University of Chittagong

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A variety of rechargeable batteries are now available in world markets for powering electric vehicles (EVs). The lithium-ion (Li-ion) battery is considered the best among all battery types and cells because of its superior characteristics and performance. The positive environmental impacts and recycling potential of lithium batteries have influenced the development of new research for improving Li-ion battery technologies. However, the cost reduction, safe operation, and mitigation of negative ecological impacts are now a common concern for advancement. This paper provides a comprehensive study on the state of the art of Li-ion batteries including the fundamentals, structures, and overall performance evaluations of different types of lithium batteries. A study on a battery management system for Li-ion battery storage in EV applications is demonstrated, which includes a cell condition monitoring, charge, and discharge control, states estimation, protection and equalization, temperature control and heat management, battery fault diagnosis, and assessment aimed at enhancing the overall performance of the system. It is observed that the Li-ion batteries are becoming very popular in vehicle applications due to price reductions and lightweight with high power density. However, the management of the charging and discharging processes, CO2 and greenhouse gases emissions, health effects, and recycling and refurbishing processes have still not been resolved satisfactorily. Consequently, this review focuses on the many factors, challenges, and problems and provides recommendations for sustainable battery manufacturing for future EVs. This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.

Hydrogels based on cellulose comprising many organic biopolymers including cellulose, chitin, and chitosan are the hydrophilic material, which can absorb and retain a huge proportion of water in the interstitial sites of their structures. These polymers feature many amazing properties such as responsiveness to pH, time, temperature, chemical species and biological conditions besides a very high-water absorption capacity. Biopolymer hydrogels can be manipulated and crafted for numerous applications leading to a tremendous boom in research during recent times in scientific communities. With the growing environmental concerns and an emergent demand, researchers throughout the globe are concentrating particularly on naturally derived hydrogels due to their biocompatibility, biodegradability and abundance. Cellulose-based hydrogels are considered as useful biocompatible materials to be used in medical devices to treat, augment or replace any tissue, organ, or help function of the body. These hydrogels also hold a great promise for applications in agricultural activity, as smart materials and some other useful industrial purposes. This review offers an overview of the recent and contemporary research regarding physiochemical properties of cellulose-based hydrogels along with their applications in multidisciplinary areas including biomedical fields such as drug delivery, tissue engineering and wound healing, healthcare and hygienic products as well as in agriculture, textiles and industrial applications as smart materials.

Heterocyclic compounds have gained a lot of attention because of their numerous significant medical and biological uses. Research interest on heterocyclic compounds is rapidly increasing due to the extensive synthetic study and functional utility. They are found in more than 90% of novel drugs, and span the gap between biology and chemistry, where so much scientific discovery and application occurs. Heterocycles also play a role in different fields, inclusive of medicinal chemistry, biochemistry, and others. Pharmaceuticals, agrochemicals, and veterinary items are the main applications of heterocyclic compounds. In our review, we cover the majority of bio-active heterocycles that have recently been synthesized and introduced a new phase of possible antifungal, anti-inflammatory, anti-bacterial, antiviral, antioxidant, anticonvulsant, anthelmintics, anthelmintic antipyretics, anti-allergic, anti-histamine, herbicidal, anticancer, antihypertensive and anti-leprosy therapeutics.

Cancer is one of the leading causes of death worldwide. Several treatments are available for cancer treatment, but many treatment methods are ineffective against multidrug-resistant cancer. Multidrug resistance (MDR) represents a major obstacle to effective therapeutic interventions against cancer. This review describes the known MDR mechanisms in cancer cells and discusses ongoing laboratory approaches and novel therapeutic strategies that aim to inhibit, circumvent, or reverse MDR development in various cancer types. In this review, we discuss both intrinsic and acquired drug resistance, in addition to highlighting hypoxia- and autophagy-mediated drug resistance mechanisms. Several factors, including individual genetic differences, such as mutations, altered epigenetics, enhanced drug efflux, cell death inhibition, and various other molecular and cellular mechanisms, are responsible for the development of resistance against anticancer agents. Drug resistance can also depend on cellular autophagic and hypoxic status. The expression of drug-resistant genes and the regulatory mechanisms that determine drug resistance are also discussed. Methods to circumvent MDR, including immunoprevention, the use of microparticles and nanomedicine might result in better strategies for fighting cancer.

