University of Dhaka

<h3>Importance</h3> Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. <h3>Objective</h3> To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. <h3>Evidence Review</h3> We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. <h3>Findings</h3> In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). <h3>Conclusions and Relevance</h3> The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care.

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

Heavy metals are well-known environmental pollutants owing to their toxicity, longevity in the atmosphere, and ability to accumulate in the human body via bioaccumulation. The pollution of terrestrial and aquatic ecosystems with toxic heavy metals is a major environmental concern that has consequences for public health. Most heavy metals occur naturally, but a few are derived from anthropogenic sources. Heavy metals are characterized by their high atomic mass and toxicity to living organisms. Most heavy metals cause environmental and atmospheric pollution, and may be lethal to humans. Heavy metals can become strongly toxic by mixing with different environmental elements, such as water, soil, and air, and humans and other living organisms can be exposed to them through the food chain. Plenty of experimental studies were performed to appraise the promising treatment options from natural products. Additionally, nanotechnology based treatment options are being constantly developed. As an emerging field, nanotechnology is making substantial advances in the analysis and removal of heavy metals from complicated matrices. Removal of heavy metal has been accomplished by the use of a variety of nanomaterials, including graphene and its derivatives, magnetic nanoparticles, metal oxide nanoparticles, and carbon nanotubes, to name a few. Using nanotechnology for heavy metal analysis and removal from food and water resources provides many benefits over traditional methods. These advantages include a broad linear range, low detection and quantification limits, a high sensitivity, and high selectivity. Therefore this review aimed to explore the environmental consequences of the heavy metals, toxicity to the human health, as well as novel therapeutics development from the natural resources. Additionally, nanotechnological and nanomedicinal applications to treat heavy metal toxicity are also highlighted in this review.

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.

Antibiotics have been used to cure bacterial infections for more than 70 years, and these low-molecular-weight bioactive agents have also been used for a variety of other medicinal applications. In the battle against microbes, antibiotics have certainly been a blessing to human civilization by saving millions of lives. Globally, infections caused by multidrug-resistant (MDR) bacteria are on the rise. Antibiotics are being used to combat diversified bacterial infections. Synthetic biology techniques, in combination with molecular, functional genomic, and metagenomic studies of bacteria, plants, and even marine invertebrates are aimed at unlocking the world's natural products faster than previous methods of antibiotic discovery. There are currently only few viable remedies, potential preventive techniques, and a limited number of antibiotics, thereby necessitating the discovery of innovative medicinal approaches and antimicrobial therapies. MDR is also facilitated by biofilms, which makes infection control more complex. In this review, we have spotlighted comprehensively various aspects of antibiotics viz. overview of antibiotics era, mode of actions of antibiotics, development and mechanisms of antibiotic resistance in bacteria, and future strategies to fight the emerging antimicrobial resistant threat.

Inflammation is a natural protective mechanism that occurs when the body's tissue homeostatic mechanisms are disrupted by biotic, physical, or chemical agents. The immune response generates pro-inflammatory mediators, but excessive output, such as chronic inflammation, contributes to many persistent diseases. Some phenolic compounds work in tandem with nonsteroidal anti-inflammatory drugs (NSAIDs) to inhibit pro-inflammatory mediators' activity or gene expression, including cyclooxygenase (COX). Various phenolic compounds can also act on transcription factors, such as nuclear factor-κB (NF-κB) or nuclear factor-erythroid factor 2-related factor 2 (Nrf-2), to up-or downregulate elements within the antioxidant response pathways. Phenolic compounds can inhibit enzymes associated with the development of human diseases and have been used to treat various common human ailments, including hypertension, metabolic problems, incendiary infections, and neurodegenerative diseases. The inhibition of the angiotensin-converting enzyme (ACE) by phenolic compounds has been used to treat hypertension. The inhibition of carbohydrate hydrolyzing enzyme represents a type 2 diabetes mellitus therapy, and cholinesterase inhibition has been applied to treat Alzheimer's disease (AD). Phenolic compounds have also demonstrated anti-inflammatory properties to treat skin diseases, rheumatoid arthritis, and inflammatory bowel disease. Plant extracts and phenolic compounds exert protective effects against oxidative stress and inflammation caused by airborne particulate matter, in addition to a range of anti-inflammatory, anticancer, anti-aging, antibacterial, and antiviral activities. Dietary polyphenols have been used to prevent and treat allergy-related diseases. The chemical and biological contributions of phenolic compounds to cardiovascular disease have also been described. This review summarizes the recent progress delineating the multifunctional roles of phenolic compounds, including their anti-inflammatory properties and the molecular pathways through which they exert anti-inflammatory effects on metabolic disorders. This study also discusses current issues and potential prospects for the therapeutic application of phenolic compounds to various human diseases.

