Mitochondrial dysfunction and metabolic reprogramming induce macrophage pro-inflammatory phenotype switch and atherosclerosis progression in aging.

Introduction: Aging increases the risk of atherosclerotic vascular disease and its complications. Macrophages are pivotal in the pathogenesis of vascular aging, driving inflammation and atherosclerosis progression. NOX4 (NADPH oxidase 4) expression increases with age, correlating with mitochondrial dysfunction, inflammation, and atherosclerosis. We hypothesized that the NOX4-dependent mitochondrial oxidative stress promotes aging-associated atherosclerosis progression by causing metabolic dysfunction and inflammatory phenotype switch in macrophages. Methods: We studied atherosclerotic lesion morphology and macrophage phenotype in young (5-month-old) and aged (16-month-old) Nox4 -/-/Apoe -/- and Apoe -/- mice fed Western diet. Results: Young Nox4-/-/Apoe-/- and Apoe-/- mice had comparable aortic and brachiocephalic artery atherosclerotic lesion cross-sectional areas. Aged mice showed significantly increased lesion area compared with young mice. Aged Nox4-/-/Apoe-/- had significantly lower lesion areas than Apoe-/- mice. Compared with Apoe-/- mice, atherosclerotic lesions in aged Nox4-/-/Apoe-/- showed reduced cellular and mitochondrial ROS and oxidative DNA damage, lower necrotic core area, higher collagen content, and decreased inflammatory cytokine expression. Immunofluorescence and flow cytometry analysis revealed that aged Apoe-/- mice had a higher percentage of classically activated pro-inflammatory macrophages (CD38+CD80+) in the lesions. Aged Nox4-/-/Apoe-/- mice had a significantly higher proportion of alternatively activated pro-resolving macrophages (EGR2+/CD163+CD206+) in the lesions, with an increased CD38+/EGR2+ cell ratio compared with Apoe-/- mice. Mitochondrial respiration assessment revealed impaired oxidative phosphorylation and increased glycolytic ATP production in macrophages from aged Apoe-/- mice. In contrast, macrophages from Nox4-/-/Apoe-/- mice were less glycolytic and more aerobic, with preserved basal and maximal respiration and mitochondrial ATP production. Macrophages from Nox4-/-/Apoe-/- mice also had lower mitochondrial ROS levels and reduced IL1ß secretion; flow cytometry analysis showed fewer CD38+ cells after IFNγ+LPS treatment and more EGR2+ cells after IL4 treatment than in Apoe-/- macrophages. In aged Apoe-/- mice, inhibition of NOX4 activity using GKT137831 significantly reduced macrophage mitochondrial ROS and improved mitochondrial function, resulting in decreased CD68+CD80+ and increased CD163+CD206+ lesion macrophage proportion and attenuated atherosclerosis. Discussion: Our findings suggest that increased NOX4 in aging drives macrophage mitochondrial dysfunction, glycolytic metabolic switch, and pro-inflammatory phenotype, advancing atherosclerosis. Inhibiting NOX4 or mitochondrial dysfunction could alleviate vascular inflammation and atherosclerosis, preserving plaque integrity.

HAPI: An efficient Hybrid Feature Engineering-based Approach for Propaganda Identification in social media.

Social media platforms serve as communication tools where users freely share information regardless of its accuracy. Propaganda on these platforms refers to the dissemination of biased or deceptive information aimed at influencing public opinion, encompassing various forms such as political campaigns, fake news, and conspiracy theories. This study introduces a Hybrid Feature Engineering Approach for Propaganda Identification (HAPI), designed to detect propaganda in text-based content like news articles and social media posts. HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). Hybrid feature engineering entails the amalgamation of various features, including Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), Sentimental features, and tweet length, among others. Multiple Machine Learning classifiers undergo training and evaluation utilizing the proposed methodology, leveraging a selection of 40 pertinent features identified through the hybrid feature selection technique. All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. The SVM-based HaPi (SVM-HaPi) exhibits superior performance among traditional algorithms, achieving precision, recall, F-Measure, and overall accuracy of 0.69, 0.69, 0.69, and 69.2%, respectively. Furthermore, the proposed approach is compared to well-known existing approaches where it overperformed most of the studies on several evaluation metrics. This research contributes to the development of a comprehensive system tailored for propaganda identification in textual content. Nonetheless, the purview of propaganda detection transcends textual data alone. Deep learning algorithms like Artificial Neural Networks (ANN) offer the capability to manage multimodal data, incorporating text, images, audio, and video, thereby considering not only the content itself but also its presentation and contextual nuances during dissemination.

