Assessment of the Utilization of HIV Interventions by Sex Workers in Selected Brothels in Bangladesh: An Exploratory Study

In this qualitative study of brothel-based Female Sex Workers (FSWs), the authors explored factors that influence safe sex practices of FSWs within an integrated HIV intervention. Qualitative methods, including focus group discussions (FGDs), in-depth interviews and key informant interviews were applied in four brothels in Bangladesh. Young and elderly FSWs, Sordarnis (Madams who own young FSWs and who may be either active or inactive sex workers themselves), program managers and providers were the participants for this study. Findings showed that condom use was high but not consistent among bonded FSWs (those who are under the control of a Sordarni) who have regular clients. The bonded FSWs reported being maltreated by the Sordarnis for refusing to have sex without a condom, and access to health services was hindered by Sordarnis. Implications of the study are that integrated HIV intervention should provide more encouragement to relevant stakeholders to promote mutual support towards safe sex practices for the FSWs

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification

Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machine learning algorithms try to maximize the accuracy classification by correctly identifying majority class samples while ignoring the minority class. However, the concept of the minority class instances usually represents a higher interest than the majority class. Recently, several cost sensitive methods, ensemble models and sampling techniques have been used in literature in order to classify imbalance datasets. In this paper, we propose MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost mixes two different weak learners with boosting to improve the performance on imbalanced datasets. MEBoost is an alternative to the existing techniques such as SMOTEBoost, RUSBoost, Adaboost, etc. The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost. Experimental results show significant improvement over the other methods and it can be concluded that MEBoost is an effective and promising algorithm to deal with imbalance datasets. The python version of the code is available here: https://github.com/farshidrayhanuiu/Comment: SKIMA-201

Costs of maternal health-related complications in Bangladesh.

This paper assesses both out-of-pocket payments for healthcare and losses of productivity over six months postpartum among women who gave birth in Matlab, Bangladesh. The hypothesis of the study objective is that obstetric morbidity leads women to seek care at which time out-of-pocket expenditure is incurred. Second, a woman may also take time out from employment or from doing her household chores. This loss of resources places a financial burden on the household that may lead to reduced consumption of usual but less important goods and use of other services depending on the extent to which a household copes up by using savings, taking loans, and selling assets. Women were divided into three groups based on their morbidity patterns: (a) women with a severe obstetric complication (n=92); (b) women with a less-severe obstetric complication (n=127); and (c) women with a normal delivery (n=483). Data were collected from households of these women at two time-points--at six weeks and six months after delivery. The results showed that maternal morbidity led to a considerable loss of resources up to six weeks postpartum, with the greatest financial burden of cost of healthcare among the poorest households. However, families coped up with loss of resources by taking loans and selling assets, and by the end of six months postpartum, the households had paid back more than 40% of the loans

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Geographical Concentration of Rural Poverty in Bangladesh

This paper was presented at the dialogue on Mapping Poverty for Rural Bangladesh: Implications for Pro-poor Development. The dialogue was organised as part of CPD's ongoing agricultural policy research and advocacy activities with IRRI under the PETRRA project. The study reported geographical concentration of rural poverty in Bangladesh for 425 upazilas in 2000-01. The study measured and mapped incidence of poverty (using Headcount Index), intensity of poverty (using Poverty Gap Index) and severity of poverty (using Squared Poverty Gap Index). It has analyzed factors contributing to the spatial concentration of poverty. It is hoped that the findings of the study would be helpful in identifying target areas and priorities for agricultural R&D interventions and poverty reduction programmes.Poverty, Rural Poverty, Bangladesh

Oxidizability assay of unfractionated plasma of patients’ with different plasma profile: a methodological study

BACKGROUND: Present study describe the in vitro model of plasma oxidation of patients with different lipid profile, that can be correlated to their invivo plasma oxidizability in order to find the arterial diseases prone patient groups. METHOD: The method applied here to measure the invitro plasma oxidizability, accounts a convenient way that can be well suited in any clinical laboratory settings. Un-fractionated plasma was exposed to CuSO4 (5.0 mmol/L), a pro-oxidant, and low frequency ultrasonic wave to induce oxidation, and finally oxidizability was calculated by TBARS and Conjugated Diene methods. RESULT: In our study, plasma LDL greater than 150 mg/dL possess 1.75 times more risk to undergo oxidation (CI, 0.7774 to 3.94; p = 0.071) than the low LDL plasma, percent of oxidation increased from 38.3% to 67.1% for the LDL level upto 150 mg/dL and high. Lag phase, which is considered as the plasma antioxidative protection, was also influenced by the higher LDL concentration. The mean lag time was 65.27 ± 20.02 (p = 0.02 compared to healthy), where as for 94.71 ± 35.11 min for the normolipidemic subject. The plasma oxidizability was also changed drastically for total cholesterol level, oxidative susceptibility shown 35% and 55.02% for 200 mg/dL and high respectively, however it didn’t appear as risk factor. Patient samples were also stratified according to their age, gender, and blood glucose level. Older persons (≥40 years) were 1.096 times (95% CL, 0.5607 to 2.141, p = 0.396) than younger (≤39 years age), males are 1.071 (95% CI, 0.5072- 2.264) times than the females, and diabetic patients are 1.091 (CI, 0.6153 to 1.934, p = 0.391) times in more risk than the non-diabetic counterpart. CONCLUSION: This method addressing its easy applicability in biomedical research. And by this we were able to show that patients with high LDL (≥150 mg/dL) are in alarming condition besides diabetic and elderly (≥40 years age) males are considered to be susceptible and more prone to develop vascular diseases

STUDY OF PROBIOTICS ON THE SEED PRODUCTION OF BLACK TIGER SHRIMP Penaeus monodon

Currently, antibiotics are widely used in shrimp hatcheries to control bacterial infections. Appearance of antibiotic resistant pathogens and restriction on the use of antibiotics have led to the development of alternatives to antibiotics in hatchery systems. In light of this, an attempt was undertaken to investigate the effects of probiotics on the larval rearing of Penaeus monodon, compared with control tanks (without probiotics). The results showed that several issues significantly improved with administering probiotics in the experimental tanks compared with the tanks without probiotics. For example, the concentration of ammonia was estimated to be 1.25 mg/L that was less than half of what was measured in the control tanks. The size variation was observed more in the control tanks than in the experimental tanks. Moreover, the muscle gut ratio of PL15 was about 85 to 92% in the probiotic treated tank and 70 to 80% in the control tank during the eight cycles of production. The fouling organisms were more in the control tank compared to the experimental tanks. The average length of PL15 was maximum when reared in the experimental tanks compared to the control tanks. The final survival rate of PL15 from the control and experimental tank was 35 and 52%, respectively. The present investigation indicated that probiotics played an important role in the growth, survival and health status of P. monodon larvae