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Background: Anemia is a major public health problem with raising prevalence worldwide, including Bangladesh. Objectives: To identify the risk factors of anemia among women in Bangladesh and its prediction using Machine Learning (ML) based techniques. Methods: The anemia dataset, comprising of 3,020 respondents, was extracted from the Bangladesh Demographic and Health Survey (BDHS). Two feature selection techniques as Logistic Regression (LR) and Random Forest (RF), have been utilized to determine the risk factors of anemia. Additionally, eight ML-based techniques, namely LR, Linear Discriminant Analysis (LDA), K-Nearest Neighborhood (KNN), Support Vector Machine (SVM), Quadratic Discriminant Analysis (QDA), Neural Network (NN), Classification And Regression Tree (CART), and RF have also been utilized to predict anemia disease among women in Bangladesh. Classification accuracy and Area Under the Curve (AUC) are used to evaluate the performances of these classifiers. Results: LR and RF-based feature selection results indicate that out of 15 factors, 13 for LR and 14 factors for RF appear to be significant risk factors for anemia among women. All predictive models provide the highest classification accuracy and AUC of 74.10-81.29% and 0.744-0.819 under RF features. However, the combination of RF-based feature selection along with RF-based classifier gives the highest classification accuracy (81.29%) and AUC (0.819). Conclusion: Out of the eight predictive models, RF-RF based combination model shows the best performance for the prediction of anemia. This study suggests policymakers to make appropriate decisions to control the anemia using RF-RF combination to save time and reduce the cost for Bangladeshi women.

Hypertension is one of the major risk factors for cardiovascular disease and the leading cause of death worldwide. Hypertension was defined as systolic or diastolic blood pressure according to Joint National Committee 7 (JNC7) and 2017 American College of Cardiology/American Hypertension Association (ACC/AHA) rules. The aims of this study was to determine the difference in hypertension prevalence and its risk factors using ACC/AHA rule, and compared its result with JNC7 rule. Data were collected using two-stage stratified cluster sample of households from 2017/18 Albanian Demographic and Health Survey. Data were analyzed using the descriptive and multivariate logistic regression model. Among 15,003 respondents aged 15–49 years, the overall prevalence of hypertension was 63.48% and 16.24%, as per ACC/AHA and JNC7 rules, respectively with an absolute increase of prevalence by 47.3% (CI 46.4–48.2%). According to the ACC/AHA rule, in most of the categories of the respondents, the prevalence of hypertension was 40% higher compared with the JNC7 rule. In multivariate analysis, age, education, richest respondents, number of living children (≥ 3), health insurance and gender had significant (p

Antioxidant properties of plants can help thwart a plethora of conditions including cancer, bronchitis, asthma, ulcers, coronary heart diseases, scabies, insomnia, senile debility, diabetes, dysentery, etc. In this study, Andrographis paniculata (Family: Acanthaceae) was investigated for antioxidant activity. 1, 1-Diphenyl-2-picryl hydrazyl (DPPH), reducing power assay, total antioxidant capacity, 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), nitric oxide test, total phenolic and flavonoid from methanol, acetone, and water extracts were investigated employing various established in-vitro system. The results divulged that methanol extract had significant activity in quenching of DPPH, reducing power, ABTS, nitric oxide, antioxidant capacity when compared to other extracts. All extracts were correlated with two specific standard antioxidants ascorbic acid and butylated hydroxyanisole. The methanol extract at 500 µg/ml showed the highest scavenging activity of DPPH (91.78 ± 0.02%), ABTS (99.76 ± 0.05%). Total antioxidant capacity was 83.35 ± 0.13 AAE/mg. Reducing the power of methanol extract was significant compared to all other fractions. Nitric oxide scavenging activity was maximum in acetone extract (80.73 ± 0.05%). The quantitative estimation of the extract revealed a considerable amount of flavonoids and conversely, the amount of phenolic compounds was poor. The flavonoid content (592.34 ± 0.13 mg QE/g of dry extract) was maximum in methanol extract but phenolic content of methanol and acetone extract was very similar. This study strongly suggests that methanol extract exhibits more potent antioxidant activity than other extracts of acetone and water. Key words: Andrographis paniculata, antioxidant potential, methanol, acetone, water, DPPH, ABTS

