A Peculiar Case of Dengue Hemorrhagic Fever: A Case Report of Radical Outcomes

Dengue is the arthropod-borne flavivirus infection, its severity increases during pregnancy, and results in worst maternofetal outcomes, which is an alarming issue. Dengue fever (DF) is the most common cause for preterm delivery and abortion. Dengue-related thrombocytopenia increases the risk of bleeding during pregnancy and delivery and can also leads to high maternal mortality rates. Globally 3.9 billion people are at risk of dengue fever, especially in Asia. This case study focuses on a 22-years old pregnant woman from Pakistan without known co-morbidities. She presented in an emergency department with high fever, nausea, vomiting, and bleeding gum. She was positive for dengue and malaria. Due to the emergence of warning sign and symptoms, rapidly decreasing platelets, deranged coagulopathy, liver and renal profile, the baby was delivered by spontaneous vaginal delivery. The woman went into post-partum hemorrhage (PPH), and due to concealed bleeding needed multiple transfusions, vaginal packaging, and balloon tamponade. She developed multiple organ failure and was on continuous renal replacement therapy (CRRT). On day eight in intensive care unit, life support was withdrawn at the family’s request, and she died. This case report of a failure to safe a pregnant woman’s life highlights the importance of being alert to early warning signs, establishing an early diagnosis, timely interventions, close monitoring, and critical consideration of physiological changes of pregnancy are important for diagnosing infectious diseases such as dengue and malaria early, especially when both infections happen at the same time. In this case the baby survived but the woman sadly died

Impact of different cut types on the quality of fresh-cut potatoes during storage

Fresh-cut vegetables can be minimally processed through cleaning/washing, trimming, peeling, slicing and dicing, followed by packaging and cold storage. This study aimed to verify the effect of different cuts on the quality and shelf life of fresh-cut potato. Different cut types, such as slices, dices, cubes and wedges, were selected for this study to evaluate the shelf-life response of potatoes. Potato pieces of these different shapes were treated with calcium chloride, citric acid, and potassium metabisulfite (3%, 2% and 0.3%, respectively), stored in plastic boxes at 4 ˚C for 60 days, and then physicochemically (firmness (N), weight loss (WL), pH, titratable acidity (TA), total soluble solids (TSS), and ascorbic acid (AA) content analyses) and microbiologically assessed. The best results were observed for the dice cut type, which showed minimal changes in TSS (5.31%), pH (5.65), TA (0.34%), WL (9.04%), and AA content (10.86%). Moreover, the microbial activity of all shapes of potato pieces remained within acceptable limits during cold storage

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney function and ultimately leads to complications such as chronic kidney disease, kidney failure, and other renal disorders. Combining patient metadata with computed tomography (CT) images is essential to accurately and timely diagnosing such complications. Deep Neural Networks (DNNs) have transformed medical fields by providing high accuracy in complex tasks. However, the high computational cost of these models is a significant challenge, particularly in real-time applications. This paper proposed SpinalZFNet, a hybrid deep learning approach that integrates the architectural strengths of Spinal Network (SpinalNet) with the feature extraction capabilities of Zeiler and Fergus Network (ZFNet) to classify kidney disease accurately using CT images. This unique combination enhanced feature analysis, significantly improving classification accuracy while reducing the computational overhead. At first, the acquired CT images are pre-processed using a median filter, and the pre-processed image is segmented using Efficient Neural Network (ENet). Later, the images are augmented, and different features are extracted from the augmented CT images. The extracted features finally classify the kidney disease into normal, tumor, cyst, and stone using the proposed SpinalZFNet model. The SpinalZFNet outperformed other models, with 99.9% sensitivity, 99.5% specificity, precision 99.6%, 99.8% accuracy, and 99.7% F1-Score in classifying kidney disease

Do oil shocks affect the green bond market?

