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Skin lesions or malignancies have been a source of worry for many individuals in recent years. The underline reason behind it mainly the diet and environmental pollution. Yet many individuals are unaware of the issue and, more importantly, many people do not want to visit a hospital for diagnostic or therapeutic purposes. So, we have come up with a pipeline to diagnose skin lesions at home. Firstly, we proposed a IoT base data collection device which is accessible by patient to capture skin lesions image. This IoT device will encrypt and send the collected image towards a cloud storage; then it will decrypt the image send to the computer assisted diagnosis system. In CAD, we have implemented ensemble classifier. Ensemble classifier created depending on four deep learning classifiers namely VGG16, DenseNet201, Inception V3 and Efficient B7; whereas encryption and decryption performed in order to secure a patient data from unauthorized access. For skin lesions classification, we have used "HAM10000" dataset where 7 kind of skin lesions data included; Although DenseNet201 performed well, the ensemble model provides the highest accuracy with 87.22\% as well as its test loss/error is lower than others with 0.4131.

This study reports on the analysis of eleven Jarrah (

Background The safety of health care workers (HCWs) in Bangladesh and the factors associated with getting COVID-19 have been infrequently studied. The aim of this study was to address this gap by assessing the capacity development and safety measures of HCWs in Bangladesh who have been exposed to COVID-19 and by identifying the factors associated with respondents’ self-reported participation in capacity development trainings and their safety practices. Methods This cross-sectional study was based on an online survey of 811 HCWs working at 39 dedicated COVID-19 hospitals in Bangladesh. A pretested structured questionnaire consisting of questions related to respondents’ characteristics, capacity development trainings and safety measures was administered. Binary logistic regressions were run to assess the association between explanatory and dependent variables. Results Among the respondents, 58.1% had been engaged for at least 2 months in COVID-19 care, with 56.5% of them attending capacity development training on the use of personal protective equipment (PPE), 44.1% attending training on hand hygiene, and 35% attending training on respiratory hygiene and cough etiquette. Only 18.1% reported having read COVID-19-related guidelines. Approximately 50% of the respondents claimed that there was an inadequate supply of PPE for hospitals and HCWs. Almost 60% of the respondents feared a high possibility of becoming COVID-19-positive. Compared to physicians, support staff [odds ratio (OR) 4.37, 95% confidence interval (CI) 2.25–8.51] and medical technologists (OR 8.77, 95% CI 3.14–24.47) were more exhausted from working in COVID-19 care. Respondents with longer duty rosters were more exhausted, and those who were still receiving infection prevention and control (IPC) trainings were less exhausted (OR 0.54, 95% CI 0.34–0.86). Those who read COVID-19 guidelines perceived a lower risk of being infected by COVID-19 (OR 0.44, 95% CI 0.29–0.67). Compared to the respondents who strongly agreed that hospitals had a sufficient supply of PPE, others who disagreed (OR 2.68, 95% CI 1.31–5.51) and strongly disagreed (OR 5.05, 95% CI 2.15–11.89) had a higher apprehension of infection by COVID-19. Conclusion The findings indicated a need for necessary support, including continuous training, a reasonable duty roster, timely diagnosis of patients, and an adequate supply of quality PPE.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may cause severe life-threatening diseases called acute respiratory distress syndrome (ARDS) owing to cytokine storms. The mortality rate of COVID-19-related ARDS is as high as 40% to 50%. However, effective treatment for the extensive release of acute inflammatory mediators induced by hyperactive and inappropriate immune responses is very limited. Many anti-inflammatory drugs with variable efficacies have been investigated. Colchicine inhibits interleukin 1 beta (IL-1β) and its subsequent inflammatory cascade by primarily blocking pyrin and nucleotide-binding domain leucine-rich repeat and pyrin domain containing receptor 3 (NLRP3) activation. Therefore, this cheap, widely available, oral drug might provide an added benefit in combating the cytokine storm in COVID-19. Here, we sought to determine whether adding colchicine to other standards of care could be beneficial for moderate COVID-19 pneumonia in terms of the requirement for advanced respiratory support and mortality. Methods and findings This blinded placebo-controlled drug trial was conducted at the Dhaka Medical College Hospital, Dhaka, Bangladesh. A total of 300 patients with moderate COVID-19 based on a positive RT-PCR result were enrolled based on strict selection criteria from June 2020 to November 2020. Patients were randomly assigned to either treatment group in a 1:1 ratio. Patients were administered 1.2 mg of colchicine on day 1 followed by daily treatment with 0.6 mg of colchicine for 13 days or placebo along with the standard of care. The primary outcome was the time to clinical deterioration from randomization to two or more points on a seven-category ordinal scale within the 14 days post-randomization. Clinical outcomes were also recorded on day 28. The primary endpoint was met by 9 (6.2%) patients in the placebo group and 4 (2.7%) patients in the colchicine group (P = 0.171), which corresponds to a hazard ratio (95% CI) of 0.44 (0.13–1.43). Additional analysis of the outcomes on day 28 revealed significantly lower clinical deterioration (defined as a decrease by two or more points) in the colchicine group, with a hazard ratio [95%CI] of 0.29 [0.098–0.917], (P = 0.035). Despite a 56% reduction in the need for mechanical ventilation and death with colchicine treatment on day 14, the reduction was not statistically significant. On day 28, colchicine significantly reduced clinical deterioration measured as the need for mechanical ventilation and all-cause mortality. Conclusion Colchicine was not found to have a significant beneficial effect on reducing mortality and the need for mechanical ventilation. However, a delayed beneficial effect was observed. Therefore, further studies should be conducted to evaluate the late benefits of colchicine. Clinical trial registration Clinical trial registration no: ClinicalTrials.gov Identifier: NCT04527562 https://www.google.com/search?client=firefox-b-d&q=NCT04527562.

