Search

Nandeeta Samad,1 Temitayo Eniola Sodunke,2 Hasan Al Banna,3 Ashmita Sapkota,4 Aneeka Nawar Fatema,5 Katia Iskandar,6 Dilshad Jahan,7 Timothy Craig Hardcastle,8 Tanzina Nusrat,9 Tajkera Sultana Chowdhury,10 Mainul Haque11 1Department of Public Health, North South University, Dhaka 1229, Bangladesh; 2University of Ilorin, Ilorin, Kwara State, Nigeria; 3Institute of Social Welfare and Research, University of Dhaka, Dhaka 1000, Bangladesh; 4Department of Microbiology, Mahidol University, Ratchathewi, Bangkok 10400, Thailand; 5Department of Microbiology, Prima Asia University, Dhaka 1213, Bangladesh; 6School of Pharmacy, Lebanese University, Beirut, Lebanon; 7Department of Hematology, Asgar Ali Hospital, Dhaka 1204, Bangladesh; 8Department of Surgery, Nelson R Mandela School of Clinical Medicine, University of KwaZulu-Natal, Umbilo, Berea 4001, South Africa; 9Department of Microbiology, Chittagong Medical College, Chattogram 4203, Bangladesh; 10Department of Urology, Shaheed Suhrawardy Medical College Hospital, Dhaka 1207, Bangladesh; 11Unit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kuala Lumpur 57000, MalaysiaCorrespondence: Mainul HaqueUnit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kem Perdana Sungai Besi, Kuala Lumpur 57000, MalaysiaTel +60 109265543Email runurono@gmail.comAbstract: The world is striving against the severe crisis of the COVID-19 pandemic. Healthcare professionals are struggling to treat their patients based on nonspecific therapies. Amidst this uncertainty, convalescent plasma therapy (CPT) has appeared to be an interim adjuvant therapy for severely ill patients of COVID-19 until long-term clinical trial treatment options are available. Considering the transfusion-related hazards, especially lung injuries and microbial transmission, where sensitivity is not ensured, rigorous trials should be conducted to determine this therapy’s efficacy. Moreover, the ratio of recovered cases to plasma donors is not satisfying, which questioning this therapy’s availability and accessibility. Although some countries are making the treatment free, the attributable cost mandates a justification for its suitability and sustainability. Our article aimed to review the published facts and findings of CPT’s effectiveness in lowering the mortality rate of COVID-19. This pandemic showed that healthcare systems worldwide need core reform. A unified global collaboration must align and coordinate to face the current pandemic and enhance world readiness for future outbreaks based on health equity and equality.Keywords: convalescent plasma therapy, safety, risk, availability, accessibility, COVID-19, global pandemic

