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Background: In-center hemodialysis (IHD) is the most common dialysis modality. Assisted peritoneal dialysis (assPD) is an option for frail and/or incapacitated patients. Both modalities can be used to alleviate uremic symptoms towards the end of life. There are few studies comparing these modalities. The primary aim is to compare hospital admissions between assPD and IHD. The secondary aim is to compare continuation of the dialysis modality and patient survival. Methods: Patients > 65 years, registered in the Swedish Renal Registry (SRR) and starting dialysis 2010–2017 were eligible for inclusion. Patients starting on assPD were matched with patients starting on IHD according to sex, Charlson Index, age and date for start of dialysis. Data were collected from SRR and other registries. Results: During the first year, patients on assPD and IHD had in median one (IQR 0–5.0; 0–4.0) hospitalization (p = 0.412). There was no significant difference after two years, in the annual number of days admitted to hospital, in hospitalizations with cardiovascular or infectious disease diagnoses or continuation of the dialysis modality, respectively. However, patients on assPD had a worse median survival (1.1 years IQR 0.6–2.1; IHD 3.1 years IQR 0.2–5.8; p < 0.001). Conclusion: In this study patients starting assPD, often as a palliative treatment, showed no difference compared to IHD concerning the number of hospitalizations, number of days in hospital/year or continuation of the dialysis modality. Patients on assPD had a worse survival, which is likely due to residual confounding. Without that, patients on assPD would probably have lower number of hospitalizations. Despite limitations due to the retrospective observational design of the study, the results indicate that assPD is a feasible alternative to IHD when self-care dialysis is not possible and/or IHD too arduous. [ABSTRACT FROM AUTHOR]

The article discusses an experimental study conducted in India to enhance diagnostic and clinical reasoning skills in interns through case-based learning of biochemistry. The study compared pre and post-test scores of interns taught with case-based learning methods, showing a significant improvement in the intervention group compared to the control group. The results suggest that reinforcing biochemistry concepts through case-based learning can improve interns' scientific reasoning skills and better management of patients, aligning with the trend of student-centered education in India. [Extracted from the article]

Objective: Idiopathic inflammatory myopathies (IIM) confer an increased risk of morbidity from atherosclerotic cardiovascular disease (ASCVD). While ASCVD risk has been studied in other countries, these results may not be applicable to patients with dermatomyositis (DM) and polymyositis (PM) in the United States. This retrospective analysis of a cohort of patients identified by ICD code from TriNetX investigated the incidence of ASCVD after International Classification of Disease (ICD) codes of DM, PM, dermatopolymyositis (DPM) or juvenile dermatomyositis (JDM). Method: Patients were identified by entry of two ICD codes separated by at least 6 months, according to their first diagnosis code; ASCVD was defined as first ICD code for myocardial infarction, ischemic stroke, transient ischemic attack, or peripheral arterial disease. Cox proportional hazards regression modeled time from first IIM ICD code to ASCVD event. Results: A total of 35,554 patients were identified with the mean age at first IIM code of 54 and 26.1% were male. The most common comorbidity for all groups except JDM was hyperlipidemia (39.9%) though 79.2% of patients were on no cholesterol lowering medication. ASCVD occurred in 30.4% of patients with PM, 24.3% of patients with DM and 0.9% of patients with JDM. Patients with PM had a median time to event of 9.7 years (95% Confidence interval (CI) 9.1, 10.7) and 14.3 years (95% CI 12.6, 14.8) for DM. This study demonstrates that ASCVD is a comorbidity occurring after a median of 12.5 years (95% CI 11.9, 13.6) in patients with IIM. Conclusions: ASCVD appears to be a long-term complication for IIM patients occurring in nearly a quarter of US patients without prior ASCVD with at least two ICD codes for IIM, with a median time to event of 12.5 years. There appears to be a practice gap in the recognition and treatment of hyperlipidemia in these patients. Key Points • Hyperlipidemia was a common comorbidity identified in patients with IIM though most patients were not on cholesterol lowering medication. • Development of ASCVD appears to be a long-term complication for patients with IIM in the United States. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus (SLE) increases cardiovascular disease (CVD) risk, and this is not explained by traditional risk factors. Characterization of blood immunologic signatures that associate with subclinical CVD and predict its progression has been challenging and may help identify subgroups at risk. Methods: Patients with SLE (n = 77) and healthy controls (HCs) (n = 27) underwent assessments of arterial stiffness, vascular wall inflammation, and coronary atherosclerosis burden with cardio‐ankle vascular index (CAVI); fluorodeoxyglucose–positron emission tomography/computed tomography (CT) (target‐to‐background ratio [TBR]); and coronary CT angiography. Whole blood bulk RNA sequencing was performed in a subset of study participants (HC n = 10, SLE n = 20). In a partially overlapping subset (HC n = 24, SLE n = 64), serum inflammatory protein biomarkers were quantified with an Olink platform. Results: CAVI, TBR, and noncalcified coronary plaque burden (NCB) were increased in patients with SLE compared to HCs. When comparing patients with SLE with high CAVI scores to those with low CAVI scores or to HCs, there was a down‐regulation of genes in pathways involved in the cell cycle and differentially regulated pathways related to metabolism. Distinct serum proteins associated with increased CAVI (CCL23, colony‐stimulating factor 1, latency‐activating peptide transforming growth factor β1, interleukin 33 [IL‐33], CD8A, and IL‐12B), NCB (monocyte chemotactic protein 4 and FMS‐like tyrosine kinase 3 ligand [Flt3L]), and TBR (CD5, IL‐1α, AXIN1, cystatin D [CST5], and tumor necrosis factor receptor superfamily 9; P < 0.05). Conclusion: Blood gene expression patterns and serum proteins that associate with worse vascular phenotypes suggest dysregulated immune and metabolic pathways linked to premature CVD. Cytokines and chemokines identified in associations with arterial stiffness, inflammation, and NCB in SLE may allow for characterization of new CVD biomarkers in lupus. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment.This study examines the full cardiac coverage using a 3D dual‐domain convolutional model and then improves this model using an innovative explainable salient region detection model and a recurrent architecture.Salient regions are extracted from the short‐axis cine CMR stacks using a three‐step proposed algorithm. Changing the architecture of the 3D dual‐domain convolutional model to a recurrent one and taking advantage of the salient region detection model creates a kind of attention mechanism that leads to improved results.The results obtained from the images of over 6200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracies of the proposed model in identifying the presence/absence of basal/apical slices are 96.22% and 95.42%, respectively.The proposed recurrent architecture of the 3D dual‐domain convolutional model can force the model to focus on the most informative areas of the images using the extracted salient regions, which can help the model improve accuracy. The performance of the proposed fully automated model indicates that it can be used for image quality control in population cohort datasets and real‐time post‐imaging quality assessments. Codes are available at https://github.com/mohammadhashemii/CMR_Cardiac_Coverage_Control [ABSTRACT FROM AUTHOR]