Acute illness iscaused by bacteria, viruses, and parasites.Co-infection is the simultaneous infectionof a host by multiple pathogens resulting in a delay in diagnosis and bad prognosis.Currentlycorona virus infection is a pandemic and global health concern. Coronavirus infection in children withconcomitant other infections is rarely reported in our country. This study was performed to find out the corona virus infectionassociated with recent illnesses to minimize morbidity and mortality. Study was done at Dr. M R Khan Shishu Hospital & ICH from April 2020 to June 2020. Patient with fever, rash, vomiting, diarrhea, cough, respiratory distress, dysuria, convulsion, jaundice and other symptoms; admitted in hospital were taken for this study. Diagnosis was made by clinical symptoms plus investigations accordingly and appropriate treatment was given for recent illnesses. During follow up these cases; there was persistence of symptoms and some other new features appeared. These arouse our suspicion that there might be associated with corona virus infection in this pandemic. Thirty cases were taken co-infection with COVID19.Age was found 4 months to 8 years.Male female ratio was 1.5: 1.Typhoid fever with COVID-1912(40%), Bacterial meningitis with COVID-196(20%), UTI with COVID-19 3(10%), Paratyphoid fever with COVID192(6.7%), Typhus/ Rickettsial fever withCOVID-19 2(6.7%),HAV infection withCOVID-192(6.7%),HEV infection withCOVID-19 1 (3.3%), Septic arthritis with COVID-191 (3.3%), Atypical pneumonia with COVID191 (3.3%) were found. RT-PCR was positive forCOVID-1930(100%), blood culture was positive for Salmonella Typhi 4(13.3%), Widal test was reactive (high rising titer) 8(26.7%), Latex agglutination test was positive for bacterial meningitis 6(20%), urine culture was positive for E.coli 3(10%), blood culture was positive for Salmonella Para typhi A 2(6.7%), Weil-Felix reaction was found (high rising titer) 2(6.7%), Anti HAV IgM was positive 2(6.7%), Anti HEV IgM was positive 1(3.3%), Blood culturewas positive for Septic arthritis 1(3.3%), blood culture was positive for Mycoplasma Pneumoniae 1(3.3%). This study showed that COVID-19 in Bangladeshi children was found associated with co-infections. So, coinfection with COVID-19 patients should be closely monitored and managed accordingly.

The timing of a woman's first birth is significant because motherhood involves a substantial commitment of time and resources. Early age at first birth is a factor that significantly influences the rapid population growth in Bangladesh. In addition, early age at first birth has a negative effect on occupational achievements, marital stability, and maternal and neonatal health. The aim of this study was to determine the factors influencing early age at first birth. Data were collected from the Bangladesh Demographic and Health Survey (BDHS) 2014. The BDHS 2014 data were obtained from 17,863 ever-married women using two-stage stratified sampling. Chi-square test and two-level multiple binary logistic regression models were performed to analyze the data. A total of 15,842 ever-married women with mean age of 18.22 ± 0.389 years were included in this study. Muslim women (AOR = 1.32, 95% CI: 1.11–1.56, p

It was intended to investigate the biological activities of different solvent extracts of the fruiting bodies of Calocybe indica (P & C). The bioactive compounds were extracted using acetone, methanol, and hot water. The antioxidant activities of three different extracts at 0.5-20.0 mg/ml of C. indica on β-carotene-linoleic acid ranged from 65.83 - 92.56, 62.79 - 93.06 and 61.42 - 92.12%, respectively. The highest (2.825) reducing power inhibition was recorded in methanolic extract, while the lowest (2.332) was in hot water at the concentration of 8 mg/ml. The free radicle scavenging activity was more pronounced in methanolic extract. Chelating effect of C. indica was significantly strong as compared to positive control. The xanthine oxidase and tyrosinase inhibitory activity of the acetone, methanol, and hot water extracts of C. indica increased with increasing concentration. Thirteen phenolic compounds i.e. biochanin-A, caffeic acid, chlorogenic acid, ferulic acid, formononetin, gallic acid, hesperetin, homogentisic acid, naringenin, naringin, protocatechuic acid, resveratrol and vanillin were detected from acetonitrile-hydrochloric acid extract. The result indicated that the maximum and minimum concentrations were recorded for gallic acid (29 μg/g) and formononetin (10 μg/g). Thus, it could be suggested that C. indica has the potential to be used as antioxidant and tyrosinase protection system of the human body against oxidative damage and other complications.

