"I Have Come Because I See You Care About Me": Recruiting Older Black Americans for Genomic Research.

The lack of diversity in genomic studies is a disparity that influences our understanding of human genomic variation and threatens equity in the benefits of precision medicine. Given our current genomic research with Black older adults, we conducted a qualitative study to elucidate participants' knowledge, attitudes, and beliefs about genomic research and research participation and what factors contribute to their willingness to participate and to gain insights into barriers that researchers may have in recruiting Black Americans. We conducted semistructured interviews (N=16) with previous genomic research participants, and an inductive thematic approach was used to code and interpret the data. The mean age was 70, 82% reported <$15,000 annual income, and 100% participated in genomic research. The results note that genomic research is poorly understood despite participation in prior genomic studies, and cultural beliefs about health and managing health impact an individual's research participation. Although not all participants identified with historical distrust, those who did report health system distrust also contributed distrust in research. Relationship building facilitates research participation, especially when perceived as personally relevant and meaningful. Participant incentives and convenience to engage in the study are less important if the personal benefits or relevance of the research are clear. Our results provide new context into the importance of relationship building and research literacy and highlight new considerations for engaging racially diverse populations in research.

Endothelial γ-protocadherins inhibit KLF2 and KLF4 to promote atherosclerosis.

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of mortality worldwide. Laminar shear stress from blood flow, sensed by vascular endothelial cells, protects from ASCVD by upregulating the transcription factors KLF2 and KLF4, which induces an anti-inflammatory program that promotes vascular resilience. Here we identify clustered γ-protocadherins as therapeutically targetable, potent KLF2 and KLF4 suppressors whose upregulation contributes to ASCVD. Mechanistic studies show that γ-protocadherin cleavage results in translocation of the conserved intracellular domain to the nucleus where it physically associates with and suppresses signaling by the Notch intracellular domain. γ-Protocadherins are elevated in human ASCVD endothelium; their genetic deletion or antibody blockade protects from ASCVD in mice without detectably compromising host defense against bacterial or viral infection. These results elucidate a fundamental mechanism of vascular inflammation and reveal a method to target the endothelium rather than the immune system as a protective strategy in ASCVD.

Environmental Heavy Metal Exposure and Associated Cardiovascular Diseases in Light of the Triglyceride Glucose Index.

Cardiovascular diseases (CVD), primarily ischemic heart disease and stroke, remain leading global health burdens. Environmental risk factors have a major role in the development of CVD, particularly exposure to heavy metals. The Triglyceride Glucose Index (TyG), a measure of insulin resistance and CVD risk, is the primary focus of this study, which summarizes the most recent findings on the effects of lead (Pb), arsenic (As), and cadmium (Cd) on CVD risk. A higher risk of CVD is correlated with an elevated TyG index, which has been linked to insulin resistance. Exposure to Cd is associated with disturbance of lipid metabolism and oxidative stress, which increases the risk of CVD and TyG. Exposure reduces insulin secretion and signaling, which raises the TyG index and causes dyslipidemia. Pb exposure increases the risk of CVD and TyG index via causing oxidative stress and pancreatic ß-cell destruction. These results highlight the need of reducing heavy metal exposure by lifestyle and environmental modifications in order to lower the risk of CVD. To comprehend the mechanisms and create practical management plans for health hazards associated with heavy metals, more study is required.

Synchronizing smart city nodes using Skew Integrated Timestamp (SIT).

Time synchronization among smart city nodes is critical for proper functioning and coordinating various smart city systems and applications. It ensures that different devices and systems in the smart city network are synchronized and all the data generated by these devices is consistent and accurate. Synchronization methods in smart cities use multiple timestamp exchanges for time skew correction. The Skew Integrated Timestamp (SIT) proposed here uses a timestamp, which has time skew calculated from the physical layer and uses just one timestamp to synchronize. The result from the experiment suggests that SIT can be used in place of multiple timestamp exchanges, which saves computational resources and energy.

Temporal and regional trends in adults with diabetics kidney disease in the US from 1999 to 2020.