Background: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in decision-making and enhance predictive abilities. Objective: The goal of this paper is to introduce a model for precise cardiovascular disease prediction by combining machine learning and deep learning. Method: Two public heart disease classification datasets with 70,000 and 1190 records besides a locally collected dataset with 600 records were used in our experiments. Then, a model which makes use of both machine learning and deep learning was proposed in this paper. The proposed model employed CNN and LSTM, as the representatives of deep learning models, besides KNN and XGB, as the representatives of machine learning models. As each classifier defined the output classes, majority voting was then used as an ensemble learner to predict the final output class. Result: The proposed model obtained the highest classification performance based on all evaluation metrics on all datasets, demonstrating its suitability and reliability in forecasting the probability of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient's circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins. [ABSTRACT FROM AUTHOR]

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner's experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and "others"). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.7–98.0) and a mean absolute error of 6.54 (95%CI 6.43–6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) simulations have shown great potentials in cardiovascular disease diagnosis and postoperative assessment. Patient‐specific and well‐tuned boundary conditions are key to obtaining accurate and reliable hemodynamic results. However, CFD simulations are usually performed under non‐patient‐specific flow conditions due to the absence of in vivo flow and pressure measurements. This study proposes a new method to overcome this challenge by tuning inlet boundary conditions using data extracted from electrocardiogram (ECG). Five patient‐specific geometric models of type B aortic dissection were reconstructed from computed tomography (CT) images. Other available data included stoke volume (SV), ECG, and 4D‐flow magnetic resonance imaging (MRI). ECG waveforms were processed to extract patient‐specific systole to diastole ratio (SDR). Inlet boundary conditions were defined based on a generic aortic flow waveform tuned using (1) SV only, and (2) with ECG and SV (ECG + SV). 4D‐flow MRI derived inlet boundary conditions were also used in patient‐specific simulations to provide the gold standard for comparison and validation. Simulations using inlet flow waveform tuned with ECG + SV not only successfully reproduced flow distributions in the descending aorta but also provided accurate prediction of time‐averaged wall shear stress (TAWSS) in the primary entry tear (PET) and abdominal regions, as well as maximum pressure difference, ∆Pmax, from the aortic root to the distal false lumen. Compared with simulations with inlet waveform tuned with SV alone, using ECG + SV in the tuning method significantly reduced the error in false lumen ejection fraction at the PET (from 149.1% to 6.2%), reduced errors in TAWSS at the PET (from 54.1% to 5.7%) and in the abdominal region (from 61.3% to 11.1%), and improved ∆Pmax prediction (from 283.1% to 18.8%) However, neither of these inlet waveforms could be used for accurate prediction of TAWSS in the ascending aorta. This study demonstrates the importance of SDR in tailoring inlet flow waveforms for patient‐specific hemodynamic simulations. A well‐tuned flow waveform is essential for ensuring that the simulation results are patient‐specific, thereby enhancing the confidence and fidelity of computational tools in future clinical applications. [ABSTRACT FROM AUTHOR]

Introduction: According to the World Health Organisation (WHO), the American College of Cardiology (ACC), and the American Heart Association (AHA), the issued prediction charts can be used to make an absolute prediction of a study variable's Cardiovascular Disease (CVD) risk. This study aimed to examine the combined effects of gender, Family History (FH) of CVD, tobacco use, alcohol consumption, smoking, physical activity levels, and other health conditions on the presence of CVD confirmed by angiography. Predicting CVD risk is crucial; particularly because young individuals aged 25 to 40 are affected by these diseases and from the foundation of any nation. Moreover, CVDs significantly contribute to human mortality compared to other ailments. This approach sought to identify the risk factors for CVD. Aim: To investigate the impact and the association between hereditary characteristics, behaviour, and self-control factors on coronary angiography findings among the study participants from a statistical viewpoint. Materials and Methods: An observational cross-sectional study related to angiography report of 274 study participants visiting the Department of cardiology at Krishna Vishwa Vidyapeeth (Deemed to be University), Karad, Maharashtra with complaints of CVD was conducted from January 2023 to May 2023. Statistical Analysis was performed with the help of Statistical Package for Social Sciences (SPSS) version 28.0, InStat and Microsoft Excel. Chi-square test was applied to study association of demographic study parameters with CVD. Logistic regression was carried out to develop the regression model. Results: Angiography significant CVD was associated with father's history of CVD (40, 71.42%), alcohol consumption (68, 70.8%), tobacco chewing (115, 69.7%), no exercise (89,79.46%), diabetes (83,75.45%), and diabetic with medication (14,93.33%) showed significant associations with CVD. Logistic regression analysis identified these variables as the best predictors of CVD. Genetics, lifestyle choices, and co-morbidities all contribute to the risk of Coronary Artery Disease (CAD). Conclusion: The study effectively identifies and quantifies the relationships between hereditary characteristics, behavioural factors, and self-control measures with coronary angiography findings. Exercise and diabetic status are significant predictors of the outcome, while tobacco-chewing leans towards significance and other studied variables are at their reference levels. This predictive model will help clinicians, patients, and their families in mentally preparing for confirmation of the disease's presence or absence. [ABSTRACT FROM AUTHOR]