Mainul Haque,1 Judy McKimm,2 Massimo Sartelli,3 Sameer Dhingra,4 Francesco M Labricciosa,5 Salequl Islam,6 Dilshad Jahan,7 Tanzina Nusrat,8 Tajkera Sultana Chowdhury,9 Federico Coccolini,10 Katia Iskandar,11 Fausto Catena,12 Jaykaran Charan13 1Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kuala Lumpur 57000, Malaysia; 2Medical Education, Swansea University School of Medicine, Grove Building, Swansea University, Swansea, Wales SA2 8PP, UK; 3Department of General and Emergency Surgery, Macerata Hospital, Macerata, Italy; 4School of Pharmacy, The University of the West Indies, St. Augustine Campus, Faculty of Medical Sciences, Eric Williams Medical Sciences Complex, Uriah Butler Highway, Trinidad & Tobago, West Indies; 5Global Alliance for Infections in Surgery, Vila Nova de Gaia, Portugal; 6Department of Microbiology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh; 7Department of Hematology, Asgar Ali Hospital, Dhaka 1204, Bangladesh; 8Department of Microbiology, Chittagong Medical College, Chattogram 4203, Bangladesh; 9Department of Urology, Shaheed Suhrawardy Medical College Hospital, Dhaka 1207, Bangladesh; 10Department of General Emergency and Trauma Surgery, Pisa University Hospital, Pisa, Italy; 11School of Pharmacy, Lebanese University, Beirut, Lebanon; 12Department of Emergency Surgery, Parma Maggiore Hospital, Parma, Italy; 13Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, Rajasthan, IndiaCorrespondence: Mainul HaqueUnit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kem Perdana Sungai Besi, Kuala Lumpur 57000, MalaysiaTel +60 10 926 5543Email runurono@gmail.comAbstract: Healthcare-associated infections (HCAIs) are a major source of morbidity and mortality and are the second most prevalent cause of death. Furthermore, it has been reported that for every one-hundred patients admitted to hospital, seven patients in high-income economies and ten in emerging and low-income economies acquire at least one type of HCAI. Currently, almost all pathogenic microorganisms have developed antimicrobial resistance, and few new antimicrobials are being developed and brought to market. The literature search for this narrative review was performed by searching bibliographic databases (including Google Scholar and PubMed) using the search terms: “Strategies,” “Prevention,” and “Healthcare-Associated Infections,” followed by snowballing references cited by critical articles. We found that although hand hygiene is a centuries-old concept, it is still the primary strategy used around the world to prevent HCAIs. It forms one of a bundle of approaches used to clean and maintain a safe hospital environment and to stop the transmission of contagious and infectious microorganisms, including multidrug-resistant microbes. Finally, antibiotic stewardship also has a crucial role in reducing the impact of HCAIs through conserving currently available antimicrobials.Keywords: prevention, hand hygiene, environmental hygiene, surveillance, antibiotic stewardship

With the increase of salinity, shrimp monoculture is a typical production system on many global coasts. They are thus inviting commercially valuable exotic shrimp species that threaten the population of natives and causing alteration of ecosystem function. This intentional introduction and redistribution of shrimp species is an Anthropocene feature of the global coast. Therefore, we investigated how the coastal system breaches a freshwater system leading to more saline-friendly production. Spatiotemporal mapping showed the scope of diversified coastal livelihood that illustrate the coexistence of saline and sweet water-based production system in Bangladesh with a mean accuracy of 89% (kappa statistics 0.86). In many parts, the once agriculturally dominated landscape has transformed into aquaculture to produce shrimp, crab, fish, and salt. Continuous Change Detection and Classification (CCDC) algorithm also depicts the breakpoint in time series of normalized difference vegetation index (NDVI) data due to coastal land-use change. This dynamism includes the strong presence of mixed rice-shrimp culture in Assasuni (west coast) and Chakaria (east coast), as well as salt panning in Chakaria and paddy rice production in Kalapara (mid-coast). Thus those areas are more resilient because many other coastal communities primarily depend on shrimp or aqua monoculture, having limited or no alternative production system. Due to semi-intensive shrimp cultivation, biotic depletion, disease, pollution like antibiotics, nutrients, and organic matter loads are also less than the global coast's intensive aquaculture area. This study revealed the need for a blended fresh and saline-based production system. The findings can be used as a reference in the formulation and implementation of sustainable coastal management policies.

BACKGROUND AND OBJECTIVES: Needle sticks and other sharps injuries are common and serious hazards in healthcare facilities, exposing healthcare workers to blood-borne pathogens like HIV, hepatitis B, and C and affecting the physical and psychological health of dental staff. Our study aims to find the prevalence of needle injuries and preventive responses by affected ones. METHODOLOGY: A cross-sectional study was conducted at various Dental hospitals and clinics in Islamabad, Rawalpindi, and Abbottabad Pakistan, from December 2023 to May 2024. A close-ended questionnaire was distributed among 400 dentists and other healthcare workers at participating dental hospitals and clinics to collect the data. A total of 345 completely filled forms were collected and assessed by using SPSS 22. RESULTS: Out of 345, 241 healthcare persons reported needle injury at least once. The majority of injuries (78%) occurred at the chairside, 14% in the operation theatre and 8% at other locations. 197(81.7%) were self-inflicted during needle uncapping/recapping, and 44 (18.3%) were by the assisting staff. About 82% of injuries were with syringes while 18% were by sutures or other sharps. To manage the Needlestick injury (NSI), 159(66%) immediately washed the injury site with water and soap, 27(11%) pressed to bleed, 19(8%) applied antiseptic, 17(7%) took post-exposure prophylaxis against Hepatitis B, C and HIV/AIDS and 8 % did nothing. CONCLUSION: This study showed that Needlestick injury (NSI) was a common finding at dental workplaces, leaving a physical and psychological fear impact on healthcare persons.