The use of dietary phytochemical rather than conventional therapies to treat numerous cancers is now a well-known approach in medical science. Easily available and less toxic dietary phytochemicals present in plants should be introduced in the list of phytochemical-based treatment areas. Sesamin, a natural phytochemical, may be a promising chemopreventive agent aiming to manage breast cancer. In this study, we discussed the pharmacological properties of sesamin that determine its therapeutics opportunity to be used in breast cancer treatment and other diseases. Sesamin is available in medicinal plants, especially in Sesamum indicum, and is easily metabolized by the liver. To better understand the antibreast cancer consequence of sesamin, we postulate some putative pathways related to the antibreast cancer mechanism: (1) regulation of estrogen receptor (ER-α and ER-β) activities, (2) suppressing programmed death-ligand 1 (PD-L1) overexpression, (3) growth factor receptor inhibition, and (4) some tyrosine kinase pathways. Targeting these pathways, sesamin can modulate cell proliferation, cell cycle arrest, cell growth and viability, metastasis, angiogenesis, apoptosis, and oncogene inactivation in various in vitro and animal models. Although the actual tumor intrinsic signaling mechanism targeted by sesamin in cancer treatment is still unknown, this review summarized that this phytoestrogen suppressed NF-κB, STAT, MAPK, and PIK/AKT signaling pathways and activated some tumor suppressor protein in numerous breast cancer models. Cotreatment with γ-tocotrienol, conventional drugs, and several drug carriers systems increased the anticancer potentiality of sesamin. Furthermore, sesamin exhibited promising pharmacokinetics properties with less toxicity in the bodies. Overall, the shreds of evidence highlight that sesamin can be a potent candidate to design drugs against breast cancer. So, like other phytochemicals, sesamin can be consumed for better therapeutic advantages due to having the ability to target a plethora of molecular pathways until clinically trialed standard drugs are not available in pharma markets.

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0$\%$, while the rest of the algorithms were over 90.0$\%$ accurate.

Background: The World Health Organization has declared human monkeypox as a global health emergency on 23 July 2022. This indicates that the outbreak poses a serious risk to global health and requires a united worldwide response to stop the virus from spreading and possibly turning into a pandemic. Vaccines can play a vital role in this context, contributing to pre- and post-exposure prophylaxis. Methods: The aim of our rapid review was to go through the background of the vaccine approach for human monkeypox over the years and to find out what current guidelines are highlighting relating to it. A rapid review with a systematic search and manual searching have been performed here. Results: 22 relevant published articles from MEDLINE bibliographic database and 8 vaccine recommendations from manual searching have been deliberated here. Conclusion: The significant synopsis of this review is that the smallpox vaccine is the only immunization option for monkeypox so far, and it is up to 85% effective to prevent the infection. Third-generation smallpox vaccines are advised over first and second generations due to their minimal side effects. Healthcare providers and lab professionals at risk are on the priority list to get vaccinated, as well as pregnant women or lactating mothers, and immunocompromised or chronically ill patients can get vaccinated if they are surely exposed to the monkeypox infection. Lastly, JYNNEOS/IMVAMUNE is the current most preferable smallpox vaccine that is highly advised for the latest outbreak of human monkeypox but more clinical trials on humans should be conducted to evaluate its safety, efficacy, and adverse events.

Honeys are commonly subjected to a series of post-harvest processing steps, such as filtration and/or radiation treatment and heating to various temperatures, which might affect their physicochemical properties and bioactivity levels. Therefore, there is a need for robust quality control assessments after honey processing and storage to ensure that the exposure to higher temperatures, for example, does not compromise the honey’s chemical composition and/or antioxidant activity. This paper describes a comprehensive short-term (48 h) and long-term (5 months) study of the effects of temperature (40 °C, 60 °C and 80 °C) on three commercial honeys (Manuka, Marri and Coastal Peppermint) and an artificial honey, using high-performance thin-layer chromatography (HPTLC) analysis. Samples were collected at baseline, at 6 h, 12 h, 24 h and 48 h, and then monthly for five months. Then, they were analysed for potential changes in their organic extract HPTLC fingerprints, in their HPTLC-DPPH total band activities, in their major sugar composition and in their hydroxymethylfurfural (HMF) content. It was found that, while all the assessed parameters changed over the monitoring period, changes were moderate at 40 °C but increased significantly with increasing temperature, especially the honeys’ HPTLC-DPPH total band activity and HMF content.

Background: In 3%–19% of patients, reintubation is needed 48–72 hours following extubation, which increases intensive care unit (ICU) morbidity, mortality, and expenses. Extubation failure is frequently caused by diaphragm dysfunction. Ultrasonography can be used to determine the mobility and thickness of the diaphragm. This study looked at the role of diaphragm excursion (DE) and thickening fraction in predicting successful extubation from mechanical ventilation.Methods: Thirty-one patients were extubated with the advice of an ICU consultant using the ICU weaning regimen and diaphragm ultrasonography was performed. Ultrasound DE and thickening fraction were measured three times: at the commencement of the t-piece experiment, at 10 minutes, and immediately before extubation. All patients' parameters were monitored for 48 hours after extubation. Rapid shallow breathing index (RSBI) was also measured at the same time.Results: Successful extubation was significantly correlated with DE (P=0.01). Receiver curve analysis for DE to predict successful extubation revealed good properties (area under the curve [AUC], 0.83; P

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

In the present era, human brain tumor is the extremist dangerous and devil to the human being that leads to certain death. Furthermore, the brain tumor arises more complexity of patients life with time. As a result, early detection of tumors is most crucial to save and prolong the patient’s lifetime. Therefore, enhanced brain tumor detection is required in medical fields. Automatic human brain tumor detection in magnetic resonance imaging (MRI) is playing a vital role in several symptomatic and cures applications. However, the existing schemes (e.g., random forest, Fuzzy C-means, artificial neural network (ANN) and wavelet transform) can detect brain tumors with insufficient accuracy and longer execution time (in minutes). In this paper, we propose an enhanced brain tumor detection scheme based on the template-based K-means (TK) algorithm with superpixels and principal component analysis (PCA) which efficiently detects the human brain tumors in lower execution time. At first, we extract essential features using both superpixels and PCA which helps accurately to detect brain tumors. Then, image enhancement is done using a filter that helps to improve accuracy. Finally, the image segmentation is performed through TK-means clustering algorithm to detect the brain tumor. The experimental results show that the proposed detection scheme achieves a better accuracy and a reduced execution time (in seconds) than other existing schemes for the detection of brain tumor in MR image.

