Heart-related Diseases Research Articles

“I think we should wait and see”: A qualitative study of call-takers’ decision-making in consultations with patients suffering unrecognized myocardial infarction

ObjectivesCall-takers face a complex situation when assessing medical problems in emergency medical services calls. Patients with myocardial infarction experiencing atypical symptoms risk misinterpretation. We examined development in call-takers' decision-making process in telephone consultations with patients having imminent myocardial infarction. MethodsRecording of 38 calls among 19 patients (two per patient) who contacted Copenhagen Emergency Medical Services (Denmark) at least twice within one week before myocardial infarction diagnosis. The penultimate and last call were compared using qualitative content analysis. ResultsCall-takers’ assessment of the condition changed from symptom picture and dismissal of heart disease in penultimate call to severe condition, not heart-related, and possible heart disease in last call. Call-takers recommended watchful waiting in the penultimate call. Both calls involved response negotiation, while caution regarding misinterpretation was only seen in the penultimate call. ConclusionCall-takers used different decision-making approaches when the caller’s symptom descriptions appeared unclear and not corresponding with the medical understanding of severe conditions. Call-takers did not negotiate the condition's assessment but engaged in discussions about the response choice. Practice implicationsA protocol to negotiate response choice with callers having unclear clinical conditions should be developed. Clarifying watchful waiting as a recommendation may assist call-takers’ decision-making.

Methylome of circulating cell free DNA as a novel biomarker tool: from acute coronary syndrome to broader risk stratification of cardiovascular disease

Abstract Background Cell death during tissue injury leads to release of chromosomal DNA which is promptly degraded. However, short DNA fragments wrapped around nucleosomes survive longer in the bloodstream and can be isolated as circulating cell-free DNA (ccfDNA). Oncology field pioneered the use of cfDNA as clinical biomarker by identifying sequence modifications induced in cancer cells. Importantly, ccfDNA also retains the same epigenetic information as the tissue of origin. Despite major advances for disease diagnosis and therapy, coronary artery diseases (CAD) and associated conditions such acute myocardial infarction (AMI) or heart failure (HF) are responsible for leading number of deaths worldwide. Preventive and surveillance strategies with non-invasive testing are needed. Purpose Evaluation of ccfDNA methylation profiles as prognostic non-invasive biomarker for risk stratification of acute coronary syndromes and their potential application in preventive and surveillance strategies in other cardiovascular disorders. Methods CcfDNA isolated from citrate-plasma collected from our discovery cohort, comprising healthy controls, STEMI, NSTEMI and unstable angina (UA) patients, was analyzed applying Whole Genome Bisulfite Sequencing (WGBS) using a low-input BS-seq (PBAT) protocol. Computational analysis generated differentially methylated regions (DMRs) by tiling CpG areas and setting a threshold of minimal 25% methylation difference compared to the healthy group. To assess the validity of our findings, a selection of disease-specific DMRs were tested using targeted methylation sequencing protocol in a new set of patients. This procedure consisted in enzymatic conversion of ccfDNA methylated sites followed by probe-specific enrichment libraries and sequencing at a Novaseq 6000 platform. Results The study identified a total of 1941 DMRs and from those 486 were STEMI, 223 NSTEMI and 684 UA specific. From the identified DMRs, 74.13% from STEMI, 72.68% from NSTEMI and 61.62% from UA annotated with heart-related diseases using DisGeNET gene-disease associations. Interestingly, most of identified DMRs located within intronic regions (~60%) and also annotated as enhancers and lncRNAs. The independent validation with targeted methylation sequencing identified 566, 306 and 248 DMRs, respectively, and reduced the number of disease-specific DMRs to 171 in STEMI, 115 in NSTEMI and 82 in UA. Conclusion and Future Approaches Methylation profiles of ccfDNA showed potential as a biomarker to stratify the severity of acute coronary syndromes, a novel non-invasive detection methodology that could be extrapolated to risk stratification in other cardiovascular disease such as heart failure or coronary artery disease.

