Disease Threats Research Articles

Mix and manage: Cultivar mixtures can maintain yield under high wheat blast disease pressure

Originating in South America, wheat blast disease has spread to both Asia and Africa and is considered a significant threat to food security. Bangladesh experienced the first outbreak of wheat blast outside of the Americas in 2016. Shortly thereafter, the blast-resistant variety BARI Gom 33 was released. Seeds of this variety are however not as widely available as required, although the disease threat remains. While varietal mixtures have been shown to mitigate some symptoms and yield losses associated with other fungal diseases in wheat, there is a complete research gap on this topic as it pertains to wheat blast. As such, we evaluated the potential of using BARI Gom 33 as a component of a variety mixture under high disease pressure in Bangladesh. During three cropping seasons, blast symptoms and yield were determined in a field experiment for the highly blast-susceptible variety BARI Gom 26, the moderately susceptible BARI Gom 30, the resistant BARI Gom 33, and seven mixture combinations of the three varieties using artificial inoculation to increase disease pressure. In addition to wheat blast, Bipolaris leaf blight (BpLB) symptoms were observed and evaluated. While yields of the susceptible varieties were severely affected by blast even after fungicide application, disease-inflicted yield loss without fungicide was only 15% for sole BARI Gom 33 and did not differ significantly from yield losses in BARI Gom 33 and BARI Gom 30 mixtures. Furthermore, in the mixture containing 67% BARI Gom 33 and 33% BARI Gom 30, blast incidence and severity were reduced by 25% and 16%, respectively, in comparison to weighted values in sole stands. Conversely, mixing varieties tended to increase the symptoms of BpLB. Under high wheat blast pressure, fungicide protection against blast was relatively weak, underscoring the importance of resistant varieties. Although variety mixtures did not increase yield, the yield advantage of BARI Gom 33 was maintained when its seeds were mixed with the less resistant BARI Gom 30. This study confirms recommendations that farmers should use BARI Gom 33 as a first line of defense against wheat blast in Bangladesh. Yet where farmers cannot access sufficient BARI Gom 33 seed for planting, our data suggest that agricultural extension services can recommend this variety with non-resistant cultivars as interim strategy without significant risk of yield loss.

Continuum of global to local dispersal frameworks highlights the increasing threat of pine wilt disease in China

The global introduction of human-mediated trade and local dispersal through transport or natural pathways are major processes and contributors to biological invasion. To effectively prevent and manage biological invasion, global introduction pathways and local dispersal processes of biological invasions need to be considered as a continuum of global to local dispersal (CGLD). However, there has been no unified CGLD framework developed that simultaneously considers global introduction pathway dynamics and multiple local dispersal processes with human-mediated and natural pathways to guide the development of effective management priority strategies for pine wilt disease. In this study, we constructed a CGLD framework to evaluate the global introduction and local dispersal risks of pine wilt disease. This is an invasive forest pathogen caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus, which causes severe harm to forestry ecosystems worldwide. The global introduction network dynamics has indicated that the introduction pressure of PWN continually increased during 2003–2016. The highest-risk source country and entry port were the United States and Jiangsu ports, respectively. The local dispersal modes of PWN were mainly presented a random structure with lots of “in-filling” dispersal events during 2010–2021. The PWN populations in China have gradually dispersed to non-analogous climatic regions compared to those in the native and other invasive ranges. The dispersal risk areas of PWN primarily cover southwestern, eastern, central, and northeastern China. Such integration can facilitate more accurate prediction of global introduction and local dispersal pathway risk of biological invasion and provide vital guidance for designing management priority strategies to prevent invasion.