Background: Within-breath analysis of oscillometry parameters is a growing research area since it increases sensitivity and specificity to respiratory pathologies and conditions. However, reference equations for these parameters in White adults are lacking and devices using multiple sinusoids or pseudorandom forcing stimuli have been underrepresented in previous studies deriving reference equations. The current study aimed to establish reference ranges for oscillometry parameters, including also the within-breath ones in White adults using multi-sinusoidal oscillations. Methods: White adults with normal spirometry, BMI ≤30 kg/m2, without a smoking history, respiratory symptoms, pulmonary or cardiac disease, neurological or neuromuscular disorders, and respiratory tract infections in the previous 4 weeks were eligible for the study. Study subjects underwent oscillometry (multifrequency waveform at 5–11–19 Hz, Resmon PRO FULL, RESTECH Srl, Italy) in 5 centers in Europe and the USA according to international standards. The within-breath and total resistance (R) and reactance (X), the resonance frequency, the area under the X curve, the frequency dependence of R (R5–19), and within-breath changes of X (ΔX) were submitted to lambda-mu-sigma models for deriving reference equations. For each output parameter, an AIC-based stepwise input variable selection procedure was applied. Results: A total of 144 subjects (age 20.8–86.3 years; height 146–193 cm; BMI 17.42–29.98 kg/m2; 56% females) were included. We derived reference equations for 29 oscillatory parameters. Predicted values for inspiratory and expiratory parameters were similar, while differences were observed for their limits of normality. Conclusions: We derived reference equations with narrow confidence intervals for within-breath and whole-breath oscillatory parameters for White adults. [ABSTRACT FROM AUTHOR]

Background: The rationale for screening for cardiovascular disease (CVD) is debated as a prevention strategy. However, research shows that when preventive medication is initiated after screening for CVD, mortality rates decrease, especially among men. When implementing screening programmes, facilitating citizens' informed decisions and empowering their autonomy in the decision‐making process are crucial. We therefore aimed to explore citizens' perspectives on and decisions regarding initiating cardiovascular preventive medication for screen‐detected CVD. Methods: We employed an exploratory qualitative design using semi‐structured interviews to investigate participants' perspectives on and decisions regarding initiating cardiovascular preventive medication for screen‐detected CVD. Twelve interviews were conducted and analysed using thematic analysis following Braun and Clarke's six‐step approach. Findings: We found one overall theme, Being on a continuum between wellness and illness, with two underlying sub‐themes. Sub‐theme (1) Navigating mixed feelings and pathways reflects participants' perspectives on CVD preventive medication, which were positioned on a continuum with shifting perspectives. The state between wellness and illness was experienced as liminal and characterised by uncertainty and concerns about CVD risk. (2) Navigating medication decisions: a negotiated process. Pivotal medical decisions hinged on trust in authorities or own judgement, influenced by attitudes towards the need for medication, perception of meaningfulness, asymptomatic condition, age, family responsibility and predisposition. Participants' medical decisions were supported and impacted by significant others. Conclusion: We uncovered a continuum of shifting perspectives ranging from feeling improved health to experiencing fear of CVD. For certain citizens, it is like navigating between these contrasting feelings. The perceived necessity and meaningfulness, in particular, shape medical decisions. Providing support for informed decisions is crucial and includes significant others. Shared decision‐making with healthcare professionals as facilitators is needed. Future research is warranted to investigate how to embrace the various perspectives on initiating CVD preventive medication in clinical practice. [ABSTRACT FROM AUTHOR]

An interview with Cardiologist Dr. Siddharth Dagli is presented. Topics include risk factors for cardiovascular disease, preventive care and lifestyle modifications, and the importance of regular health check-ups and screenings for early detection. Dr. Dagli emphasizes that managing these risk factors through lifestyle changes can significantly reduce the likelihood of developing heart disease.

Cardiac imaging plays a pivotal role in the diagnosis and management of cardiovascular diseases. In the burgeoning landscape of digital technology and social media platforms, it becomes essential for cardiac imagers to know how to effectively increase the visibility and the impact of their activity. With the availability of social sites like X (formerly Twitter), Instagram, and Facebook, cardiac imagers can now reach a wider audience and engage with peers, sharing their findings, insights, and discussions. The integration of persistent identifiers, such as digital object identifiers (DOIs), facilitates traceability and citation of cardiac imaging publications across various digital platforms, further enhancing their discoverability. To maximize visibility, practical advice is provided, including the use of visually engaging infographics and videos, as well as the strategic implementation of relevant hashtags and keywords. These techniques can significantly improve the discoverability of cardiac imaging research through search engine optimization and social media algorithms. Tracking impact and engagement is crucial in the digital age, and this review discusses various metrics and tools to gauge the reach and influence of cardiac imaging publications. This includes traditional citation-based metrics and altmetrics. Moreover, this review underscores the importance of creating and updating professional profiles on social platforms and participating in relevant scientific communities online. The adoption of digital technology, social platforms, and a strategic approach to publication sharing can empower cardiac imaging professionals to enhance the visibility and impact of their research, ultimately advancing the field and improving patient care. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management. [ABSTRACT FROM AUTHOR]