Autotaxin Inhibition Reduces Post-Ischemic Myocardial Inflammation via Epigenetic Gene Modifications.

Myocardial infarction (MI) triggers a complex inflammatory response that is essential for cardiac repair but can also lead to adverse outcomes if left uncontrolled. Recent studies have highlighted the importance of epigenetic modifications in regulating post-MI inflammation. This study investigated the role of the autotaxin (ATX)/lysophosphatidic acid (LPA) signaling axis in modulating myocardial inflammation through epigenetic pathways in a mouse model of MI. C57BL/6 J mice underwent left anterior descending coronary artery ligation to induce MI and were treated with the ATX inhibitor, PF-8380, or vehicle. Cardiac tissue from the border zone was collected at 6 h, 1, 3, and 7 days post-MI for epigenetic gene profiling using RT2 Profiler PCR Arrays. The results revealed distinct gene expression patterns across sham, MI + Vehicle, and MI + PF-8380 groups. PF-8380 treatment significantly altered the expression of genes involved in inflammation, stress response, and epigenetic regulation compared to the vehicle group. Notably, PF-8380 downregulated Hdac5, Prmt5, and Prmt6, which are linked to exacerbated inflammatory responses, as early as 6 h post-MI. Furthermore, PF-8380 attenuated the reduction of Smyd1, a gene important in myogenic differentiation, at 7 days post-MI. This study demonstrates that the ATX/LPA signaling axis plays a pivotal role in modulating post-MI inflammation via epigenetic pathways. Targeting ATX/LPA signaling may represent a novel therapeutic strategy to control inflammation and improve outcomes after MI. Further research is needed to validate these findings in preclinical and clinical settings and to elucidate the complex interplay between epigenetic mechanisms and ATX/LPA signaling in the context of MI.

Inherent Metabolic Adaptations in Adult Spiny Mouse ( Acomys ) Cardiomyocytes Facilitate Enhanced Cardiac Recovery Following Myocardial Infarction.

The adult mammalian heart has limited regenerative capacity following injury, leading to progressive heart failure and mortality. Recent studies have identified the spiny mouse ( Acomys ) as a unique model for mammalian cardiac isch3emic resilience, exhibiting enhanced recovery after myocardial infarction (MI) compared to commonly used laboratory mouse strains. However, the underlying cellular and molecular mechanisms behind this unique response remain poorly understood. In this study, we comprehensively characterized the metabolic characteristics of cardiomyocytes in Acomys compared to the non-regenerative Mus musculus . We utilized single-nucleus RNA sequencing (snRNA-seq) in sham-operated animals and 1, 3, and 7 days post-myocardial infarction to investigate cardiomyocytes' transcriptomic and metabolomic profiles in response to myocardial infarction. Complementary targeted metabolomics, stable isotope-resolved metabolomics, and functional mitochondrial assays were performed on heart tissues from both species to validate the transcriptomic findings and elucidate the metabolic adaptations in cardiomyocytes following ischemic injury. Transcriptomic analysis revealed that Acomys cardiomyocytes inherently upregulate genes associated with glycolysis, the pentose phosphate pathway, and glutathione metabolism while downregulating genes involved in oxidative phosphorylation (OXPHOS). These metabolic characteristics are linked to decreased reactive oxygen species (ROS) production and increased antioxidant capacity. Our targeted metabolomic studies in heart tissue corroborated these findings, showing a shift from fatty acid oxidation to glycolysis and ancillary biosynthetic pathways in Acomys at baseline with adaptive changes post-MI. Functional mitochondrial studies indicated a higher reliance on glycolysis in Acomys compared to Mus , underscoring the unique metabolic phenotype of Acomys hearts. Stable isotope tracing experiments confirmed a shift in glucose utilization from oxidative phosphorylation in Acomys . In conclusion, our study identifies unique metabolic characteristics of Acomys cardiomyocytes that contribute to their enhanced ischemic resilience following myocardial infarction. These findings provide novel insights into the role of metabolism in regulating cardiac repair in adult mammals. Our work highlights the importance of inherent and adaptive metabolic flexibility in determining cardiomyocyte ischemic responses and establishes Acomys as a valuable model for studying cardiac ischemic resilience in adult mammals.