Identification of environmental, socioeconomic, water, sanitation, and hygiene (WaSH) factors associated with COVID-19 incidence in the Philippines: A nationwide modelling study

Despite the implementation of non-pharmaceutical interventions, the threat of coronavirus disease 2019 (COVID-19) remains significant on a global scale. Identifying external factors contributing to its spread is crucial, especially given the World Health Organization's recommendation emphasizing access to water, sanitation, and hygiene as essential in curbing COVID-19. There is a notable discrepancy in access to sanitation facilities, particularly evident in low- and middle-income countries. However, there is a lack of quantitative assessments regarding these factors. This study examines various environmental, socioeconomic, water, sanitation, and hygiene factors and their associations with COVID-19 incidence. All regions in the Philippines were categorized into clusters based on socioeconomic factors. A conceptual structural equation model (SEM) was developed using domain knowledge. The best-fitting SEM for each cluster was determined, and associations between factors and COVID-19 incidence were estimated.The correlation analysis revealed that rainfall, minimum temperature, and relative humidity were positively correlated with weekly COVID-19 incidence in urban regions. Maximum temperature, mean temperature, wind speed, and wind direction were negatively correlated with weekly COVID-19 incidence in rural regions, with time lags of 0, 3, and 7 weeks. In urban regions (Cluster 1), factors such as urbanization rate (1.00), area (−0.93), and population (0.54) were found to be associated with weekly COVID-19 incidence. Conversely, in rural regions (Cluster 2), factors including area (0.17), basic sanitation (0.84), and wind direction (0.83) showed associations with weekly COVID-19 incidence. These factors were causally associated with a latent variable reflecting the hidden confounders associated with COVID-19 incidence. It is important to note that sanitation factors were associated only in rural regions. Improving access to sanitation facilities in rural regions of the Philippines is imperative to effectively mitigate disease transmission in future pandemics. Identification of the causal effect of unobserved confounders with COVID-19 incidence is recommended for future research.

New Method for Identification and Response to Infectious Disease Patterns Based on Comprehensive Health Service Data

Infectious diseases continue to pose a major threat to global public health and require early detection and effective response strategies. Despite advances in information technology and data analysis, the full potential of health data in identifying disease patterns and trends remains underutilised. This study aims to propose a comprehensive new mathematical model (new method) that utilises health data to identify infectious disease patterns and trends by exploring the potential of data-driven care approaches in addressing public health challenges associated with infectious diseases. The research methods used are exploratory data collection and analytical model development. The research results obtained mathematical models and algorithms that consider data of period, time, patterns, and trends of dangerous diseases, statistical analysis, and recommendations. Data visualisation and in-depth analysis were conducted in the research to improve the ability to respond to infectious disease threats and provide better decision-making solutions in improving outbreak response, as well as improving preparedness in addressing public health challenges. This research contributes to health practitioners and decision-makers.

The Establishment of the Multi-Visual Loop-Mediated Isothermal Amplification Method for the Rapid Detection of Vibrio harveyi, Vibrio parahaemolyticus, and Singapore grouper iridovirus

Groupers are valuable economic fish in the southern sea area of China, but the threat of disease is becoming more and more serious. Vibrio harveyi, V. parahaemolyticus, and Singapore grouper iridovirus (SGIV) are three important pathogens that cause disease in groupers, and infection with either a single one or a mix of these pathogens poses a serious threat to the healthy development of grouper culture. To enhance the rapid diagnosis and screening in the early stages, it is necessary to develop rapid detection methods of these pathogens. To simultaneously and rapidly detect the three pathogens, in this study, we utilized the TolC of V. harveyi, DNAJ of V. parahaemolyticus, and RAD2 of SGIV as the target genes and established a triple visual loop-mediated isothermal amplification (LAMP) method. This LAMP method showed a detection time as fast as 30 min and a high sensitivity of 100 fg/μL. Moreover, this method exhibited strong specificity and no cross-reaction with seven types of Vibrio and Staphylococcus aureus, as well as five common viruses in aquatic animals. Then, the LAMP products were enzymically cut, and three characteristic strips were used to identify the pathogen species. The results of the clinical trials demonstrated that the method could accurately and specifically detect V. harveyi, V. parahaemolyticus, and SGIV in grouper tissues. In summary, this study successfully established a triple visual LAMP rapid detection method for V. harveyi, V. parahaemolyticus, and SGIV. The method offers several advantages including simple equipment, easy operation, rapid reaction, high specificity, high sensitivity, and visual results. It is suitable for the early and rapid diagnosis of groupers infected with V. harveyi, V. parahaemolyticus, and SGIV, thereby providing useful technical support for further application in the large-scale disease surveillance of aquaculture animals.