Design of a Heart Rate Monitoring Application Based on Internet of Things (IoT) Technology with WhatsApp Blast

The highest death rate based on data from WHO (World Health Organization) is due to heart-related diseases. In 2015, more than 17 million people in the world died from heart disease and blood vessel disorders. About 75% of deaths from heart disease occur in developing countries with low to moderate income. Based on SRS (sample registration system) data in 2014, heart disease was the highest cause in Indonesia, namely 12.9%. With IoT (Internet of Things) technology, it can make it easier to provide information to survivors, namely carrying out early heart detection if there are several anomalies that occur in the human body, especially heartbeats that do not comply with the provisions. Utilizing the Max30102 sensor in collaboration with several other IoT devices can be integrated with a web-based system which is used for monitoring based on incoming data from IoT devices. This system is capable of recording heart data in humans in real time by providing notifications to heart survivors via WhatsApp blasting technology.

Abstract We146: CHD8 is Essential for Postnatal Heart Function through Preserving Mitochondrial Integrity

Objective: Epigenetic regulation plays a crucial role in heart development and disease, although its mechanisms remain elusive. Known mutations in epigenetic regulators have been linked to cardiac disease. For instance, the mutation in the chromodomain helicase DNA-binding (CHD) protein 7 gene, which leads to CHARGE syndrome, is known to results in heart defects. However, the role of CHD8, an interacting partner of CHD7, in the heart has not been investigated. This study aims to explore the function of CHD8 in the heart. Methods and Results: We generated a cardiac-specific Chd8 conditional knockout mouse line (Chd8 cKO) by breeding Chd8 fl/fl mice with aMHC Cre mice. Over 60% of Chd8 cKO mice died between 7 and 9 months of age, with increased heart weight to bodyweight ratio and reduced cardiac function, increased fibrosis, and signs of dilated cardiomyopathy. Electron microscopy analysis revealed disordered sarcomeres and escalated mitophagy in CHD8-deficient hearts. Using Genome-wide transcriptome analysis though RNA sequencing, we discovered that genes involved in mitochondrial oxidative phosphorylation and muscle contraction were down-regulated in Chd8 cKO hearts. Proteins in the electron transport chain complexes were down regulated and mitophagy marker LC3 increased in the Chd8 cKO hearts compared with the control mice. Upregulated pathways include inflammatory response suggesting that the innate immune response may contribute to dilated cardiomyopathy. Conclusions: Our findings underscore CHD8 as a key regulator of cardiac function and regeneration by influencing chromatin accessibility of genes involved in mitochondrial oxidative phosphorylation and contractility. Overall, our study provides new insights into the epigenetic regulation of cardiac function and the CHD8’s role in heart development and disease. It implies that activating CHD8 may be a promising therapeutic strategy for treating dilated cardiomyopathy and other heart-related diseases.

THE PREDICTION OF HEART DISEASE USING MACHINE LEARNING ALGORITHMS

Heart disease threatens the lives of around one individual per minute, establishing it as the foremost cause of mortality in the contemporary era. A wide range of individuals over the globe has encountered the intricacies associated with cardiovascular illness. Various factors, such as hypertension, elevated levels of cholesterol, and an irregular pulse rhythm hinder the early identification of a cardiovascular disease. In cardiology, similar to other branches of Medicine, timely and precise identification of cardiac diseases is of utmost importance. Anticipating the onset of heart failure at the appropriate moment can provide challenges, particularly for cardiologists and surgeons. Fortunately, categorisation and forecasting models can assist the medical business and provide real applications for medical data. Regarding this, Machine Learning (ML) algorithms and techniques have benefited from the automated analysis of several medical datasets and complex data to aid the medical community in diagnosing heart-related diseases. Predicting if the patient has early-stage cardiac disease is the primary goal of this paper. A prior study that worked on the Erbil Heart Disease dataset has proved that Naïve Bayes (NB) got an accuracy of 65%, which is the worst classifier, while Decision Tree (DT) obtained the highest accuracy of 98%. In this article, a comparison study has been applied using the same dataset (i.e., Erbil Heart Disease dataset) between multiple ML algorithms, for instance, LR (Logistic Regression), KNN (K-Nearest Neighbours), SVM (Support Vector Machine), DT (Decision Tree), MLP (Multi-Layer Perceptron), NB (Naïve Bayes) and RF (Random Forest). Surprisingly, we obtained an accuracy of 98% after applying LR, MLP, and RF, which was the best outcome. Furthermore, the accuracy obtained by the NB classifier differed incredibly from the one received in the prior work.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L0 +L1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Application value of real-time 3D speckle tracking imaging in left atrial function evaluation of patients with paroxysmal atrial fibrillation.