Environmental pollution, particularly from heavy metal ions in the wastewater, is one of the most serious concerns of the world. In the pursuit of remedial action, various conventional methods such as ion exchange, chemical precipitation, coagulation, membrane separation, reverse osmosis, and adsorption methods have so far been used for the removal of heavy metal ions. A good variety of adsorbents have been developed to remove different heavy metal ions from wastewater in particular those which have been detrimental to living organisms. Adsorption processes have been very demanding for high removal efficiency of heavy metal ions even at trace levels and they are low cost as compared to conventional methods. It has therefore been crucial to develop low cost and readily available adsorbents for the adsorption of heavy metal ions from wastewater. The adsorbents may be collected from agricultural and animal waste and industrial by-products. All adsorbents, by their intrinsic nature, have functional groups to play the key role in metal ion adsorption. Generally, chemically modified adsorbents enhance the surface area of the adsorbent and exhibit higher adsorption capacity than unmodified adsorbents. In this review, a series of natural waste materials and their modified forms have been evaluated for the removal of various metals from potable and wastewater. The major focus has been an accumulation of comprehensive knowledge on of the use of the low-cost adsorbents for removal of heavy metal ions.

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

In recent years, there have been increasingly rapid advances of using bioactive materials in tissue engineering applications. Bioactive materials constitute many different structures based upon ceramic, metallic or polymeric materials, and can elicit specific tissue responses. However, most of them are relatively brittle, stiff, and difficult to form into complex shapes. Hence, there has been a growing demand for preparing materials with tailored physical, biological, and mechanical properties, as well as predictable degradation behavior. Chitosan-based materials have been shown to be ideal bioactive materials due to their outstanding properties such as formability into different structures, and fabricability with a wide range of bioactive materials, in addition to their biocompatibility and biodegradability. This review highlights scientific findings concerning the use of innovative chitosan-based bioactive materials in the fields of tissue engineering, with an outlook into their future applications. It also covers latest developments in terms of constituents, fabrication technologies, structural, and bioactive properties of these materials that may represent an effective solution for tissue engineering materials, making them a realistic clinical alternative in the near future.

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.

Concern has been raised by Bangladeshi and international scientists about elevated levels of arsenic in Bengali food, particularly in rice grain. This is the first inclusive food market-basket survey from Bangladesh, which addresses the speciation and concentration of arsenic in rice, vegetables, pulses, and spices. Three hundred thirty aman and boro rice, 94 vegetables, and 50 pulse and spice samples were analyzed for total arsenic, using inductivity coupled plasma mass spectrometry (ICP-MS). The districts with the highest mean arsenic rice grain levels were all from southwestern Bangladesh: Faridpur (boro) 0.51 > Satkhira (boro) 0.38 > Satkhira (aman) 0.36 > Chuadanga (boro) 0.32 > Meherpur (boro) 0.29 microg As g(-1). The vast majority of food ingested arsenic in Bangladesh diets was found to be inorganic; with the predominant species detected in Bangladesh rice being arsenite (AsIII) or arsenate (AsV) with dimethyl arsinic acid (DMAV) being a minor component. Vegetables, pulses, and spices are less important to total arsenic intake than water and rice. Predicted inorganic arsenic intake from rice is modeled with the equivalent intake from drinking water for a typical Bangladesh diet. Daily consumption of rice with a total arsenic level of 0.08 microg As g(-1) would be equivalent to a drinking water arsenic level of 10 microg L(-1).

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.

Bangladesh - one of the most densely populated countries of the world- has plentiful water sources, but these sources are being polluted continuously. Both surface water and groundwater sources are contaminated with different contaminants like toxic trace metals, coliforms as well as other organic and inorganic pollutants. As most of the population uses these water sources, especially groundwater sources which contain an elevated amount of arsenic throughout the country; health risk regarding consuming water is very high. Death due to water-borne diseases is widespread in Bangladesh, particularly among children. Anthropogenic sources such as untreated industrial effluents, improper disposal of domestic waste, agricultural runoffs are the main contributors regarding water pollution. A total water pollution status of this country, as well as the sources of this severe condition, is crucial to evaluate public health risk. For this purpose, we reviewed hundreds of well recognized international and national journals, conference proceedings and other related documents to draw a complete picture of recent water pollution status and its impact on public health; also the sources of water pollution are identified.

Abstract This study characterized Pokkali-derived quantitative trait loci (QTLs) for seedling stage salinity tolerance in preparation for use in marker-assisted breeding. An analysis of 100 SSR markers on 140 IR29/Pokkali recombinant inbred lines (RILs) confirmed the location of the Saltol QTL on chromosome 1 and identified additional QTLs associated with tolerance. Analysis of a series of backcross lines and near-isogenic lines (NILs) developed to better characterize the effect of the Saltol locus revealed that Saltol mainly acted to control shoot Na + /K + homeostasis. Multiple QTLs were required to acquire a high level of tolerance. Unexpectedly, multiple Pokkali alleles at Saltol were detected within the RIL population and between backcross lines, and representative lines were compared with seven Pokkali accessions to better characterize this allelic variation. Thus, while the Saltol locus presents a complex scenario, it provides an opportunity for marker-assisted backcrossing to improve salt tolerance of popular varieties followed by targeting multiple loci through QTL pyramiding for areas with higher salt stress.