Tamsulosin 0.8 mg daily dose in management of BPH patients with failed tamsulosin 0.4 mg monotherapy and unfit for surgical intervention.

AIM: This study aims to evaluate the effectiveness and safety of administering double-dose tamsulosin (0.8 mg) for treating patients with benign prostatic hyperplasia (BPH) who have not responded to the standard single dose of tamsulosin (0.4 mg) and are deemed unsuitable for transurethral resection (TUR) intervention. MATERIALS AND METHODS: Between November 2022 and July 2023, we prospectively analyzed 111 patients who were experiencing severe BPH symptoms. These patients received a double dose of tamsulosin for one month. We collected baseline characteristics such as age, body mass index, and underlying medical conditions. Various parameters including the International Prostate Symptom Score (IPSS), prostate-specific antigen (PSA) levels, prostate volume, peak urinary flow rate (Qmax), voided volume, and post-void residual volume were evaluated before and after treatment. RESULTS: All 111 patients completed the study. The mean age, PSA level, and prostate volume were 63.12 ± 4.83 years, 3.42 ± 0.93 ng/ml, and 50.37 ± 19.23 ml, respectively. Of these patients, 93 showed improvement in Qmax, post-void residual volume, and IPSS score (p-value = 0.001). The total IPSS score and total Qmax improved from 24.03 ± 2.49 and 7.72 ± 1.64 ml/sec to 16.41 ± 3.84 and 12.08 ± 2.37 ml/sec, respectively. CONCLUSION: Double-dose 0.8mg tamsulosin as an alpha-blocker therapy appears to be a viable temporary management option for BPH patients who have not responded to the standard single dose 0.4mg tamsulosin and are not suitable candidates for TUR intervention.

Custom Patient-Specific 3D-Printed Titanium Truss Tibiotalocalcaneal Arthrodesis Implants for Failed Total Ankle Replacements: Classification, Technical Tips, and Treatment Algorithm.

BACKGROUND: The management of failed total ankle replacements, with significant loss of bone stock, is challenging with high rates of complications and associated morbidity. Recent technological advances have enabled the development of patient-customized 3D-printed titanium truss arthrodesis implants, which offer an alternative salvage option for failed total ankle replacements. METHODS: A prospective observational study was performed of 6 cases of failed total ankle replacements that were managed using custom patient-specific 3D-printed titanium truss arthrodesis implants. Technical tips, classification, and a treatment algorithm were developed based on our initial experience. RESULTS: Between November 2018 and March 2022, 6 patients underwent arthrodesis for failed total ankle replacements. Follow-up was available for all cases. The mean follow-up was 3.0 years (range 1-4.5). The mean MOXFQ Index improved from 73.1 to 32.3 (P < .05). The mean EQ-5D-5L Index improved from 0.366 to 0.743 (P < .05) and the EQ-VAS also improved from 53.0 to 63.3 (P = .36). The mean VAS-Pain score at final follow-up was 27.5. There were no cases of nonunion. None of the patients were smokers. The overall complication rate was 50%. Two patients returned to surgery: one for wound washout following TAR explantation and a second for removal of metalwork 2 years following surgery for a prosthetic joint infection secondary to hematogenous spread. No patients underwent revision fixation or amputation. CONCLUSION: Custom patient-specific 3D-printed titanium truss arthrodesis implants are a viable treatment option for failed total ankle replacements.

