Graphic Era University

Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. This paper introduces the Big data technology along with its importance in the modern world and existing projects which are effective and important in changing the concept of science into big science and society too. The various challenges and issues in adapting and accepting Big data technology, its tools (Hadoop) are also discussed in detail along with the problems Hadoop is facing. The paper concludes with the Good Big data practices to be followed.

The biosorption process has been established as characteristics of dead biomasses of both cellulosic and microbial origin to bind metal ion pollutants from aqueous suspension. The high effectiveness of this process even at low metal concentration, similarity to ion exchange treatment process, but cheaper and greener alternative to conventional techniques have resulted in a mature biosorption technology. Yet its adoption to large scale industrial wastewaters treatment has still been a distant reality. The purpose of this review is to make in-depth analyses of the various aspects of the biosorption technology, staring from the various biosorbents used till date and the various factors affecting the process. The design of better biosorbents for improving their physico-chemical features as well as enhancing their biosorption characteristics has been discussed. Better economic value of the biosorption technology is related to the repeated reuse of the biosorbent with minimum loss of efficiency. In this context desorption of the metal pollutants as well as regeneration of the biosorbent has been discussed in detail. Various inhibitions including the multi mechanistic role of the biosorption technology has been identified which have played a contributory role to its non-commercialization.

Cadmium is a widespread toxic pollutant of occupational and environmental concern because of its diverse toxic effects: extremely protracted biological half-life (approximately 20-30 years in humans), low rate of excretion from the body and storage predominantly in soft tissues (primarily, liver and kidneys). It is an extremely toxic element of continuing concern because environmental levels have risen steadily due to continued worldwide anthropogenic mobilization. Cadmium is absorbed in significant quantities from cigarette smoke, food, water and air contamination and is known to have numerous undesirable effects in both humans and animals. Cadmium has a diversity of toxic effects including nephrotoxicity, carcinogenicity, teratogenicity and endocrine and reproductive toxicities. At the cellular level, cadmium affects cell proliferation, differentiation, apoptosis and other cellular activities. Current evidence suggests that exposure to cadmium induces genomic instability through complex and multifactorial mechanisms. Most important seems to be cadmium interaction with DNA repair mechanism, generation of reactive oxygen species and induction of apoptosis. In this article, we have reviewed recent developments and findings on cadmium toxicology.

Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.

Circular supply chain (CSC) emphasises surge in application of reuse, recycling, remanufacturing and thereby promotes transformation from linear to circular model of flow of products. Supply chains of manufacturing industries have become global over the years. Products manufactured in developing nations are being sent to developed nations for mass consumption. Developed nations have regulatory policies, technological knowhow and modern infrastructure to adopt CSC model. Their counterpart is trailing in these aspects. In literature, limited work has been performed on identifying challenges of implementing CSC in developing nations. Therefore, employing literature review and feedback received from experts, 16 important barriers were identified to CSC adoption in India. These barriers were analysed using integrated Interpretive Structural Modelling ? MICMAC approach. The findings will contribute in transforming supply chains thereby bringing economic prosperity, addressing global warming and generating employment opportunities. Finally, crucial policy measures and recommendations are proposed to assist managers and government bodies.

Cancer, one of the leading illnesses, accounts for about 10 million deaths worldwide. The treatment of cancer includes surgery, chemotherapy, radiation therapy, and drug therapy, along with others, which not only put a tremendous economic effect on patients but also develop drug resistance in patients with time. A significant number of cancer cases can be prevented/treated by implementing evidence-based preventive strategies. Plant-based drugs have evolved as promising preventive chemo options both in developing and developed nations. The secondary plant metabolites such as alkaloids have proven efficacy and acceptability for cancer treatment. Apropos, this review deals with a spectrum of promising alkaloids such as colchicine, vinblastine, vincristine, vindesine, vinorelbine, and vincamine within different domains of comprehensive information on these molecules such as their medical applications (contemporary/traditional), mechanism of antitumor action, and potential scale-up biotechnological studies on an in-vitro scale. The comprehensive information provided in the review will be a valuable resource to develop an effective, affordable, and cost effective cancer management program using these alkaloids.

