Severe Cardiovascular Diseases Research Articles

Acupoint Application of Cinnamon and Coptis Combined for Abdominal Distension

Femoral neck fracture is more common in the elderly, with surgery being the primary treatment approach. However, elderly patients are susceptible to symptoms such as abdominal distension and constipation after surgery, which can be caused by secondary factors such as loss of appetite and electrolyte imbalances. In severe cases, cardiovascular and cerebrovascular diseases can be induced, which is not conducive to postoperative rehabilitation of patients. Choosing a safe, effective and low-cost method to intervene patients with abdominal distension and constipation is conducive to promoting postoperative rehabilitation of patients with femoral neck fracture. Acupoint application of cinnamon coptis contains cinnamon, coptis and other traditional Chinese medicine, which has the effect of dredge viscera and promote intestinal peristalsis. The active ingredients of these herbs, such as cinnamaldehyde and berberine, have anti-inflammatory and laxative effects. Through clinical trials, this study found that acupoint application of cinnamon coptis positively impacts the recovery of abdominal distension and constipation in elderly patients following femoral neck fracture surgery. This intervention significantly enhances clinical efficacy, inhibits the levels of inflammatory factors, reduces constipation symptoms, and improves gastrointestinal function (P <0.05). However, acupoint application of cinnamon and coptis can effectively promote postoperative constipation recovery of patients, which may be related to the effect of effective ingredients such as cinnamaldehyde and berberine, which have anti-inflammatory and bowel soothing effects. Therefore, this study suggested that acupoint application of cinnamon and coptis combined with acupoint massage could be the optimal treatment for elderly patients with abdominal distention and constipation after femoral neck fracture.

Dynamic variations of plasma and urine metabolic profiles in left anterior descending coronary artery ligation-induced myocardial infarction rats and the regulatory effects of Chinese patent medicine Xin-Ke-Shu

Myocardial infarction (MI) is one of the most severe cardiovascular diseases (CVD). Traditional Chinese medicines have unique advantages in the treatment of CVD, with Xin-Ke-Shu (XKS) being a commonly used Chinese patent medicine for the prevention and treatment of MI patients. This study aimed to investigate the dynamic metabolic profiles of plasma and urine in left anterior descending coronary artery ligation (LAD) -induced MI rats at days 3, 12, and 21 after surgery, and to evaluate the regulatory effects of XKS at these time points using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) metabolomics. The metabolic profiles of plasma and urine in the LAD-induced MI rats showed significant variations at days 3, 12, and 21 after MI. We identified a total of 23 plasma metabolites and 12 urine metabolites as potential pathological markers related to MI progression. These metabolites were mainly involved in pathways such as TCA cycle, arachidonic acid metabolism, glutathione metabolism, glycerophospholipid metabolism, sphingolipid metabolism, and fatty acid metabolism, all of which were associated with imbalance of myocardial energy metabolism, oxidative stress, and calcium overload. Disturbances in the TCA cycle, arachidonic acid metabolism, glutathione metabolism, and purine metabolism in plasma and urine were observed as early as day 3 after MI. By day 12, we noted significant changes in fatty acid metabolism in plasma and urine, along with notable alterations in sphingolipid metabolism in plasma. Disorders in plasma glycerophospholipid metabolism were first evident at day 12 and reached their peak severity by day 21. Treatments with XKS significantly regulated the disturbances in the plasma and urine metabolic profiles of MI rats at days 3, 12, and 21, with medium dose of XKS displaying a particularly strong regulatory effect, especially at day 12. Our study demonstrates that host metabolism undergoes dynamical changes following MI with most metabolic disorders manifesting in the early stage of MI. XKS effectively regulates nearly all of these disturbances and can be administered as soon as possible after MI. These findings provide valuable insights into the metabolic progression of MI and highlight the therapeutic potential of XKS in the treatment of MI.

Effect of Cannabistilbene I in Attenuating Angiotensin II-Induced Cardiac Hypertrophy: Insights into Cytochrome P450s and Arachidonic Acid Metabolites Modulation.

