Khulna University

The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media-Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961-8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736-1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students.

Transforming growth factor-beta (TGF-β)/bone morphogenetic protein (BMP) plays a fundamental role in the regulation of bone organogenesis through the activation of receptor serine/threonine kinases. Perturbations of TGF-β/BMP activity are almost invariably linked to a wide variety of clinical outcomes, i.e., skeletal, extra skeletal anomalies, autoimmune, cancer, and cardiovascular diseases. Phosphorylation of TGF-β (I/II) or BMP receptors activates intracellular downstream Smads, the transducer of TGF-β/BMP signals. This signaling is modulated by various factors and pathways, including transcription factor Runx2. The signaling network in skeletal development and bone formation is overwhelmingly complex and highly time and space specific. Additive, positive, negative, or synergistic effects are observed when TGF-β/BMP interacts with the pathways of MAPK, Wnt, Hedgehog (Hh), Notch, Akt/mTOR, and miRNA to regulate the effects of BMP-induced signaling in bone dynamics. Accumulating evidence indicates that Runx2 is the key integrator, whereas Hh is a possible modulator, miRNAs are regulators, and β-catenin is a mediator/regulator within the extensive intracellular network. This review focuses on the activation of BMP signaling and interaction with other regulatory components and pathways highlighting the molecular mechanisms regarding TGF-β/BMP function and regulation that could allow understanding the complexity of bone tissue dynamics. How bone morphogenetic protein (BMP), discovered in 1965, interacts within a complex network to form bone is not yet clearly understood. Disturbances in BMP activity are linked to a wide range of autoimmune diseases, cancers and skeletal diseases including fibrodysplasia ossificans progressiva. In their review Md. Shaifur Rahman from Tissue Banking and Biomaterial Research Unit, Atomic Energy Research Establishment, Dahka, Bangladesh, and his colleagues focus on the structure of BMP and its receptors, and the pathways it uses to regulate and stimulate bone growth. A large number of factors and targets have now been identified for the BMP pathways. This has led to the discovery of a complex interactive network that researchers are still trying to understand. Knowing more about how the pathways affect the transformation of bone-forming cells at different developmental stages would help to develop potential therapies for bone-related diseases.

INTRODUCTION: Rumors and conspiracy theories, can contribute to vaccine hesitancy. Monitoring online data related to COVID-19 vaccine candidates can track vaccine misinformation in real-time and assist in negating its impact. This study aimed to examine COVID-19 vaccine rumors and conspiracy theories circulating on online platforms, understand their context, and then review interventions to manage this misinformation and increase vaccine acceptance. METHOD: In June 2020, a multi-disciplinary team was formed to review and collect online rumors and conspiracy theories between 31 December 2019-30 November 2020. Sources included Google, Google Fact Check, Facebook, YouTube, Twitter, fact-checking agency websites, and television and newspaper websites. Quantitative data were extracted, entered in an Excel spreadsheet, and analyzed descriptively using the statistical package R version 4.0.3. We conducted a content analysis of the qualitative information from news articles, online reports and blogs and compared with findings from quantitative data. Based on the fact-checking agency ratings, information was categorized as true, false, misleading, or exaggerated. RESULTS: We identified 637 COVID-19 vaccine-related items: 91% were rumors and 9% were conspiracy theories from 52 countries. Of the 578 rumors, 36% were related to vaccine development, availability, and access, 20% related to morbidity and mortality, 8% to safety, efficacy, and acceptance, and the rest were other categories. Of the 637 items, 5% (30/) were true, 83% (528/637) were false, 10% (66/637) were misleading, and 2% (13/637) were exaggerated. CONCLUSIONS: Rumors and conspiracy theories may lead to mistrust contributing to vaccine hesitancy. Tracking COVID-19 vaccine misinformation in real-time and engaging with social media to disseminate correct information could help safeguard the public against misinformation.

Arsenic removal by passive treatment, in which naturally present Fe(II) is oxidized by aeration and the forming iron(III) (hydr)oxides precipitate with adsorbed arsenic, is the simplest conceivable water treatment option. However, competing anions and low iron concentrations often require additional iron. Application of Fe(II) instead of the usually applied Fe(III) is shown to be advantageous, as oxidation of Fe(II) by dissolved oxygen causes partial oxidation of As(III) and iron(III) (hydr)oxides formed from Fe(II) have higher sorption capacities. In simulated groundwater (8.2 mM HCO3(-), 2.5 mM Ca2+, 1.6 mM Mg2+, 30 mg/L Si, 3 mg/L P, 500 ppb As(III), or As(V), pH 7.0 +/- 0.1), addition of Fe(II) clearly leads to better As removal than Fe(III). Multiple additions of Fe(II) further improved the removal of As(II). A competitive coprecipitation model that considers As(III) oxidation explains the observed results and allows the estimation of arsenic removal under different conditions. Lowering 500 microg/L As(III) to below 50 microg/L As(tot) in filtered water required > 80 mg/L Fe(III), 50-55 mg/L Fe(II) in one single addition, and 20-25 mg/L in multiple additions. With As(V), 10-12 mg/L Fe(II) and 15-18 mg/L Fe(III) was required. In the absence of Si and P, removal efficiencies for Fe(II) and Fe(III) were similar: 30-40 mg/L was required for As(II), and 2.0-2.5 mg/L was required for As(V). In a field study with 22 tubewells in Bangladesh, passive treatment efficiently removed phosphate, but iron contents were generally too low for efficient arsenic removal.