Background and Aims It is postulated that molecular methods along with mathematical modeling can provide critical inference regarding epidemiological parameters, transmission dynamics, spatiotemporal characteristics, and intervention efficacy. Hence, studying molecular epidemiology of human immunodeficiency virus (HIV)‐1 infection, especially in resource‐limited settings and with a large diaspora of the migrant population such as that of Bangladesh, is of paramount importance. The purpose of this systematic review was to concisely present and discuss the findings from previous studies conducted in Bangladesh regarding HIV‐1 subtype prevalence. Methods Articles were retrieved from six publicly available databases regarding HIV‐1 molecular epidemiology using keywords HIV, HIV‐1, subtype(s), Bangladesh, and any combination of aforementioned keywords using Boolean operators. A total of 14 articles were downloaded and screened for suitability. Finally, five studies, containing pooled sequences from 317 individuals, were included in this systematic review. Results Results revealed a preponderance of subtype C among HIV‐1 infected population (51.10%), followed by circulating recombinant form (CRF)_07BC (15.46%), CRF_01AE (5.68%), A1 (4.73%), CRF_02AG (3.47%), G (3.15%), CRF_62BC (2.84%), B (2.21%), and other subtypes and recombinant forms in small percentages. Subtype C was largely predominant in intravenous drug users as well as female sex workers, whereas the migrant population exhibited a diverse subtype including rare recombinant forms, largely due to their travel in the Middle East and other South East Asian countries. Conclusion With the number of HIV‐1 infections increasing among the general population and a steady increase in the migrant population, molecular epidemiological data are required to curb the progression of the HIV‐1 epidemic in Bangladesh.

Optical urethrotomy has been considered standard therapy for anterior urethral stricture since its introduction in 1976. Now optical internal urethrotomy (OIU) with intralesional triamcinolone injection is a safe and effective, minimally invasive therapeutic modality. The aim of the study is to compare the outcome of OIU alone and OIU with intralesional triamcinolone injection in the treatment of anterior urethral stricture. This Quasi Experimental study was carried out among 50 male patients with bulbar urethral stricture in the Department of Urology, Dhaka Medical College Hospital, Dhaka, over a period of six months. The age range of the patients were 32-46 years and patients were divided equally into two groups, OIU with and without intralesional triamcinolone acetonide injection as Group- A (experimental group, 25 patients) and Group- B (control group, 25 patients). Post-operative evaluation was done on the basis of history and uroflowmetry. Retrograde urethrography and micturating cystourethrography were done only in patient who developed obstructive voiding problems or flow rate below 10 ml/second. Follow up was done at regular interval on 7th day, 3rd month and 6th month. Post-operative outcomes were compared between two groups. Post-operative infection was significantly higher among those OIU with intralesional Triamcinolone acetonide injection (8%) than patients without intralesional Triamcinolone acetonide injection (4%). Per operative extravasations of urine were significantly higher among those without intralesional Triamcinolone acetonide injection (4%) than subjects with intralesional Triamcinolone acetonide injection. Extravasation not influenced by steroid but this patient subsequently suffered recurrence of stricture. In Group-A, pre and post-operative Q-max were 10.25±2.21 and 22.11±2.96 ml/sec respectively. In Group-B, pre and post-operative follow up Q-max were 10.37±2.55 and 19.54±2.65 mi/sec respectively. In Group-A, pre and post-operative voiding time was 85.20±4.20 and 27.10±3.36 sec respectively. In Group-B, pre and post-operative follow up voiding time were 86.37±4.55 and 31.45±2.55 sec respectively. Post-operative recurrences of stricture were significantly higher among those without intralesional Triamcinolone acetonide injection (24%) than subjects with intralesional Triamcinolone acetonide injection (12%). Post-operative it seems that triamcinolone injection after OIU is safe method to prevent the recurrence of urethral stricture Bangladesh Med J. 2019 Jan; 48 (1): 31-38

Poor hand hygiene and food handling put consumers of restaurant and street food at risk of enteric disease, especially in low-income countries. This study aimed to collect hygiene indicators from a nationally representative sample of restaurants and street food vendors. The field team collected data from 50 rural villages and 50 urban administrative units (mahallas). We explored restaurant service staff, cook, and food vendor hygiene practices (N = 300 restaurants and 600 street food vendors), by observing hygiene facilities, food handling, and utensil cleaning. A qualitative assessment explored perceptions of hygiene related to food handling. During restaurant spot checks, 91% (273/300) had soap and water at handwashing location for customers but in only 33% (100) at locations convenient for restaurant staff. Among street food–vending stalls, 11% (68/600) had soap and water when observed. During 90-minute structured observations, cooks used soap to wash hands on 14/514 (3%) of occasions before food preparation, 6/82 (8%) occasions after cutting fish/meat/vegetables, 3/71 (4%) occasions before serving food, and 0/49 (0%) occasions) before hand-mashing food/salad preparation; no street food vendors washed hands with soap during these food-handling events. Most of the qualitative study participants perceived that customers select a vendor based on tastiness of the food, whereas no one mentioned the importance of food hygiene. The study demonstrates widespread poor hygiene and food-handling practices in restaurants and among food vendors. Based on our study findings, we proposed a food premises Hygiene Investigation Model to create action plans to improve food safety.

In this work, Ba0.9Sr0.1TiO3, Ba0.7Sr0.3TiO3, Ba0.5Sr0.5TiO3, Ba0.5Ca0.25Sr0.25TiO3 and Ba0.5Ca0.5TiO3 have been synthesized to evaluate the influence of mono and co-substitution of A-site dopants (Sr2+ and Ca2+) on the structural, electrical and optical properties of BaTiO3 ceramics. Sr2+ added samples showed a tetragonal structure which became slightly distorted with increasing Sr2+ concentration and finally achieved a cubic structure for x = 0.50. Ba0.5Ca0.5TiO3 also retained their tetragonality with limited solubility. Presence of second phase, CaTiO3 demonstrated the fact of restricted solubility. The concurrent effect of Sr2+ and Ca2+ didn't alter the tetragonal structure. Sr2+ substitution enhanced the apparent density as well as grain size which stimulated the domain wall motion and improved dielectric properties. However, the ferroelectric nature of Ba1-xSrxTiO3 was poor due to the redistribution of point defect at grain boundary. The optical band gap was found to be reduced from 3.48 eV to 3.28 eV with increasing Sr2+ content. Co-substitution of cations improved the electrical property significantly. The highest value of dielectric constant was found to be ∼547 for Ba0.5Ca0.25Sr0.25TiO3 ceramics. Both Ba0.5Ca0.25Sr0.25TiO3 and Ba0.5Ca0.5TiO3 had developed P-E loop having lower coercive field and moderate optical band gap energy. Co-doping with Sr2+ and Ca2+ was a good approach enhancing materials electrical as well as optical property.

