and Personalized Medicine: Interrelated Concepts in Clinical Cardiology

Systems biology is established as an integrative computational analysis methodology with practical and theoretical applications in clinical cardiology. The integration of genetic and molecular components of a disease produces interacting networks, modules and phenotypes with clinical applications in complex cardiovascular entities. With the holistic principle of systems biology, some of the features of complexity and natural progression of cardiac diseases are approached and explained. Two important interrelated holistic concepts of systems biology are described; the emerging field of personalized medicine and the constraint-based thinking with downward causation. Constraints in cardiovascular diseases embrace three scientific fields related to clinical cardiology: biological and medical constraints; constraints due to limitations of current technology; and constraints of general resources for better medical coverage. Systems healthcare and personalized medicine are connected to the related scientific fields of: ethics and legal status; data integration; taxonomic revisions; policy decisions; and organization of human genomic data.

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review.

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

Virtual Reality (VR) Applications in Cardiology: A Review

Virtual reality (VR) has applications in cardiology to create enhancement, thereby improving the quality of associated planning, treatment and surgery. The need is to study different applications of this technology in the field of cardiology. We have studied research papers on VR and its applications in cardiology through a detailed bibliometric analysis. The study identified five significant steps for proper implementation of this technology in cardiology. Some challenges are to be undertaken by using this technology, and they can provide some benefits; thus, authors contemplate extensive research and development. This study also identifies 10 major VR technology applications in cardiology and provided a brief description. This innovative technology helps a heart surgeon to perform complex heart surgery effectively. Thus, VR applications have the potential for improving decision-making, which helps save human life. VR plays a significant role in the development of a surgical procedure. This technology undertakes 3D heart model information in full colour, which helps to analyze the overall heart vane, blockage and blood flow. With the help of this digital technology, a surgeon can improve the accuracy of heart surgery, and he can simulate the surgery. A surgeon can undertake surgery in a virtual environment on a virtual patient. The unique purpose of this technology is to practice pre-operatively on the specific circumstance. A cardiologist can also check the proper status of inner and outer heart wall layer. Thus, by using this 3D information, the surgeon can now interact with heart data/information without any physical touch. This technology opens a new opportunity to improve the heart surgery and development in cardiovascular treatment to improve patient outcome.

Fast Acoustic Steering Via Tilting Electromechanical Reflectors (FASTER): A Novel Method for High Volume Rate 3-D Ultrasound Imaging.

The 3-D ultrasound imaging is essential for a wide range of clinical applications in diagnostic and interventional cardiology, radiology, and obstetrics for prenatal imaging. 3-D ultrasound imaging is also pivotal for advancing technical developments of emerging imaging technologies, such as elastography, blood flow imaging, functional ultrasound (fUS), and super-resolution microvessel imaging. At present, however, existing 3-D ultrasound imaging methods suffer from low imaging volume rate, suboptimal imaging quality, and high costs associated with 2-D ultrasound transducers. Here, we report a novel 3-D ultrasound imaging technique, fast acoustic steering via tilting electromechanical reflectors (FASTER), which provides both high imaging quality and fast imaging speed while at low cost. Capitalizing upon unique water immersible and fast-tilting microfabricated mirror to scan ultrafast plane waves in the elevational direction, FASTER is capable of high volume rate, large field-of-view (FOV) 3-D imaging with conventional 1-D transducers. In this article, we introduce the fundamental concepts of FASTER and present a series of calibration and validation studies for FASTER 3-D imaging. In a wire phantom and tissue-mimicking phantom study, we demonstrated that FASTER was capable of providing spatially accurate 3-D images with a 500-Hz imaging volume rate and an imaging FOV with a range of 48° (20 mm at 25-mm depth) in the elevational direction. We also showed that FASTER had comparable imaging quality with conventional mechanical translation-based 3-D imaging. The principles and results presented in this study establish the technical foundation for the new paradigm of high volume rate 3-D ultrasound imaging based on ultrafast plane waves and fast-tilting, water-immersible microfabricated mirrors.

