Genomic Insights of a Methicillin-Resistant Biofilm-Producing Staphylococcus aureus Strain Isolated From Food Handlers.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important zoonotic pathogen associated with a wide range of infections in humans and animals. Thus, the emergence of MRSA clones poses an important threat to human and animal health. This study is aimed at elucidating the genomics insights of a strong biofilm-producing and multidrug-resistant (MDR) S. aureus MTR_BAU_H1 strain through whole-genome sequencing (WGS). The S. aureus MTR_BAU_H1 strain was isolated from food handlers hand swabs in Bangladesh and phenotypically assessed for antimicrobial susceptibility and biofilm production assays. The isolate was further undergone to high throughput WGS and analysed using different bioinformatics tools to elucidate the genetic diversity, molecular epidemiology, sequence type (ST), antimicrobial resistance, and virulence gene distribution. Phenotypic analyses revealed that the S. aureus MTR_BAU_H1 strain is a strong biofilm-former and carries both antimicrobial resistance (e.g., methicillin resistance; mecA, beta-lactam resistance; blaZ and tetracycline resistance; tetC) and virulence (e.g., sea, tsst, and PVL) genes. The genome of the S. aureus MTR_BAU_H1 belonged to ST1930 that possessed three plasmid replicons (e.g., rep16, rep7c, and rep19), seven prophages, and two clustered regularly interspaced short palindromic repeat (CRISPR) arrays of varying sizes. Phylogenetic analysis showed a close evolutionary relationship between the MTR_BAU_H1 genome and other MRSA clones of diverse hosts and demographics. The MTR_BAU_H1 genome harbours 42 antimicrobial resistance genes (ARGs), 128 virulence genes, and 273 SEED subsystems coding for the metabolism of amino acids, carbohydrates, proteins, cofactors, vitamins, minerals, and lipids. This is the first-ever WGS-based study of a strong biofilm-producing and MDR S. aureus strain isolated from human hand swabs in Bangladesh that unveils new information on the resistomes (ARGs and correlated mechanisms) and virulence potentials that might be linked to staphylococcal pathogenesis in both humans and animals

Access to mass media and awareness of sexually transmitted diseases (STDs) among the truck drivers in Dhaka City : do mass media make them aware?

The major objective of the study is to determine the association between access to mass media such as television, newspaper, radio and internet and level of STDs awareness among the truck drivers in Dhaka city. This research utilized purposive sampling technique to select 250 respondents from the study areas. The results of the study demonstrate that a significant number of respondents (88%) had heard of STDs. However, most of them (70%) did not have the awareness of STDs. It again reveals that radio, newspaper and internet did not play significant role in making them aware of STDs. Bivariate results of the study indicate that respondents with higher degree of exposure to television were more likely to be aware of STD like HIV. This study concludes that mass media may play vibrant role in disseminating information about not only HIV but also other STDs such as Chlamydia, Herpes, Hepatitis B and Human Papilloma Virus (HPV)

Internet of Things (IoT) based ECG System for Rural Health Care

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future

Exploring knowledge and practices regarding menstrual hygiene management among Bihari women in the Geneva Camp in Bangladesh

Background: Research into menstrual hygiene management (MHM) has been mainly based on menstruation-related knowledge and practices of women and girls in the mainstream Bangladeshi society; socially disadvantaged groups, such as the Bihari refugee women, have largely been ignored. Purpose: This study aims to assess knowledge and practices about MHM among Bihari women in the Mohammadpur Geneva Camp in Dhaka, Bangladesh. Methods: In 2017, a cross-sectional survey was conducted among Bihari women and girls by the trained interviewers using a structured questionnaire. The purposive sampling was applied to select 160 Bihari women aged between 15 and 49. Data were entered, cleaned, and analysed using SPSS software. Both univariate and bivariate analyses were undertaken to examine knowledge and MHM-related practices with a significance level of p<0.01. Results: Overall, most women (59.4%) had low knowledge about menstruation. More than one-quarter (27.0%) used disposable sanitary napkins. The Bihari women who did not use sanitary pads (73%) reported that they used old disposable clothes (59.83%), reusable cloths (25.64%), cotton (9.40%), or toilet tissue paper (4.27%). Around two-thirds of the women (68.0%) performed special baths and 36.9% followed socio-cultural taboos during menstruation. The bivariate analyses revealed that higher menstruation knowledge was associated with higher use of disposable sanitary napkins (low knowledge: 18.9%, high knowledge: 38.5%; p<0.01). Conclusions: The findings suggest that it is imperative for Bihari women to have adequate and appropriate menstruation knowledge so that they can maintain good menstrual hygiene practices. The findings highlight challenges experienced by the refugee women in maintaining MHM and can be used to improve women’s reproductive health and well-being and reduce the risk of reproductive tract infections (RTI) among socially disadvantaged women

Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimisation stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimisation outcome

A hybrid method for analyzing the situation based on cumulative fully vaccinated and confirmed cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters

An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach

SARS-CoV-2 is a multi-organ disease characterized by a wide range of symptoms, which also causes severe acute respiratory syndrome. When it initially began, it rapidly spread from its origin to adjacent nations, infecting millions of people around the globe. In order to take appropriate preventative and precautionary actions, it is necessary to anticipate positive Covid19 instances in order to better comprehend future risk. Therefore, it is vital to build mathematical models that are resilient and have as few prediction mistakes as feasible. This research recommends an optimization based Least Square Support Vector Machines (LSSVM) for forecasting Covid19 confirmed cases along with the daily total vaccination frequency. In this work, a novel hybrid Barnacle Mating Optimizer (BMO) via Gauss Distribution is combined with Least Squares Support Vector Machines algorithm for time series forecasting. The data source consists of the daily occurrences of cases and frequency of total vaccination since 24 February,2021 to 27 July,2022 in Malaysia. LSSVM will thereafter conduct the prediction job with the optimized hyper-parameter values using BMO via gauss distribution. This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters

A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics

Isolation and characterization of Staphylococcus aureus from raw cow milk in Bangladesh

The study was intended for identification and characterization of Staphylococcus aureus isolated from raw cow milk. A total of 47 milk samples were collected from Sheshmore, Shutiakhali and Bangladesh Agricultural University Dairy Farm, Mymensingh. Using bacteriological, biochemical and PCR-based identification schemes, 12 (25.53%) isolates were confirmed as S. aureus. All the isolates showed ?-hemolysis on 5% sheep blood agar. S. aureus specific nuc gene (target size 279-bp) was amplified in the cases of all isolates. The isolates were found as resistant to Penicillin (100%), Erythromycin (75%) and Amoxicillin (100%). On the other hand, the isolates were sensitive to Ciprofloxacin (83.33%), Oxacillin (100%), Cloxacillin (100%) and Neomycin (100%). The isolated S. aureus showed increased resistance to broad spectrum antibiotic (e.g., Ciprofloxacin). As many people have a tendency to drink raw milk and raw milk products, there is high risk of S. aureus infection in human

Antinociceptive Activity of Methanol Extract of Areca catechu L. (Arecaceae) Stems and Leaves in Mice

ABSTRACT The antinociceptive effect of crude methanol extracts of stems and leaves of Areca catechu L. (Arecaceae) was evaluated in acetic acid-induced gastric pain writhing model in Swiss albino mice. The methanol extract of Areca catechu stems dose-dependently reduced the number of writhings (constrictions) in mice, when tested at doses of 50, 100, 200, and 400 mg extract administered per kg body weight. Significant reductions in the number of writhings were noted with all administered doses. The percent inhibitions of acetic acid-induced writhings with the four different doses were, respectively, 30.8, 36.6, 40.9 and 59.6. The standard antinociceptive drug, aspirin, when administered at doses of 200 and 400 mg per kg body weight reduced writhings by 42.3 and 55.8%, respectively. A significant dose-dependent inhibition of writhings was also observed with crude methanol extract of Areca catechu leaves, where the extract at doses of 50, 100, 200 and 400 mg per kg body weight significantly inhibited writhings by 55.8, 57.7, 86.5 and 88.5%, respectively. Dose for dose, the leaf extract demonstrated higher antinociceptive activity than the stem extract. At even the lowest dose of 50 mg extract per kg body weight, the antinociceptive activity of leaf extract was comparable to that of 400 mg aspirin per kg body weight. The results suggest that both stem and leaf extract possess good antinociceptive activity, which merits further scientific studies as to isolation of responsible phytochemical component(s)