Alternative therapeutic approach of ovine subclinical mastitis using the ethanolic roots extract of Capparis spinosa.

Background: Over the past decades, Capparis spinosa has been considered a traditional therapy for relieving different illnesses. Mastitis causes a decrease in milk production and is usually treated with injectable and intra-mammary antibiotics. Aim: Investigating the therapeutic effects of C. spinosa root extract on subclinically mastitic ewes. Methods: Totally, 164 lactating ewes were selected randomly from the flocks that existed in some areas belonging to Al-Najaf City (Najaf, Iraq) from September to December (2022). Each study animal was subjected to direct sampling of milk before and once each week for 6 weeks (42 days) post treatment to be tested directly by the California mastitis test (CMT). Results: Concerning phytochemical testing of ethanolic root extract, the findings revealed a significant increase in the concentration of alkaloids, flavonoids, polyphenols, and tannins when compared to other components such as coumarins, saponin, glycosides, amino acids, and steroids. In this study, there were 44.51% infected ewes with subclinical mastitis, involving 25.61%, 13.41%, and 5.49% for scores 1, 2, and 3, respectively. In comparison with pre-treatment week, insignificant alteration was seen in the values of all scores in therapeutic week 1. However, significant differences were initiated in values of score 0 in week 2; score 0 and score 2 in week 3; score 0, score 1, and score 2 in week 4; and values of all scores in weeks 5 and 6. Conclusion: This represents the first Iraqi study aimed at the treatment of subclinical mastitis in sheep using the root extract of C. spinosa. Phytochemical testing of ethanolic extract revealed the presence of variable amounts of chemical compounds that reflect their effects on treated animals by decreasing the number of infected ewes with the disease. Moreover, studies are greatly important to estimate the therapeutic effects of other parts of C. spinosa such as leaves and seeds, on the disease and other animal diseases.

Lysophosphatidic Acid-Mediated Inflammation at the Heart of Heart Failure.

PURPOSE OF REVIEW: The primary aim of this review is to provide an in-depth examination of the role bioactive lipids-namely lysophosphatidic acid (LPA) and ceramides-play in inflammation-mediated cardiac remodeling during heart failure. With the global prevalence of heart failure on the rise, it is critical to understand the underlying molecular mechanisms contributing to its pathogenesis. Traditional studies have emphasized factors such as oxidative stress and neurohormonal activation, but emerging research has shed light on bioactive lipids as central mediators in heart failure pathology. By elucidating these intricacies, this review aims to: Bridge the gap between basic research and clinical practice by highlighting clinically relevant pathways contributing to the pathogenesis and prognosis of heart failure. Provide a foundation for the development of targeted therapies that could mitigate the effects of LPA and ceramides on heart failure. Serve as a comprehensive resource for clinicians and researchers interested in the molecular biology of heart failure, aiding in better diagnostic and therapeutic decisions. RECENT FINDINGS: Recent findings have shed light on the central role of bioactive lipids, specifically lysophosphatidic acid (LPA) and ceramides, in heart failure pathology. Traditional studies have emphasized factors such as hypoxia-mediated cardiomyocyte loss and neurohormonal activation in the development of heart failure. Emerging research has elucidated the intricacies of bioactive lipid-mediated inflammation in cardiac remodeling and the development of heart failure. Studies have shown that LPA and ceramides contribute to the pathogenesis of heart failure by promoting inflammation, fibrosis, and apoptosis in cardiac cells. Additionally, recent studies have identified potential targeted therapies that could mitigate the effects of bioactive lipids on heart failure, including LPA receptor antagonists and ceramide synthase inhibitors. These recent findings provide a promising avenue for the development of targeted therapies that could improve the diagnosis and treatment of heart failure. In this review, we highlight the pivotal role of inflammation induced by bioactive lipid signaling and its influence on the pathogenesis of heart failure. By critically assessing the existing literature, we provide a comprehensive resource for clinicians and researchers interested in the molecular mechanisms of heart failure. Our review aims to bridge the gap between basic research and clinical practice by providing actionable insights and a foundation for the development of targeted therapies that could mitigate the effects of bioactive lipids on heart failure. We hope that this review will aid in better diagnostic and therapeutic decisions, further advancing our collective understanding and management of heart failure.