Artificial intelligence, fetal echocardiography, and congenital heart disease

There has been a recent explosion in the use of artificial intelligence (AI), which is now part of our everyday lives. Uptake in medicine has been more limited, although in several fields there have been encouraging results showing excellent performance when AI is used to assist in a well‐defined medical task. Most of this work has been performed using retrospective data, and there have been few clinical trials published using prospective data. This review focuses on the potential uses of AI in the field of fetal cardiology. Ultrasound of the fetal heart is highly specific and sensitive in experienced hands, but despite this there is significant room for improvement in the rates of prenatal diagnosis of congenital heart disease in most countries. AI may be one way of improving this. Other potential applications in fetal cardiology include the provision of more accurate prognoses for individuals, and automatic quantification of various metrics including cardiac function. However, there are also ethical and governance concerns. These will need to be overcome before AI can be widely accepted in mainstream use. It is likely that a familiarity of the uses, and pitfalls, of AI will soon be mandatory for many healthcare professionals working in fetal cardiology.

Carbon-11: Radiochemistry and Target-Based PET Molecular Imaging Applications in Oncology, Cardiology, and Neurology.

The positron emission tomography (PET) molecular imaging technique has gained its universal value as a remarkable tool for medical diagnosis and biomedical research. Carbon-11 is one of the promising radiotracers that can report target-specific information related to its pharmacology and physiology to understand the disease status. Currently, many of the available carbon-11 (t1/2 = 20.4 min) PET radiotracers are heterocyclic derivatives that have been synthesized using carbon-11 inserted different functional groups obtained from primary and secondary carbon-11 precursors. A spectrum of carbon-11 PET radiotracers has been developed against many of the upregulated and emerging targets for the diagnosis, prognosis, prediction, and therapy in the fields of oncology, cardiology, and neurology. This review focuses on the carbon-11 radiochemistry and various target-specific PET molecular imaging agents used in tumor, heart, brain, and neuroinflammatory disease imaging along with its associated pathology.

Performance testing of dosimeters used in interventional radiology: Results from the VERIDIC project

Interventional procedures in radiology and cardiology are associated with high dose to the patient. Accurate dosimetry is essential and calibration of the equipment is a means to provide the necessary accuracy of dose assessment.The objective of this work is to investigate the performance of dosimeters used in interventional procedures in different standard and non-standard X-ray radiation qualities, and to investigate potential uncertainties related to dose measurements, thus improving accuracy of patient dosimetry in interventional procedures.Four new reference radiation qualities dedicated to interventional cardiology applications have been established, allowing calibration of dosimeters used in clinical conditions with appropriate traceability to primary standards. Testing of solid-state semiconductor detectors and thermoluminescent dosimeter properties, e.g. influence of photon energy, angle of incidence and dose rate, was performed in the standard and non-standard radiation qualities.Both dosimeter types showed good performance in the non-standard beams during all performance tests. Solid-state dosimeters displayed weak dependence on energy, angle of incidence and dose rate, in the range defined by the manufacturer and requirements of the international standard. Thermoluminescent dosimeters displayed excellent linearity and angular dependence. The influence of energy dependence on measurement uncertainty can be reduced if appropriate radiation quality is selected for calibration.

Artificial intelligence in cardiology : Relevance, current applications, and future developments

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as abasis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.