Bangladesh, a low-lying deltaic plain formed by the convoluted network of streams within the Ganges-Brahmaputra-Meghna basin, is one of the worst climate change-induced natural hazards sufferers. Among all the riverine and coastal hazards, floods alone have wreaked havoc on the national economy. This study focused on determining the level of potential hazard of different crop stages of Transplanted Aman (T. Aman) rice based on the coincidence of the crop cycle stages with the duration and depth of flood of the Brahmaputra-Jamuna River system. A 2D hydrodynamic model has been developed using TUFLOW for the Brahmaputra-Jamuna River system under this study. The model simulated results for 2008 and 2019, representing bank-full flow conditions (2008) and a recent flood event (2019). It is seen that the vegetative stage for both the flooding scenarios is most affected for all selected monitoring locations, and the effect of flood events of the other two stages (reproductive and ripening stage) varied spatially. The analysis presented in the study can be used to determine the economic damage incurred to the lower-income community, primarily associated with agriculture. Adaptive actions and mitigation measures can be prioritized to depreciate the loss and damage due to these catastrophic flooding events.

Abstract The detrimental impact of reactive oxygen species on D.N.A. repair processes is one of the contributing factors to colon cancer. The idea that oxidative stress may be a significant etiological element for carcinogenesis is currently receiving more and more support. The goal of the current study is to evaluate the anti-inflammatory and anticancer activity of three powerful phytocompounds—sitosterol, amyrin, and epiafzelechin—alone and in various therapeutic combinations against colon cancer to identify the critical mechanisms that mitigate nickel's carcinogenic effect. To evaluate the ligand–protein interaction of four selected components against Vascular endothelial growth factor (VEGF), Matrix metalloproteinase-9 (MMP9) inhibitor and Interleukin-10 (IL-10) molecular docking approach was applied using PyRx bioinformatics tool. For in vivo analysis, hundred albino rats were included, divided into ten groups, each containing ten rats of weight 160–200 g. All the groups were injected with 1 ml/kg nickel intraperitoneally per week for three months, excluding the negative control group. Three of the ten groups were treated with β-sitosterol (100 mg/kg b wt), β-amyrin (100 mg/kg b wt), and epiafzelechin (200 mg/kg b wt), respectively, for one month. The later four groups were fed with combinatorial treatments of the three phyto compounds for one month. The last group was administered with commercial drug Nalgin (500 mg/kg b wt). The biochemical parameters Creatinine, Protein carbonyl, 8-hydroxydeoxyguanosine (8-OHdG), VEGF, MMP-9 Inhibitor, and IL-10 were estimated using ELISA kits and Glutathione (G.S.H.), Superoxide dismutase (S.O.D.), Catalase (C.A.T.) and Nitric Oxide (NO) were analyzed manually. The correlation was analyzed through Pearson’s correlation matrix. All the parameters were significantly raised in the positive control group, indicating significant inflammation. At the same time, the levels of the foresaid biomarkers were decreased in the serum in all the other groups treated with the three phytocompounds in different dose patterns. However, the best recovery was observed in the group where the three active compounds were administered concomitantly. The correlation matrix indicated a significant positive correlation of IL-10 vs VEGF (r = 0.749**, p = 0.009), MMP-9 inhibitor vs SOD (r = 0.748**, p = 0.0 21). The study concluded that the three phytocompounds β-sitosterol, β-amyrin, and epiafzelechin are important anticancer agents which can target the cancerous biomarkers and might be used as a better therapeutic approach against colon cancer soon.