Background: Isolated pulmonic valve endocarditis is a rare heart valve infection, and constitutes about 1% to 2% of all infective endocarditis cases. Modified Duke's criteria were used to diagnose culture negative pulmonic valve endocarditis. Case presentation: A 52-year-old male patient presented with generalized body swelling of 1 month duration associated with prolonged fever, malaise, fatigue, and lassitude. He had productive cough, dyspnea on mild exertion, and reddish discoloration of urine. Upon physical examination, blood pressure (BP) = 140/90 mmHg, pulse rate (PR) = 104 beats per minute, respiratory rate (RR) = 26 breaths per minute, temperature (T0) = 38.3°C, and Sp02 = 90% at ambient air. He had signs of bilateral pleural effusion. Cardiovascular examination revealed tachycardia, raised jugular venous pressure, murmurs of pulmonic regurgitation, and tricuspid regurgitation. There was grade 2 ascites and bilateral leg edema. On laboratory investigation, there were normochromic, normocytic anemia; raised ESR; positive Rheumatoid factor, elevated serum creatinine; and active urinary sediments on urinalysis. Two sets of blood culture were negative on days 1, 5, and 7. Chest-X-ray showed cardiomegaly with bilateral pleural effusion. ECG revealed sinus tachycardia with regular P-waves and QRS complexes. 2D Transthoracic echo showed vegetation on pulmonic valves, pulmonary valve lesions, dilated right atrium and right ventricle, and elevated right ventricular systolic pressure. Abdominal ultrasound revealed enlarged and echogenic kidneys, and ascites. Definitive diagnosis of PVE was made using modified Duke's criteria which was evidenced by 1 major (echo-proven vegetation on pulmonic valve), and 3 minors (suspected congenital pulmonic stenosis, fever, and immunologic phenomena [acute glomerulonephritis, positive rheumatoid factor]). The patient's clinical condition markedly improved after 2 weeks of intravenous antibiotics and loop diuretics, and discharged home after completing 6 weeks of parenteral antibiotics. Conclusion: Modified Duke's criteria could play a major role in the management decision about diagnosis and empiric treatment of infective endocarditis in the absence of positive bacterial cultures. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To investigate whether a four-electrode arrangement could provide enough information to diagnose other CVDs, further research is necessary. At the University Medical Center Utrecht in a multidisciplinary team, we developed the miniECG, a four-electrode ECG handheld system for scientific research in clinical environments (TRL6). This paper describes the process followed during the development of the miniECG. From assembling a multidisciplinary team, which includes engineers, cardiologists, and clinical physicians to the contribution of team members in the design input, design, and testing for safety and functionality of the device. Finally, we detail how the development process was composed by iterative design steps based on user input and intended use evolution. The miniECG is a device compliant for scientific research with patients within Dutch Medical Centers. We believe that hospital-based development led to a streamlined process, which could be applied for the design and development of other technologies used for scientific research in clinical environments. [ABSTRACT FROM AUTHOR]

The global population is at risk from both communicable and non-communicable deadly diseases, including cardiovascular disease. Early detection and prevention of cardiovascular disease require an accurate self-detection model. Therefore, this study introduces a novel fuzzy entropy DEMATEL inference system for accurate self-detection of cardiovascular disease. It combines fuzzy DEMATEL, entropy, and Mamdani fuzzy inference, utilizing innovative strategies like attribute reduction, entropy-based clustering, influential factor selection, and rule reduction. The system achieves high accuracy (98.69%) and sensitivity (98.62%), outperforming existing methods. Validation includes satisfactory factor analysis, performance measures and statistical analysis, demonstrating its effectiveness in addressing complexity and prioritizing factors. [ABSTRACT FROM AUTHOR]

Purpose of review: Atherosclerosis, a highly pathogenic and lethal disease, is difficult to locate accurately via conventional imaging because of its scattered and deep lesions. However, second near-infrared (NIR-II) nanomaterials show great application potential in the tracing of atherosclerotic plaques due to their excellent penetration and angiographic capabilities. Recent findings: With the development of nanotechnology, among many nanomaterials available for the visual diagnosis and treatment of cardiovascular diseases, optical nanomaterials provide strong support for various biomedical applications because of their advantages, such as noninvasive, nondestructive and molecular component imaging. Among optical nanomaterials of different wavelengths, NIR-II-range (900 ~ 1700 nm) nanomaterials have been gradually applied in the visual diagnosis and treatment of atherosclerosis and other vascular diseases because of their deep biological tissue penetration and limited background interference. Summary: This review explored in detail the prospects and challenges of the biological imaging and clinical application of NIR-II nanomaterials in treating atherosclerosis. [ABSTRACT FROM AUTHOR]

Cardiovascular studies, including nursing research, frequently integrate biomarkers for diagnostic, prognostic, monitoring, and therapeutic insights. However, effective utilization of biomarker data demands careful consideration. In the study design phase, researchers must select biomarkers that align with study objectives while considering resources and logistical factors. Additionally, a nuanced understanding of disease pathophysiology and biomarker characteristics is needed. During data collection, suitable experimental conditions and assays need to be defined. Whether researchers opt to manage these steps internally or outsource some, a comprehensive understanding of biomarker selection and experiments remains crucial. In this article, part 1 of 2, we provide an overview of considerations for the design to measurement phases of biomarker studies. [ABSTRACT FROM AUTHOR]