Introduction: This research investigated the impact of Cannabistilbene I on Angiotensin II (Ang II)-induced cardiac hypertrophy and its potential role in cytochrome P450 (CYP) enzymes and arachidonic acid (AA) metabolic pathways. Cardiac hypertrophy, a response to increased stress on the heart, can lead to severe cardiovascular diseases if not managed effectively. CYP enzymes and AA metabolites play critical roles in cardiac function and hypertrophy, making them important targets for therapeutic intervention. Methods: Adult human ventricular cardiomyocyte cell line (AC16) was cultured and treated with Cannabistilbene I in the presence and absence of Ang II. The effects on mRNA expression related to cardiac hypertrophic markers and CYP were analyzed using real-time polymerase chain reaction, while CYP protein levels were measured by Western blot analysis. AA metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results: Results showed that Ang II triggered hypertrophy, as evidenced by the increase in hypertrophic marker expression, and enlarged the cell surface area, effects that were alleviated by Cannabistilbene I. Gene expression analysis indicated that Cannabistilbene I upregulated CYP1A1, leading to increased enzymatic activity, as evidenced by 7-ethoxyresorufin-O-deethylase assay. Furthermore, LC-MS/MS analysis of AA metabolites revealed that Ang II elevated midchain (R/S)-hydroxyeicosatetraenoic acid (HETE) concentrations, which were reduced by Cannabistilbene I. Notably, Cannabistilbene I selectively increased 19(S)-HETE concentration and reversed the Ang II-induced decline in 19(S)-HETE, suggesting a unique protective role. Conclusion: This study provides new insights into the potential of Cannabistilbene I in modulating AA metabolites and reducing Ang II-induced cardiac hypertrophy, revealing a new candidate as a therapeutic agent for cardiac hypertrophy.

Synthesized economic evidence on the cost-effectiveness of screening familial hypercholesterolemia

BackgroundFamilial hypercholesterolemia (FH) is a prevalent genetic disorder with global implications for severe cardiovascular diseases. Motivated by the growing recognition of the need for early diagnosis and treatment of FH to mitigate its severe consequences, alongside the gaps in understanding the economic implications and equity impacts of FH screening, this study aims to synthesize the economic evidence on the cost-effectiveness of FH screening and to analyze the impact of FH screening on health inequality.MethodsWe conducted a systematic review on the economic evaluations of FH screening and extracted information from the included studies using a pre-determined form for evidence synthesis. We synthesized the cost-effectiveness components involving the calculation of synthesized incremental cost-effectiveness ratios (ICERs) and net health benefit (NHB) of different FH screening strategies. Additionally, we applied an aggregate distributional cost-effectiveness analysis (DCEA) to assess the impact of FH screening on health inequality.ResultsAmong the 19 studies included, over half utilized Markov models, and 84% concluded that FH screening was potentially cost-effective. Based on the synthesized evidence, cascade screening was likely to be cost-effective, with an ICER of $49,630 per quality-adjusted life year (QALY). The ICER for universal screening was $20,860 per QALY as per evidence synthesis. The aggregate DCEA for six eligible studies presented that the incremental equally distributed equivalent health (EDEH) exceeded the NHB. The difference between EDEH and NHB across the six studies were 325, 137, 556, 36, 50, and 31 QALYs, respectively, with an average positive difference of 189 QALYs.ConclusionsOur research offered valuable insights into the economic evaluations of FH screening strategies, highlighting significant heterogeneity in methods and outcomes across different contexts. Most studies indicated that FH screening is cost-effective and contributes to improving overall population health while potentially reducing health inequality. These findings offer implications that policies should promote the implementation of FH screening programs, particularly among younger population. Optimizing screening strategies based on economic evidence can help identify the most effective measures for improving health outcomes and maximizing cost-effectiveness.