AIMS: We aim to analyze trends in mortality rates among adults with diabetic kidney disease (DKD) in the US from 1999 to 2020. METHODS: We queried the Centers for Disease Control Wide-Ranging Online Data for Epidemiologic Research database for mortality statistics from 1999 to 2020 associated with DKD in adults aged ≥25 years. Age-adjusted mortality rates (AAMRs) were calculated and trends were analyzed using the Joinpoint Regression Program. RESULTS: From 1999 to 2020, a total of 528,430 deaths were reported among adults with DKD. The mortality rates increased over time with males consistently exhibiting higher AAMR than females. NH American Indian or Alaska Native individuals had the highest AAMR, followed by NH Blacks, Hispanics, NH Whites, and NH Asians. The West region had the highest AAMR, followed by the Midwest, South, and Northeast. Rural regions had higher AAMR than urban areas, and mortality rates increased with age. CONCLUSIONS: This study reveals notable disparities in DKD mortality rates across demographic groups and geographic regions. NH American Indians or Alaska Natives, males, elderly individuals, rural residents, and those in the West region were disproportionately affected. Understanding these trends is crucial for developing targeted interventions to reduce DKD-related mortality and address healthcare disparities.

Is Lipid Metabolism of Value in Cancer Research and Treatment? Part I- Lipid Metabolism in Cancer.

For either healthy or diseased organisms, lipids are key components for cellular membranes; they play important roles in numerous cellular processes including cell growth, proliferation, differentiation, energy storage and signaling. Exercise and disease development are examples of cellular environment alterations which produce changes in these networks. There are indications that alterations in lipid metabolism contribute to the development and progression of a variety of cancers. Measuring such alterations and understanding the pathways involved is critical to fully understand cellular metabolism. The demands for this information have led to the emergence of lipidomics, which enables the large-scale study of lipids using mass spectrometry (MS) techniques. Mass spectrometry has been widely used in lipidomics and allows us to analyze detailed lipid profiles of cancers. In this article, we discuss emerging strategies for lipidomics by mass spectrometry; targeted, as opposed to global, lipid analysis provides an exciting new alternative method. Additionally, we provide an introduction to lipidomics, lipid categories and their major biological functions, along with lipidomics studies by mass spectrometry in cancer samples. Further, we summarize the importance of lipid metabolism in oncology and tumor microenvironment, some of the challenges for lipodomics, and the potential for targeted approaches for screening pharmaceutical candidates to improve the therapeutic efficacy of treatment in cancer patients.

Bleeding Outcomes of Direct Oral Anticoagulants and Vitamin K Antagonists for Acute Venous Thromboembolism: A Cross-Sectional Study.

BACKGROUND: Venous thromboembolism (VTE) is a widespread and significant cause of morbidity and mortality on a global scale. The primary objective of this cross-sectional study is to examine the impact of anticoagulant therapy on major organ hemorrhage events in patients diagnosed with acute venous thromboembolism (VTE). Specifically, this research compares the effects of vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs). MATERIALS AND METHODS: This retrospective observational study examined the medical records of 46 patients who had been diagnosed with VTE and were receiving treatment with DOACs or VKAs. The documentation of patient characteristics encompassed demographic information, comorbidities, and treatment particulars. Within 30 days of hospital admission, the incidence of significant organ bleeding events, with an emphasis on gastrointestinal and intracranial hemorrhage, was the primary outcome evaluated. RESULTS: Overall, 46 patients with VTE who were treated with oral anticoagulation therapy participated in the study. Twenty-four and 22 patients were administered VKAs and DOACs, respectively. The similarity in baseline characteristics between the DOAC and VKA groups ensured that the analyses were well-matched. The examination of bleeding sites unveiled subtle variations, as the DOAC group exhibited a progressive increase in the incidence of intracranial bleeding (12, 55.5%), while the VKA group demonstrated a surge in upper gastrointestinal bleeding (12, 50%) as well. While lacking statistical significance, these observed patterns are consistent with prior research that indicates that DOACs may have a lower risk of catastrophic hemorrhage in comparison to VKAs. The overall in-hospital mortality rate for patients treated with VKA was 33.3% (n=8), while that treated with DOAC was 18.2% (n=4). These differences did not reach statistical significance (P>0.05). In a similar vein, the evaluation of mortality associated with hemorrhage revealed six (25%) in the group receiving VKA and three (13.6%) in the group receiving DOAC; the P value was not statistically significant (P>0.05). CONCLUSIONS: This study contributes valuable insights into bleeding outcomes associated with anticoagulant therapy for acute VTE. The nuanced differences in bleeding patterns highlight the complexity of anticoagulant selection, emphasizing the importance of considering bleeding site considerations. The comparable mortality rates support existing evidence regarding the favorable safety profile of DOACs.

Is Lipid Metabolism of Value in Cancer Research and Treatment? Part II: Role of Specialized Pro-Resolving Mediators in Inflammation, Infections, and Cancer.

