Jahangirnagar University

A good number of abstracts and research articles (in total 74) published, so far, for evaluating antioxidant activity of various samples of research interest were gone through where 407 methods were come across, which were repeated from 29 different methods. These were classified as in vitro and in vivo methods. And those are described and discussed below in this review article. In the later part of this review article, frequency of in vitro as well as in vivo methods is analyzed with a bar diagram. Solvents are important for extracting antioxidants from natural sources. Frequency of solvents used for extraction is also portrayed and the results are discussed in this article. As per this review there are 19 in vitro methods and 10 in vivo methods that are being used for the evaluation of antioxidant activity of the sample of interest. DPPH method was found to be used mostly for the in vitro antioxidant activity evaluation purpose while LPO was found as mostly used in vivo antioxidant assay. Ethanol was with the highest frequency as solvent for extraction purpose.

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

With the development of nanotechnology, silver nanoparticles (Ag-NPs) have become one of the most in-demand nanoparticles owing to their exponential number of uses in various sectors. The increased use of Ag-NPs-enhanced products may result in an increased level of toxicity affecting both the environment and living organisms. Several studies have used different model cell lines to exhibit the cytotoxicity of Ag-NPs, and their underlying molecular mechanisms. This review aimed to elucidate different properties of Ag-NPs that are responsible for the induction of cellular toxicity along with the critical mechanism of action and subsequent defense mechanisms observed in vitro. Our results show that the properties of Ag-NPs largely vary based on the diversified synthesis processes. The physiochemical properties of Ag-NPs (e.g., size, shape, concentration, agglomeration, or aggregation interaction with a biological system) can cause impairment of mitochondrial function prior to their penetration and accumulation in the mitochondrial membrane. Thus, Ag-NPs exhibit properties that play a central role in their use as biocides along with their applicability in environmental cleaning. We herein report a current review of the synthesis, applicability, and toxicity of Ag-NPs in relation to their detailed characteristics.

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Abstract Systematic assessments of species extinction risk at regular intervals are necessary for informing conservation action 1,2 . Ongoing developments in taxonomy, threatening processes and research further underscore the need for reassessment 3,4 . Here we report the findings of the second Global Amphibian Assessment, evaluating 8,011 species for the International Union for Conservation of Nature Red List of Threatened Species. We find that amphibians are the most threatened vertebrate class (40.7% of species are globally threatened). The updated Red List Index shows that the status of amphibians is deteriorating globally, particularly for salamanders and in the Neotropics. Disease and habitat loss drove 91% of status deteriorations between 1980 and 2004. Ongoing and projected climate change effects are now of increasing concern, driving 39% of status deteriorations since 2004, followed by habitat loss (37%). Although signs of species recoveries incentivize immediate conservation action, scaled-up investment is urgently needed to reverse the current trends.

Out-of-pocket (OOP) payments are the principal means of financing health care throughout much of Asia. We estimate the magnitude and distribution of OOP payments for health care in fourteen countries and territories accounting for 81% of the Asian population. We focus on payments that are catastrophic, in the sense of severely disrupting household living standards, and approximate such payments by those absorbing a large fraction of household resources. Bangladesh, China, India, Nepal and Vietnam rely most heavily on OOP financing and have the highest incidence of catastrophic payments. Sri Lanka, Thailand and Malaysia stand out as low to middle income countries that have constrained both the OOP share of health financing and the catastrophic impact of direct payments. In most low/middle-income countries, the better-off are more likely to spend a large fraction of total household resources on health care. This may reflect the inability of the poorest of the poor to divert resources from other basic needs and possibly the protection of the poor from user charges offered in some countries. But in China, Kyrgyz and Vietnam, where there are no exemptions of the poor from charges, they are as, or even more, likely to incur catastrophic payments.