Disease resistance of Brassica juncea to Sclerotinia sclerotiorum is established through the induction of indole glucosinolate biosynthesis

Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, is the main disease threat to oilseeds in Brassiceae, causing significant yield losses and reduction in oil content and quality. While the genetic mechanism underlying its lethal phenotype, particularly in B. juncea, remains largely unexplored. Transcriptome analysis revealed a large number of defense-related genes and response processes in B. napus and B. oleracea. However, similarities and differences in the defense responses to S. sclerotiorum on B. juncea are rarely reported. In the present study, we reported a B. juncea breeding line of H83 with high S. sclerotiorum resistance, which was used for transcriptome analysis compared to L36 with low resistance. A novel regulatory network was proposed to defend against S. sclerotiorum invasion in B. juncea. Upon infection of S. sclerotiorum, a series of were initiated within 12 h, and then defenses were activated to restrict the development and spread of S. sclerotiorum by inducing the massive synthesis of indole glucosinolates after 24 h. Twelve hub genes involved in the network were identified by the weighted gene co-expression network (WGCNA), which are involved in plant-pathogen interaction, signaling pathway genes, indole glucosinolate biosynthesis and cell wall formation. The hub genes were further validated by qRT-PCR. The research revealed a new resistant line of H83 against S. sclerotiorum and a different regulatory network in B. juncea, which would be beneficial for the future effective breeding of Sclerotinia-resistant varieties.

Assessment of the pathogen genomics landscape highlights disparities and challenges for effective AMR Surveillance and outbreak response in the East African community

The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC’s NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.

Artificial intelligence for drug repurposing against infectious diseases

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.

Mild phototherapy strategies for efficient treatment of bacterial keratitis while avoiding side effects

Bacterial keratitis (BK) is one of the most threatening ocular diseases, leading to stromal injury, corneal perforation, or even blindness. Avoiding side effect is necessary for efficient BK treatment. Herein, we develop GDY-Ag@AgCl heterojunction catalysts to achieve BK treatment via mild phototherapy strategy. Mild photothermal therapy (PTT) can greatly enhance the generation of ROS produced by photocatalytic therapy (PCT), which can decrease the dosage of photosensitizer, as well as solve the issue of uncontrolled local overheating and the accumulation of PTT agents, minimizing side effects and avoiding secondary injuries. The results demonstrate that lower dosage of PTT/PCT agents, and milder and controlled thermal effect of GDY-Ag@AgCl exhibits excellent inactivation performance against ampicillin-resistant Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA). Moreover, this phototherapy strategy not only achieves more effective treatment of BK induced by MRSA with no side effects but also shows a superior ability to alleviate inflammation. This strategy offers a safe and promising theragnostic approach for treating ocular drug-resistant bacterial infections.

Polarized Perspectives on Health Equity: Results from a Nationally Representative Survey on US Public Perceptions of COVID-19 Disparities in 2023

Republicans and Democrats responded to the COVID-19 pandemic in starkly different ways, from their attitudes in 2020 about whether the virus posed a threat to whether the pandemic ended in 2023. The consequences of COVID-19 for health equity have been a central concern in public health, and the concept of health equity has also been beset by partisan polarization. In this article, the authors present and discuss nationally representative survey data from 2023 on US public perceptions of disparities in COVID-19 mortality (building on a previous multiwave survey effort) as well as causal attributions for racial disparities, the contribution of structural racism, and broader attitudes about public health authority. The authors find anticipated gulfs in perspectives between Democrats on the one hand and independents and Republicans on the other. The results offer a somewhat pessimistic view of the likelihood of finding common ground in how the general public understands health inequities or the role of structural racism in perpetuating them. However, the authors show that those who acknowledge racial disparities in COVID-19 are more likely to support state public health authority to act in response to other infectious disease threats. The authors explore the implications of these public opinion data for advocacy, communication, and future needed research.

Usefulness of aircraft and airport wastewater for monitoring multiple pathogens including SARS-CoV-2 variants.