Hybrid polymer matrix composites of waste cotton fabric and wood saw dust reinforcements were studied with a view to recycling the wastes from garments and carpentry industries. Polymer composites with cotton fabric and wood-saw-dust reinforcements were fabricated using hot press machine. Their physical, thermal and mechanical behaviors were discussed in terms of moisture absorption, thermal stability, tensile strength, elastic modulus, flexural strength and flexural modulus. Compositional analyses of fibers, matrix and composites were carried out with FTIR spectroscopy. Experimental results revealed that tensile and flexural strength of the composites increased with cotton fabric reinforcement. On the other hand, with increasing wood saw dust strength decreased up to a certain limit and then increased again. Water absorption of the hybrid composites increased substantially with increasing natural filler contents. Maximum water absorption was observed in 20% fabric/wood-saw-dust reinforced polymer composite. Furthermore, TGA graphs suggest better thermal stability of the hybrid composites than that of pure polypropylene. Bangladesh J. Sci. Ind. Res.54(1), 21-30, 2019

This study, for the Farmer-Bepari system of agricultural products in Bangladesh, can be formulated as a mixed integer linear programming (MILP) model. Further, it will be investigated that the significant impact of profit the attributes such as labour cost, fertilizer cost, the raw material cost of different firms and also to estimate the product distribution in different locations. To solve this MILP model, with the help of a branch and bound algorithm by using A Mathematical Programming Language (AMPL). To investigate the model we have to collect data from seven locations of three districts in Bangladesh. Also, a numerical example presented this study, which objectives illustrate the models. From the sensitivity of the production, if the raw material cost, labour cost and fertilizer cost increase is about 5%, then decrease the profit by MILP model have 0.004%, 1.6% and 1.2% respectively. Labour cost is a significant factor in profit, which changes the profit more than the raw material cost and fertilizer cost of the product. The results are helping decision-makers to identify the desired agricultural production and distribution structure optimization strategy.

We present a theoretical study on probe field propagation through a three-level Ξ type system formed in an atomic vapour medium in presence of a strong control field. The model is based on density matrix method and is solved analytically, without neglecting any term in any power of probe and control Rabi frequencies, to find out the probe response under Doppler free condition as well as at room temperature. The probe response for a four-level Ξ type system is also determined analytically. Electromagnetically induced transparency is formed in the simulated probe response for both the level schemes. We also present the switching of probe field propagation from subluminal to superluminal as a function of probe detuning for different control transitions, one of which is to a Rydberg state. The switching window becomes asymmetric under detuned control field with the asymmetry depending on the wavelength mismatching factor between the control field and the probe field. Under strong probe regime probe propagation switches from superluminal to subluminal on resonance when the wavelength mismatch between the probe and the control field is increased, an effect which can be explained with the help of the analytical model that is free from any perturbative treatment.

Introduction: The aim of this study was to assess the induction of solvents on the total phenol and flavonoid content and also the antioxidant activity of Ganoderma lucidum extracts. Materials & Methods: In this study, two concentrations (100% and 75%) of diethyl ether, ethanol, butanol, chloroform, and acetone were used as extractants of Ganoderma lucidum. Total phenol and flavonoid contents were measured by spectrophotometric methods and 1, 1-diphenyl-2-picrylhydrazyl (DPPH). Free radical scavenging assay was used for the investigation of antioxidant activity. Results & Discussion: Extractants significantly affected the % yield of extract, the quantity of phenol and flavonoids and antioxidant activity of Ganoderma lucidum mushroom. The highest extraction yield, around 38%, was achieved by 75% acetone, followed by 100% acetone (about 36%) and 75% chloroform (approximate 21%). Hydro-acetone extract exhibited the most significant antioxidative properties (EC50 value; 645.55 µg/mL) comprised of a higher total of phenol content. In conclusion, the total phenol content encouraged the antioxidative potential of Ganoderma lucidum mushroom. Conclusion: These findings indicate that the selective extraction of Ganoderma lucidum shows significant biological activities.

This study was conducted to evaluate the toxic effects of an azo dye carmoisine widely used in foods and to investigate its relation to carcinogenicity. Carmoisine administered into mice orally in four different doses as control, low, medium, and high equivalent to 0, 4, 200, and 400 mg/kg bw, respectively, for 120 days. The key toxicological endpoint was observed including animal body weight, organ weights, hematology, biochemistry, and molecular biology assessment. The body weights of medium‐ and high‐dose carmoisine‐treated mice group were significantly decreased as compared to the control mice group. Platelet, white blood cell and monocyte counts of treated group were considerably higher, while Hb and red blood cell counts were drastically lower than the control group. The biochemical parameters such as serum alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, total protein, globulin, urea, and creatinine level were significantly increased, while serum cholesterol level was decreased after treatment as compared to the control. RT‐PCR results showed that expression of Bcl‐x and PARP gene was intensively increased, whereas expression of p53 gene was decreased in the mouse liver tissues treated with carmoisine. This study revealed that high‐dose (400 mg/kg bw) treatment of carmoisine was attributable to renal failure and hepatotoxicity. It also would be suspected as a culprit for liver oncogenesis.