Electroporation is regularly used to deliver agents into cells, including transgenic materials, but it is not used for mutating zebrafish embryos due to the lack of suitable systems, information on appropriate operating parameters, and the challenges posed by the protective chorion. Here, a novel method for gene delivery in zebrafish embryos was developed by combining microinjection into the space between the chorion and the embryo followed by electroporation. This method eliminates the need for chorion removal and injecting into the space between the chorion and embryo eliminates the need for finding and identifying key cell locations before performing an injection, making the process much simpler and more automatable. We also developed a microfluidic electroporation system and optimized electric pulse parameters for transgenesis of embryos. The study provided a novel method for gene delivery in zebrafish embryos that can be potentially implemented in a high throughput transgenesis or mutagenesis system.

Background: Thumb-sucking, a non-nutritive sucking behavior, is a natural reflex seen in infants but can persist into childhood if not addressed leading to various dental and orofacial issues, including malocclusion, dental arch deformities, speech difficulties, and facial asymmetry. The aim of the study was to explore the consequences of thumb-sucking habits among the school going children. Methods: This cross-sectional study involved among purposively selected 124 school going children, aged between 5-8 years with their parents attended in the outpatient department of University Dental College at Moghbazar, Dhaka, Bangladesh. The data were obtained through a pretested structured questionnaire and checklist through face-to-face interview from parents regarding background characteristics, consequences of thumbs sucking and oral examination of children respectively. Then, data were analyzed using statistical package for social sciences (SPSS) software version 27 for descriptive analysis (frequency, percentage) and inferential statistics (chi-square test or fisher’s exact test) at 5% level of significance (P

Coronavirus Disease (COVID-19), which began as a small outbreak in Wuhan, China, in December 2019, became a global pandemic within months due to its high transmissibility. In the absence of pharmaceutical treatment, various non-pharmaceutical interventions (NPIs) to contain the spread of COVID-19 brought the entire world to a halt. After almost a year of seemingly returning to normalcy with the world's quickest vaccine development, the emergence of more infectious and vaccine resistant coronavirus variants is bringing the situation back to where it was a year ago. In the light of this new situation, we conducted a study to portray the possible scenarios based on the three key factors: impact of interventions (pharmaceutical and NPIs), vaccination rate, and vaccine efficacy. In our study, we assessed two of the most crucial factors, transmissibility and vaccination rate, in order to reduce the spreading of COVID-19 in a simple but effective manner. In order to incorporate the time-varying mutational landscape of COVID-19 variants, we estimated a weighted transmissibility composed of the proportion of existing strains that naturally vary over time. Additionally, we consider time varying vaccination rates based on the number of daily new cases. Our method for calculating the vaccination rate from past active cases is an effective approach in forecasting probable future scenarios as it actively tracks people's attitudes toward immunization as active case changes. Our simulations show that if a large number of individuals cannot be vaccinated by ensuring high efficacy in a short period of time, adopting NPIs is the best approach to manage disease transmission with the emergence of new vaccine breakthrough and more infectious variants.

Zebrafish have emerged as a useful model for biomedical research and have been used in environmental toxicology studies. However, the presence of the chorion during the embryo stage limits cellular exposure to toxic elements and creates the possibility of a false-negative or reduced sensitivity in fish embryo toxicity testing (FET). This paper presents the use of electroporation as a technique to improve the delivery of toxic elements inside the chorion, increasing the exposure level of the toxins at an early embryo stage (

Shea tree (Vitellaria paradoxa) is an essential tree crop with great potential economic value mainly because of its seed oil (shea butter) which is of high demand for manufacturing assorted products in food, cosmetic, and rubber industries. Propagation of this species is, however, hindered by relative unavailability of seed (nuts), erratic seed germination, a long vegetative phase, and latex exudation from cuttings. Thus, another method of propagation through in vitro culture is recommended for rapid multiplication of shea genotypes for large-scale cultivation. In the present study, the effects of two cytokinins, namely, 6-benzylaminopurine (BAP) and kinetin (KIN), and one auxin, namely 1-naphthaleneacetic acid (NAA), on shoot and/or root induction in vitro were asses sed at various combinations/concentrations. The inclusion of these growth regulators in the culture medium significantly improved (P