Lipases are industrial biocatalysts, which are involved in several novel reactions, occurring in aqueous medium as well as non-aqueous medium. Furthermore, they are well-known for their remarkable ability to carry out a wide variety of chemo-, regio- and enantio-selective transformations. Lipases have been gained attention worldwide by organic chemists due to their general ease of handling, broad substrate tolerance, high stability towards temperatures and solvents and convenient commercial availability. Most of the synthetic reactions on industrial scale are carried out in organic solvents because of the easy solubility of non-polar compounds. The effect of organic system on their stability and activity may determine the biocatalysis pace. Because of worldwide use of lipases, there is a need to understand the mechanisms behind the lipase-catalyzed reactions in organic solvents. The unique interfacial activation of lipases has always fascinated enzymologists and recently, biophysicists and crystallographers have made progress in understanding the structure-function relationships of these enzymes. The present review describes the advantages of lipase-catalyzed reactions in organic solvents and various effects of organic solvents on their activity.

Microplastics (MPs) are regarded as a global issue due to their toxicity effects on fish and humans. Fish is a vital origin of human protein, which is necessary for body growth. Contamination of fish by MPs is a major hazard that requires special focus. After exposure to MPs alone or in combination with other pollutants, fish may experience a variety of health issues. MPs can cause tissue damage, oxidative stress, and changes in immune-related gene expression as well as antioxidant status in fish. After being exposed to MPs, fish suffer from neurotoxicity, growth retardation, and behavioral abnormalities. The consequences of MPs on human health are poorly understood. Due to the abundance of MPs in environment, exposure may occur via consumption, inhalation, and skin contact. Humans may experience oxidative stress, cytotoxicity, neurotoxicity, immune system disruption, and transfer of MPs to other tissues after being exposed to them. The toxic effects of MPs in both fish and human are still unknown. This detailed review has the potential to add to existing knowledge about the ecotoxicity effects of MPs in both fish and humans, which will be useful for the forthcoming study.

Natural products derived from microorganisms serve as a vital resource of valuable pharmaceuticals and therapeutic agents. Streptomyces is the most ubiquitous bacterial genus in the environments with prolific capability to produce diverse and valuable natural products with significant biological activities in medicine, environments, food industries, and agronomy sectors. However, many natural products remain unexplored among Streptomyces . It is exigent to develop novel antibiotics, agrochemicals, anticancer medicines, etc., due to the fast growth in resistance to antibiotics, cancer chemotherapeutics, and pesticides. This review article focused the natural products secreted by Streptomyces and their function and importance in curing diseases and agriculture. Moreover, it discussed genomic-driven drug discovery strategies and also gave a future perspective for drug development from the Streptomyces .

The proliferation of misinformation on social media platforms is faster than the spread of Corona Virus Diseases (COVID-19) and it can generate hefty deleterious consequences on health amid a disaster like COVID-19. Drawing upon research on the stimulus-response theory (hypodermic needle theory) and the resilience theory, this study tested a conceptual framework considering general misinformation belief, conspiracy belief, and religious misinformation belief as the stimulus; and credibility evaluations as resilience strategy; and their effects on COVID-19 individual responses. Using a self-administered online survey during the COVID-19 pandemic, the study obtained 483 useable responses and after test, finds that all-inclusive, the propagation of misinformation on social media undermines the COVID-19 individual responses. Particularly, credibility evaluation of misinformation strongly predicts the COVID-19 individual responses with positive influences and religious misinformation beliefs as well as conspiracy beliefs and general misinformation beliefs come next and influence negatively. The findings and general recommendations will help the public, in general, to be cautious about misinformation, and the respective authority of a country, in particular, for initiating proper safety measures about disastrous misinformation to protect the public health from being exploited.

BACKGROUND: To limit the rapid spread of COVID-19, countries have asked their citizens to stay at home. As a result, demographic and cultural factors related to home life have become especially relevant to predict population well-being during isolation. This pre-registered worldwide study analyses the relationship between the number of adults and children in a household, marital status, age, gender, education level, COVID-19 severity, individualism-collectivism, and perceived stress. METHODS: We used the COVIDiSTRESS Global Survey data of 53,524 online participants from 26 countries and areas. The data were collected between 30 March and 6 April 2020. RESULTS: Higher levels of stress were associated with younger age, being a woman, lower level of education, being single, staying with more children, and living in a country or area with a more severe COVID-19 situation. CONCLUSIONS: The COVID-19 pandemic revealed that certain people may be more susceptible to experience elevated levels of stress. Our findings highlight the need for public health to be attentive to both the physical and the psychological well-being of these groups.

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.

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.