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions.

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

A Novel 3D Reversible Data Hiding Scheme Based on Integer-Reversible Krawtchouk Transform for IoMT.

To avoid rounding errors associated with the limited representation of significant digits when applying the floating-point Krawtchouk transform in image processing, we present an integer and reversible version of the Krawtchouk transform (IRKT). This proposed IRKT generates integer-valued coefficients within the Krawtchouk domain, seamlessly aligning with the integer representation commonly utilized in lossless image applications. Building upon the IRKT, we introduce a novel 3D reversible data hiding (RDH) algorithm designed for the secure storage and transmission of extensive medical data within the IoMT (Internet of Medical Things) sector. Through the utilization of the IRKT-based 3D RDH method, a substantial amount of additional data can be embedded into 3D carrier medical images without augmenting their original size or compromising information integrity upon data extraction. Extensive experimental evaluations substantiate the effectiveness of the proposed algorithm, particularly regarding its high embedding capacity, imperceptibility, and resilience against statistical attacks. The integration of this proposed algorithm into the IoMT sector furnishes enhanced security measures for the safeguarded storage and transmission of massive medical data, thereby addressing the limitations of conventional 2D RDH algorithms for medical images.

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection.

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.

Prognostic Significance of Activated Monocytes in Patients with ST-Elevation Myocardial Infarction.

Circulating monocytes have different subsets, including classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16++), which play different roles in cardiovascular physiology and disease progression. The predictive value of each subset for adverse clinical outcomes in patients with coronary artery disease is not fully understood. We sought to evaluate the prognostic efficacy of each monocyte subset in patients with ST-elevation myocardial infarction (STEMI). We recruited 100 patients with STEMI who underwent primary percutaneous coronary intervention (PCI). Blood samples were collected at the time of presentation to the hospital (within 6 h from onset of symptoms, baseline (BL)) and then at 3, 6, 12, and 24 h after presentation. Monocytes were defined as CD45+/HLA-DR+ and then subdivided based on the expression of CD14, CD16, CCR2, CD11b, and CD42. The primary endpoint was a composite of all-cause death, hospitalization for heart failure, stent thrombosis, in-stent restenosis, and recurrent myocardial infarction. Univariate and multivariate Cox proportional hazards models, including baseline comorbidities, were performed. The mean age of our cohort was 58.9 years and 25% of our patients were females. Patients with high levels (above the median) of CD14+CD16++ monocytes showed an increased risk for the primary endpoint in comparison to patients with low levels; adjusted hazard ratio (aHR) for CD14+/CD16++ cells was 4.3 (95% confidence interval (95% CI) 1.2-14.8, p = 0.02), for CD14+/CD16++/CCR2+ cells was 3.82 (95% CI 1.06-13.7, p = 0.04), for CD14+/CD16++/CD42b+ cells was 3.37 (95% CI 1.07-10.6, p = 0.03), for CD14+/CD16++/CD11b+ was 5.17 (95% CI 1.4-18.0, p = 0.009), and for CD14+ HLA-DR+ was 7.5 (95% CI 2.0-28.5, p = 0.002). CD14++CD16-, CD14++CD16+, and their CD11b+, CCR2+, and CD42b+ aggregates were not significantly predictive for our composite endpoint. Our study shows that CD14+ CD16++ monocytes and their subsets expressing CCR2, CD42, and CD11b could be important predictors of clinical outcomes in patients with STEMI. Further studies with a larger sample size and different coronary artery disease phenotypes are needed to verify the findings.

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model.

In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system's complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.

C-Reactive Protein Levels and Risk of Cardiovascular Diseases: A Two-Sample Bidirectional Mendelian Randomization Study.