Ethical aspects in the context of extracorporeal life support systems (ECLS): consensus paper of the DGK, DGTHG and DGAI

Extracorporeal life support systems (ECLS) are life-sustaining measures for severe cardiovascular diseases, serving as bridging treatment either until cardiovascular function is restored or alternative treatment, such as heart transplantation or the implantation of permanent ventricular assist devices is performed. Given the insufficient evidence and frequent urgency of implantation without initial patient consent, the ethical challenges and psychological burden for patients, relatives and the interprofessional intensive care team are significant. As with any treatment, an appropriate therapeutic goal for ECLS treatment based on the indications and patient informed consent is mandatory. In order to integrate the necessary ethical considerations into everyday clinical practice, astructured algorithm for handling ECLS is proposed here, which takes ethical aspects into due account.

Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement

Human health risks from particles with a diameter of less than 2.5 µm (PM2.5) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear. This study investigates how leaf attributes affect the capture of PM2.5, which can penetrate deep into the lungs and pose significant health risks. Using a self-developed particulate matter (PM) resuspension chamber and single-particle aerosol mass spectrometer, we measured the size distribution and mass spectra of particles captured by ten tree species. Notably, Cinnamomum camphora (L.) J.Presl and Osmanthus fragrans Lour. are more effective at capturing particles under 1 µm, which are most harmful because they can reach the alveoli, whereas Ginkgo biloba L. and Platanus × acerifolia (Aiton) Willd. tend to capture larger particles, up to 1.6 µm, which are prone to being trapped in the upper respiratory tract. Leaf physiological traits such as stomatal conductance and water potential significantly enhance the capture of larger particles. The Adaptive Resonance Theory neural network (ART-2a) algorithm classified a large number of single particles to determine their composition. Results indicate distinct inter-species variations in chemical composition of particles captured by leaves. Moreover, we identified how specific leaf wax compositions—beyond the known sticky nature of hydrophobic waxes—contribute to particle adhesion, particularly highlighting the roles of fatty acids and alkanes in adhering particles rich in organic carbon and heavy metals, respectively. This research advances our understanding by linking leaf physiological and wax characteristics to the selective capture of PM2.5, providing actionable insights for urban forestry management. The detailed exploration of particle size and composition, tied to specific tree species, enriches the current literature by quantifying how and why different species contribute variably to air quality improvement. This adds a crucial layer of specificity to the general knowledge that trees serve as bio-filters, offering a refined strategy for planting urban trees based on their particulate capture profiles.

Incidence of heart disease and stroke among patients with bipolar disorder

Abstract Background Increased vulnerability to cardiovascular diseases (CVD) and stroke has been observed among people with mental disorders, such as bipolar disorder (BP). Yet, the evidence for the association between severe mental disorders and CVD and stroke events remains inconclusive. Purpose By running a cohort study based on large retrospective data, we aimed to establish the relative risk of CVD problems and stroke in patients admitted to the hospital and diagnosed with BP. Methods The study was conducted on electronic health records provided by 113 healthcare organizations worldwide from the TriNetX Global Collaborative Network, containing at least 690,946 patients with BP symptoms. The assessment was focused on the first incidence of ischemic heart diseases, nontraumatic brain hemorrhage, or cerebral infarction among patients after BP diagnosis. After propensity score matching age, gender, race and comorbidities, the rates were compared against a cohort of patients with no mental, behavioral, or neurodevelopmental disorders who also suffered from similar CVD and stroke events. In addition, Kaplan-Meier Analysis was performed to compare their survivability to CVD, taking into account patient censoring. Results Patient count after applying the score matching yielded 532,082 per cohort. The odds ratio (OR) of ischemic heart diseases, nontraumatic brain hemorrhage, or cerebral infarction on BP compared to people without any mental disorder was equal to 2.349, indicating a significantly elevated risk for such events. The Kaplan-Meier analysis showed that patients with BP diagnosis had 9.88% less survival probability to having CVD problems after 20 years since the diagnosis than patients without any mental disorder. With a hazard ratio of 1.970 (95%CI 1.935-2.005), the risk of developing a CVD problem on the former (patients with BP diagnosis) was almost twice greater than for patients suffering without mental health disorders. Conclusion There is an increased risk of developing cardiovascular problems and stroke among patients with bipolar disorder compared to patients affected by such symptoms without mental disorders. The robust results were ensured by limiting the potential confounders with the propensity score matching procedure, such as demographics, comorbidity, and medication. The study implies that monitoring and prevention approaches for cardiovascular and cerebral vascular diseases must be tailored to symptoms of mental disorders.