Purpose: The extraaortic‐valvular cardiac damage (EVCD) Stage has shown potential for risk stratification for patients with aortic stenosis (AS). This study aimed to examine the usefulness of the EVCD Stage in risk stratification of patients with moderate AS and reduced left ventricular ejection fraction (LVEF). Methods: Clinical data from patients with moderate AS (aortic valve area,.60–.85 cm2/m2; peak aortic valve velocity, 2.0–4.0 m/s) and reduced LVEF (LVEF 20%–50%) were analyzed during 2010–2019. Patients were categorized into three groups: EVCD Stages 1 (LV damage), 2 (left atrium and/or mitral valve damage), and 3/4 (pulmonary artery vasculature and/or tricuspid valve damage or right ventricular damage). The primary endpoint included a composite of cardiac death and heart failure hospitalization, with non‐cardiac death as a competing risk. Results: The study included 130 patients (mean age 76.4 ± 6.8 years; 62.3% men). They were categorized into three groups: 26 (20.0%) in EVCD Stage 1, 66 (50.8%) in Stage 2, and 48 (29.2%) in Stage 3/4. The endpoint occurred in 54 (41.5%) patients during a median follow‐up of 3.2 years (interquartile range, 1.4–5.1). Multivariate analysis indicated EVCD Stage 3/4 was significantly associated with the endpoint (hazard ratio 2.784; 95% confidence interval 1.197–6.476; P =.017) compared to Stage 1, while Stage 2 did not (hazard ratio 1.340; 95% confidence interval.577–3.115; P =.500). Conclusion: The EVCD staging system may aid in the risk stratification of patients with moderate AS and reduced LVEF. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are classed as diseases of aging, which are associated with an increased prevalence of atherosclerotic lesion formation caused by such diseases and is considered as one of the leading causes of death globally, representing a severe health crisis affecting the heart and blood vessels. Atherosclerosis is described as a chronic condition that can lead to myocardial infarction, ischemic cardiomyopathy, stroke, and peripheral arterial disease and to date, most pharmacological therapies mainly aim to control risk factors in patients with cardiovascular disease. Advances in transformative therapies and imaging diagnostics agents could shape the clinical applications of such approaches, including nanomedicine, biomaterials, immunotherapy, cell therapy, and gene therapy, which are emerging and likely to significantly impact CVD management in the coming decade. This review summarizes the current anti-atherosclerotic therapies' major milestones, strengths, and limitations. It provides an overview of the recent discoveries and emerging technologies in nanomedicine, cell therapy, and gene and immune therapeutics that can revolutionize CVD clinical practice by steering it toward precision medicine. CVD-related clinical trials and promising pre-clinical strategies that would significantly impact patients with CVD are discussed. Here, we review these recent advances, highlighting key clinical opportunities in the rapidly emerging field of CVD medicine. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is vital for diagnosing carotid artery vascular lesions, highlighting the importance of accurately segmenting lumens in ultrasound images to prevent, diagnose and treat vascular diseases. However, noise artifacts, blood residue and discontinuous lumens significantly affect segmentation accuracy. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm which is guided by an adaptively generated shape prior. To tackle the above challenges, we introduce a shape-prior-based segmentation method for carotid artery lumen walls. The shape prior in this study is adaptively generated based on the evolutionary trend of vessel growth. Shape priors guide and constrain the active contour, resulting in precise segmentation. The efficacy of the proposed model was confirmed using 247 carotid artery ultrasound images, with experimental results showing an average Dice coefficient of 92.38%, demonstrating superior segmentation performance compared to existing mathematical models. Our method can quickly and effectively perform accurate lumen segmentation on low-quality carotid artery ultrasound images, which is of great significance for the diagnosis of cardiovascular and cerebrovascular diseases. [ABSTRACT FROM AUTHOR]

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The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6 ± 4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings. [ABSTRACT FROM AUTHOR]

The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk. [ABSTRACT FROM AUTHOR]

The article explores the role of echocardiography in critical care settings, focusing on essential skills and applications. Topics include the importance of training healthcare professionals in echocardiography to ensure effective assessment of cardiac function; advances in imaging techniques that enhance diagnostic accuracy in critical care; and specific clinical indications for echocardiography, highlighting when it is most beneficial in patient management.

Background: Research suggests that people with intellectual disabilities have a higher risk for cardiovascular disease than the general population. The aim of this study was to identify barriers for the prevention, diagnosis, and treatment of cardiovascular disease for people with intellectual disabilities. Method: We conducted individual interviews with relatives and general practitioners and focus group interviews with staff working at an assisted home facility, a cardiac ward, an obesity clinic and two rehabilitation centres (n = 33) in Norway. Inductive approach and thematic analysis were used to analyse the data. Results: We identified barriers on an individual and a structural level. The underlying reason for these barriers is that health problems, such as cardiovascular disease, are regularly overlooked as the condition of intellectual disability overshadows other possible diagnoses. Conclusion: This focus on intellectual disability rather than other explanations leads to shortcomings in the prevention, diagnoses, and treatment of cardiovascular disease in this group. [ABSTRACT FROM AUTHOR]

The Canadian Association of Radiologists (CAR) Cardiovascular Expert Panel is made up of physicians from the disciplines of radiology, cardiology, and emergency medicine, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 30 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 48 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 125 recommendation statements across the 30 scenarios (27 unique scenarios as 2 scenarios point to the CAR Thoracic Diagnostic Imaging Referral Guideline and the acute pericarditis subscenario is included under 2 main scenarios). This guideline presents the methods of development and the referral recommendations for acute chest pain syndromes, chronic chest pain, cardiovascular screening and risk stratification, pericardial syndromes, intracardiac/pericardial mass, suspected valvular disease cardiomyopathy, aorta, venous thrombosis, and peripheral vascular disease. [ABSTRACT FROM AUTHOR]

This article examines the early adoption of semaglutide, a drug used to treat type 2 diabetes, in Scandinavia, with a focus on Denmark. The study analyzes nationwide prescription data from Denmark, Norway, and Sweden to compare the use of semaglutide with other glucose-lowering drugs and to compare the characteristics of semaglutide initiators with initiators of other drugs. The findings reveal that semaglutide is widely used in Scandinavia and that the characteristics of semaglutide initiators differ slightly from initiators of other drugs. The study also identifies differences between early and late initiators of semaglutide, suggesting potential off-label use for weight loss. However, the study acknowledges limitations, such as the lack of data on specific indications for use. [Extracted from the article]

Automatic segmentation of retinal vessels plays an important role in computer-aided diagnosis of ophthalmology and cardiovascular diseases. Attention mechanism can improve the efficiency and accuracy of image feature extraction in classical neural network models, so attention mechanism is widely used in retinal vessel segmentation models. This paper firstly reviews the commonly used datasets and evaluation metrics for retinal vessel segmentation, subsequently, attention mechanisms are categorized into two types: selective attention mechanisms and self-attention mechanisms, based on their working principles. Meanwhile, according to the data domain of computer vision tasks, attention methods are divided into three categories: channel attention, spatial attention, and mixed attention. Combined with the task of retinal vessel segmentation, the paper highlights the specific applications of representative attention models of these three types and conducts performance comparisons and evaluations of relevant models. Finally, the problems of attention mechanism and the development trend in the future are discussed. [ABSTRACT FROM AUTHOR]