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

Tannins are found in most of the species throughout the plant kingdom, where their functions are to protect the plant against predation and might help in regulating the plant growth. There are two major groups of tannins, i.e., hydrolyzable and condensed tannins. The tannins are being used as important and effective chemicals for the tanning of animal hides in the leather processing industry since the beginning of the industry. Additionally, the tannins have been using as mineral absorption and protein precipitation purposes since 1960s. These are also used for iron gall ink production, adhesive production in wood-based industry, anti-corrosive chemical production, uranium recovering chemical from seawater, and removal of mercury and methylmercury from solution. Presently, tannins are considering as bioactive compound in nutrition science. It has also been considered for advanced applications, i.e., 3D printing and biomedical devices. The application of tannins as medicine is another new dimension in medical science. This paper outlines the general information about tannins followed by their extraction process. The utilization of tannins has also been presented in a broader scale. Depending on all these information, the article also describes the impending utilization of tannins for ensuring high-sustainability and better environmental performance.

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.

Healthcare waste comprises the waste generated by healthcare facilities, medical laboratories and biomedical research facilities. Improper treatment of this waste poses serious risks of disease transmission to waste pickers, waste workers, health workers, patients, and the community in general through exposure to infectious agents. Poor management of the waste emits harmful and deleterious contaminants into society. However, contamination of highly contagious agents such as the COVID-19 virus has created enormous instability in healthcare waste handling and subsequent recycling because of the volume of the waste generated and its contagious nature. Several countries have adopted safety measures to combat this contamination and manage healthcare waste; however, these measures are insufficient and vary depending on the context of the country. In addition, the WHO has set out guidelines for management of healthcare waste. These guidelines are helping to manage the highly contagious healthcare waste resulting from the current pandemic. Proper healthcare waste management may add value by reducing the spread of the COVID-19 virus and increasing the recyclability of materials instead of sending them to landfill. Disinfecting and sorting out healthcare waste facilitates sustainable management and allows their utilization for valuable purposes. This review discusses the different healthcare solid waste management strategies practiced in different countries, the challenges faced during this management, and the possible solutions for overcoming these challenges. It also provides useful insights into healthcare solid waste management scenarios during the COVID-19 pandemic and a possible way forward.

The spatial and temporal distribution of heavy metals in water, sediment and fish (dry weight basis) of Buriganga River, Bangladesh were determined by atomic absorption spectrophotometer. In water concentration of Pb, Cd, Ni, Cu and Cr varied seasonally and spatially from 58.17 to 72.45 μg/L, 7.08 to 12.33 μg/L, 7.15 to 10.32 μg/L, 107.38 to 201.29 μg/L and 489.27 to 645.26 μg/L, respectively. Chromium was the most abundant in the water of Balughat during pre-monsoon, whereas, Cd was the most scarce in the water of Shawaryghat during monsoon. The sediment also showed spatial and temporal variation of Pb, Cd, Ni, Cu and Cr ranged from 64.71 to 77.13 mg/kg, 2.36 to 4.25 mg/kg, 147.06 to 258.17 mg/kg, 21.75 to 32.54 mg/kg and 118.63 to 218.39 mg/kg, respectively. Among all the metals studied in sediment, Ni was the highest at Foridabad during pre-monsoon and Cd was the lowest at Shawaryghat during monsoon. In six species of fish studied, the concentration of Pb, Cd, Ni, Cu and Cr varied seasonally from 8.03 to 13.52 mg/kg, 0.73 to 1.25 mg/kg, 8.25 to 11.21 mg/kg, 3.36 to 6.34 mg/kg and 5.27 to 7.38 mg/kg, respectively. Of the five metals studied Pb concentration was the highest in Gudusia chapra during monsoon, in contrast, Cd concentration was the lowest in Cirrhinus reba during post-monsoon. Some of the heavy metals’ concentrations are higher than the recommended value, which suggest that the Buriganga is to a certain extent a heavy metal polluted river and the water, sediment and fish are not completely safe for health.