Discovery of Highly Selective, Potent, Covalent, and Orally Bioavailable Factor XIIa Inhibitors for the Treatment of Thrombo-Inflammation.

Thrombo-inflammation is closely associated with a few severe cardiovascular and infectious diseases. Factor XIIa (FXIIa) in the intrinsic coagulation pathway plays a pivotal role in the development of thrombo-inflammation and its inhibition has emerged as a potential therapeutic approach for thrombo-inflammatory disorders. Nonetheless, as of now, few small-molecule FXIIa inhibitors have demonstrated notable effectiveness against thrombo-inflammation, with none progressing into clinical stages. Herein, we present potent, covalent, reversible, and selective small-molecule FXIIa inhibitors such as 4a and 4j obtained through structure-based drug design. Compounds 4a and 4j showed significant anticoagulation and substantial anti-inflammatory effects in vitro, coupled with exceptional plasma stability. Furthermore, in carrageenan-induced thrombosis models, 4a and 4j demonstrated remarkable dual antithrombotic and anti-inflammatory activity when administered orally. Compound 4j exhibited a favorable safety profile without obvious tissue toxicity in mice, suggesting its potential as an oral therapeutic option for thrombo-inflammation.

Copy number variations: The potential association genetic cause in severe cardiovascular diseases with unknown aetiology

AbstractCardiovascular diseases (CVDs) are the leading cause of mortality worldwide. While both genetic and environmental factors significantly contribute to the pathogenesis of CVDs, recent advancements in genetic technology have further emphasized the significance of genetic factors in CVDs. Growing evidence suggests genetic changes as a primary cause of CVDs and their susceptibility. The major genetic changes include chromosomal aneuploidy, abnormal chromosomal structure, and single gene mutations. Copy number variation (CNV) is a type of newly introduced structural change that influences the copy number of a genome region. CNVs could manifest with more severe phenotypes compared to single‐nucleotide polymorphism (SNP) as they affect a considerably larger segment of the genome. Additionally, for the same reason, CNVs tend to manifest earlier in life compared to SNP. Recent studies have demonstrated the fundamental role of CNVs in the development or susceptibility to cardiovascular disorders. However, it remains to be determined which CVDs should prompt CNV tests in clinical practice. In this review, we discussed the extent to which CNV could take part in CVDs and further hypothesized that testing for CNV might be most beneficial for selective patients with CVDs.

Herbs Used for the Management of Hypertension: A Systematic Review

Background: Hypertension is characterized by persistent high blood pressure and has emerged as a critical risk factor for severe cardiovascular diseases. Although several drugs have been designated to reduce blood pressure, these drugs can potentially cause side effects for patients. Therefore, medicinal plants are used to complement conventional drugs to treat various ailments. Methods: This study reviews the anti-hypertensive potential of herbs and plants and their mechanism of action in reducing blood pressure at their evaluated effective doses. Databases such as PubMed, Scopus, Science Direct, and Google Scholar were used to search articles from January 2016 to Sept 2022. The key search terms included “hypertension”, “lowers blood pressure”, “anti-hypertensive”, and “plants”. These generated 807 documents and using the PRISMA guidelines, thirty plants were identified for this review. Research studies with similar plant parts extracted from similar solvents, more than one dose, and animal models with a positive control were considered in this review. Studies that did not show significant blood pressure reduction were excluded. Results: Based on this criterion, the mechanism of actions of these plants was thematically grouped into three categories, namely 1) antioxidant, 2) angiotensin-converting enzyme (ACE) inhibition, and 3) calcium influx in vascular smooth muscle. The effective doses, plant parts used, and traditional medicine usage are presented in this review. Conclusion: Further research is highly recommended to identify the active compounds and to potentially develop them into anti-hypertensive drugs as well as to establish the safe doses and standardization of these plant extracts. This review is partially funded by grants from the Ministry of Higher Education.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning

Purpose:Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic and hemorrhagic. Clinically, brain images such as computed tomography (CT) and computed tomography angiography (CTA) are widely used to recognize stroke types. However, few studies have combined imaging and clinical data to classify stroke or consider a factor as an Independent etiology. Methods:In this work, we propose a classification model that automatically distinguishes stroke types with hypertension as an independent etiology based on brain imaging and clinical data. We first present a preprocessing workflow for head axial CT angiograms, including noise reduction and feature enhancement of the images, followed by an extraction of regions of interest. Next, we develop a multi-scale feature fusion model that combines the location information of position features and the semantic information of deep features. Furthermore, we integrate brain imaging with clinical information through a multimodal learning model to achieve more reliable results. Results:Experimental results show our proposed models outperform state-of-the-art models on real imaging and clinical data, which reveals the potential of multimodal learning in brain disease diagnosis. Conclusion:The proposed methodologies can be extended to create AI-driven diagnostic assistance technology for categorizing strokes.

A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats

Cardiac hypertrophy (CH) is one of the stages in the occurrence and development of severe cardiovascular diseases, and exploring its biomarkers is beneficial for delaying the progression of severe cardiovascular diseases. In this research, we established a comprehensive and highly efficient pseudotargeted metabolomics method, which demonstrated a superior capacity to identify differential metabolites when compared to traditionaluntargeted metabolomics. The intra/inter-day precision and reproducibility results proved the method is reliable and precise. The established method was then applied to seek the potential differentiated metabolic biomarkers of cardiac hypertrophy (CH) rats, and oxylipins, phosphorylcholine (PC), lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), Krebs cycle intermediates, carnitines, amino acids, and bile acids were disclosed to be the possible differentiate components. Their metabolic pathway analysis revealed that the potential metabolic alterations in CH rats were mainly associated with phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and tyrosine metabolism. In sum, this research provided a comprehensiveand reliable LC-MS/MS MRM platform for pseudo-targeted metabolomics investigation of disease condition, and some interesting potential biomarkers were disclosed for CH, which merit further exploration in the future.

Lifestyle Behaviors of Childhood and Adolescence: Contributing Factors, Health Consequences, and Potential Interventions

In this narrative review, we summarized evidence from peer-reviewed articles, published before February 2024, that investigated lifestyle behaviors among children (≤12 years) and adolescents (13-18 years) representing different geographic descents. These behaviors included dietary patterns, screen time, physical activity, smoking, alcohol consumption, oral hygiene, unsafe sex, and sleep duration and quality. We documented the significant impacts of parental, school, and sociodemographic factors on the adoption of numerous lifestyle behaviors in this age category. Several health consequences could be attributed to unhealthy lifestyle behaviors during childhood and adolescence. For example, poor dietary habits can lead to a higher risk of obesity and cardiovascular disease. Physical inactivity contributes to the development of musculoskeletal and psychological disorders. Excessive screen time is associated with visual acuity problems, poor sleep, and psychological and behavioral problems. Tobacco use poses a significant risk for severe respiratory and cardiovascular diseases. Risky sexual behaviors are related to sexually transmitted infections and exposure to violence. Short sleep duration is associated with a lack of physical fitness and poor cognitive function. However, potential interventions, such as school-based health programs, community outreach initiatives, and national health policies and regulations, can improve lifestyle behaviors among children and adolescents.

Silica nanoparticles enhance the cyto- and hemocompatibility of a multilayered extracellular matrix scaffold for vascular tissue regeneration.