Acute inflammation is the body's first defense in response to pathogens or injury that is partially governed by a novel genus of endogenous lipid mediators that orchestrate the resolution of inflammation, coined specialized pro-resolving mediators (SPMs). SPMs, derived from omega-3-polyunstaturated fatty acids (PUFAs), include the eicosapentaenoic acid-derived and docosahexaenoic acid-derived Resolvins, Protectins, and Maresins. Herein, we review their biosynthesis, structural characteristics, and therapeutic effectiveness in various diseases such as ischemia, viral infections, periodontitis, neuroinflammatory diseases, cystic fibrosis, lung inflammation, herpes virus, and cancer, especially focusing on therapeutic effectiveness in respiratory inflammation and ischemia-related injuries. Resolvins are sub-nanomolar potent agonists that accelerate the resolution of inflammation by reducing excessive neutrophil infiltration, stimulating macrophage functions including phagocytosis, efferocytosis, and tissue repair. In addition to regulating neutrophils and macrophages, Resolvins control dendritic cell migration and T cell responses, and they also reduce the pro-inflammatory cytokines, proliferation, and metastasis of cancer cells. Importantly, several lines of evidence have demonstrated that Resolvins reduce tumor progression in melanoma, oral squamous cell carcinoma, lung cancer, and liver cancer. In addition, Resolvins enhance tumor cell debris clearance by macrophages in the tumor's microenvironment. Resolvins, with their unique stereochemical structure, receptors, and biosynthetic pathways, provide a novel therapeutical approach to activating resolution mechanisms during cancer progression.

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

Examination of social worlds of risky drinking. Insights from Twitter data analysis.

Rich nature of social media data offers a great opportunity to examine social worlds of its users. Further to wide range of topics being discussed on social media, alcohol-related content is prevalent on social media and studies have found an association between this content and increased consumption of alcohol, cravings for alcohol and addiction. This study analyses social media data to examine social worlds of risky drinking in Victoria, Australia. This study conducted a scoping literature review and two online surveys, one with the general community and the other with health professionals, to determine key words to search for on social media sites. These keywords were used in a social media analytics tool called Talkwalker to generate quantitative and qualitative data on the social media users and their conversations. NVIVO was used for developing categories and themes in a sample of 172 posts. A total of 1,021 results were obtained from Twitter. The main demographic group found to be involved in conversations about drinking alcohol on Twitter was young fathers aged 25-34 years. The culture of alcohol consumption in Victoria for Twitter users is reflective of Australia's drinking culture within which risky drinking, and in particular binge drinking, is normalised.

Tirzepatide: A novel cardiovascular protective agent in type 2 diabetes mellitus and obesity.

Cardiovascular disease (CVD) remains a major global health concern, and obesity and diabetes mellitus have been found to be important risk factors. Tirzepatide a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP1) receptor agonist has been shown to have cardioprotective effects. Noteworthy benefits of Tirzepatide include decreased cardiovascular risk factors in people with Type 2 diabetes mellitus (T2DM). In the SURPASS-4 trial, tirzepatide significant decreased blood pressure, body weight, and HbA1c. Furthermore, the SURMOUNT-1 trial demonstrated the effectiveness of tirzepatide in reducing cardiometabolic risk factors in people with obesity without T2DM. Together, the dual receptor agonism improves lipid profiles, increases insulin secretion, reduces inflammation, and promotes endothelial integrity. Tirzepatide shows promise as a comprehensive therapeutic option for managing cardiovascular risk factors in patients with T2DM and obesity. While further studies are needed to assess the long-term cardiovascular benefits, current evidence supports tirzepatide's potential impact on cardiovascular health beyond its antidiabetic properties.

Heavy metal-resistant rhizobacteria fosters to alleviate the cadmium toxicity in Arabidopsis by upregulating the plant physiological responses.