In Bangladesh, an array of measures have been adopted to control the rapid spread of the COVID-19 epidemic. Such general population control measures could significantly influence perception, knowledge, attitudes, and practices (KAP) towards COVID-19. Here, we assessed KAP towards COVID-19 immediately after the lock-down measures were implemented and during the rapid rise period of the outbreak. Online-based cross-sectional study conducted from March 29 to April 19, 2020, involving Bangladeshi residents aged 12-64 years, recruited via social media. After consenting, participants completed an online survey assessing socio-demographic variables, perception, and KAP towards COVID-19. Of the 2017 survey participants, 59.8% were male, the majority were students (71.2%), aged 21-30 years (57.9%), having a bachelor's degree (61.0%), having family income >30,000 BDT (50.0%), and living in urban areas (69.8). The survey revealed that 48.3% of participants had more accurate knowledge, 62.3% had more positive attitudes, and 55.1% had more frequent practices regarding COVID-19 prevention. Majority (96.7%) of the participants agreed 'COVID-19 is a dangerous disease', almost all (98.7%) participants wore a face mask in crowded places, 98.8% agreed to report a suspected case to health authorities, and 93.8% implemented washing hands with soap and water. In multiple logistic regression analyses, COVID-19 more accurate knowledge was associated with age and residence. Sociodemographic factors such as being older, higher education, employment, monthly family income >30,000 BDT, and having more frequent prevention practices were the more positive attitude factors. More frequent prevention practice factors were associated with female sex, older age, higher education, family income > 30,000 BDT, urban area residence, and having more positive attitudes. To improve KAP of general populations is crucial during the rapid rise period of a pandemic outbreak such as COVID-19. Therefore, development of effective health education programs that incorporate considerations of KAP-modifying factors is needed.

An organic compound known as 5-hydroxymethylfurfural (HMF) is formed from reducing sugars in honey and various processed foods in acidic environments when they are heated through the Maillard reaction. In addition to processing, storage conditions affect the formation HMF, and HMF has become a suitable indicator of honey quality. HMF is easily absorbed from food through the gastrointestinal tract and, upon being metabolized into different derivatives, is excreted via urine. In addition to exerting detrimental effects (mutagenic, genotoxic, organotoxic and enzyme inhibitory), HMF, which is converted to a non-excretable, genotoxic compound called 5-sulfoxymethylfurfural, is beneficial to human health by providing antioxidative, anti-allergic, anti-inflammatory, anti-hypoxic, anti-sickling, and anti-hyperuricemic effects. Therefore, HMF is a neo-forming contaminant that draws great attention from scientists. This review compiles updated information regarding HMF formation, detection procedures, mitigation strategies and effects of HMF on honey bees and human health.

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.

The global outbreak of coronavirus disease 2019 (COVID-19) is affecting every part of human lives, including the physical world. The measures taken to control the spread of the virus and the slowdown of economic activities have significant effects on the environment. Therefore, this study intends to explore the positive and negative environmental impacts of the COVID-19 pandemic, by reviewing the available scientific literatures. This study indicates that, the pandemic situation significantly improves air quality in different cities across the world, reduces GHGs emission, lessens water pollution and noise, and reduces the pressure on the tourist destinations, which may assist with the restoration of the ecological system. In addition, there are also some negative consequences of COVID-19, such as increase of medical waste, haphazard use and disposal of disinfectants, mask, and gloves; and burden of untreated wastes continuously endangering the environment. It seems that, economic activities will return soon after the pandemic, and the situation might change. Hence, this study also outlines possible ways to achieve long-term environmental benefits. It is expected that the proper implementation of the proposed strategies might be helpful for the global environmental sustainability.

Tomatoes are consumed worldwide as fresh vegetables because of their high contents of essential nutrients and antioxidant-rich phytochemicals. Tomatoes contain minerals, vitamins, proteins, essential amino acids (leucine, threonine, valine, histidine, lysine, arginine), monounsaturated fatty acids (linoleic and linolenic acids), carotenoids (lycopene and β-carotenoids) and phytosterols (β-sitosterol, campesterol and stigmasterol). Lycopene is the main dietary carotenoid in tomato and tomato-based food products and lycopene consumption by humans has been reported to protect against cancer, cardiovascular diseases, cognitive function and osteoporosis. Among the phenolic compounds present in tomato, quercetin, kaempferol, naringenin, caffeic acid and lutein are the most common. Many of these compounds have antioxidant activities and are effective in protecting the human body against various oxidative stress-related diseases. Dietary tomatoes increase the body's level of antioxidants, trapping reactive oxygen species and reducing oxidative damage to important biomolecules such as membrane lipids, enzymatic proteins and DNA, thereby ameliorating oxidative stress. We reviewed the nutritional and phytochemical compositions of tomatoes. In addition, the impacts of the constituents on human health, particularly in ameliorating some degenerative diseases, are also discussed.