Reaction of N-2-(R-pyridyl)-2-hydroxy-1-naphthaldimine {R = H (HL1), 4-CH3 (HL2) and 6-CH3 (HL3)} with copper(II) nitrate provides the new complex of bis[2-oxo-1-naphthaldehydato-κO,O']copper(II) (1) via in-situ hydrolysis of the Schiff base back to 2-hydroxy-1-naphthaldehyde (HL′). Elemental analyses reveal that there are no nitrogen atoms in the complex. X-ray diffraction (PXRD) data indicate that the complex is monoclinic with space group P21/a and Z = 2 and that the ligand acts as bidentate through OˆO-chelate system forming a bis-complex with O2O2-coordination sphere around the copper(II) ion. Electrochemical results show two quasi-reversible one electron charge transfer processes attributed to [Cu(L′)2]2−/[Cu(L′)2]− and [Cu(L′)2]−/[Cu(L′)2] (L' = deprotonated aldehyde) couples in acetonitrile. The magnetic data confirm the paramagnetic property of the complex with one unpaired electron in the metallic centre. The results suggest that the complex assumes a geometry between tetrahedral and square-planar supported by DFT calculations. Thermal analysis shows an irreversible phase transformation from solid to isotropic liquid phase. EPR spectrum in chloroform exhibits an isotropic pattern with four lines due to nuclear hyperfine splitting from the copper(II) ion with spin 3/2. The structural analyses, electrochemical and paramagnetic properties of these complexes explore greater interests for their use in the supramolecular chemistry.

Aptamers are short single-stranded DNA or RNA molecules, which have recently been developed for potential broad applications such as clinical therapeutics, diagnosis and tumor-targeted drug delivery. However, the selection of specific aptamers is often unsatisfactory using the classical protein or cell-based SELEX. Herein, we modified the paired cell line approach to identify aptamers targeting leukemia cells expressing the CD33 antigen. Our strategy artfully used the same cells for negative (HEK293T cells) and positive (CD33 transfected-HEK293T cells) aptamer selections, and the negative selections were performed adequately before the positive selection to remove unspecific sequences. The advantages of this strategy are that it is fast and accurate, where only a few rounds of selection together with PCR amplifications are sufficient to obtain high binding affinity antigen-targeted aptamers. By using our modified approach, we successfully obtained the CD33-targeting aptamer S30, which could highly recognize the C2 domain of the CD33 antigen in vitro and in vivo. Moreover, the optimized aptamer S30-T1 (i.e., core region of S30) was conjugated with doxorubicin (Dox) to synthesize S30-T1-Dox conjugates, which could specifically inhibit CD33 positive acute myeloid leukemia HL-60 cell proliferation by arresting the cell cycle at the G2 phase. Thus, our modified approach can rapidly screen reliable, stable and high binding affinity aptamers for precise cancer treatment.

Background: The aim of this study was to determine the amount of refractive changes after pterygium surgery and to compare the relationship between amounts of astigmatism with grade of pterygium.Methods: A prospective, interventional, comparative study was conducted on one hundred eyes of 100 patients of primary pterygium attending the outpatients department of Ispahani Islamia eye hospital and Institute, Dhaka from May, 2019 to November, 2019. Pterygium was graded according to morphology and the extent of corneal encroachment. Manifest refraction was performed preoperatively and at 1, 3 and 6 months postoperatively. All patients underwent stem cell conjunctival autograft by fibrin glue by different surgeons.Results: The astigmatism decreased significantly following pterygium excision. The mean pre-operative astigmatism was 1.69±1.13 D and reduced post-operatively to 0.23±0.27 D (p value

Purpose: Providing high-quality e-banking services and trust is considered a basic strategy for attracting and retaining customers with mobile banking that is less investigated particularly in Iraq. A review of the literature was conducted to achieve these goals. Theoretical framework: The scarcity of literature in terms of mobile banking in Iraqi setting was the theoretical basis of the current study. Previous related research was collected and reviewed thematically to understand the trends of the latest literature in mobile banking and customer satisfaction. Design/methodology/approach: A narrative approach was utilized in the context of this study. Several studies published between 2016 and 2022 were reviewed. Through this semi systematic literature review different key words were keyed in numerous e-journal database. Findings: the review revealed that most of the studies in this article utilized qualitative approach as an appropriate method to elicit data. The participants were from ordinary people one study investigated the variables of mobile banking and customer satisfaction among bank staff. Service quality was the highest variable compare with security and ease of use. The review indicated also there a limitation in the previous studies in the Iraqi mobile banking setting. Research, Practical & Social implications: Mobile banking as an emerging research area specially in Iraqi environment needs more investigation since such kind of research could increase people awareness about the advantage of such technology in the banking industry. Originality/value: Even though mobile banking is not a novel concept in Iraq, there is no literature that discusses the use of service quality modal to evaluate a user's level of satisfaction in the mobile banking research area. As a result, the purpose of this paper is to make up for this void.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein–protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Condensation of 2-hydroxy-benzophenone (HL') with (R or S)-(Ar)ethylamine yields the enantiopure Schiff bases (S or R)-2-((E)-1-(1-(Ar)ethylimino)ethyl)phenol {Ar = C6H5 (S- or R-HL1), p-CH3OC6H4 (S- or R-HL2)}. These Schiff bases react with copper(ii) acetate under reflux to give green microcrystals of bis[(R or S)-2-((E)-1-(1-(Ar)ethylimino)ethyl)phenolato-κ2N,O]-Λ/Δ-copper(ii), {Ar = C6H5 (Λ/Δ-Cu-R- or S-L1), p-CH3OC6H4 (Λ/Δ-Cu-R- or S-L2)} with induction of Λ/Δ-chirality at-metal. The presence of Schiff base ligands in the paramagnetic green microcrystals is confirmed by decomplexation reaction with NaCN via reduction of Cu(ii) to Cu(i) in DMSO-d6 solution. Crystallization attempts of the green microcrystalline Schiff-base Cu complexes provide deep-green block-shaped crystals of an about equal admixture of bis[2-oxo-benzophenonato-κ2O,O']-copper(ii), (CuL'2) and bis[2-(imino(phenyl)methyl)phenolato-κ2N,O]copper(ii), (CuL''2) via in situ hydrolysis of the coordinated Schiff base ligands back to 2-hydroxy-benzophenone (HL') and to 2-(imino(phenyl)methyl)phenol (HL''), which in-turn bind with the copper(ii) ion. Powder X-ray diffraction (PXRD) patterns of R-HL1 and Cu-R-L1 allowed their structure determinations using the program Expo-2014 followed by Rietveld refinement. The Cu structures refined to four-coordinated Λ/Δ-copper(ii)-complexes by the two phenolate-oxygen and two imine-nitrogen atoms from two Schiff base ligands in a pseudotetrahedral geometry. DFT optimized structures (at gas-phase) reveal the Δ-Cu-S-L1 or Λ-Cu-R-L1 diastereomer as slightly more stable than the corresponding Λ-Cu-S-L1 or Δ-Cu-R-L1 by ca. 7.60 kcal mol-1, resulting from diastereoselectively induced Λ vs. Δ-chirality at-metal. Electronic circular dichroism (ECD) spectra display mirror-image relationships and comparisons of experimental and simulated ECD spectra by TDDFT suggest an excess of the Δ-Cu-S-L1 or Λ-Cu-R-L1 diastereomer in solution. The cyclic voltammograms demonstrate two one electron charge transfer processes for Cu2+/Cu+ and Cu+/Cu0 couples in acetonitrile, respectively.

