Asthma prevalence among United States population insights from NHANES data analysis.

Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. POPULATION: Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. POPULATION: We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with the prevalence of asthma. The study found that asthma affected 8.7% of the U.S. POPULATION: Gender emerged as a significant factor, with 36.0% of asthma patients being male and 64.0% female (p < 0.001). Individuals aged 60 and older having the highest asthma prevalence at 34.0%. Non-Hispanic whites had the highest prevalence at 46.4%, followed by non-hispanic blacks at 26.0%. In contrast, Mexican Americans and other hispanic individuals had lower rates, at 9.6% and 9.0%, respectively. Females were 1.76 times more likely to have asthma than males (p < 0.001). Obese individuals had a 1.74 times higher likelihood of current asthma compared to underweight individuals (p < 0.001). Notably, both Non-Hispanic Whites and Non-Hispanic Blacks showed higher odds of current asthma compared to Mexican Americans (with adjusted odds ratios of 2.084 and 2.096, respectively, p < 0.001). The research findings indicate that asthma is prevalent in 8.7% of the U.S. POPULATION: Our study highlights that individuals who are female, have low income, are obese, and smoke have the highest likelihood of being affected by asthma. Therefore, public health policies should prioritize addressing these risk factors in their preventive strategies.

Impact of aerobic and resistance training on fatigue, quality of life, and physical activity in prostate cancer patients: a systematic review and meta-analysis.

BACKGROUND: Prostate cancer (PCa) is a prevalent cancer with significant morbidity and mortality rates. In most cases, prostate cancer remains asymptomatic until advanced disease manifests with symptoms, such as benign prostate hyperplasia (BPH). Timely detection and better management have improved overall survival in patients with prostate cancer, and fatigue, reduced physical activity, and impaired quality of life (QoL) remain major challenges that impact daily life. OBJECTIVE: This study aimed to systematically review and conduct a meta-analysis to evaluate the impact of aerobic and resistance training on fatigue, quality of life, and physical activity in prostate cancer patients undergoing treatment. MATERIAL METHODS: A comprehensive literature search was conducted using the PubMed, Cochrane Library, and clinicaltrials.gov databases, adhering to the PRISMA guidelines. Twenty studies, involving 1393 participants, were included in the final analysis. The inclusion criteria were Studies that evaluated the effects of exercise interventions relative to passive controls in patients with prostate cancer were included. The primary outcomes of interest were fatigue, QoL, and PA.. Data from eligible studies were extracted, and a meta-analysis was performed using RevMan 5.40. RESULTS: Twenty studies met our inclusion criteria. Data Analysis of the included studies demonstrated a significant improvement in quality of life among prostate cancer patients in the exercise group compared to the control group (SMD=0.20, 95% CI=0.07 to 0.34, P=0.003). However, there was no significant association between exercise and fatigue (SMD=0.07, 95% CI=-0.13, 0.26, P=0.51). Sensitivity analysis did not alter these findings. Regarding physical activity outcomes, the control group exhibited superior performance in the 400-meter walk test (P<0.05). No significant associations were found between exercise and the 6-meter walk test or up-and-go time. CONCLUSION: This systematic review revealed that aerobic and resistance training enhance the quality of life of patients with prostate cancer, although it has a limited impact on fatigue and physical activity levels. These findings advocate a shift in clinical practice and positioning exercise as a core component of comprehensive cancer care. Tailoring exercise regimens according to individual patient needs and treatment stages should become the norm in treatment planning. This approach goes beyond physical wellness and addresses the psychological and emotional facets of cancer management. Moreover, there is an evident need for further research to develop holistic exercise interventions that effectively address the complex dynamics of fatigue, physical activity, and QoL in this patient group.

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES.

ABSTRACT: Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION?

ABSTRACT: This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.

COVID-19 Patient Count Prediction Using LSTM.

In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients' estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model's prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients' count of the proposed model is much closer to the actual patient count.

The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey.

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today's society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today's hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.