Introduction: Cadmium(Cd) an industrial poison present abundantly in the environment, causes human toxicity by an inflammatory process. Chronic exposure of cadmium can cause a number of molecular lesions that could be relevant to oncogenesis, through indirect or epigenetic mechanisms, potentially including abnormal activation of oncogenes and suppression of apoptosis by depletion of antioxidants. As induction of cyclooxygenase (COX)-2 is linked to inflammatory processes, use of luteolin, epiafzelechin, and albigenin alone or in different combinations may be used as anti-inflammatory therapeutic agents.Methods: We, herein, performed in silico experiments to check the binding affinity of phytochemicals and their therapeutic effect against COX-2 in cadmium administered rats. Wistar albino rats were given phytochemicals in different combinations to check their anti-inflammatory activities against cadmium intoxication. The level of alanine aminotransferases (ALT), 4-hydroxynonenal (4HNE), 8-hydroxy-2-deoxyguanosine (8-OHdG), tumor necrosis factor-alpha (TNF-α), isoprostanes (IsoP-2α), COX-2, and malondialdehyde (MDA) were estimated with their respective ELISA and spectrophotometric methods.Results: The generated results show that phytocompounds possessed good binding energy potential against COX-2, and common interactive behavior was observed in all docking studies. Moreover, the level of ALT, 4HNE, 8-OHdG, TNF-α, IsoP-2α, malondialdehyde, and COX-2 were significantly increased in rats with induced toxicity compared to the control group, whereas in combinational therapy of phytocompounds, the levels were significantly decreased in the group. Discussion: Taken together, luteolin, epiafzelechin, and albigenin can be used as anti-inflammatory therapeutic agents for future novel drug design, and thus it may have therapeutic importance against cadmium toxicity.

Alongside pesticides and specialist predators, natural communities of generalist beetle and spider predators play an important role in suppressing agricultural pests. However, the predation pressure of natural communities can be unpredictable. Overall predation pressure is influenced by a dense network of potential intraguild interactions, which are further shaped by species traits and environmental factors. Understanding how these different influences combine to impact pest control is especially important in the context of changing global temperatures. Recent empirical studies have demonstrated that the foraging behavior of arthropod predators is influenced by an interaction between temperature and predator body size. To explore the consequences of these findings for intraguild interactions and pest control, we expand a previously published model describing interactions between arthropod predators and a pest population. The model assumed that interaction strengths are influenced by body size and habitat preference. In our updated model, we incorporate the effect of temperature on predator foraging activity. We parameterize the model to match empirically observed predator community composition in 10 agricultural fields and use simulations to demonstrate how temperature-dependent behaviors change the expected efficiency of the natural predator community. Then, we use an optimization approach to identify the most efficient composition of natural predators for pest control. We then evaluate whether the most efficient predator compositions would change with increasing average daily temperature and its variability, as is expected under future temperature change. We find that optimal communities often include predators with complementary foraging activity and that in 2 fields, the optimal community changes drastically under future temperatures. We also note that at some temperatures, foraging activity reduces the negative effects of intraguild interference on pest consumption. This work allows us to assess the effect of climate change on the efficiency of natural predator communities to control pest populations and provide guidance for farmers to design pest management strategies tailored to different climate scenarios.