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.

Plants are often exposed to abiotic stresses such as drought, salinity, heat, cold, and heavy metals that induce complex responses, which result in reduced growth as well as crop yield. Phytohormones are well known for their regulatory role in plant growth and development, and they serve as important chemical messengers, allowing plants to function during exposure to various stresses. Seed priming is a physiological technique involving seed hydration and drying to improve metabolic processes prior to germination, thereby increasing the percentage and rate of germination and improving seedling growth and crop yield under normal and various biotic and abiotic stresses. Seed priming allows plants to obtain an enhanced capacity for rapidly and effectively combating different stresses. Thus, seed priming with phytohormones has emerged as an important tool for mitigating the effects of abiotic stress. Therefore, this review discusses the potential role of priming with phytohormones to mitigate the harmful effects of abiotic stresses, possible mechanisms for how mitigation is accomplished, and roles of priming on the enhancement of crop production.

BACKGROUND: Chronic exposure to arsenic through drinking water has the potential to cause adverse pregnancy outcomes, although the association has not been demonstrated conclusively. This cross-sectional study assessed the association between arsenic in drinking water and spontaneous abortion, stillbirth, and neonatal death. METHODS: In this cross-sectional study, 533 women were interviewed. Information on sociodemographic characteristics, drinking water use, and adverse pregnancy outcomes was obtained through a structured pretested interviewer-administered questionnaire. The respondents reported use of a total of 223 tube wells; for 208 wells, water samples were measured using an ultraviolet/visible spectrophotometry method, whereas 15 were measured by flow-injection hydride generation atomic absorption spectrometry (FIHG-AAS). RESULTS: Excess risks for spontaneous abortion and stillbirth were observed among the participants chronically exposed to higher concentrations of arsenic in drinking water after adjusting for participant's height, history of hypertension and diabetes, and (for neonatal death only) age at first pregnancy. Comparing exposure to arsenic concentration of greater than 50 microg/L with 50 microg/L or less, the odds ratios were 2.5 (95% confidence interval=1.5-4.3) for spontaneous abortion, 2.5 (1.3-4.9) for stillbirth, and 1.8 (0.9-3.6) for neonatal death. CONCLUSIONS: These study findings suggest that chronic arsenic exposure may increase the risk of fetal and infant death.

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Natural dietary agents have attracted considerable attention due to their role in promoting health and reducing the risk of diseases including cancer. Ginger, one of the most ancient known spices, contains bioactive compounds with several health benefits. [6]-Gingerol constitutes the most pharmacologically active among such compounds. The aim of the present work was to review the literature pertaining to the use of ginger extract and [6]-gingerol against tumorigenic and oxidative and inflammatory processes associated with cancer, along with the underlying mechanisms of action involved in signaling pathways. This will shed some light on the protective or therapeutic role of ginger derivatives in oxidative and inflammatory regulations during metabolic disturbance and on the antiproliferative and anticancer properties. Data collected from experimental (in vitro or in vivo) and clinical studies discussed in this review indicate that ginger extract and [6]-gingerol exert their action through important mediators and pathways of cell signaling, including Bax/Bcl2, p38/MAPK, Nrf2, p65/NF-κB, TNF-α, ERK1/2, SAPK/JNK, ROS/NF-κB/COX-2, caspases-3, -9, and p53. This suggests that ginger derivatives, in the form of an extract or isolated compounds, exhibit relevant antiproliferative, antitumor, invasive, and anti-inflammatory activities.

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused increasing challenges for healthcare professionals globally. However, there is a dearth of information about these challenges in many developing countries, including Bangladesh. This study aims to explore the challenges faced by healthcare professionals (doctors and nurses) during COVID-19 in Bangladesh. Methods: We conducted qualitative research among healthcare professionals of different hospitals and clinics in Khulna and Dhaka city of Bangladesh from May 2020 to August 2020. We conducted 15 in-depth telephone interviews using a snowball sampling technique. We used an in-depth interview guide as data were collected, audiotaped, and transcribed. The data were analyzed both manually and using QDA Miner software as we used thematic analysis for this study. Results: Seven themes emerged from the study. Participants experienced higher workload, psychological distress, shortage of quality personal protective equipment (PPE), social exclusion/stigmatization, lack of incentives, absence of coordination, and proper management during their service. These healthcare professionals faced difficulty coping with these challenges due to situational and organizational factors. They reported of faith in God and mutual support to be the keys to adapt to adversities. Adequate support to address the difficulties faced by healthcare professionals is necessary for an overall improved health outcome during the pandemic. Conclusion: The findings highlight the common challenges faced by healthcare professionals during the COVID-19 outbreak. This implies the need to support adequate safety kits, protocols, and support for both physical and mental health of the healthcare professionals.