The limited availability of autologous vessels for vascular bypass surgeries is a major roadblock to treating severe cardiovascular diseases. Based on this clinical priority, our group has developed a novel engineered vascular graft by rolling human amniotic membranes into multilayered extracellular matrixes (ECM). When treated with silica nanoparticles (SiNP), these rolled scaffolds showed a significant improvement in their structural and mechanical properties, matching those from gold standard autologous grafts. However, it remained to be determined how cells respond to SiNP-treated materials. As a first step toward understanding the biocompatibility of SiNP-dosed biomaterials, we aimed to assess how endothelial cells and blood components interact with SiNP-treated ECM scaffolds. To test this, we used established in vitro assays to study SiNP and SiNP-treated scaffolds' cyto and hemocompatibility. Our results showed that SiNP effects on cells were concentration-dependent with no adverse effects observed up to 10μg/ml of SiNP, with higher concentrations inducing cytotoxic and hemolytic responses. The SiNP also enhanced the scaffold's hydrophobicity state, a feature known to inhibit platelet and immune cell adhesion. Accordingly, SiNP-treated scaffolds were also shown to support endothelial cell growth while preventing platelet and leukocyte adhesion. Our findings suggest that the addition of SiNP to human amniotic membrane extracellular matrixes improves the cyto- and hemocompatibility of rolled scaffolds and highlights this strategy as a robust mechanism to stabilize layered collagen scaffolds for vascular tissue regeneration.

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals.

Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

Impact of Comprehensive Primary Care in Patients With Complex Chronic Diseases: Nationwide Cohort Database Analysis in Korea.

More comprehensive healthcare services should be provided to patients with complex chronic diseases to better manage their complex care needs. This study examined the effectiveness of comprehensive primary care in patients with complex chronic diseases. We obtained 2002-2019 data from the National Health Insurance Sample Cohort Database. Participants were individuals aged ≥ 30 years with at least two of the following diseases: hypertension, diabetes mellitus, and hyperlipidemia. Doctors' offices were classified into specialized, functional, and gray-zone based on patient composition and major diagnostic categories. The Cox proportional hazard model was used to examine the association between office type and hospital admission due to all-causes, severe cardiovascular or cerebrovascular diseases (CVDs), hypertension, diabetes mellitus, or hyperlipidemia. The mean patient age was 60.3 years; 55.8% were females. Among the 24,906 patients, 12.8%, 38.3%, and 49.0% visited specialized, functional, and gray-zone offices, respectively. Patients visiting functional offices had a lower risk of all-cause admission (hazard ratio [HR], 0.935; 95% confidence interval [CI], 0.895-0.976) and CVD-related admission (HR, 0.908; 95% CI, 0.844-0.977) than those visiting specialized offices. However, the admission risks for hypertension, diabetes mellitus, and hyperlipidemia were not significantly different among office types. This study provides evidence of the effectiveness of primary care in functional doctors' offices for patients with complex chronic diseases beyond a single chronic disease and suggests the need for policies to strengthen functional offices providing comprehensive care.

Measurement of Retrobulbar Blood Flow and Vascular Reactivity—Relevance for Ocular and Cardiovascular Diseases

Abnormal retrobulbar hemodynamics have been linked to the development of various ocular diseases, including glaucoma, age-related macular degeneration, and diabetic retinopathy. Additionally, altered retrobulbar blood flow has been observed in patients with severe cardiovascular diseases, including carotid artery occlusion, stroke, heart failure, and acute coronary syndrome. Due to the complex and intricate anatomy of retrobulbar blood vessels and their location behind the eyeball, measurement of retrobulbar blood flow and vascular reactivity, as well as the interpretation of the findings, are challenging. Various methods, such as color Doppler imaging, computed tomography angiography or magnetic resonance imaging, have been employed to assess retrobulbar blood flow velocities in vivo. Color Doppler imaging represents a fast and non-invasive method to measure retrobulbar blood flow velocities in vivo. While no information about vessel diameter can be gained performing this method, computed tomography angiography and magnetic resonance imaging provide information about vessel diameter and detailed information on the anatomical course. Additionally, ex vivo studies, such as myography, utilizing genetically modified animal models may provide high optical resolution for functional vascular investigations in these small vessels. To our best knowledge, this is the first review, presenting a detailed overview of methods aiming to evaluate retrobulbar blood flow and vascular reactivity in both humans and laboratory animals. Furthermore, we will summarize the disturbances observed in retrobulbar blood flow in retinal, optic nerve, and cardiovascular diseases.

MiR-223-3p Prevents Necroptotic Macrophage Death by Targeting Ripk3 in a Negative Feedback Loop and Consequently Ameliorates Advanced Atherosclerosis.

