Monitoring Of Cardiovascular Disease Research Articles

A Cross-talk between Nanomedicines and Cardiac Complications: Comprehensive View.

Cardiovascular Diseases (CVDs) are the leading cause of global morbidity and mortality, necessitating innovative approaches for both therapeutics and diagnostics. Nanoscience has emerged as a promising frontier in addressing the complexities of CVDs. This study aims to explorethe interaction of CVDs and Nanomedicine (NMs), focusing on applications in therapeutics and diagnostics. In the realm of therapeutics, nanosized drug delivery systems exhibit unique advantages, such as enhanced drug bioavailability, targeted delivery, and controlled release. NMs platform, including liposomes, nanoparticles, and carriers, allows the precise drug targeting to the affected cardiovascular tissues with minimum adverse effects and maximum therapeutic efficacy. Moreover, nanomaterial (NM) enables the integration of multifunctional components, such as therapeutic agents and target ligands, into a single system for comprehensive CVD management. Diagnostic fronts of NMs offer innovative solutions for early detection and monitoring of CVDs. Nanoparticles and nanosensors enable highly sensitive and specific detection of Cardiac biomarkers, providing valuable insights into a disease state, its progression, therapeutic outputs, etc. Further, nano-based technology via imaging modalities offers high high-resolution imaging, aiding in the vascularization of cardiovascular structures and abnormalities. Nanotechnology-based imaging modalities offer high-resolution imaging and aid in the visualization of cardiovascular structures and abnormalities. The cross-talk of CVDs and NMs holds tremendous potential for revolutionizing cardiovascular healthcare by providing targeted and efficient therapeutic interventions, as well as sensitive and early detection for the improvement of patient health if integrated with Artificial Intelligence (AI).

ECG segmentation algorithm based on bidirectional hidden semi-Markov model

Accurate segmentation of electrocardiogram (ECG) waves is crucial for cardiovascular diseases (CVDs). In this study, a bidirectional hidden semi-Markov model (BI-HSMM) based on the probability distributions of ECG waveform duration was proposed for ECG wave segmentation. Four feature-vectors of ECG signals were extracted as the observation sequence of the hidden Markov model (HMM), and the statistical probability distribution of each waveform duration was counted. Logistic regression (LR) was used to train model parameters. The starting and ending positions of the QRS wave were first detected, and thereafter, bidirectional prediction was employed for the other waves. Forwardly, ST segment, T wave, and TP segment were predicted. Backwardly, P wave and PQ segments were detected. The Viterbi algorithm was improved by integrating the recursive formula of the forward prediction and backward backtracking algorithms. In the QT database, the proposed method demonstrated excellent performance (Acc = 97.98%, F1 score of P wave = 98.37%, F1 score of QRS wave = 97.60%, F1 score of T wave = 97.79%). For the wearable dynamic electrocardiography (DCG) signals collected by the Shandong Provincial Hospital (SPH), the detection accuracy was 99.71% and the F1 of each waveform was above 99%. The experimental results and real DCG signal validation confirmed that the proposed new BI-HSMM method exhibits significant ability to segment the resting and DCG signals; this is conducive to the detection and monitoring of CVDs.

The impact of the strategic action plan to combat chronic non-communicable diseases on hospital admissions and deaths from cardiovascular diseases in Brazil.

Chronic Non-Communicable Diseases (NCDs) are the main causes of death worldwide, responsible for millions of hospital admissions per year, especially cardiovascular diseases (CVD). Several strategies for controlling and coping with these diseases have been developed in several countries. The aim of the study was to evaluate the impact of the Strategic Action Plan to Combat NCDs (2011-2022) on hospital admissions, deaths and mortality rate in Brazil, classified by CVD. This is a descriptive study, with secondary data from the Hospital Information System of the Unified Health System (SIH/SUS). Hospital admissions, deaths and mortality rate due to CVD in the Brazilian population aged over 20 years were analyzed, according to region, sex and age group. Statistical analysis was performed using the GraphPad Prism program. Data normality was assessed using the Komogorov Smirnov test and the comparison between groups and year periods was performed using the two-way ANOVA test with Tukey's post hoc test. A value of p<0.05 was considered significant. In this study, in most analyses, a reduction in the hospitalization rates of the adult population was observed after the implementation of the plan, however, there was no improvement in relation to the number of deaths and mortality rate from CVD. This shows that there is still a long way to go to reduce the impact of these diseases in Brazil, and they reaffirm the need for and importance of maintaining the prevention of their risk factors, the social determinants of health and the reorganization of care in the face of to population aging. Such findings contribute with information that allow better control and monitoring of CVD and should be considered when implementing new strategies for prevention, care and control of risk factors.

Predictors of cardiovascular diseases among people living with HIV initiated on antiretroviral therapy in Khomas region, Namibia: A cross-sectional study.