Phase equilibria of CaO-Al2O3-SiO2-Na2O slags were studied by thermal equilibration and quenching. The primary phases of the quenched slags were identified, and their equilibrium compositions determined by electron probe microanalysis. Liquidus temperatures of the slags were bracketed within an uncertainty of ± 10–20°C. Anorthite (CaO·Al2O3·2SiO2), gehlenite (2CaO·Al2O3·SiO2), pseudowollastonite (CaO·SiO2), and larnite (2CaO·SiO2) were observed as primary phases. Progressive doping by Na2O substantially changed the slag liquidus temperature, shifting the primary phase from anorthite to pseudowollastonite and larnite, and gehlenite to larnite. The liquidus temperature decreased significantly with increasing Na2O in slags with CaO/SiO2 (C/S) ratios of 0.3 and 0.6, while the liquidus temperature increased for slags with a C/S ratio of 1.0. The solid solubility of Na2O in the phases was quantified. Finally, the relevance of the phase equilibria study of the CaO-Al2O3-SiO2-Na2O system was discussed with regard to the optimum design of smelting with a focus on value recovery from e-waste.

Skin cancer is basically the unnatural growth of skin tissues and it can be fatal. Lately, it has evolved into one of the most perilous types of other cancers in the human body. Premature detection can help to endure the patient. Detection of skin cancer is quite difficult. At present in medical image diagnosis, the performance of computer vision is quite conducive. Together with the progress in technology and impetuous increment in computer provision, different types of machine learning techniques and deep learning models have arisen for the analysis of medical images particularly skin lesion images. In this study, we propose a deep learning model with some image pre-processing steps that help to categorize skin lesions with a better classification rate than other existing models. Normalization, data reduction, and data augmentation are used in pre-processing steps to classify benign and malignant cancer lesions from the HAM10000 dataset. From the experimental result, the proposed model gained an accuracy of 96.10% in training and 90.93% during testing. This model reduces the execution time and performs well-handled.

Our society is getting more and more technology dependent day by day. Nevertheless, agriculture is imperative for our survival. Rice is one of the primary food grains. It provides sustenance to almost fifty percent of the world population and promotes huge amount of employments. Hence, proper mitigation of rice plant diseases is of paramount importance. A model to detect three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut is proposed in this paper. Backgrounds of the images are removed by saturation threshold while disease affected areas are segmented using hue threshold. Distinctive features from color, shape, and texture domain are extracted from affected areas. These features can robustly describe local and global statistics of such images. Trying a couple of classification algorithms, extreme gradient boosting decision tree ensemble is incorporated in this model for its superior performance. Our model achieves 86.58% accuracy on rice leaf diseases dataset from UCI, which is higher than previous works on the same dataset. Class-wise accuracy of the model is also consistent among the classes. &nbsp

We synthesized spinel ferrite nanoensembles (MnFe2O4, CoFe2O4, and Fe3O4) using the chemical co-precipitation method and characterized their physical, chemical, and magnetic properties by X-ray diffraction (XRD), transmission electron microscopy (TEM), physical properties measurement system (PPMS), Mössbauer spectroscopy, Fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS) and Raman spectroscopy. Their relaxation properties and potential for hyperthermia therapy were determined using nuclear magnetic resonance (NMR) and cell viability assay, respectively. XRD and TEM data confirmed that the particle core sizes were 6–9 nm before coating while their sizes increased to 10–14 nm and 14–20 nm after coating with chitosan and polyethylene glycol (PEG), respectively. Mössbauer spectroscopy showed superparamagnetic behavior for MnFe2O4 nanoparticles and ferrimagnetic behavior for the CoFe2O4 and Fe3O4 nanoparticles. A detailed studies of MH loops of all three ferrites before and after coating showed surface functionalization by a large reduction of coercivity and anisotropy. The successful coating was further confirmed by the peak shifts in the FTIR spectra of the particles whereas Raman spectra of coated ferrites also displayed the characteristic absorption patterns and suppression of the ferrite peaks suggesting successful coating. The induced heating profile of the nanoparticles in stable suspension was tested with a radio frequency magnetic field of 76 mT and a frequency of 400 kHz. High mortality (>98%) of 9 L gliosarcoma cancer cells by hyperthermia suggested that these nanoparticles could be used for cancer therapy. Transverse relaxivities (r2) determined by NMR for chitosan-coated MnFe2O4, CoFe2O4, and Fe3O4 nanoparticles were 297 (±22), 353 (±26), and 345 (±13), mM−1S−1, while for PEG-coated nanoparticles are 165 (±22), 146 (±14), and 159 (±07) mM−1S−1, respectively. Overall these spinel ferrite nanoensembles show great promise for cancer theranostics research applications.