To evaluate left atrial volume and function in patients with paroxysmal atrial fibrillation (AF) combined with left atrial appendage thrombosis and patients with paroxysmal AF without left atrial appendage thrombosis by 3-dimensional speckle tracking imaging (3D-STI), and to explore the application value of this set of parameters in the evaluation of left atrial function in patients with paroxysmal AF. A total of 40 patients with paroxysmal AF admitted from December 2018 to December 2020 were selected as the observation group. All patients with paroxysmal AF in the observation group underwent transesophageal echocardiography. According to the presence of left atrial appendage thrombosis, the patients were divided into the AF without thrombosis group (24 cases) and the AF with thrombosis group (16 cases). Thirty normal people were selected as control group who were chosen as having no heart-related disease. The left atrial volume parameters (Left atrial maximum volume LAVmax, Left atrial minimum volume LAVmin, Left atrial volume before atrial contraction LAVpre-A, Left atrial stroke volume LAEV), left atrial ejection fraction (LAEF) and left atrial strain parameters (Left atrial reservoir longitudinal strain LASr, Left atrial conduit longitudinal strain LAScd, Left atrial contraction longitudinal strain LASct, Left atrial reservoir circumferential strain LASr-c, Left atrial conduit circumferential strain LAScd-c, Left atrial contraction circumferential strain LASct-c) of the 3 groups were measured by 3D-STI. With the progression of paroxysmal AF, the left atrial volume increased, and the reservoir, conduit and contractile function were damaged. The left atrial volume continued to increase, and the reservoir, conduit and contractile function further decreased significantly in patients with AF combined with left atrial appendage thrombosis. LAEF was positively correlated with LASr and LASr_c. Real-time 3-dimensional spot tracking imaging (3D-STI) can evaluate the changes in left atrial volume and function in patients with paroxysmal AF, and has a certain reference value for clinical judgment of disease progression and prognosis.

Efficient IoT-based heart disease prediction framework with Weight Updated Trans-Bidirectional Long Short Term Memory-Gated Recurrent Unit

The integrated system has generated numerous features for the users, like as identifying heart disease by its symptoms, forwarding the information to the doctors regarding the phase of the probability of disease as well as aiding to fix it. When an emergency situation exists, the system forwards the emergency alert to the respective doctor. Moreover, the automatic system is needed to diagnose heart disease but, the larger data is not sufficient to train the model. Thus, the Internet of Things (IoT) is employed to manage the huge amount of data. Therefore, a novel prediction of heart diseases is implemented with the aid of IoT-based deep learning approaches. Here, the collected data is collected from the three standard databases and then perform preprocessed over the gathered data. Here, the IoT assisted deep learning model is performed to predict heart related diseases accurately. Further, the acquired features of heart diseases are selected using the developed Hybrid Chameleon Electric Fish Swarm Optimization (HCEFSO) via Chameleon Swarm Algorithm (CSA) and Electric Fish Optimization (EFO). Then, the optimally selected features are fed to the training process, where the Trans-Bi-directional Long Short-Term Memory with Gated Recurrent Unit (Trans-Bi-LSTM-GRU) is adopted for predicting heart diseases. Here, the weights are updated with the developed HCEFSO while validating the training phase. The trained Trans-Bi-LSTM-GRU network is used in the testing phase for predicting heart diseases.

Lifestyle interventions improving health-related quality of life: A systematic review and meta-analysis of randomized control trials.