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Neuroinflammation, a protective response of the central nervous system (CNS), is associated with the pathogenesis of neurodegenerative diseases. The CNS is composed of neurons and glial cells consisting of microglia, oligodendrocytes, and astrocytes. Entry of any foreign pathogen activates the glial cells (astrocytes and microglia) and overactivation of these cells triggers the release of various neuroinflammatory markers (NMs), such as the tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-1β (IL-10), nitric oxide (NO), and cyclooxygenase-2 (COX-2), among others. Various studies have shown the role of neuroinflammatory markers in the occurrence, diagnosis, and treatment of neurodegenerative diseases. These markers also trigger the formation of various other factors responsible for causing several neuronal diseases including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), multiple sclerosis (MS), ischemia, and several others. This comprehensive review aims to reveal the mechanism of neuroinflammatory markers (NMs), which could cause different neurodegenerative disorders. Important NMs may represent pathophysiologic processes leading to the generation of neurodegenerative diseases. In addition, various molecular alterations related to neurodegenerative diseases are discussed. Identifying these NMs may assist in the early diagnosis and detection of therapeutic targets for treating various neurodegenerative diseases.

The food processing and handling industry is the most significant business among the various manufacturing industries in the entire world that subsidize the highest employability. The human workforce plays an essential role in the smooth execution of the production and packaging of food products. Due to the involvement of humans, the food industries are failing to maintain the demand-supply chain and also lacking in food safety. To overcome these issues of food industries, industrial automation is the best possible solution. Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. This article is going to explain the AI applications in the food industry which recommends a huge amount of capital saving with maximizing resource utilization by reducing human error. Artificial intelligence with data science can improve the quality of restaurants, cafes, online delivery food chains, hotels, and food outlets by increasing production utilizing different fitting algorithms for sales prediction. AI could significantly improve packaging, increasing shelf life, a combination of the menu by using AI algorithms, and food safety by making a more transparent supply chain management system. With the help of AI and ML, the future of food industries is completely based on smart farming, robotic farming, and drones.

Composite materials are emerging as a vital entity for the sustainable development of both humans and the environment. Polylactic acid (PLA) has been recognized as a potential polymer candidate with attractive characteristics for applications in both the engineering and medical sectors. Hence, the present article throws lights on the essential physical and mechanical properties of PLA that can be beneficial for the development of composites, biocomposites, films, porous gels, and so on. The article discusses various processes that can be utilized in the fabrication of PLA-based composites. In a later section, we have a detailed discourse on the various composites and nanocomposites-based PLA along with the properties' comparisons, discussing our investigation on the effects of various fibers, fillers, and nanofillers on the mechanical, thermal, and wear properties of PLA. Lastly, the various applications in which PLA is used extensively are discussed in detail.

A well-regarded as well as powerful method named the ‘analytic hierarchy process’ (AHP) uses mathematics and psychology for making and analysing complex decisions. This article aims to present a brief review of the consistency measure of the judgments in AHP. Judgments should not be random or illogical. Several researchers have developed different consistency measures to identify the rationality of judgments. This article summarises the consistency measures which have been proposed so far in the literature. Moreover, this paper describes briefly the functional relationships established in the literature among the well-known consistency indices. At last, some thoughtful research directions that can be helpful in further research to develop and improve the performance of AHP are provided as well.

Neurodegenerative disorders (NDs) are expected to pose a significant challenge for both medicine and public health in the upcoming years due to global demographic changes. NDs are mainly represented by degeneration/loss of neurons, which is primarily accountable for severe mental illness. This neuronal degeneration leads to many neuropsychiatric problems and permanent disability in an individual. Moreover, the tight junction of the brain, blood-brain barrier (BBB)has a protective feature, functioning as a biological barrier that can prevent medicines, toxins, and foreign substances from entering the brain. However, delivering any medicinal agent to the brain in NDs (i.e., Multiple sclerosis, Alzheimer's, Parkinson's, etc.) is enormously challenging. There are many approved therapies to address NDs, but most of them only help treat the associated manifestations. The available therapies have failed to control the progression of NDs due to certain factors, i.e., BBB and drug-associated undesirable effects. NDs have extremely complex pathology, with many pathogenic mechanisms involved in the initiation and progression; thereby, a limited survival rate has been observed in ND patients. Hence, understanding the exact mechanism behind NDs is crucial to developing alternative approaches for improving ND patients' survival rates. Thus, the present review sheds light on different cellular mechanisms involved in NDs and novel therapeutic approaches with their clinical relevance, which will assist researchers in developing alternate strategies to address the limitations of conventional ND therapies. The current work offers the scope into the near future to improve the therapeutic approach of NDs.