Water Stress 2

Infectious diseases pose a formidable global challenge, compounded by the emergence of antimicrobial resistance. Consequently, researchers are actively exploring novel antimicrobial compounds as potential solutions. This endeavor underscores the pivotal role of methods employed for screening and evaluating antimicrobial activity-a critical step in discovery and characterization of antimicrobial agents. While traditional techniques such as well-diffusion, disk-diffusion, and broth-dilution are commonly utilized in antimicrobial assays, they may encounter limitations concerning reproducibility and speed. Additionally, a diverse array of antimicrobial assays including cross-streaking, poisoned-food, co-culture, time-kill kinetics, resazurin assay, bioautography, etc., are routinely employed in antimicrobial evaluations. Advanced techniques such as flow-cytometry, impedance analysis, and bioluminescent technique may offer rapid and sensitive results, providing deeper insights into the impact of antimicrobials on cellular integrity. However, their higher cost and limited accessibility in certain laboratory settings may present challenges. This article provides a comprehensive overview of assays designed to characterize antimicrobial activity, elucidating their underlying principles, protocols, advantages, and limitations. The primary objective is to enhance understanding of the methodologies designed for evaluating antimicrobial agents in our relentless battle against infectious diseases. By selecting the appropriate antimicrobial testing method, researchers can discern suitable conditions and streamline the identification of effective antimicrobial agents.

Metal complexes of Schiff bases synthesized from condensation of an amine with carbonyl compounds are widely used for industrial purposes such as catalysts, pigments and dyes, intermediates in organic synthesis, polymer stabilizers, and corrosion inhibitors. For design and development of various bioactive compounds, Schiff bases carrying imine or azomethine (−C = N–) functional group, are versatile pharmacophores. Medicinal chemists give attention toward new chemotherapeutic Schiff bases and their metal complexes owing to numerous applications in pharmacology as antiviral, antibacterial, antifungal, antimalarial, antituberculosis, anticancer, anti-HIV, anti-inflammatory, and antipyretic agents. This review highlights recently synthesized Schiff bases as well as their metal complexes as potential bioactive core.

The degraded Chunati wildlife sanctuary (CWS) has undergone various land use changes since 1980s. In this study, land use changes of CWS were assessed from 2005 to 2015 by using Landsat TM and Landsat 8 OLI/TIRS images. The ArcGIS v10.1 and ERDAS Imagine v14 were used to process satellite imageries and assessed quantitative data for land use change assessment of this study area. Maximum likelihood classification algorithm was used in order to derive supervised land use classification. It was found that about 256 ha of degraded forest area had been increased within 10 years (2005–2015) and the annual rate of change was 25.56%. Another 159 ha of naturally forested land had been changed to other land uses having an (−) annual rate of change of 15.88%. The overall supervised classification accuracy was found 92.16% for 2015, 86.15% for 2010, and 83.96% for 2005 with Kappa values of 0.89, 0.82, and 0.81 for 2015, 2010, and 2005, respectively and these were fairly satisfactory. The results of this study would be helpful to plan and implement important management decisions in order to conserve the rich biodiversity of Chunati wildlife sanctuary. Keywords: Land use/land cover change (LULCC), Remote sensing (RS), Geographic information system (GIS), Chunati wildlife sanctuary (CWS), Land use classification, Protected area

Among the nitrogen-containing heterocyclic compounds, triazoles emerge with superior pharmacological applications. Structurally, there are two types of five-membered triazoles: 1,2,3-triazole and 1,2,4-triazole. Due to the structural characteristics, both 1,2,3- and 1,2,4-triazoles are able to accommodate a broad range of substituents (electrophiles and nucleophiles) around the core structures and pave the way for the construction of diverse novel bioactive molecules. Both the triazoles and their derivatives have significant biological properties including antimicrobial, antiviral, antitubercular, anticancer, anticonvulsant, analgesic, antioxidant, anti-inflammatory, and antidepressant activities. These are also important in organocatalysis, agrochemicals, and materials science. Thus, they have a broad range of therapeutic applications with ever-widening future scope across scientific disciplines. However, adverse events such as hepatotoxicity and hormonal problems lead to a careful revision of the azole family to obtain higher efficacy with minimum side effects. This review focuses on the structural features, synthesis, and notable therapeutic applications of triazoles and related compounds.

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

For generations, cyclones and tidal surges have frequently devastated lives and property in coastal and island Bangladesh. This study explores vulnerability to cyclone hazards using first-hand coping recollections from prior to, during and after these events. Qualitative field data suggest that, beyond extreme cyclone forces, localised vulnerability is defined in terms of response processes, infrastructure, socially uneven exposure, settlement development patterns, and livelihoods. Prior to cyclones, religious activities increase and people try to save food and valuable possessions. Those in dispersed settlements who fail to reach cyclone shelters take refuge in thatched-roof houses and big-branch trees. However, women and children are affected more despite the modification of traditional hierarchies during cyclone periods. Instinctive survival strategies and intra-community cooperation improve coping post cyclone. This study recommends that disaster reduction programmes encourage cyclone mitigation while being aware of localised realities, endogenous risk analyses, and coping and adaptation of affected communities (as active survivors rather than helpless victims).