As global travel resumed in coronavirus disease 2019 (COVID-19) endemicity, the potential of aircraft wastewater monitoring to provide early warning of disease trends for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and other infectious diseases, particularly at international air travel hubs, was recognized. We therefore assessed and compared the feasibility of testing wastewater from inbound aircraft and airport terminals for 18 pathogens including SARS-CoV-2 in Singapore, a popular travel hub in Asia. Wastewater samples collected from inbound medium- and long-haul flights and airport terminals were tested for SARS-CoV-2. Next Generation Sequencing was carried out on positive samples to identify SARS-CoV-2 variants. Airport and aircraft samples were further tested for 17 other pathogens through quantitative reverse transcription polymerase chain reaction. The proportion of SARS-CoV-2-positive samples and the average virus load was higher for wastewater samples from aircraft as compared with airport terminals. Cross-correlation analyses indicated that viral load trends from airport wastewater led local COVID-19 case trends by 2-5 days. A total of 10 variants (44 sub-lineages) were successfully identified from aircraft wastewater and airport terminals, and four variants of interest and one variant under monitoring were detected in aircraft and airport wastewater 18-31days prior to detection in local clinical cases. The detection of five respiratory and four enteric viruses in aircraft wastewater samples further underscores the potential to expand aircraft wastewater to monitoring pathogens beyond SARS-CoV-2. Our findings demonstrate the feasibility of aircraft wastewater testing for monitoring infectious diseases threats, potentially detecting signals before clinical cases are reported. The triangulation of similar datapoints from aircraft wastewater of international travel nodes could therefore serve as a useful early warning system for global health threats.

Exploring the potential of Bacillus for crop productivity and sustainable solution for combating rice false smut disease.

Rice false smut, which is caused by the soil-borne fungal pathogen Ustilaginoidea virens (U. virens), is one of the most threatening diseases in most of the rice-growing countries including India that causes 0.5-75% yield loss, low seed germination, and a reduction in seed quality. The assessment of yield loss helps to understand the relevance of disease severity and facilitates the implementation of appropriate management strategies. This study aimed to mitigate biotic stress in rice by employing a rhizobacterial-based bioformulation, which possesses diverse capabilities as both a plant growth promoter and a biocontrol agent against U. virens. Rhizobacteria were isolated from the soil of the rice rhizospheres from the healthy plant of the false smut affected zone. Furthermore, they were identified as Bacillus strains: B. subtilis (BR_4), B. licheniformis (BU_7), B. licheniformis (BU_8), and B. vallismortis (KU_7) via sequencing. Isolates were screened for their biocontrol potential against U. virens under in vitro conditions. The antagonistic study revealed that B. vallismortis (KU_7) inhibited U. virens the most (44.6%), followed by B. subtilis BR_4 (41.4%), B. licheniformis BU_7 (39.8%), and B. licheniformis BU_8 (43.5%). Various biochemical and plant growth promoting attributes, such as phosphate and Zn solubilization, IAA, ammonium, siderophore, and chitinase production, were also investigated for all the selected isolates. Furthermore, the potential of the isolates was tested in both in vitro and field conditions by employing talc-based bioformulation through bio-priming and root treatment. The application of bioformulation revealed a 20% decrease in disease incidence in plants treated with B. vallismortis (KU_7), a 60.5% increase in the biological yield, and a 45% increase in the grain yield. This eco-friendly approach not only controlled the disease but also improved the grain quality and reduced the chaffiness.

Design and fabrication of dual-layer PCL nanofibrous scaffolds with inductive influence on vascular cell responses

Confronted with the profound threat of cardiovascular diseases to health, vascular tissue engineering presents potential beyond the limitations of autologous and allogeneic grafts, offering a promising solution. This study undertakes an initial exploration into the impact of a natural active protein, elastin, on vascular cell behavior, by incorporating with polycaprolactone to prepare fibrous tissue engineering scaffold. The results reveal that elastin serves to foster endothelial cell adhesion and proliferation, suppress smooth muscle cell proliferation, and induce macrophage polarization. Furthermore, the incorporation of elastin contributes to heightened scaffold strength, compliance, and elongation, concomitantly lowering the elastic modulus. Subsequently, a bilayer oriented polycaprolactone (PCL) scaffold infused with elastin is proposed. This design draws inspiration from the cellular arrangement of native blood vessels, leveraging oriented fibers to guide cell orientation. The resulting fiber scaffold exhibits commendable mechanical properties and cell infiltration capacity, imparting valuable insights for the rapid endothelialization of vascular scaffolds.