This study was designed to investigate the role of Morganella morganii strains in alleviating Cd stress in Arabidopsis seedlings under controlled conditions. Both M. morganii strains ABT3 (ON316873) and ABT9 (ON316874) strains isolated from salt-affected areas showed higher resistance against Cd and possess plant growth-promoting traits such as nitrogen fixation, indole-acetic acid production, ammonia production, phosphate solubilization, and, catalase, gelatinase and protease enzyme production. Plant inoculation assay showed that varying concentration of Cd (1.5 mM and 2.5 mM) significantly reduced Arabidopsis growth, quantum yield (56.70%-66.49%), and chlorophyll content (31.90%-42.70%). Cd toxicity also triggered different associations between lipid peroxidation (43.61%-69.77%) and enzymatic antioxidant mechanisms. However, when both strains were applied to the Arabidopsis seedlings, the shoot and root length and fresh and dry weights were improved in the control and Cd-stressed plants. Moreover, both strains enhanced the resistance against Cd stress by increasing antioxidant enzyme activities [catalase (19.47%-27.39%) and peroxidase (37.50%-48.07%)]that ultimately cause a substantial reduction in lipid peroxidation (27.71%-41.90%). Both strains particularly ABT3 also showed positive results in improving quantum yield (73.84%-98.64%) and chlorophyll content (41.13%-48.63%), thus increasing the growth of Arabidopsis seedlings. The study suggests that PGPR can protect plants from Cd toxicity, and Cd-tolerant rhizobacterial strains can remediate heavy metal polluted sites and improve plant growth.

In order to develop sustainable and effective agricultural techniques in areas polluted with heavy metals, it is important to have a deeper understanding of the characteristics of metal-resistant PGPR. Hence, this study focuses on the efficacy of M. morganii in promoting the growth and increasing the photosynthetic pigments of Arabidopsis seedlings under Cd toxicity.

Correction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

[This corrects the article DOI: 10.1371/journal.pone.0287755.].

An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing.

The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: IoT-edge and Cloud-processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. Optimized Yet Another Resource Negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic dataset using Apache Spark. The comparative analysis is decorated with existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.

Should aspirin be replaced with ADP blockers and anti-GPVI to manage thrombosis?

Platelets have a pivotal role in maintaining cardiovascular homeostasis. They are kept docile by endothelial-derived mediators. Aberration in haemostatic balance predisposes an individual to an elevated risk of a prothrombotic environment. Anti-platelet therapy has been a key component to reduce this risk. However, understanding how these medications affect the balance between the activation and inhibition of platelets is critical. There is no evidence that a key anti-platelet therapy - aspirin, may not be the most efficacious medicine of choice, as it can compromise both platelet inhibition and activation pathways. In this review, the rationale of aspirin as an anti-thrombotic drug has been critically discussed. This review looks at how recently published trials are raising key questions about the efficacy and safety of aspirin in countering cardiovascular diseases. There is an increasing portfolio of evidence that identifies that although aspirin is a very cheap and accessible drug, it may be used in a manner that is not always beneficial to a patient, and a more nuanced and targeted use of aspirin may increase its clinical benefit and maximize patient response. The questions about the use of aspirin raise the potential for changes in its clinical use for dual anti-platelet therapy. This highlights the need to ensure that treatment is targeted in the most effective manner and that other anti-platelet therapies may well be more efficacious and beneficial for CVD patients in their standard and personalized approaches.

Ensemble learning for multi-class COVID-19 detection from big data.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Fall Prevalence and Associated Risk Factors in the Hospitalised Adult Population: A Crucial Step Towards Improved Hospital Care.

Background Falls among the adult population are a major global health concern with severe repercussions for individuals and healthcare systems. The purpose of this study was to investigate the prevalence and associated risk factors of falls in hospitalized patients in order to improve hospital care for elderly adults. Materials and methods The research was conducted at two institutions of tertiary care in Abbottabad, Pakistan. After extensive screening and obtaining informed consent, a total of 210 participants aged 50 and older were enrolled in the study. Mental status, history of falls, ambulation/elimination status, vision, gait/balance, systolic blood pressure, medication use, and predisposing diseases were evaluated using the Long Term Care Fall Risk Assessment Form. Additionally, the Dynamic Gait Index was utilized to evaluate various aspects of gait. Results 58.6% of participants reported a history of falls in the previous year, according to the findings. BMI, imbalance, vertigo, and fear of falling were significantly associated with an increased risk of falls in older individuals. The Long-Term Care Fall Risk Assessment, the Montreal Cognitive Assessment (MoCA), the Dynamic Gait Index (DGI), and the Mini-BESTest scores revealed that patients with a history of falls had inferior functional and cognitive outcomes. Falls were more common among individuals with a robust BMI, especially men. Conclusions The study results highlight the multifactorial nature of falls in the adult population and the need for targeted interventions to address modifiable risk factors. To enhance hospital care for high-risk patients, proactive fall prevention strategies, including regular risk assessments and individualized interventions, should be implemented. This study provides important insights into the prevalence and causes of accidents among hospitalized patients, particularly in developing nations such as Pakistan. ​​​​​​.

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.