This article, published in the journal Circulation, discusses the past, present, and future of coronary computed tomography angiography (CCTA) as a diagnostic tool for cardiovascular disease. The essay traces the historical milestones and technological advancements that have shaped cardiac CT, highlighting its ability to provide noninvasive assessment of coronary artery disease and other cardiovascular conditions. The article also discusses the role of CCTA in the evaluation of acute and stable chest pain, as well as its potential for risk stratification and guiding preventive treatments. The authors conclude that ongoing advancements in technology and clinical research will continue to enhance the role of cardiac CT in modern cardiology. [Extracted from the article]

Purpose: We aimed to evaluate the ability of the peripheral perfusion index (PPI) to predict reintubation of critically ill surgical patients. Methods: This prospective observational study included mechanically ventilated adults who were extubated after a successful spontaneous breathing trial (SBT). The patients were followed up for the next 48 h for the need for reintubation. The heart rate, systolic blood pressure, respiratory rate, peripheral arterial oxygen saturation (SpO2), and PPI were measured before-, at the end of SBT, 1 and 2 h postextubation. The primary outcome was the ability of PPI 1 h postextubation to predict reintubation using area under the receiver operating characteristic curve (AUC) analysis. Univariate and multivariate analyses were performed to identify predictors for reintubation. Results: Data from 62 patients were analysed. Reintubation occurred in 12/62 (19%) of the patients. Reintubated patients had higher heart rate and respiratory rate; and lower SpO2 and PPI than successfully weaned patients. The AUC (95%confidence interval) for the ability of PPI at 1 h postextubation to predict reintubation was 0.82 (0.71–0.91) with a negative predictive value of 97%, at a cutoff value of ≤ 2.5. Low PPI and high respiratory rate were the independent predictors for reintubation. Conclusion: PPI early after extubation is a useful tool for prediction of reintubation. Low PPI is an independent risk factor for reintubation. A PPI > 2.5, one hour after extubation can confirm successful extubation. [ABSTRACT FROM AUTHOR]

Background: According to the World Health Organisation's Health Report 2019, approximately 17.18 million people die from cardiovascular disease each year, accounting for more than 30% of all global deaths. Therefore, the occurrence of cardiovascular disease is still a global concern. The transcription factor 21 (TCF21) plays an important role in cardiovascular diseases. This article reviews the regulation mechanism of TCF21 expression and activity and focuses on its important role in atherosclerosis in order to contribute to the development of diagnosis and treatment of cardiovascular diseases. Summary: TCF21 is involved in the phenotypic regulation of vascular smooth muscle cells (VSMCs), promotes the proliferation and migration of VSMCs, and participates in the activation of inflammatory sequences. Increased proliferation and migration of VSMCs can lead to neointimal hyperplasia after vascular injury. Abnormal hyperplasia of neointima and inflammation are one of the main features of atherosclerosis. Therefore, targeting TCF21 may become a potential treatment for relieving atherosclerosis. Key Messages: TCF21 as a member of basic helix-loop-helix transcription factors regulates cell growth and differentiation by modulating gene expression during the development of different organs and plays an important role in cardiovascular development and disease. VSMCs and cells derived from VSMCs constitute the majority of plaques in atherosclerosis. TCF21 plays a key role in regulation of VSMCs' phenotype, thus accelerating atherogenesis in the early stage. However, TCF21 enhances plaque stability in late-stage atherosclerosis. The dual role of TCF21 should be considered in the translational medicine. [ABSTRACT FROM AUTHOR]

Aim: Intermediate altitude (IA) is a risk factor for the development of cardiovascular disease due to low oxygen levels. At IA, adaptive mechanisms develop both in the pulmonary and systemic circulatory systems from an early period. The effects of IA on atrial functions were investigated in the present study by using speckle-tracking echocardiography. Materials and Methods: The present study was on the subjects living at IA for 1 year. The effects of IA on atrial functions were investigated by comparing the baseline and first-year echocardiographic data of the subjects. Results: A total of 67 subjects were included in this study. The mean age of the patients was 44.5 ± 6.2 years, and 62% of them were males. At the end of the first year, the left atrial (LA) conduit strain (p:0.011), reservoir strain (p:0.032), and contractile strain (p:0.037) rates were found to be significantly higher, whereas the left atrial volume index (p:0.005) was determined to be lower. Conclusion: IA leads to the development of adaptive mechanisms in the cardiovascular system, and the maintenance of tissue oxygenation is not affected by these mechanisms. These adaptive changes can be detected in LA structure and functions from the first year in individuals living at high altitudes. [ABSTRACT FROM AUTHOR]

Identifying patients with left ventricular ejection fraction (EF), either reduced [EF < 40% (rEF)], mid-range [EF 40–50% (mEF)], or preserved [EF > 50% (pEF)], is considered of primary clinical importance. An end-to-end video classification using AutoML in Google Vertex AI was applied to echocardiographic recordings. Datasets balanced by majority undersampling, each corresponding to one out of three possible classifications, were obtained from the Standford EchoNet-Dynamic repository. A train–test split of 75/25 was applied. A binary video classification of rEF vs. not rEF demonstrated good performance (test dataset: ROC AUC score 0.939, accuracy 0.863, sensitivity 0.894, specificity 0.831, positive predicting value 0.842). A second binary classification of not pEF vs. pEF was slightly less performing (test dataset: ROC AUC score 0.917, accuracy 0.829, sensitivity 0.761, specificity 0.891, positive predicting value 0.888). A ternary classification was also explored, and lower performance was observed, mainly for the mEF class. A non-AutoML PyTorch implementation in open access confirmed the feasibility of our approach. With this proof of concept, end-to-end video classification based on transfer learning to categorize EF merits consideration for further evaluation in prospective clinical studies. [ABSTRACT FROM AUTHOR]