Data on comprehensive population-based surveillance of antimicrobial resistance is lacking. In low- and middle-income countries, the challenges are high due to weak laboratory capacity, poor health systems governance, lack of health information systems, and limited resources. Developing countries struggle with political and social dilemma, and bear a high health and economic burden of communicable diseases. Available data are fragmented and lack representativeness which limits their use to advice health policy makers and orientate the efficient allocation of funding and financial resources on programs to mitigate resistance. Low-quality data means soaring rates of antimicrobial resistance and the inability to track and map the spread of resistance, detect early outbreaks, and set national health policy to tackle resistance. Here, we review the barriers and limitations of conducting effective antimicrobial resistance surveillance, and we highlight multiple incremental approaches that may offer opportunities to strengthen population-based surveillance if tailored to the context of each country.

Abstract The recently developed Fear of COVID-19 Scale (FCV-19S) is a seven-item uni-dimensional scale that assesses the severity of fears of COVID-19. Given the rapid increase of COVID-19 cases in Bangladesh, we aimed to translate and validate the FCV-19S in Bangla. The forward-backward translation method was used to translate the English version of the questionnaire into Bangla. The reliability and validity properties of the Bangla FCV-19S were rigorously psychometrically evaluated (utilizing both confirmatory factor analysis and Rasch analysis) in relation to socio-demographic variables, national lockdown variables, and response to the Bangla Health Patient Questionnaire. The sample comprised 8550 Bangladeshi participants. The Cronbach α value for the Bangla FCV-19S was 0.871 indicating very good internal reliability. The results of the confirmatory factor analysis showed that the uni-dimensional factor structure of the FCV-19S fitted well with the data. The FCV-19S was significantly correlated with the nine-item Bangla Patient Health Questionnaire (PHQ-90) ( r = 0.406, p < 0.001). FCV-19S scores were significantly associated with higher worries concerning lockdown. Measurement invariance of the FCV-19S showed no differences with respect to age or gender. The Bangla version of FCV-19S is a valid and reliable tool with robust psychometric properties which will be useful for researchers carrying out studies among the Bangla speaking population in assessing the psychological impact of fear from COVID-19 infection during this pandemic.

The incidence and mortality of the coronavirus-2019 disease (COVID-19) have increased dramatically around the world. The effects of COVID-19 pandemic are not limited to health, but also have a major impact on the social and economic aspects. Meanwhile, developing and less developed countries are arguably experiencing more severe crises than developed countries, with many small and medium-sized businesses being disrupted and even bankrupt (Fernandes 2020). Consequently, some individuals' mental health is very fragile (Lin 2020). Sahoo et al. ( Moreover, impacts on mental health (e.g., depression, anxiety, panic, and traumatic stress) can also occur due to the lack of accurate information

In Uganda, Ghana and Bangladesh, participatory tools were used for a socio-economic and gender analysis of three topics: climate-smart agriculture (CSA), climate analogue approaches, and climate and weather forecasting. Policy and programme-relevant results were obtained. Smallholders are changing agricultural practices due to observations of climatic and environmental change. Women appear to be less adaptive because of financial or resource constraints, because of male domination in receiving information and extension services and because available adaptation strategies tend to create higher labour loads for women. The climate analogue approach (identifying places resembling your future climate so as to identify potential adaptations) is a promising tool for increasing farmer-to-farmer learning, where a high degree of climatic variability means that analogue villages that have successfully adopted new CSA practices exist nearby. Institutional issues related to forecast production limit their credibility and salience, particularly in terms of women's ability to access and understand them. The participatory tools used in this study provided some insights into women's adaptive capacity in the villages studied, but not to the depth necessary to address women's specific vulnerabilities in CSA programmes. Further research is necessary to move the discourse related to gender and climate change beyond the conceptualization of women as a homogenously vulnerable group in CSA programmes.