In this study, a novel nanohybrid composite was fabricated via the incorporation of manganese ferrite (MnFe2O4) nanoparticles into the integrated surface of reduced graphene oxide (rGO) and black cumin seeds (BC). The nanohybrid composite was prepared by a simple co-precipitation method and characterized by several spectroscopic and microscopic techniques. The characterization analysis revealed that the rGO-BC surface was decorated with the MnFe2O4. The strong chemical interaction (via electrostatic and H-bonding) between the integrated surface of rGO-BC and MnFe2O4 nanoparticles has been reported. The prepared composite was highly porous with a heterogeneous surface. The average size of the prepared composite was reported in the ranges of 2.6–7.0 nm. The specific surface area of the prepared composite was calculated to be 50.3 m2/g with a pore volume of 0.061 cc/g and a half pore width of 8.4 Å. As well, many functional sites on the nanohybrid composite surface were also found. This results in the excellent adsorption properties of nanohybrid composite and the effectual elimination of methylene blue dye from water. The nanohybrid was tested for various linear isotherms, such as Langmuir and Freundlich, for the adsorption of methylene blue dye. The Freundlich isotherm was the well-fitted model, proving the adsorption is multilayer. The maximum Langmuir adsorption capacity of nanohybrid composite for methylene blue was reported to be 74.627 mg/g at 27 °C. The adsorption kinetics followed the pseudo-second-order recommended surface interaction between the dye and nanohybrid composite. The interaction between methylene blue and the nanohybrid composite was also confirmed from the FTIR spectrum of the methylene blue-loaded adsorbent. The rate-determining step for the present study was intraparticle diffusion. Temperature-dependent studies of methylene blue adsorption were also carried out to estimate adsorption’s free energy, enthalpy, and entropy. The methylene blue adsorption was feasible, spontaneous, and endothermic. A comparison study revealed that the present materials could be successfully prepared and used for wastewater treatment.

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient’s health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods.

Predicting the kinetics of recrystallisation in metals, and recrystallised grain size distributions, is one the key approaches to controlling and refining grain size during metal processing, which typically increases strength and toughness/ductility. Recrystallisation prediction models and equations are supported by lab-based simulations that can systematically assess recrystallisation over a range of temperatures and strains for different materials and starting grain sizes. This work uses modelling and experimental verification to assess the different commonly used compression test sample geometries to determine strain uniformity and potential sources of error in microstructural assessment and proposes a modified geometry that increases the area of constant known strain. Whilst flow stress measurements in all samples showed good agreement. It has been shown that the new plane strain geometry offers a more consistent, homogeneous strain through the sample such that the large number of grains needed for accurate grain size distribution measurement can be readily achieved. Over double the area of ±10% of the target strain was achieved in the modified plane strain sample compared to a conventional uniaxial specimen, this area was also shown to be more conducive to metallographic assessment and offers in excess of 1500 grains of 250 μm to be assess per cross-sectional slice.

This research was aimed to assess the human health risk by measuring concentration of seven heavy metals (Fe, Mn, Ni, Cu, Zn, As and Pb) in some selected vegetables, fruits and cereals in Khulna, Bangladesh. Environmental Protection Agency (US EPA) deterministic approaches were used to assess the carcinogenic and non-carcinogenic health risk which was measured by Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), Hazard Index (HI) and Target cancer Risk (TR) values. The contents of Mn, Ni, As and Pb except Fe, Cu and Zn were above the recommended maximum acceptable levels proposed by the Joint FAO/WHO Expert Committee on Food Additives. Assessment of non-carcinogenic health hazard by THQ indicated no concern from consumption of these selected cereals except As, Pb and vegetables except for Pb. But the combined impacts of all metals (HI) were higher than the acceptable limit of 1 for all vegetables and cereals. So, the consumption of these vegetables and cereals are of biggest concern for non-carcinogenic health effects and all metals together may affect human health as revealed by HI. Upon consumption of studied vegetables, fruits and cereals the probability of developing cancer was greater than USEPA threshold risk limit (>10-5) for Pb and (>10-3) for As and Ni. Whereas the TR value indicates moderate cancer risk for Pb and highly cancer risk for As, Ni from this study area.

Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields of computer vision and image-processing, particularly human action recognition. Many methods have been developed to recognize human activity, which is limited to effectiveness, efficiency, and use of data modalities. Very few methods have used depth sequences in which they have introduced different encoding techniques to represent an action sequence into the spatial format called dynamic image. Then, they have used a 2D convolutional neural network (CNN) or traditional machine learning algorithms for action recognition. These methods are completely dependent on the effectiveness of the spatial representation. In this article, we propose a novel ranking-based approach to select key frames and adopt a 3D-CNN model for action classification. We directly use the raw sequence instead of generating the dynamic image. We investigate the recognition results with various levels of sampling to show the competency and robustness of the proposed system. We also examine the universality of the proposed method on three benchmark human action datasets: DHA (depth-included human action), MSR-Action3D (Microsoft Action 3D), and UTD-MHAD (University of Texas at Dallas Multimodal Human Action Dataset). The proposed method secures better performance than state-of-the-art techniques using depth sequences.