Objective Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, but their performance has not been entirely satisfactory. This study aimed to develop a more accurate LDL-C prediction model using machine learning methods. Methods The study involved predicting directly measured LDL-C, using individual characteristics, lipid profiles, and other laboratory results as predictors. The models applied to predict LDL-C values were multiple regression, penalized regression, random forest, and XGBoost. Additionally, a novel 2-step prediction model was developed and introduced. The machine learning methods were evaluated against the Friedewald, Martin, and Sampson equations. Results The Friedewald, Martin, and Sampson equations had root mean squared error (RMSE) values of 12.112, 8.084, and 8.492, respectively, whereas the 2-step prediction model showed the highest accuracy, with an RMSE of 7.015. The LDL-C levels were also classified as a categorical variable according to the diagnostic criteria of the dyslipidemia treatment guideline, and concordance rates were calculated between the predictive values obtained from each method and the directly measured ones. The 2-step prediction model had the highest concordance rate (85.1%). Conclusion The machine learning method can calculate LDL-C more accurately than existing equations. The proposed 2-step prediction model, in particular, outperformed the other machine learning methods. [ABSTRACT FROM AUTHOR]

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Cardiovascular diseases detection or diagnosis on appropriate time is crucial to avoid health complications. In this study, an advanced procedure for classifying changes in the blood pressure has been used analyzing the wave-forms inside the arterial system where such variation can occur due to improper timing in intra-aortic balloon pump (IABP) control. Inaccurate pressure extends with probable injury can be caused by improper timing in the heart valve in both pumping and compression of the balloon. This investigation focuses on accurately recognizing and classifying any irregularities in the artery wave-forms in IABP in the blood pressure initiated by mistiming. Accumulated blood pressure records are used for the progression of providing information to IABP trainer. The wave-forms require pre-handling employing image digitizing software to acquire automated identifications. Any undesirable image features have been removed using Wavelet in MATLAB software. Afterward, such features can be employed to develop a technique for arrangement depending on neural networks. The artificial neural network technique has used marked data to properly detect irregularities in wave-forms in vascular blood pressure due to improper IABP timing. As a result, the validation has proved to appropriately recognize and classify such anomalies, denoting a considerable prospect to improve patient protection with an efficacy of treatment in the area of cardiovascular prescription. [ABSTRACT FROM AUTHOR]

Background: Peripheral artery disease (PAD) is common and associated with future cardiovascular events. PAD is underdiagnosed, which limits opportunities to address secondary prevention of cardiovascular disease. It is unknown how closely guidelines for detection of PAD are followed in primary care. Aim: To survey GPs' attitudes to diagnosis and follow-up of patients with PAD. Design & setting: Online survey of GPs in England and the Republic of Ireland (RoI). Method: GPs’ approaches to management of PAD were assessed using likelihood ratings (scales of 0–10) and discrete questions. Findings were summarised as proportions, or median and interquartile ranges (IQR). Results: In total, 111 responses were analysed; 68 (61%) from England and 43 (39%) from the RoI. Considering a hypothetical patient at risk of PAD, likelihood of GPs enquiring about PAD symptoms (leg pains: 3/10 or erectile dysfunction: 2/10) was low. GPs in the RoI compared with GPs in England more often examined the heart (10/10 versus 7/10) or carotid vessels (5/10 versus 1/10). Lower limb pulses were palpated in response to symptoms or signs of PAD. In England 25% of practitioners, and in the RoI 55% of practitioners, reported that they do not measure ankle-brachial index (ABI). Conclusion: Currently, detection of PAD is generally triggered by 'classical' leg claudication symptoms, while known vascular risk factors appear to elicit little consideration. ABI measurement is not performed by many practitioners, suggesting that a proportion of vascular referrals must be based on history and examination findings alone. Opportunities to recognise PAD are missed. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing these challenges, providing promising solutions in medical diagnostics. However, traditional ML approaches often need to be improved in feature selection and optimization, leading to suboptimal performance in complex diagnostic tasks. To overcome these limitations, this study introduces a new hybrid method called CSA-DE-LR, which combines the clonal selection algorithm (CSA) and differential evolution (DE) with logistic regression. This integration is designed to optimize logistic regression weights efficiently for the accurate classification of CVD. The methodology employs three optimization strategies based on the F1 score, the Matthews correlation coefficient (MCC), and the mean absolute error (MAE). Extensive evaluations on benchmark datasets, namely Cleveland and Statlog, reveal that CSA-DE-LR outperforms state-of-the-art ML methods. In addition, generalization is evaluated using the Breast Cancer Wisconsin Original (WBCO) and Breast Cancer Wisconsin Diagnostic (WBCD) datasets. Significantly, the proposed model demonstrates superior efficacy compared to previous research studies in this domain. This study's findings highlight the potential of hybrid machine learning approaches for improving diagnostic accuracy, offering a significant advancement in the fields of medical data analysis and CVD diagnosis. [ABSTRACT FROM AUTHOR]

Background: General practitioners (GPs) specialized in cardiovascular disease (GPSI-CVD) may suspect heart failure (HF) more easily than GPs not specialized in CVD. We assessed whether GPSI-CVD consider investigations aimed at detecting HF more often than other GPs in two clinical scenarios of an older male person with respiratory and suggestive HF symptoms. Methods: In this vignette study, Dutch GPs evaluated two vignettes. The first involved a 72-year-old man with hypertension and a 30 pack-year smoking history who presented himself with symptoms of a common cold, but also shortness of breath, reduced exercise tolerance, and signs of fluid overload. The second vignette was similar but now the 72-year-old man was known with chronic obstructive pulmonary disease (COPD). GPs could select diagnostic tests from a multiple-choice list with answer options targeted at HF, COPD or exacerbation of COPD, or lower respiratory tract infection. With Pearson Chi-square or Fisher's exact test differences between the two GP groups were assessed regarding the chosen diagnostic tests. Results: Of the 148 participating GPs, 25 were GPSI-CVD and 123 were other GPs. In the first vignette, GPSI-CVD more often considered performing electrocardiography (ECG) than other GPs (64.0% vs. 32.5%, p = 0.003). In the second vignette, GPSI-CVD were more inclined to perform both ECG (36.0% vs. 12.2%, p = 0.003) and natriuretic peptide testing (56.0% vs. 32.5%, p = 0.006). Conclusions: Most GPs seemed to consider multiple diagnoses, including HF, with GPSI-CVD more likely performing ECG and natriuretic peptide testing in an older male person with both respiratory and suggestive HF symptoms. [ABSTRACT FROM AUTHOR]