Abstract Ecoregionization is a process of delineating and classifying ecologically distinctive areas of ecological land. Each area can be viewed as a discrete system that has resulted from the mesh and interplay of the geologic, landform, soil, vegetative, climatic, wildlife, water, and human factors where ecological functions and processes are continuing. The dominance of any one or more of these factors varies with the given ecological land unit. This holistic approach to land classification can be applied incrementally on a scale‐related basis from site‐specific ecosystems to very broad ecosystems (1). Ecological processes, evolutionary mechanisms, and geological forces are continually reshaping landscapes across various scales of time and space and result in distinctive but dynamic ecoregions. All of the world's food and most medicines and raw materials are derived from these processes and associated biodiversity. Thus ecoregions gain their identity through spatial differences in a combination of landscape characteristics. Several factors such as topography, hydrology, and nutrients are important to identify these characteristics that may vary from one place to another in an ecoregion.

Several recent studies have shown that citric acid/citrate (CA) can confer abiotic stress tolerance to plants. Exogenous CA application leads to improved growth and yield in crop plants under various abiotic stress conditions. Improved physiological outcomes are associated with higher photosynthetic rates, reduced reactive oxygen species, and better osmoregulation. Application of CA also induces antioxidant defense systems, promotes increased chlorophyll content, and affects secondary metabolism to limit plant growth restrictions under stress. In particular, CA has a major impact on relieving heavy metal stress by promoting precipitation, chelation, and sequestration of metal ions. This review summarizes the mechanisms that mediate CA-regulated changes in plants, primarily CA's involvement in the control of physiological and molecular processes in plants under abiotic stress conditions. We also review genetic engineering strategies for CA-mediated abiotic stress tolerance. Finally, we propose a model to explain how CA's position in complex metabolic networks involving the biosynthesis of phytohormones, amino acids, signaling molecules, and other secondary metabolites could explain some of its abiotic stress-ameliorating properties. This review summarizes our current understanding of CA-mediated abiotic stress tolerance and highlights areas where additional research is needed.

Several corona viral infections have created serious threats in the last couple of decades claiming the death of thousands of human beings. Recently, corona viral epidemic raised the issue of developing effective antiviral agents at the earliest to prevent further losses. Natural products have always played a crucial role in drug development process against various diseases, which resulted in screening of such agents to combat emergent mutants of corona virus. This review focuses on those natural compounds that showed promising results against corona viruses. Although inhibition of viral replication is often considered as a general mechanism for antiviral activity of most of the natural products, studies have shown that some natural products can interact with key viral proteins that are associated with virulence. In this context, some of the natural products have antiviral activity in the nanomolar concentration (e.g., lycorine, homoharringtonine, silvestrol, ouabain, tylophorine, and 7-methoxycryptopleurine) and could be leads for further drug development on their own or as a template for drug design. In addition, a good number of natural products with anti-corona virus activity are the major constituents of some common dietary supplements, which can be exploited to improve the immunity of the general population in certain epidemics.

Global climate change is expected to further raise the frequency and severity of extreme events, such as droughts. The effects of extreme droughts on trees are difficult to disentangle given the inherent complexity of drought events (frequency, severity, duration, and timing during the growing season). Besides, drought effects might be modulated by trees' phenotypic variability, which is, in turn, affected by long-term local selective pressures and management legacies. Here we investigated the magnitude and the temporal changes of tree-level resilience (i.e., resistance, recovery, and resilience) to extreme droughts. Moreover, we assessed the tree-, site-, and drought-related factors and their interactions driving the tree-level resilience to extreme droughts. We used a tree-ring network of the widely distributed Scots pine (Pinus sylvestris) along a 2,800 km latitudinal gradient from southern Spain to northern Germany. We found that the resilience to extreme drought decreased in mid-elevation and low productivity sites from 1980-1999 to 2000-2011 likely due to more frequent and severe droughts in the later period. Our study showed that the impact of drought on tree-level resilience was not dependent on its latitudinal location, but rather on the type of sites trees were growing at and on their growth performances (i.e., magnitude and variability of growth) during the predrought period. We found significant interactive effects between drought duration and tree growth prior to drought, suggesting that Scots pine trees with higher magnitude and variability of growth in the long term are more vulnerable to long and severe droughts. Moreover, our results indicate that Scots pine trees that experienced more frequent droughts over the long-term were less resistant to extreme droughts. We, therefore, conclude that the physiological resilience to extreme droughts might be constrained by their growth prior to drought, and that more frequent and longer drought periods may overstrain their potential for acclimation.