Elevated C-reactive protein (CRP) levels are an indicator of inflammation, a major risk factor for cardiovascular disease (CVD). However, this potential association in observational studies remains inconclusive. We performed a two-sample bidirectional Mendelian randomization (MR) study using publicly available GWAS summary statistics to evaluate the relationship between CRP and CVD. Instrumental variables (IVs) were carefully selected, and multiple approaches were used to make robust conclusions. Horizontal pleiotropy and heterogeneity were evaluated using the MR-Egger intercept and Cochran's Q-test. The strength of the IVs was determined using F-statistics. The causal effect of CRP on the risk of hypertensive heart disease (HHD) was statistically significant, but we did not observe a significant causal relationship between CRP and the risk of myocardial infarction, coronary artery disease, heart failure, or atherosclerosis. Our primary analyses, after performing outlier correction using MR-PRESSO and the Multivariable MR method, revealed that IVs that increased CRP levels also increased the HHD risk. However, after excluding outlier IVs identified using PhenoScanner, the initial MR results were altered, but the sensitivity analyses remained congruent with the results from the primary analyses. We found no evidence of reverse causation between CVD and CRP. Our findings warrant updated MR studies to confirm the role of CRP as a clinical biomarker for HHD.

Immune mechanisms of cardiac aging.

Advances in healthcare and improvements in living conditions have led to rising life expectancy worldwide. Aging is associated with excessive oxidative stress, a chronic inflammatory state, and limited tissue healing, all of which result in an increased risk of heart failure. In fact, the prevalence of heart failure approaches 40% in the ninth decade of life, with the majority of these cases suffering from heart failure with preserved ejection fraction (HFpEF). In cardiomyocytes (CMs), age-related mitochondrial dysfunction results in disrupted calcium signaling and covalent protein-linked aggregates, which cause cardiomyocyte functional disturbances, resulting in increased stiffness and diastolic dysfunction. Importantly, aging is also associated with chronic low-grade, sterile inflammation, which alters the function of interstitial cardiac cells and leads to cardiac fibrosis. Taken together, cardiac aging is associated with cellular, structural, and functional changes in the heart that contribute to the rising prevalence of heart failure in older people.

Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD.

Synthesis and In Vitro Anticancer Evaluation of Flavone-1,2,3-Triazole Hybrids.

Hybrid compounds of flavones, namely chrysin and kaempferol, and substituted 1,2,3-triazole derivatives, were synthesized by click reaction of the intermediate O-propargyl derivatives. 4-Fluoro- and 4-nitrobenzyl-1,2,3-triazole-containing hybrid molecules were prepared. The mono- and bis-coupled hybrids were investigated on 60 cell lines of 9 common cancer types (NCI60) in vitro as antitumor agents. Some of them proved to have a significant antiproliferative effect.

iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection.

Traffic sign detection (TSD) in real-time environment holds great importance for applications such as automated-driven vehicles. Large variety of traffic signs, different appearances, and spatial representations causes a huge intraclass variation. In this article, an extreme learning machine (ELM), convolutional neural network (CNN), and scale transformation (ST)-based model, called improved extreme learning machine network, are proposed to detect traffic signs in real-time environment. The proposed model has a custom DenseNet-based novel CNN architecture, improved version of region proposal networks called accurate anchor prediction model (A2PM), ST, and ELM module. CNN architecture makes use of handcrafted features such as scale-invariant feature transform and Gabor to improvise the edges of traffic signs. The A2PM minimizes the redundancy among extracted features to make the model efficient and ST enables the model to detect traffic signs of different sizes. ELM module enhances the efficiency by reshaping the features. The proposed model is tested on three publicly available data sets, challenging unreal and real environments for traffic sign recognition, Tsinghua-Tencent 100K, and German traffic sign detection benchmark and achieves average precisions of 93.31%, 95.22%, and 99.45%, respectively. These results prove that the proposed model is more efficient than state-of-the-art sign detection techniques.