The formation of large necrotic cores results in vulnerable atherosclerotic plaques, which can lead to severe cardiovascular diseases. However, the specific regulatory mechanisms underlying the development of necrotic cores remain unclear. To evaluate how the modes of lesional cell death are reprogrammed during the development of atherosclerosis, the expression levels of key proteins that are involved in the necroptotic, apoptotic, and pyroptotic pathways were compared between different stages of plaques in humans and mice. Luciferase assays and loss-of-function studies were performed to identify the microRNA-mediated regulatory mechanism that protects foamy macrophages from necroptotic cell death. The role of this mechanism in atherosclerosis was determined by using a knockout mouse model with perivascular drug administration and tail vein injection of microRNA inhibitors in Apoe-/- mice. Here, we demonstrate that the necroptotic, rather than the apoptotic or pyroptotic, pathway is more activated in advanced unstable plaques compared with stable plaques in both humans and mice, which closely correlates with necrotic core formation. The upregulated expression of Ripk3 (receptor-interacting protein kinase 3) promotes the C/EBPβ (CCAAT/enhancer binding protein beta)-dependent transcription of the microRNA miR-223-3p, which conversely inhibits Ripk3 expression and forms a negative feedback loop to regulate the necroptosis of foamy macrophages. The knockout of the Mir223 gene in bone marrow cells accelerates atherosclerosis in Apoe-/- mice, but this effect can be rescued by Ripk3 deficiency or treatment with the necroptosis inhibitors necrostatin-1 and GSK-872. Like the Mir223 knockout, treating Apoe-/- mice with miR-223-3p inhibitors increases atherosclerosis. Our study suggests that miR-223-3p expression in macrophages protects against atherosclerotic plaque rupture by limiting the formation of necrotic cores, thus providing a potential microRNA therapeutic candidate for atherosclerosis.

Utilisation of artificial intelligence to predict early readmission to the cardiac intensive care unit in critically ill patients

Abstract Background The need for cardiac intensive care unit (ICU) beds remains high in order to treat patients with severe cardiovascular diseases. Therefore, safe and timely discharge policies from the cardiac ICU to normal wards are crucial to meet the continuously increasing demand for cardiac ICU beds. ICU readmission after ICU discharge has been shown to increase mortality, hospital length of stay and costs. Artificial intelligence may provide a timely and accurate discharge decision support system for intensivists based on comprehensive, multi-dimensional ICU data. Purpose This is the first study to develop and validate a neuronal network, called Feedforward Neural Network (FNN), to safely discharge patients from the cardiac ICU and predict ICU readmission within 72 hours. Methods Adult patients who were admitted to the cardiac ICU due to a primary cardio-pulmonary admission diagnosis (such as acute myocardial infarction, cardiogenic shock or cardiac arrest) at a German tertiary care center between 2003 and 2021 were included. The dataset was split into a training and test dataset. The FNN was trained with 127 variables, including patient demographics, co-morbidities, medical diagnoses, disease acuity levels, laboratory values, vital parameters, ICU procedures and administered ICU medications. FNN was compared to the predictability of a classic logistic regression model and the Stability and Workload Index for Transfer (SWIFT) score, which is a clinical score to predict ICU readmission. Results 2,498 adult patients in the cardiac ICU met the inclusion criteria, out of which the FNN was trained in 1,998 (80.0%) patients and tested in 500 (20.0%) patients. The FNN had a 97.9% sensitivity, 86.9% specificity, 99.4% precision and 97.4% accuracy. FNN showed a better predictability of early ICU readmission than the logistic regression model and the SWIFT score: areas under the receiver operating characteristic (AUC ROC) curves were 90.78% vs. 71.34% vs. 71.13% (P <0.001) (Figure), respectively. Conclusions ICU transfer to the normal ward is considered to be a high-risk event and the vast amount of daily clinical, physiologic, laboratory and monitoring data may overwhelm critical care providers in making well informed decisions on patient discharge readiness. Artificial intelligence might be used as a sensitive, specific and accurate decision support system to help with patient discharge management and reduce ICU readmission rates.