Docosahexaenoic acid (C22:6, n-3), a major n-3 fatty acid of the brain, has been implicated in restoration and enhancement of memory-related functions. Because Alzheimer's disease impairs memory, and infusion of amyloid-beta (Abeta) peptide (1-40) into the rat cerebral ventricle reduces learning ability, we investigated the effect of dietary pre-administration of docosahexaenoic acid on avoidance learning ability in Abeta peptide-produced Alzheimer's disease model rats. After a mini-osmotic pump filled with Abeta peptide or vehicle was implanted in docosahexaenoic acid-fed and control rats, they were subjected to an active avoidance task in a shuttle avoidance system apparatus. Pre-administration of docosahexaenoic acid had a profoundly beneficial effect on the decline in avoidance learning ability in the Alzheimer's disease model rats, associated with an increase in the cortico-hippocampal docosahexaenoic acid/arachidonic acid molar ratio, and a decrease in neuronal apoptotic products. Docosahexaenoic acid pre-administration furthermore increased cortico-hippocampal reduced glutathione levels and glutathione reductase activity, and suppressed the increase in lipid peroxide and reactive oxygen species levels in the cerebral cortex and hippocampus of the Alzheimer's disease model rats, suggesting an increase in antioxidative defence. Docosahexaenoic acid is thus a possible prophylactic means for preventing the learning deficiencies of Alzheimer's disease.

Honey is a popular natural food product with a very complex composition mainly consisting of both organic and inorganic constituents. The composition of honey is strongly influenced by both natural and anthropogenic factors, which vary based on its botanical and geographical origins. Although minerals and heavy metals are minor constituents of honey, they play vital role in determining its quality. There are several different analytical methods used to determine the chemical elements in honey. These methods are typically based on spectroscopy or spectrometry techniques (including atomic absorption spectrometry, atomic emission spectrometry, inductively coupled plasma mass spectrometry, and inductively coupled plasma optical emission spectrometry). This review compiles available scientific information on minerals and heavy metals in honey reported from all over the world. To date, 54 chemical elements in various types of honey have been identified and can be divided into 3 groups: major or macroelements (Na, K, Ca, Mg, P, S, Cl), minor or trace elements (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, La, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr, As, B, Br, Cd, Hg, Se, Sr), and heavy metals (trace elements that have a specific gravity at least 5 times higher than that of water and inorganic sources). Chemical elements in honey samples throughout the world vary in terms of concentrations and are also influenced by environmental pollution.

The World Health Organization (WHO) defines mental health as the state of wellbeing in which an individual realizes their capabilities to combat with normal life stressors and work competencies in contributing to the belonged community, which is underpinned by six psychological elements comprising (i) self-acceptance, (ii) meaning in life, (iii) autonomy, (iv) healthy relationships with others, (v) environmental mastery, and (vi) personal growth (Mukhtar 2020). These mental health and emotional issues are now among the foremost public health concerns throughout the world because of the novel coronavirus 2019 (COVID-19) pandemic, due to fear of infection or fear of death from the virus. Consequently, many individuals are suffering from elevated anxiety, anger, confusion, and posttraumatic symptoms (Mukhtar 2020; Pakpour and Griffiths 2020). Studies have reported that the spatial distancing, self-isolation, quarantine, social and economic discord, and misinformation (particularly on social media) are among the major contributing factors towards unusual sadness, fear

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders-focusing on Alzheimer's disease, Parkinson's disease and schizophrenia-from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

Antibiotics changed medical practice by significantly decreasing the morbidity and mortality associated with bacterial infection. However, infectious diseases remain the leading cause of death in the world. There is global concern about the rise in antimicrobial resistance (AMR), which affects both developed and developing countries. AMR is a public health challenge with extensive health, economic, and societal implications. This paper sets AMR in context, starting with the history of antibiotics, including the discovery of penicillin and the golden era of antibiotics, before exploring the problems and challenges we now face due to AMR. Among the factors discussed is the low level of development of new antimicrobials and the irrational prescribing of antibiotics in developed and developing countries. A fundamental problem is the knowledge, attitude, and practice (KAP) regarding antibiotics among medical practitioners, and we explore this aspect in some depth, including a discussion on the KAP among medical students. We conclude with suggestions on how to address this public health threat, including recommendations on training medical students about antibiotics, and strategies to overcome the problems of irrational antibiotic prescribing and AMR.