Comparing social responses to Ebola and Covid-19 in Sierra Leone: an institutional analysis.

This paper compares community responses to Ebola and Covid-19 in two regions of southern and eastern Sierra Leone with reference to the theory of institutional dynamics proposed by the anthropologist Mary Douglas. Institutions, Douglas argued, are conveyed by styles of thought, shaped by the ways human communities, through everyday practices, reinforce systems of classification and denotation. Pandemic advice to 'follow the science' proved problematic, since there is no single institution of science, and institutions never stand alone but are bundled with other institutions, reflecting the manifold and intertwined practices of human social life. The paper explores some of the ways a traumatic epidemic of Ebola Virus Disease in Sierra Leone shaped a distinctive local response to this deadly infectious disease in the absence of an effective vaccine. This local approach emphasised social rules based on ideas about sequestration and testing. Communities then proposed to continue this rules-based approach to the pandemic of Covid-19 and showed little initial enthusiasm for vaccination. With Ebola, the adoption of rules resulted in dramatic drops in infection rates. But Covid-19 spreads in different ways, and good results from the application of social rules were much less apparent. The paper shows how communities began to grapple with this new situation. In some cases, vaccine hesitation was overcome by treating the requirement for vaccination as a new form of social discipline. More generally, it is concluded that epidemiologists need to pay specific attention to institutions and institutional dynamics in order to better understand and anticipate public reactions to new disease threats.

Data-Driven Face Mask Detection and Alert System Utilizing Haar Cascade Classifier

The COVID-19 pandemic has brought to light the pivotal role of face masks in curbing the transmission of contagious illnesses. As societies strive to uphold mask mandates in public areas, there has been a surge of interest in automated face mask detection systems. This study presents a novel data-centric methodology for crafting a reliable face mask detection and notification system leveraging the Haar cascade classifier. Our approach hinges on a meticulously curated dataset comprising images of individuals both with and without masks. Through the adept utilization of feature extraction techniques and machine learning algorithms, our system learns to discern masked faces from their unmasked counterparts in real-time scenarios. The experimental outcomes of our study attest to the efficacy of our proposed system, showcasing commendable accuracy in detecting face masks. This substantiates its viability for deployment across diverse public settings, bolstering the arsenal of public health safety measures. The seamless integration of our system stands poised to fortify efforts aimed at safeguarding community well-being amidst the persistent threat of infectious diseases. Key words: Data, Mask , Deep Learning, Hybrid.

Adjuvant-dependent impact of inactivated SARS-CoV-2 vaccines during heterologous infection by a SARS-related coronavirus

Whole virus-based inactivated SARS-CoV-2 vaccines adjuvanted with aluminum hydroxide have been critical to the COVID-19 pandemic response. Although these vaccines are protective against homologous coronavirus infection, the emergence of novel variants and the presence of large zoonotic reservoirs harboring novel heterologous coronaviruses provide significant opportunities for vaccine breakthrough, which raises the risk of adverse outcomes like vaccine-associated enhanced respiratory disease. Here, we use a female mouse model of coronavirus disease to evaluate inactivated vaccine performance against either homologous challenge with SARS-CoV-2 or heterologous challenge with a bat-derived coronavirus that represents a potential emerging disease threat. We show that inactivated SARS-CoV-2 vaccines adjuvanted with aluminum hydroxide can cause enhanced respiratory disease during heterologous infection, while use of an alternative adjuvant does not drive disease and promotes heterologous viral clearance. In this work, we highlight the impact of adjuvant selection on inactivated vaccine safety and efficacy against heterologous coronavirus infection.