This study examines the predictive power of oil shocks for the green bond markets. In line with this aim, we investigated the extent to which oil shocks could be used to accurately make in- and out-of-sample forecasts for green bond returns. Three striking findings emanated from our results: First, the three types of oil shock are reliable predictors for green bond indices. Second, the performances of the predictive models were consistent across the different forecasting horizons (i.e. H = 1 to H = 24). Third, our findings were sensitive to classifying the dataset into pre-COVID and COVID eras. For instance, the results confirmed that the predictive power of oil shocks declined during the crisis period. We also discuss some policy implications of this study's findings

Impact of novel processing techniques on the functional properties of egg products and derivatives: a review

Eggs are an excellent source of quality proteins. Eggs as a whole and its components (egg white and egg yolk) are employed in a range of food preparations. Thermal processing employed for stabilizing and improving shelf‐life of egg components is known to have adverse effect on heat‐sensitive proteins leading to protein denaturation and aggregation thus, reducing the required functional, technological, and overall quality of egg proteins and other constituents. Therefore, the current challenge is to identify novel processing techniques that not only improve the intrinsic functional properties of eggs or its components, but also improve the quality of the product. This review focuses on the use of technologies such as ultrasound, pulsed electric field, high‐pressure processing, radiofrequency, ultraviolet light, microwave, and cold plasma for egg products. These novel technologies are known for their advantages over thermal treatments especially in protecting the heat sensitive nature and retaining the overall quality of the egg and egg products. Availability of alternatives processing has significantly improved the structural properties, techno‐functional, nutritional and as well improving the safety egg and egg products. PRACTICAL APPLICATION: Eggs are consumed worldwide as whole egg or in some cases, consumed partly as egg whites or egg yolks. Egg components with improved techno‐functional properties can be used in various food industries (such as baking, confectionery, and culinary preparation, etc.). Value addition of new products can be achieved through modification of egg proteins. Additionally, these techniques also provide microbial safety and have a reduced impact on nutritional content and overall food quality

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Does Adaptation of Renewable Energy and Use of Service Industry Growth Diminution CO2 Emissions: Evidence of ASEAN Economies

According to recent years, ASEAN economies mainly focused on the development of renewable energy, which contributes to leading role of changing the economic structure towards service sector industry. So, most of the studies ignored the effect of heterogeneity and cross-sectional independence. It caused the biased and spurious results. Hence this study used the panel of 9 ASEAN economies of the time from 2000 to 2018, and Arelleno Bond Generalized Method of Moments (GMM) is used to examine the impact of renewable energy and the development of the service sector on Carbon emissions in ASEAN economies. Moreover, GMM overcomes the problem of cross-sectional dependence and heterogeneity so that the results will be unbiased and consistent. Results showed that an increase in the level of renewable energy usage and economic development leads to decrease in the level of CO2 emissions. Furthermore, development in the service sector industry and urbanization boost the level of emissions of carbon dioxide. So, the policymakers need a revolution in the renewable energy sector, which increased economic growth and total energy production and keep the environment safe, healthy and clean

Environmental Kuznets Curve (EKC): Empirically Examined Long Run Association Between Globalization, Financial Development and CO2 Emission for ASEAN Countries

oai:ojs2.journals.internationalrasd.org:article/485This study mainly inspects the effect of globalization and financial expansion on CO2 emissions in the existence of the EKC (Environmental Kuznets Curve) framework for ASEAN economies, firstly the study employs the cross-sectional dependence econometric test. Results of CADF, CIPS unit root test, LM test, panel Kao Cointegration, Johansen Fisher test and Panel ARDL investigation revealed that (i) the hypothesis of EKC supports in ASEAN economies (ii) financial expansion and consumption of energy subsidize to the Co2 productions while urbanization has positive and globalization negative affiliation with carbon dioxide emissions (iii) the data is heterogeneous and cross-sectional dependence test confirms that there exit cross sections dependency (iv) Co-integration test confirms that variables are co-integrated, urbanization has an order of integration is I(0) and a square of GDP, economic development, globalization, financial expansion, use of energy and CO2 emission have an order of integration is I(1). Moreover, it is recommended that the authorities of ASEAN economies give some special consideration to the globalization level. Since better institutional reforms, institutional quality is vital to upsurge financial development and globalization improved financial growth