Development and validation of an automated tool to scan scientific literature for the use of specific technologies in the field of cardiology

Abstract Background Review of scientific literature is a time consuming but fundamental step in any kind of scientific research. A consistent manual filtering of papers is always necessary in order to evaluate their relevance with respect to the topic of interest, as the sorting provided by most common research engines is rarely efficient in terms of matching with the desired contents. Purpose The aim of this study was to develop, and validate versus manual analysis, an automated tool for performing an efficient search through medical scientific literature, according to keywords relevant to the application of specific technologies in the field of cardiology. Methods Using this multiplatform tool implemented in Python, PyQt5 library, the user is required to insert a list of keywords, from which all the possible search strings were built by connecting them with logical operators. The algorithm automatically queries the on-line database PubMed (NCBI) and downloads all the resulting abstracts, with titles and keywords. Results related to the field of cardiology are identified counting the occurrences of “marker” words collected in a dedicated dictionary, developed on the base of the Unified Medical Language System (U.S. NLM). Then, a search-specific dictionary is automatically developed according to the statistical distribution of words in the texts of abstracts, titles and keywords and weighting them according to their relative frequency (ratio between occurrences and number of considered papers). Finally, for each paper the occurrences of these “marker” words are counted and a matching-probability score is assigned, providing a sorting of the results according to expected matching with the topic of interest, together with a threshold-based binary classification. In order to validate the algorithm, three different technologies with potential applications in cardiology were considered: smartphone applications (App), machine learning (ML) and virtual reality (VR). The related dictionaries were developed with the dedicated function embedded in the tool, while, for the validation of the results, a dataset of 461 manually-classified abstracts was considered, and algorithm thresholds were iteratively adjusted on the base of validation results. Results The algorithm applied to the validation dataset showed an overall accuracy (acc) of 88.5% (sensitivity (se) 85.78%, specificity (sp) 91.27%) in the identification of cardiology papers, while the results for the three inspected technologies were: App: acc 90.89% (se 92.16%, sp 90.53%) ML: acc 82.65% (se 94.06%, sp 79.44%) VR: acc 91.54% (se 96%, sp 90.3%) The algorithm can process 5000 abstracts in around 2 hours. Conclusions Results of the validation revealed that the proposed approach is highly valuable in speeding-up any search of medical literature focused on a specific technology or application, enabling a quick overview regarding its diffusion and maturity in a specific scientific domain. Algorithm schema Funding Acknowledgement Type of funding source: None

PET Parametric Imaging: Past, Present, and Future.

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.

Antibacterial Activity of Silver and Its Application in Dentistry, Cardiology and Dermatology.

The problem of antimicrobial resistance is increasingly present and requires the discovery of new antimicrobial agents. Although the healing features of silver have been recognized since ancient times, silver has not been used due to newly discovered antibiotics. Thanks to technology development, a significant step forward has been made in silver nanoparticles research. Nowadays, silver nanoparticles are a frequent target of researchers to find new and better drugs. Namely, there is a need for silver nanoparticles as alternative antibacterial nanobiotics. Silver nanoparticles (AgNPs), depending on their size and shape, also have different antimicrobial activity. In addition to their apparent antibacterial activity, AgNPs can serve as drug delivery systems and have anti-thrombogenic, anti-platelet, and anti-hypertensive properties. Today they are increasingly used in clinical medicine and dental medicine. This paper presents silver antimicrobial activity and its use in dentistry, cardiology, and dermatology, where it has an extensive range of effects.

A primer on artificial intelligence for the paediatric cardiologist.

The combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient's profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient's genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

Artificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging.

In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applications of Artificial Intelligence is also huge in clinical medicine and cardiovascular diseases. To describe for the first time in literature, the main results of articles about Artificial Intelligence potential for clinical applications in molecular imaging techniques, and to describe its advancements in cardiovascular diseases assessed with nuclear medicine imaging modalities. A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the most relevant articles that evaluated the technological insights of AI in nuclear cardiology applications. Artificial Intelligence may improve patient care in many different fields, from the semi-automatization of the medical work, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to the prognostic prediction and risk-- group selection. Myocardial implementation of Artificial Intelligence algorithms in nuclear cardiology can improve and facilitate the diagnostic and predictive process, and global patient care. Building large databases containing clinical and image data is a first but essential step to create and train automated diagnostic/prognostic models able to help the clinicians to make unbiased and faster decisions for precision healthcare.