Lifestyle interventions have garnered significant research interest for their potential to enhance health-related quality of life (HRQoL). Understanding the impact of these interventions on various dimensions of HRQoL is crucial for effective healthcare strategies. This study aims to systematically review and meta-analyze the effects of lifestyle interventions on HRQoL in randomized control trials. A systematic search was conducted across five scientific databases, including PubMed, Web of Science, Scopus, the Cochrane Library, and gray literature, with a filter applied to include only English language publications. Study selection was carried out by two independent reviewers in several steps, including duplicate removal and eligibility evaluation for meta-analysis. Information extracted from the studies included authors, countries, study designs, target populations, ages, genders, number of participants, interventions, outcomes, and results. A total of 61 randomized control trials were included in this meta-analysis. The meta-analysis revealed that lifestyle interventions significantly improved healthrelated quality of life compared to control groups, with Hedges' g of 0.38 (95% CI 0.25-0.50, Z = 5.94; P < 0.001; I2 = 84.59%). This positive effect was consistently observed in patients with heart-related diseases and metabolic disorders. Meta-regression analysis indicated that lifestyle interventions had the most substantial impact on health-related quality of life in the 1-month follow-up period. Considering the cost-effectiveness of lifestyle interventions compared to other intervention types, they can benefit various patient groups. This systematic review contributes to health policy goals by advocating focused preventive strategies in alignment with the observed benefits of lifestyle interventions.

Congestive Heart Failure (CHF) by Petrochemicals among Fuel Attendants

Introduction: Petroleum also known as crude oil is a naturally occurring yellowish-black liquid found in geological formations beneath the Earth's surface. It is commonly refined into various types of fuels. Congestive heart failure has been defined as global pandemic, since it affects around 26 million people worldwide. Fuel attendants engaged in these environments are continuously exposed to petrol and diesel fumes which results in hazards of the human system such as the heart, liver and kidney. The purpose of this study is to determine the congestive heart failure by petrochemicals among fuel attendants. Methods: The total sample size for this study was 200 samples. Of them, 100 were included as case group. They were fuel attendants who work at the various filling stations visited in the LGA. Their main direct sources of exposure were inhalation, accidental ingestion, and skin contact, among others. These samples were collected from people who are within the age range of 15-50 years. Result: This study shows that cardiac enzymes Troponin I has a mean value of 16.83 in fuel attendants compared to the mean value of 7.12 in non-fuel attendants in Oluyole Area, Ibadan. This is statistically significant (p &lt;0.01) with 133%. Troponin C has a mean value of 16.69 in fuel attendants compared to the mean value of 7.31 among non-fuel attendants in Oluyole Area, Ibadan. This is statistically significant (p &lt;0.01) with 114%. Troponin T has a mean value of 16.94 in fuel attendants compared to the mean value of 7.56 in non-fuel attendants in Oluyole Area, Ibadan. This is statistically significant (p &lt;0.01) with 112%. Conclusion: This study shows that exposure to petrochemicals can cause biochemical changes in the heart leading to the release of enzymes into the blood stream which serve as biomarker tools for the diagnosis of the risk of cardiovascular diseases such as congestive heart failure. Other petrochemical effect includes elevated blood pressure. Fuel attendants should be screened for heart related diagnosis before employed, this will reduce the risk of employing attendants with heart related disease leading to heart failure. Timely, regular medical checkup should be done while working as a fuel attendant and it is also achievable to create time in having physical exercise which reduces the risk of being above the BMI which consistently reduces the risk of heart related disease.

A case report on digoxin toxicity

Digoxin is an inotropic drug that is commonly prescribed in patient with heart related diseases. The effective dose of digoxin is 0.8-2.0 ng/ml beyond which digoxin causes toxic effects like visual dysfunction, irregular heartbeat, and cardiovascular collapse. Digoxin toxicity is caused due to increased automaticity and inotropy due to intracellular calcium and decreased dromotropy due to poisoning of sodium potassium transporter and AV nodal blockade. Digoxin toxicity should be assessed by continuous hemodynamic and cardiac monitoring including 12-lead electrocardiogram. There should be timely and immediate evaluation of electrolyte levels like potassium, calcium, serum creatinine and digoxin levels and prompt intensive care unit admission, if necessary. In this case study we studied a 43 old female patient prescribed with digoxin 0.25 mg, who had a medical history of congestive cardiac failure presented with symptoms of digoxin toxicity. The patient was assessed with electrocardiography (ECG) and managed by keeping digoxin on hold and effective patient counselling.

DETECTA – MULTIPLE DISEASE DETECTION

Machine Learning Techniques for Predictive Analytics in Healthcare There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavor, but it can eventually assist practitioners in making timely decisions regarding patients' health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India's doctor-to- population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, Heart diseases, Covid, Pneumonia, Alzheimer.