Water is essential for the life, but many people lack the accessibility to clean and healthy drinking water and die as a consequence of water-borne infections. Microorganism-mediated water pollution is considered as one of the great concerns to the aquatic environment across the globe. The effluent of fecal matter, hospitals, industry, and cattle farms increase the bacterial load in a water body. Coliform groups of bacteria have long been typically applied as an indicator organism of microbial contamination of the water and historically led to the public health security perception. Among the coliform, Escherichia coli is the indicator of fecal contamination. The multiple tube fermentation technique has been applied as a conventional way to detect coliform in water samples through the fermentation of lactose sugar with production of acid and gas. The potability of water has been measured by the absence or presence of coliform bacteria within the permissible limit referencing the most probable number index value (MPN/100 ml). As fecal pollution indicators, fecal streptococci and Clostridium perfringens are widely used as an alternative to coliform bacteria and have been confirmed via esculin hydrolyzing or catalase-negativity and sulfite reduction tests. Molecular (PCR-based) and enzymatic methods have been applied as a rapid way to detect indicators and other enteric isolates in water samples. Apart from that standard plate count (SPC) of heterotrophic bacteria and biochemical oxygen demand (BOD) techniques also determine the bacterial and organic pollution load in a water sample. Therefore, bacteriological analysis of water indicated that water is polluted by sewage to the extent that it is unsuitable for drinking and also unsuitable for recreation purposes. This is one of the big problems in the twenty-first century is providing everybody with safe drinking or domestic water. The main objective of this article is to highlight the microbial pollution of water with special reference to coliform and its nexus with the environment.

AbstractGlobal businesses are actively embracing Industry 4.0 and digital transformation. Simultaneously, the education sector is leveraging digital tools to foster personalized learning and equity. Universities transcending borders and students becoming increasingly global have opened new frontiers through the use of artificial intelligence (AI)-based tools in education. Since the role of AI is inevitable in future education, current research aims to identify the level of awareness of faculty members on the applicability and adoption of artificial intelligence. The study also intended to discover how AI enhanced their learning experience and impacted the degree of work engagement of teachers in higher education. A cluster and multi-stage sampling method was employed to select 250 faculty members from QS (Quacquarelli Symonds) ranked institutions operating in hybrid education modes. Utilizing a quantitative research approach and a structural equation model, factors influencing AI adoption in this context were explored. The findings revealed that AI implementation led to the evolution of robust evaluation and assessment methods, resulting in heightened faculty engagement levels. The study identified that perceived risk, performance expectancy, and awareness play significant roles in influencing work engagement and the adoption of AI within the higher education system through mediating variables, specifically attitude, and behavior.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.

Several flavonoids have been recognized as nutraceuticals, and myricetin is a good example. Myricetin is commonly found in plants and their antimicrobial and antioxidant activities is well demonstrated. One of its beneficial biological effects is the neuroprotective activity, showing preclinical activities on Alzheimer, Parkinson, and Huntington diseases, and even in amyotrophic lateral sclerosis. Also, myricetin has revealed other biological activities, among them as antidiabetic, anticancer, immunomodulatory, cardiovascular, analgesic and antihypertensive. However, few clinical trials have been performed using myricetin as nutraceutical. Thus, this review provides new insights on myricetin preclinical pharmacological activities, and role in selected clinical trials.

Abstract The circular economy (CE) is a more holistic approach that advocates towards extracting the value from the waste and reaching sustainability goals. The objective of the present study is to highlight the prospects, impediments, and prerequisites while transiting from the linear economy (LE) to CE of SMEs. The study gathers information on prospects, impediments, and prerequisites for the transition of LE to a CE from recent studies . A semi‐structured interview questionnaire was prepared, and a survey was conducted on representatives of six SMEs . Further, six caselets were developed to understand the prospects, impediments, and prerequisites based on the findings of the interview and previous information gained from existing literature . The major prospects favoring transition from LE to CE found in the study are significance of 3R (reduce and reuse and recycling) approach, CE leads to competitive advantage, recycling attracts consumers in few cases, CE helps in achieving sustainability goals and reuse of materials are significant in resource conservation. There are certain impediments found such as issues associated with awareness, recyclability issues, financial challenges, and weak management vision of SMEs towards CE implementation. Other resource‐based impediments were found related to trained employees, lack of experience. Whereas, consumer acceptability is also a major concern towards implementing CE. The findings of the study suggest major prerequisites towards CE implementations such as strong “management will,” innovation, technology up‐gradation, training to employees, motivation, and appropriate guidelines. Government pressure to implement CE cannot be an effective step towards the transition of LE to CE.