Coronavirus Disease-2019 (COVID-19) quickly surged the whole world and affected people’s physical, mental, and social health thereby upsetting their quality of life. Therefore, we aimed to investigate the quality of life (QoL) of COVID-19 positive patients after recovery in Bangladesh. This was a study of adult (aged ≥18 years) COVID-19 individuals from eight divisions of Bangladesh diagnosed and confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) from June 2020 to November 2020. Given a response rate of 60% in a pilot study, a random list of 6400 COVID-19 patients was generated to recruit approximately 3200 patients from eight divisions of Bangladesh and finally a total of 3244 participants could be recruited for the current study. The validated Bangla version of the World Health Organization Quality of Life Brief (WHOQOL-BREF) questionnaire was used to assess the QoL. Data were analyzed by STATA (Version 16.1) and R (Version 4.0.0). All the procedures were conducted following ethical approval and in accordance with the Declaration of Helsinki. The mean scores of QoL were highest for the physical domain (68.25±14.45) followed by social (65.10±15.78), psychological (63.28±15.48), and environmental domain (62.77±13.07). Psychological and physical domain scores among females were significantly lower than the males (p<0.001). The overall quality of life was lower in persons having a chronic disease. Participants over 45 years of age were 52% less likely to enjoy good physical health than the participants aged below 26 years (AOR: 0.48, CI: 0.28–0.82). The quality of life of employed participants was found 1.8 times higher than the unemployed (AOR: 1.80, CI: 1.11–2.91). Those who were admitted to hospitals during infection had a low QoL score in physical, psychological, and socials domains. However, QoL improved in all aspect except the psychological domain for each day passed after the diagnosis. These findings call for a focus on the quality of life of the COVID-19 affected population, with special emphasis given to females, older adults, unemployed, and people with comorbidities.

Effective utilization of cellulose and hemicelluloses is essential to sustainable bioconversion of lignocellulose. A newly isolated xylose-utilizing strain, Klebsiella pneumoniae PM2, was introduced to convert the biomass “whole sugars” into high value 2,3-butanediol (2,3-BDO) in a biorefinery process. The fermentation conditions were optimized (30°C, pH 7, and 150 rpm agitation) using glucose for maximum 2,3-BDO production in batch systems. A sulfite pretreated oil palm empty fruit bunches (EFB) whole slurry (substrate hydrolysate 119.5 g/L total glucose mixed with pretreatment spent liquor 80 g/L xylose) was fed to strain PM2 for fermentation. The optimized biorefinery process resulted in 75.03 ± 3.17 g/L of 2,3-BDO with 0.78 ± 0.33 g/L/h productivity and 0.43 g/g yield (87% of theoretical value) via a modified staged separate hydrolysis and fermentation process. This result is equivalent to approximately 135 kg 2,3-BDO and 14.5 kg acetoin precursors from 1 ton of EFB biomass without any wastage of both C6 and C5 sugars.

Human activity recognition has become a significant research trend in the fields of computer vision, image processing, and human–machine or human–object interaction due to cost-effectiveness, time management, rehabilitation, and the pandemic of diseases. Over the past years, several methods published for human action recognition using RGB (red, green, and blue), depth, and skeleton datasets. Most of the methods introduced for action classification using skeleton datasets are constrained in some perspectives including features representation, complexity, and performance. However, there is still a challenging problem of providing an effective and efficient method for human action discrimination using a 3D skeleton dataset. There is a lot of room to map the 3D skeleton joint coordinates into spatio-temporal formats to reduce the complexity of the system, to provide a more accurate system to recognize human behaviors, and to improve the overall performance. In this paper, we suggest a spatio-temporal image formation (STIF) technique of 3D skeleton joints by capturing spatial information and temporal changes for action discrimination. We conduct transfer learning (pretrained models- MobileNetV2, DenseNet121, and ResNet18 trained with ImageNet dataset) to extract discriminative features and evaluate the proposed method with several fusion techniques. We mainly investigate the effect of three fusion methods such as element-wise average, multiplication, and maximization on the performance variation to human action recognition. Our deep learning-based method outperforms prior works using UTD-MHAD (University of Texas at Dallas multi-modal human action dataset) and MSR-Action3D (Microsoft action 3D), publicly available benchmark 3D skeleton datasets with STIF representation. We attain accuracies of approximately 98.93%, 99.65%, and 98.80% for UTD-MHAD and 96.00%, 98.75%, and 97.08% for MSR-Action3D skeleton datasets using MobileNetV2, DenseNet121, and ResNet18, respectively.

Objectives While COVID-19 vaccine is on the train, this study aimed to understand belief, attitude, and intention to take the COVID-19 vaccine in South Asian region. Methods The cross-sectional study was conducted among 18201 individuals in four South Asian countries; Bangladesh, India, Pakistan, and Nepal between January 17 and February 2, 2021 using semi-structured questionnaires. We have used the health belief model (HBM) to identify the predictors related to vaccine acceptance. STATA (v16.1) was used for all analyses. Results The country wise percentage of participants willing to accept the COVID-19 vaccine was found 65%, 66%, 72%, and 74% in Bangladesh, India, Pakistan, and Nepal, respectively, whereas destructive impact of COVID-19, positive conception regarding vaccines and the concern for possible side effects were found significant in modifying respondent's intention according to health belief model among the study population. After adjusting the variables age, sex, marital status, education, presence of comorbidities, worriness of getting affected, perceived COVID-19 impact, efficiency of vaccine, positive attitude of mandatory implication and availability were found associated with people's intention to take vaccine across different countries. Conclusion This study assessed the belief, attitude, and intention to take the COVID-19 vaccine along with associated factors.

In most agricultural countries, farmers face a great loss every year due to diseases in crops. So, early detection of tomato plant diseases has achieved a great concern of the researchers. In this paper a deep convolutional neural network model is used to recognize unhealthy plants from the healthy plants and to classify the tomato plant diseases. We have used VGG16 deep cnn classifier to recognize unhealthy plants and their diseases from the images of tomato plants. We have used Plant Village dataset which contains ten different classes of tomato leaf images including healthy plants. Using transfer learning method in a pre-trained VGG16 model, this dataset shows a satisfying classification performance which about 95.5%. Top 2 accuracy of this model reaches to 99% to recognize tomato plant diseases. Without using any segmentation or preprocessing of leaves images our trained model shows a performance of approximately 100% to differentiate unhealthy plants from healthy plants.

The use of High-Performance Thin-Layer Chromatography (HPTLC) coupled with the use of DPPH* (2,2-diphenyl-1-picrylhydrazyl) as a derivatisation reagent is a novel approach to the analysis of antioxidant activity of honeys. The method facilitates the visualisation of individual constituents that contribute to the overall antioxidant activity of the honey, even if they are not yet chemically identified, and allows for the quantification of their antioxidant activity as gallic acid equivalents. The method supports a more in-depth study of the antioxidant activity of honey as it allows for a comparative analysis of the antioxidant fingerprints of honeys of different floral origin and is able to capture differences in their individual bioactive constituents. Further, it supports the tracking of changes in antioxidant activity of individual honey constituents over time upon exposure to different temperature conditions, which demonstrates the potential value of the method for in-process quality control.