It is commonly thought that human genetic diversity in non-African populations was shaped primarily by an out-of-Africa dispersal 50-100 thousand yr ago (kya). Here, we present a study of 456 geographically diverse high-coverage Y chromosome sequences, including 299 newly reported samples. Applying ancient DNA calibration, we date the Y-chromosomal most recent common ancestor (MRCA) in Africa at 254 (95% CI 192-307) kya and detect a cluster of major non-African founder haplogroups in a narrow time interval at 47-52 kya, consistent with a rapid initial colonization model of Eurasia and Oceania after the out-of-Africa bottleneck. In contrast to demographic reconstructions based on mtDNA, we infer a second strong bottleneck in Y-chromosome lineages dating to the last 10 ky. We hypothesize that this bottleneck is caused by cultural changes affecting variance of reproductive success among males.

There has been a growing interest in utilizing fibers as reinforcement to produce composite materials. Scientists prefer thermoplastic polymeric matrices than thermosets due to the low production cycle, lower cost of processing and high reparability of thermoplastics. Fiber-reinforced thermoplastic matrix composites have gained commercial success in the semistructural and structural applications. Various fibers are widely used as reinforcement in thermoplastic polypropylene (PP) matrix to prepare composites. Mechanical properties of fiber-reinforced PP composites (FRPCs) are studied by many researchers and few of them are discussed in this article. Various fiber treatments, which are carried out to improve the fiber–matrix adhesion to get improved mechanical properties, are also discussed in this article. This article also focuses on coupling agents and fiber loading which affect the mechanical properties of FRPCs significantly.

The degree of crystallinity in cellulose significantly affects the physical, mechanical, and chemical properties of cellulosic materials, their processing, and their final application. Measuring the crystalline structures of cellulose is a challenging task due to inadequate consistency among the variety of analytical techniques available and the lack of absolute crystalline and amorphous standards. Our article reviews the primary methods for estimating the crystallinity of cellulose, namely, X-ray diffraction (XRD), nuclear magnetic resonance (NMR), Raman and Fourier-transform infrared (FTIR) spectroscopy, sum-frequency generation vibrational spectroscopy (SFG), as well as differential scanning calorimetry (DSC), and evolving biochemical methods using cellulose binding molecules (CBMs). The techniques are compared to better interrogate not only the requirements of each method, but also their differences, synergies, and limitations. The article highlights fundamental principles to guide the general community to initiate studies of the crystallinity of cellulosic materials.

Redox degenerative reactions of the biological system inevitably produce reactive oxygen species (ROS) and their derivatives. Oxidative stress is the result of an imbalance in pro-oxidant/antioxidant homeostasis that leads to the generation of toxic reactive oxygen species (ROS), such as hydrogen peroxide, organic hydro peroxides, nitric oxide, superoxide and hydroxyl radicals etc. Information are accumulating steadily, supporting the general importance of oxidative damage of tissue and cellular components as a primary or secondary causative factor in many different human diseases and aging processes. Many of the recent landmarks in scientific research have shown that in human beings, oxidative stress has been implicated in the progression of major health problems by inactivating the metabolic enzymes and damaging important cellular components, oxidizing the nucleic acids, leading to cardiovascular diseases, eye disorders, joint disorders, neurological diseases (Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis), atherosclerosis, lung and kidney disorders, liver and pancreatic diseases, cancer, ageing, disease of the reproductive system including the male and female infertility etc. The advent of a growing number of in vitro and in vivo models for evaluating the human disease pathology is aiding scientists in deciphering the detailed mechanisms of the point of intersection of the oxidative stress with other cellular components or events in the growing roadmap leading to different human disorders. The toxic effect of reactive oxygen and nitrogen species in human is balanced by the antioxidant action of non-enzymatic antioxidants, as well as by antioxidant enzymes. Such antioxidant defences are extremely important as they represent the direct removal of free radicals (prooxidants), thus providing maximal protection for biological sites. These systems not only assert with the problem of oxidative damage, but also play a crucial role in wellness, health maintenance, and prevention of chronic and degenerative diseases. In this review we have tried to generate a gross picture on the critical role of ROS in deteriorating human health and the importance of antioxidative defense system in ameliorating the toxicity of ROS.

bacterial strains. Although the cytotoxic effect of nanoparticles against Hela, BHK-21 and Vero cell line was found to be toxic at maximum doses but it can be considered for tumor cell damage because it showed excellent activity against the Hela and BHK-21 cell lines.