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

Design of adaptive fuzzy gain scheduling fast terminal sliding mode to control the radius of bubble in the blood vessel with application in cardiology

The nonlinear behavior of bubble in the blood vessel restricts the applications of bubble technology in the cardiology. In this paper, a novel engineering solution is proposed to control the radius of the bubble and prevent bubble collapse. For this purpose, the nonlinear dynamics of the bubble in the blood vessel is introduced and then presented into the state-space form. The next part of this paper is devoted to designing a nonlinear control method, where the ultrasonic wave plays the role of control input and the output is the radius of the bubble in the blood vessel. In order to achieve faster, finite-time convergence and higher control precision, fast terminal sliding mode is proposed. The stability of the closed-loop system is ensured through the Lyapunov stability analysis. In order to tackle the chattering problem, the switching gain is adaptively tuned according to the fuzzy rule. In this method, the tracking error and its differential are considered as the inputs and the switching gain is considered as the output of the fuzzy logic system. Simulation results confirm the effectiveness of the proposed control method. Finally, the results of this research are of immediate interest for modern medical applications such as ultrasonic imaging, targeted drug delivery, and cancer treatment.

Study of mechanical behavior of additive manufacturing bioresorbable polymeric stents models

Stents are devices with important applications in cardiology. They can be used in different cases, as coronary artery disease, peripheral interventions, such as the iliac and carotid arteries, and in very specific applications, as congenital heart disease in aortic coarctation. Studies point to biodegradability of stents as one of the main properties of future generations of these devices. The development of stents made from fully bioresorbable polymeric materials, with appropriate mechanical properties for different applications, is considered an interesting point to be studied. Thus, the objective of this project was to study models of bioabsorbable stents of poly (-L-lactic acid) (PLLA), poly (-LD lactic acid) (PLDLA) produced by additive manufacturing. The mechanical performances were evaluated by stress and tensile strain tests of different blends compositions of these materials. A heatshock shape memory allocation process and a flexible tube model for simulating implant conditions were developed. PLLA has higher mechanical strength and hardness, while PLDLA is more elastic. Thus, it was possible to study the adequacy of the composition and mechanical properties of the prototypes for each different situation studied (coronary, peripheral and Aortic coarctation implants).

Experimental Validation of the Field and Beam Dynamics Simulations for a Superconducting Cyclotron

The Ion-12SC is a sub-compact, 12.5 MeV proton super-conducting isochronous cyclotron for commercial medical isotope production recently developed at Ionetix Corporation. The machine features a patented cold steel and cryogen-free conduction cooling magnet, a low power internal cold-cathode PIG ion source, and an internal liquid target. It is designed to produce N-13 ammonia for dose on-demand cardiology applications but can also be used to produce F-18, Ga-68 and other medical isotopes widely used in Positron Emission Tomography (PET). The cyclotron is being installed and commissioned at several medical centers. During the initial machine operating periods, some issues appeared. In particular, imperfections in manufacturing and assembly of the magnet introduced unwanted low order magnetic harmonics that resulted in beam losses. This report presents results of analysis of the measured magnetic field and beam dynamics in the cyclotron that were initiated to solve the problem. The calculations led to a process for implementing compensating magnetic shims to improve the beam transmission.

Conceptualization of Heterogeneity of Chronic Diseases and Atherosclerosis as a Pathway to Precision Medicine: Endophenotype, Endotype, and Residual Cardiovascular Risk.

The article discusses modern approaches to the conceptualization of pathogenetic heterogeneity in various branches of medical science. The concepts of endophenotype, endotype, and residual cardiovascular risk and the scope of their application in internal medicine and cardiology are considered. Based on the latest results of studies of the genetic architecture of atherosclerosis, five endotypes of atherosclerosis have been proposed. Each of the presented endotypes represents one or another pathophysiological mechanism of atherogenesis, having an established genetic substrate, a characteristic panel of biomarkers, and a number of clinical features. Clinical implications and perspectives for the study of endotypes of atherosclerosis are briefly reviewed.