Left ventricular strain changes at high altitude in rats: a cardiac magnetic resonance tissue tracking imaging study

BackgroundLong-term exposure to a high altitude environment with low pressure and low oxygen could cause abnormalities in the structure and function of the heart. Myocardial strain is a sensitive indicator for assessing myocardial dysfunction, monitoring myocardial strain is of great significance for the early diagnosis and treatment of high altitude heart-related diseases. This study applies cardiac magnetic resonance tissue tracking technology (CMR-TT) to evaluate the changes in left ventricular myocardial function and structure in rats in high altitude environment.Methods6-week-old male rats were randomized into plateau hypoxia rats (plateau group, n = 21) as the experimental group and plain rats (plain group, n = 10) as the control group. plateau group rats were transported from Chengdu (altitude: 360 m), a city in a plateau located in southwestern China, to the Qinghai-Tibet Plateau (altitude: 3850 m), Yushu, China, and then fed for 12 weeks there, while plain group rats were fed in Chengdu(altitude: 360 m), China. Using 7.0 T cardiac magnetic resonance (CMR) to evaluate the left ventricular ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV) and stroke volume (SV), as well as myocardial strain parameters including the peak global longitudinal (GLS), radial (GRS), and circumferential strain (GCS). The rats were euthanized and a myocardial biopsy was obtained after the magnetic resonance imaging scan.ResultsThe plateau rats showed more lower left ventricular GLS and GRS (P < 0.05) than the plain rats. However, there was no statistically significant difference in left ventricular EDV, ESV, SV, EF and GCS compared to the plain rats (P > 0.05).ConclusionsAfter 12 weeks of exposure to high altitude low-pressure hypoxia environment, the left ventricular global strain was partially decreased and myocardium is damaged, while the whole heart ejection fraction was still preserved, the myocardial strain was more sensitive than the ejection fraction in monitoring cardiac function.

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Exploring early indications of heart disease is challenging in today's society. It may lead to death if not detected early. Early identification of cardiac disease in remote, rural, and semi-urban locations in developing nations can be greatly aided by an accurate decision support system (DSS). To assist in heart-related disease detection at an early stage, this system offers a DSS which uses artificial learning techniques using the patient's clinical details. A hybridized class balancing methodology using the chi2 feature selection algorithm, random oversampling, and undersampling techniques were used to find the suitable features from the presented dataset. Standard scalar approaches have also been utilized for data preprocessing. In the last stage of developing the suggested Artificial Intelligence (AI) system, the system used support vector machines (SVM), naive Bayes, random forest classifiers, logistic regression, and improved Artificial Neural Network (ANN) classifiers. Tests were conducted on the Python-based simulation environment to evaluate the proposed system. Evaluation of the system is completed using the Cleveland heart disease dataset by the machine repository at UCI. A 96.74% accuracy rate was reached, which is better than some previously published models for heart-related disease prediction.

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Exploring early indications of heart disease is challenging in today's society. It may lead to death if not detected early. Early identification of cardiac disease in remote, rural, and semi-urban locations in developing nations can be greatly aided by an accurate decision support system (DSS). To assist in heart-related disease detection at an early stage, this system offers a DSS which uses artificial learning techniques using the patient's clinical details. A hybridized class balancing methodology using the chi2 feature selection algorithm, random oversampling, and undersampling techniques were used to find the suitable features from the presented dataset. Standard scalar approaches have also been utilized for data preprocessing. In the last stage of developing the suggested Artificial Intelligence (AI) system, the system used support vector machines (SVM), naive Bayes, random forest classifiers, logistic regression, and improved Artificial Neural Network (ANN) classifiers. Tests were conducted on the Python-based simulation environment to evaluate the proposed system. Evaluation of the system is completed using the Cleveland heart disease dataset by the machine repository at UCI. A 96.74% accuracy rate was reached, which is better than some previously published models for heart-related disease prediction.