Summary Background As evidence is mounting regarding irrational and often unnecessary use of antibiotics during the COVID-19 pandemic a cross-sectional Point Prevalence Survey (PPS) (in accordance with WHO guideline) was conducted across COVID-19 dedicated wards in Dhaka Medical College and Hospital (DMCH). Methodology Antibiotic usage data were collected from 193 patients at different COVID-19 dedicated wards at DMCH on 11 June 2020. Comparisons in antibiotic usage were made between different groups using Pearson chi-square and Fisher's exact test. Result Findings reveal all surveyed patients (100%) were receiving at least one antibiotic with 133 patients (68.91%) receiving multiple antibiotics. Overall, patients presenting with the severe disease received more antibiotics. Third-generation cephalosporins (i.e. ceftriaxone) (53.8%), meropenem (40.9%), moxifloxacin (29.5%), and doxycycline (25.4%) were the four most prescribed antibiotics among surveyed patients. Diabetes mellitus (DM) was independently associated with multiple antibiotic prescribing. Abnormal C-reactive protein (CRP) and serum d-dimer were linked with higher odds of multiple antibiotic prescribing among study patients. Conclusion Prevalence of multiple antibiotic prescriptions was high among severely ill patients and those with abnormal CRP and d-dimer levels. Data regarding the quality of antibiotic prescribing were lacking.

In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post.

Background: Health professionals, especially in third world countries like Bangladesh, where the doctor-patient ratio is poor, have witnessed mental distress and deaths helplessly while working in different hospitals with limited resources during the COVID-19 pandemic. This study intended to determine the prevalence and associated job stressors with PTSD among Bangladesh front-liner healthcare workers. Methodology: We conducted this cross-sectional analytical study from January to March 2021, in Dhaka city among the healthcare workers working in COVID-19 dedicated hospitals. We conveniently choose 25 hospitals involved with COVID-19 treatment. A total of 454 healthcare workers were recruited from those 25 hospitals conveniently. We conducted face-to-face interviews with structured questionnaires. Data were analyzed using SPSS version 25.0. We conducted a Chi-square test, univariate analysis, and adjusted logistic regression to explore the association of different independent variables with PTSD. Result: Most participants were doctors (44.9%), and females were the predominant respondents (61.5%). PTSD was more prevalent among doctors (38.7%) and male respondents (28.6%). In binary logistic regression analysis adjusting for other covariates, developing PTSD were found to be significantly associated with previously diagnosed as COVID-19 (OR=1.85, p = 0.045, 95% CI=1.013,3.394), unavailability of the service benefits (OR=0.466, p = 0.020, 95% CI=0.245,0.887), having overtime night duty (OR=3.090, p

Sheet Molding Compound (SMC) is specifically a fiber Reinforced plastic which has usually composed with glass fibers impregnated between two layers of thermosetting resin, fillers, additives, catalysts, inhibitors, impact modifiers and also internal mold release agent. In this study, carbon nanotube has initiated as a reinforcing element for the sheet molding compound (SMC) so that the mechanical strength of the composite can be more intensified as carbon nanotubes have an extraordinary mechanical properties. In this consequence, the mechanical properties of sheet molding compound (SMC), containing carbon nanotubes manufactured following the hand layup process were investigated where carbon nanotubes up to 25 wt. % instead of glass fiber was used in the composition of Sheet Molding Compound (SMC) but that didn’t increase the mechanical properties which were highly recommended for our research purpose. However, carbon nanotubes up to 4.5 wt.% were dispersed in the resin formulation to produce the sheet molding compound (SMC) and studies resulted in increases in tensile strength and flexural strength by 2 times and 8 times respectively from the conventional tensile and flexural strength of sheet molding compound, which turned out a positive result. Besides all of this, we also introduced glass microsphere with calcium carbonate where both performed as a filler. This can be stated another significant cause for increasing the mechanical properties of the sheet molding compound (SMC) because glass microspheres have improved dimension stability, improved stiffness at a given weight. The main advantage of using glass microspheres lies in the weight reduction of the composite which is highly recommended because the main purpose of a composite production is to reduce the total weight of the produced material.

A published article in the Journal of National Institute of Mental Health (NIMH), Dhaka, Bangladesh. This article estimates the extent of depression among doctors in Bangladesh. The results are rather fascinating.

Printed circuit boards, collected from Bangladesh, were melted to determine the proportion of metal, ceramic and volatile components. The concentration and amount of valuable elements in the e-waste were calculated from the analysis of the metal and ceramic phases. This information was used to design a simple three-stage process to recover the valuable components. The stages included smelting, electrorefining of a copper rich anode and melting of anode slimes and reduction of a tin rich slag. In this process, copper would be recovered as a high purity cathode, silver and gold recovered as a precious metal bullion from the processed anode slimes and tin recovered from the reduction of tin rich slag. A flowsheet simulation of this process was used to estimate the size of unit operations and process streams. Capital costs of the process situated in Bangladesh were estimated based on the equipment required and included capital on-costs. Operating costs were estimated from power, labour and consumables required as well as operating factors such as maintenance and administration. Cash inputs to the process were estimated from the value of product streams. The preliminary financial viability of the process was estimated, and net present value and internal rate of return are determined.

The recovery of valuable and critical metals from electronic wastes (e-waste) via the pyrometallurgical route has some challenges including high processing temperatures. Designing appropriate slag systems based on the major elemental components in e-waste could bring operational advantages by lowering the liquidus temperature. In this study, the quaternary slag system CaO–Al2O3–SiO2–Na2O was investigated to determine the liquidus temperature and phase equilibria of slags relevant to e-waste smelting. The slags were thermally equilibrated at different temperatures inside a vertical tube furnace followed by rapid quenching. The quenched slags were examined by SEM to observe the phase formed and the equilibrium compositions were determined using energy dispersive (ED) spectrometry. The liquidus temperature of the slags in the anorthite (CaO·Al2O3·2SiO2) phase field was significantly decreased with increasing levels of Na2O. The slag composition moved towards the pseudo wollastonite (CaO·SiO2) region upon the addition of Na2O.