Abstract Polymeric membranes are usually prepared from solvents like n-methylpyrrolidone (NMP) because of the strong dissolving power and high boiling point. Yet, the solvent is costly, toxic and has environmental issues. In this work, nontoxic solvents such as methyl l-lactate, ethyl lactate, propylene carbonate, tributyl o-acetylcitrate, tributyl citrate, triethyl phosphate, and γ-butyrolactone (GBL) were introduced during membrane preparation. It was found that all the solvents were unable to dissolve polyetherimide except GBL. The membranes made by GBL and NMP were evaluated for gas separation, and they have almost similar hydrogen-to-methane selectivity, but, hydrogen permeance was better in NMP membranes.

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.

Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimensional space is still a challenging problem. In this paper, we conceive an efficacious method for action recognition using 3D skeleton data. First, large-scale 3D skeleton joints information was analyzed and accomplished some meaningful pre-processing. Then, a simple straight-forward deep convolutional neural network (DCNN) was designed for the classification of the desired actions in order to evaluate the effectiveness and embonpoint of the proposed system. We also conducted prior DCNN models such as ResNet18 and MobileNetV2, which outperform existing systems using human skeleton joints information.

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

ABSTRACT A cross sectional study was conducted in two types of respiratory patients in hospital population. It was found that tuberculosis (T.B) was the most common type (29.66%) followed by the asthma (28.08%) and chronic obstructive pulmonary disease (COPD) (11.31%). Average age of diagnosis was 42.15 ± 0.65 years and average age at present 47.99 ± 0.70 years. Age group 51-60 years was more prone to this disease (21.13%). Most of the patients were married (80.06%). The highest representation of patients with respiratory diseases was observed in 1st birth order (30.36%) followed by 2nd (26.49%) and 3rd (18.45%), while the lowest was in 10th birth order (0.40%). Tuberculosis, asthma and COPD are the most prevalent types of respiratory diseases. Respiratory diseases were more common in males, in first birth order and in people of age group 51-60 years. This disease was more common in married, unemployed, less educated, and lower socioeconomic status people. Socioeconomic status and urban and rural living had a profound effect on the onset of disease.

Mecillinam is one of the very few oral antibacterial agents used against extended spectrum b-lactamase (ESBL) producing Escherichia coli (E. coli) causing urinary tract infection (UTI)). It is reported that, resistance to mecillinam can be reversed to some extent by adding beta lactamase inhibitor like clavulanic acid. The present study was aimed to determine in-vitro activity of mecillinam and mecillinam-clavulanic acid combination on the susceptibility of ESBL producing and non-ESBL producing E. coli. Total 124 E. coli (78 ESBL positive and 46 ESBL negative) isolates from urine samples of patients with UTI were included in the study. Organisms were isolated from patients attending BIRDEM General Hospital from July 2012 to December 2012. ESBL production was tested by double disc synergy test. Minimum inhibitory concentration (MIC) of mecillinam and clavulanic acid against E. coli was determined by agar dilution method. Of the total E. coli isolates, 62.9% was ESBL positive and 37.1% was negative for ESBL. Out of ESBL positive isolates, 75.6% was sensitive to mecillinam while ESBL negative isolates showed the sensitivity as 67.4%. The sensitivity to mecillinam of ESBL positive and negative isolates increased to 85.9% and 86.9% respectively by addition of clavulanic acid with mecillinam. The MIC values of intermediate and resistant isolates converted to sensitive MIC range after addition of clavulanic acid with mecillinam. Conversion of resistance of ESBL producing isolates by adding clavulanic acid was also evident by the reduction of MIC50 and MIC90 from 4µg/ml to £1 µg/ml and from 128 µg/ml to 64 µg/ml respectively. Similar trend of reduction of MICs was also observed in non-ESBLs. In conclusion, both ESBL positive and negative E. coli demonstrated considerable sensitivity to mecillinam and the sensitivity increased significantly (p