Heart Disease Prediction Using Machine Learning

Over the past few decades, heart-related diseases, or cardiovascular diseases (CVDs) have emerged as the leading cause of death not only in India but worldwide. They are the main cause of many diseases in the world. Machine learning algorithms and techniques have been applied to various clinical trials to automate the analysis of large and complex data. Recently, many scientists have been using AI techniques to support life and experts to detect heart-related diseases in the care industry. Compared to the brain, which is the largest organ in the human body, the heart is the next largest organ. Blood is pumped and sent to all the organs of the body. Cardiovascular disease prediction plays an important role in clinical practice. Informative studies will be useful in anticipating more data that can help predict various diseases and focus on treatment. There is a lot of data about the patients that are seen each month. The stored data can be a source for predicting future disease outbreaks. Some methods of data mining and artificial intelligence are used to predict heart diseases, such as artificial neural networks (ANN), animation, etc. To reduce the number of people who die from heart disease, we need to do it quickly and have a way to detect it. Data mining techniques and artificial intelligence calculations help experts make better cancer predictions and diagnoses. The main goal of this research project is to use AI statistics to predict coronary heart disease in patients.

CRIF1 attenuates doxorubicin-mediated mitochondrial dysfunction and myocardial senescence via regulating PXDN.

CR6-interacting factor 1 (CRIF1), a multifunctional protein that affects mitochondrial function and cell senescence, plays a regulatory role in heart-related diseases. However, whether CRIF1 participates in myocardial senescence by regulating mitochondrial function remains unclear. Doxorubicin (DOX)-induced C57BL/6 mice to construct mouse myocardial senescence model, and the myocardial function indicators including lactate dehydrogenase (LDH) and Creatine kinase isoform MB (CK-MB) were assessed. The expression of CRIF1 was detected by western blot. Myocardial pathological changes were examined by transthoracic echocardiography and haematoxylin and eosin (H&E) staining. Cell senescence was detected by SA-β-gal staining. JC-1 staining was used to detect mitochondrial membrane potential. Biochemical kits were used to examine oxidative stress-related factors. Additionally, AC16 cardiomyocytes were treated with DOX to mimic the cellular senescence model in vitro. Cell activity was detected by cell counting kit-8 (CCK-8) assay. Co-immunoprecipitation (CO-IP) was used to verify the relationship between CRIF1 and peroxidasin (PXDN). The CRIF1 expression was significantly decreased in DOX-induced senescent mice and AC16 cells. Overexpression of CRIF1 significantly ameliorated DOX-induced myocardial dysfunction and myocardial senescence. Additionally, CRIF1 overexpression attenuated DOX-induced oxidative stress and myocardial mitochondrial dysfunction. Consistently, CRIF1 overexpression also inhibited DOX-induced oxidative stress and senescence in AC16 cells. Moreover, CRIF1 was verified to bind to PXDN and inhibited PXDN expression. The inhibitory effects of CRIF1 overexpression on DOX-induced oxidative stress and senescence in AC16 cells were partly abolished by PXDN expression. CRIF1 plays a protective role against DOX-caused mitochondrial dysfunction and myocardial senescence partly through downregulating PXDN.

Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches.

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

Hardware Implementation of FIR Filter for ECG Signal Processing: Design, Optimization, and Performance Analysis on an FPGA

Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

Automatic classification of sleep stages using EEG signals and convolutional neural networks.

Sleep stages classification is one of the new topics in studying human life quality because it plays a crucial role in getting a healthy lifestyle. Abnormal changes or absence of normal sleep may lead to different diseases such as heart-related diseases, diabetes, and obesity. In general, sleep staging analysis can be performed using electroencephalography (EEG) signals. This study proposes a convolutional neural network (CNN) based methodology for sleep stage classification using EEG signals taken by six channels and transformed into time-frequency analysis images. The proposed methodology consists of three major steps: (i) segment the EEG signal into epochs with 30 seconds in length, (ii) convert epochs into 2D representation using time-frequency analysis, and (iii) feed the 2D time-frequency analysis to the 2D CNN. The results showed that the proposed methodology is robust and achieved a very high accuracy of 99.39% for channel C4-A1. All other channels have accuracy values above 98.5%, which indicates that any channel can be used for sleep stage classification with high accuracy. The proposed methodology outperformed the methods in the literature in terms of overall accuracy or single channel accuracy. It is expected to provide a great benefit for physicians, especially neurologists; by providing them with a new powerful tool to support the clinical diagnosis of sleep-related diseases.