Cardiovascular diseases (CVDs) have been identified as the leading cause of morbidity and mortality among people living with human immunodeficiency virus (PLHIV). However, it is evident that there is a lack of effective surveillance and monitoring of CVDs. Salient side-effects of antiretroviral therapy (ART) exacerbate pre-existing co-morbidities, hence the need for CVDs and its predictors to be monitored closely to ensure life-long care. Personal health records play a crucial role in the field of health information extraction because of their factuality and reliability.The current study assessed the predictors associated with CVDs among PLHIV initiated on ART in the Khomas health district in NamibiaA cross-sectional quantitative descriptive study was conducted to extract CVDs predictors from 529 patient care booklets (PCBs) between 2004 and 2018 at purposely selected health facilities in Khomas health district. Data was matched with the electronic Patient Monitoring System(ePMS) and statistical analyses were performed. The study found that prominent CVDs predictors were found to be greatly prevalent among PLHIV initiated on ART with an adjusted variation (p<0.001). The mean ± SD age of all participants was 38.10, ± range 64 and 55.1% of them were males and 44.9% were females. Data from this study suggest that high blood pressure, obesity, smoking, and alcohol use are greatly prevalent among PLHIV, particularly among males. Systems that provide accurate information, early screening with subsequent treatment for PLHIV, is recommended by this study.

Prognostic biomarkers in cardiovascular diseases

The use of biomarkers as a reliable and reproducible indicative of the risk, severity, and progression of cardiovascular diseases (CVDs) may greatly enhance the prognostic capability of primary healthcare clinicians. In primary healthcare, the realistic and wise use of reliable biomarkers could minimize the time and costs for effective diagnosis and suitable personalized therapy for CVD patients. Therefore, the aim of the present scoping review is to evaluate the prognostic significance of biomarkers in the progression and monitoring of CVDs. The review was conducted according to the PRISMA-ScR guidelines. Eight databases were searched for articles published as of June 2021 using search terms: cardiovascular diseases AND biomarkers AND prognosis. A total of 21 studies were included in this scoping review. This review identified biomarkers BNP, cTnT yielded better accuracy of disease progression prediction in ACS and HF respectively. The availability of CVDs prognostic biomarkers in primary healthcare clinics could promote improved clinical outcomes of patients.

Investigation of the Attachment of Circulating Endothelial Cells to a Cell Probe: Combined Experimental and Numerical Study

Circulating endothelial cells (CECs) are a reliable biomarker for cardiovascular diseases (CVDs). A major unresolved challenge limiting the widespread use of CECs for the diagnosis and monitoring of CVDs is their unreliable detection. This problem is mainly attributed to the low sample volume (5–10 mL) of commonly used ex vivo CEC isolation methods. To overcome this limitation, the BMProbe for the in vivo isolation of CECs is proposed. It consists of a twisted medical flat wire with a polymer‐coated surface functionalized with anti‐CD105 antibodies. A combined experimental and numerical study is performed to investigate which flow conditions lead to an increased cell attachment to the probe's surface. Endothelial cells are solved in a dextran solution and circulated in a flow system containing the BMProbes. Microscopic images of the attached CECs are taken. In addition, the experiments are simulated using a computational fluid dynamics (CFD) flow solver to quantify the flow conditions at the probe's surface. The microscopic images are superimposed with the CFD data to investigate the influence of wall shear rate and wall normal rate on the attachment of CECs to the probe. Most of all attached cells (85.5%) are found in areas of negative wall normal rate.

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

BackgroundCardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.MethodsDeep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV).ResultsBy combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability.ConclusionsWe show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Method of Photoplethysmography Diagnostics of Domesticated Animals Cardiovascular Diseases

At present time the standard procedure to diagnose cardiovascular diseases (CVD) of domesticated animals (dogs) is electrocardiography (ECG) monitoring at animal clinic. Sadly, results, obtained that way, are often mistaken because of stressful state of the animal, caused by medical manipulations [1]. The usage of photoplethysmography (PPG) method can become a good substitute for CVD monitoring. Main advantages of PPG, in compare with ECG, are single probe placement (on the ear or tailset), no need of gel electrodes and capability of measuring actual CVS parameters without need of special medical training or laboratory conditions. It is one of non-invasive methods for measuring the amount of the blood volume changes inside the blood vessels, applicable for cardio activity estimation by calculating heart rate variability. In most cases, PPG monitoring devises are small, easy to place and can monitor different physiological parameters of the animal, such as heart rate, pulse wave characteristics, blood oxygen saturation, etc.

Comparison between fat intake assessed by a 3-day food record and phospholipid fatty acid composition of red blood cells: Results from the monitoring of cardiovascular disease-Lille study

We investigated the relationship between assessment of fatty acid intake by a 3-day food record and by capillary gas chromatography of erythrocyte phospholipid fatty acid. The study was performed in a sample of 244 men aged 45 to 66 years from the general population who were participating in the Monitoring of Cardiovascular Disease (MONICA)-Lille survey. The relationship between each nutrient and food item and erythrocyte phospholipid fatty acid was investigated by a regression model on proportion including each food item and nutrient as a dependent variable and percentage of fatty acid and covariables (nonalcoholic energy intake, age, alcohol intake, and smoking) as independent variables. Polyunsaturated fat and linoleic acid intake were positively correlated with linoleic acid content of erythrocytes (β = 0.641 and 0.604, respectively, P < .001). Monounsaturated and saturated fat intake were correlated with oleic acid (β = 0.375 and 0.373, respectively, P < .01). Fish intake correlated positively with docosahexaenoic acid (DHA) β = 0.383, P < .001) and negatively with arachidonic acid (β = −0.509, P < .01). These data confirm, on a group level, a good relationship between assessment of polyunsaturated fat intake by a 3-day record and linoleic acid content of erythrocyte membranes. These data suggest that erythrocyte oleic acid content is a marker of both saturated and monounsaturated fat intake.