Background: Beneath the surface of the acute ST-elevation myocardial infarction (STEMI) iceberg lies a hidden peril, obscured by the well-known cardiovascular risk factors that tip the iceberg. Before delving into the potential time bomb these risk factors represent, it is crucial to recognize the obscured danger lurking under the surface. What secrets does the STEMI iceberg hold? To unveil these mysteries, a closer look at the pathophysiology of STEMI is imperative. Inflammation, the catalyst of the STEMI cascade, sets off a chain reaction within the cardiovascular system. Surprisingly, the intricate interplay between red blood cells (RBC) and lymphocytes remains largely unexplored in previous research. Materials and methods: The study encompassed 163 patients diagnosed with STEMI. Utilizing linear and logistic regression, the lymphocyte-to-red blood cell ratio (LRR) was scrutinized as a potential predictive biomarker. Results: There was a statistically significant correlation between LRR and the prognosis of STEMI patients. Building upon this discovery, an innovative scoring system was proposed that integrates LRR as a crucial parameter. Conclusions: Uncovering novel predictive markers for both immediate and delayed complications in STEMI is paramount. These markers have the potential to revolutionize treatment strategies by tailoring them to individual risk profiles, ultimately enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

Platelets, anucleate blood cells derive from megakaryocytes, are involved in cardiovascular diseases and tumors. FcγRIIA, the only FcγR expressed on human platelets, is known for its role in immune-related diseases. A growing body of evidence reveals that platelet FcγRIIA is a potential target for the prevention and control of cardiovascular disease and cancer, and is an advantageous biomarker. In this review, we describe the structure and physiological function of platelet FcγRIIA, its regulatory role in cardiovascular disease and cancer, and its potential clinical application. • Platelets are a group of highly reactive and sensitive cells that play a crucial role in human disease. • FcγRIIA, the only FcγR expressed on human platelets, is abnormal expressed in cardiovascular diseases and cancers. • Platelet FcγRIIA is a good biomarker for the diagnosis and treatment of cardiovascular diseases and tumors. [ABSTRACT FROM AUTHOR]

Aim: According to delivery room guidelines, an optimal position is not specified for the stabilization of the baby. This study aimed to define the positions effects on postnatal adaptation parameters and cerebral oxygenation in non-resuscitated neonates. Materials and Methods: A total of 60 neonates delivered by cesarean section stabilized randomly in the supine, right-side, left-side, or prone positions were enrolled. Apgar scores, heart rates (HR), arterial oxygen saturations via pulse oximetry, and perfusion indexes (PI) at the 2nd, 5th and 10th minutes were recorded. Cerebral regional oxygen saturation (SpO2) of the patients was monitored by near-infrared spectroscopy. Results: In the prone position, the 1st minute Apgar score was significantly lower than other groups, but no difference was observed at the 5th minute Apgar scores (1st min Apgarprone, p=0.05). Although there was no statistically significant difference, the prone position had the lowest HR at the 2nd minute, while the supine posture had the greatest HR at the 5th and 10th minutes. While the groups' SpO2 values were similar, the left-side group's perfusion rates increased at the 5th and 10th minute marks (5th and 10th min PIleft-side, p=0.67, p=0.21, respectively). Regional cerebral oxygen saturation (rScO2) and cerebral fractional oxygen extraction did not differ significantly between groups at the 5th and 10th minute time intervals. Although right and left rScO2 were found to be higher in the first 5 minutes in the prone position, this elevation did not lead to a statistically significant difference, and right and left rScO2 values were found to be similar in all groups at the 10th minute. Conclusion: Adaptation parameters were not affected by position, except for lower 1st minute Apgar scores in the prone group and higher perfusion indices in the left lateral position. Cerebral perfusion was similar in all groups. The left-side position, which results in a higher PI, may be a good alternative. Studies with larger case series may provide further information. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases remain a major health challenge, leading to high rates of death and hospitalization globally. In the battle against these ailments, echocardiography stands as the frontline tool for diagnosis. Pioneering the charge in innovation, real-time remote tele-mentored ultrasound echocardiography (RTMUS echo) has emerged. This cutting-edge technique facilitates the instant transmission of cardiac imaging from the patient's side to experts in far-off locations, enabling prompt diagnosis and expert consultation. To bridge this gap, a systematic review was conducted to understand RTMUS echo's current applications in diagnosing heart diseases. Searches across six databases, guided by strict inclusion and exclusion criteria, yielded nine relevant articles. These studies assessed the feasibility of RTMUS echo and the technology behind it, confirming its potential for high-quality cardiac imaging. The findings reveal that RTMUS echo could notably improve care for cardiac patients, especially those in resource-constrained settings or in isolation because of infection risks. This technology enables quick access to diagnostic expertise, which is otherwise unavailable in such areas. Future research should aim to optimize the cost-effectiveness and application of RTMUS echo to enhance its benefits for global healthcare. [ABSTRACT FROM AUTHOR]

Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information. Although multiple diagnostic challenges such as evaluation of calcified segments, stents, and small distal vessels still exist, the technologic developments in hardware as well as growing incorporation of artificial intelligence to daily practice are all set to augment the diagnostic and prognostic role of CCTA in cardiovascular disorders. [ABSTRACT FROM AUTHOR]