Identification of tumor necrosis factor receptor 1 and its association with LPS-induced apoptosis in Onchidium reevesii

The sea slug (Onchidium reevesii) inhabits intertidal environments and face environmental pressures and disease threats. The immune system of mollusks plays a crucial role in their defense against pathogens and maintaining homeostasis. The molecular mechanisms underlying its immune defense and response to pathogens remain largely unexplored. This study aimed to isolate and identify the cDNA sequence of tumor necrosis factor receptor 1 (TNFR1) in the sea slug. The obtained cDNA sequence was named OrTNFR1. Like other members of the TNF receptor superfamily, OrTNFR1 contains a signal peptide, TNF homologous domain, and death-related domain. qPCR results showed that OrTNFR1 was highly expressed in the hepatopancreas, blood, and intestines of sea slug. This indicates that OrTNFR1 plays a role in the immune-related tissues of sea slug and is consistent with the broad expression of TNFR1 in bivalves. To investigate the inducible expression pattern and downstream effects of OrTNFR1 in response to lipopolysaccharide (LPS) stimulation, we studied the functional role of the OrTNFR1 gene by stimulating with LPS in vivo and suppressing OrTNFR1 expression using siRNA technology. Our findings demonstrate that LPS stimulation rapidly up-regulates the expression of OrTNFR1 in the hepatopancreas, blood, and intestine of the sea slug. Moreover, si-TNFR1 effectively inhibits LPS-induced TNFR1 expression and its downstream genes. Additionally, we discovered that inhibiting OrTNFR1 reduces LPS-induced cell apoptosis and the expression of apoptosis-associated protein Caspase.TNFR1 is involved in the immune defense of the sea slug, and blocking TNFR1 effectively attenuates LPS-induced cell apoptosis through the TNFR1 pathway. This finding suggests that inhibiting TNFR1 in the sea slug may serve as a potential approach to reduce cell apoptosis. As a shell-less living in harsh intertidal environments, TNFR1 likely participates in its innate immunity. Therefore, studying the TNFR1 pathway contributes to understanding the innate immune signaling pathway in the sea slug, further elucidating host defense against pathogens and apoptotic functions.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features

Objective. Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG). Approach. A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result. Main results. According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset. Significance. This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.

A comparative study of the Hungarian-speaking Roma population living in the Carpathian Basin with regard to obesity during the COVID-19 pandemic

AbstractIntroductionObesity is the most threatening non-infectious disease of our time, the basis of many chronic diseases, increasing the mortality rate. The Roma ethnic minority is particularly affected.Materials and methodsCross-sectional–questionnaire and physical–survey in rural settlements of Hungary, Romania, Slovakia, where Roma live with the non-Roma, Hungarian-speaking population (N = 1893).ResultsThe average BMI of the Hungarian (P < 0.001) and Romanian (P = 0.018) samples was significantly higher than that of the Slovak sample. In the case of Roma and non-Roma subjects, we found a significant difference in Hungary (P = 0.006) based on body composition, as well as in the case of visceral fat (P < 0.001). The extremely obese (<40 BMI) are mostly low educated in Romania and Hungary (P < 0.001), while those in normal weight have a tertiary education in Slovakia (P = 0.027). Hungarian Roma and non-Roma participants show significant differences in the physical activity dimension of the SF-36 questionnaire (P < 0.001), as well as in Romania (P < 0.001) and Slovakia (P = 0.002).ConclusionsIn summary, it can be stated that rural Roma subjects in Hungary are in the worst situation in terms of obesity in the three countries studied. In our study, the results in Slovakia clearly suggest a healthier lifestyle.

Innovative pathological network-based multitarget approaches for Alzheimer's disease treatment.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and isa major health threat globally. Its prevalence is forecasted to exponentially increase during the next 30 years due to the global aging population. Currently, approved drugs are merely symptomatic, being ineffective in delaying or blocking the relentless disease advance. Intensive AD research describes this disease as a highly complex multifactorial disease. Disclosure of novel pathological pathways and their interconnections has had a major impact on medicinal chemistry drug development for AD over the last two decades. The complex network of pathological events involved in the onset of the disease has prompted the development of multitarget drugs. These chemical entities combine pharmacological activities towardtwo or more drug targets of interest. These multitarget-directed ligands are proposed to modify different nodes in the pathological network aiming to delay or even stop disease progression. Here, we review the multitarget drug development strategy for AD during the last decade.