Breast Cancer (BC) is a cancerous growth that is a result of uncontrolled cell division in the mammary tissues, usually in the ducts and in the lobules. BC is the most dominant fast-growing cancer and one of the leading cause of cancer mortality in women. BC incidents are increasing swiftly every year around the world especially in developing countries due to grown life expectancy and assumption of western culture. The conventional process of detecting BC involves a clinical expert who observed the medical images of affected breast tissues and looks for structural changes, irregularities in cell forms, ordination of cells in the tissue and determining the stage of the cancer. As conventional interpretation is often time consuming, expensive and error prone; computer-aided detection (CAD) technique is used as an alternative to provide a more accurate, automatic, fast and reproducible procedure to detect BC. This research presents a fully automatic process of BC detection. Two well know filter such as Gaussian Blur (GB) and Detail Enhanced (DE) filter has been used here for the preprocessing purpose. Convolutional Neural Network (CNN) classifier has been used here for classification. The proposed model is performed on an openly accessible dataset named Breast Histopathology Image dataset and the outcome exhibits the sharpness of our proposed model. The obtained accuracy is 87.49%, 88.46% and 88.10% in Case-I, Case-II and Case-III, respectively.

Aims This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) prediction of DN patients using ML-based techniques. Methods The combination of PCA and ML-based techniques has been implemented to select the best features at different PCA cutoff values and choose the optimal PCA cutoff in which ML-based techniques give the highest accuracy. These optimum features are fed into six ML-based techniques: linear discriminant analysis, support vector machine (SVM), logistic regression, K-nearest neighborhood, naive Bayes, and artificial neural network. The leave-one-out cross-validation protocol is executed and compared ML-based techniques performance using accuracy and area under the curve (AUC). Results The data utilized in this work consists of 133 respondents having 73 DN patients with an average age of 69.6 ± 10.2 years and 54.2% of DN patients are female. Our findings illustrate that PCA combined with SVM-RBF classifier yields 88.7% accuracy and 0.91 AUC at 0.96 PCA cutoff. Conclusions This study also suggests that PCA combined with SVM-RBF classifier may correctly classify DN patients with the highest accuracy when compared to the models published in the existing research. Prospective studies are warranted to further validate the applicability of our model in clinical settings.

Background and aims Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based classifiers for automated detection and classification of diabetes. Methods The diabetes dataset have taken from Bangladesh demographic and health survey, 2011 data having 1569 respondents are 127 diabetes. Two statistical tests as independent t for continuous and chi-square for categorical variables are used to determine the risk factors of diabetes. Six ML-based classifiers as support vector machine, random forest, linear discriminant analysis, logistic regression, k-nearest neighborhood, bagged classification and regression tree (Bagged CART) have been adopted to predict and classify of diabetes. Results Our findings show that 11 factors out of 15 factors are significantly associated with diabetes. Bagged CART provides the highest accuracy and area under the curve of 94.3% and 0.600. Conclusions Bagged CART anticipates a very supportive computational resource for classification of diabetes and it would be very helpful to the doctors for making a decision to control diabetes disease in Bangladesh.

Child undernutrition is a major public health problem throughout the world, particularly in developing countries. The main objective of this study was to identify the risk factors for acute undernutrition among under-5 children in Bangladesh. Data were taken from the nationally representative Bangladesh Demographic Health and Survey conducted in 2014. The study sample comprised 7131 under-5 children. Of these, 4.6% were found to be severely wasted (Z-score < −3.0), 11.1% moderately wasted (−3.0≤Z-score < −2.0) and 84.3% adequately nourished (Z-score ≥−2.0). Chi-squared analysis was used to investigate the association between child nutrition status and selected covariates. Multinomial logistic regression was applied to identify the risk factors for acute undernutrition. The selected factors division, place of residence, sex of child, place of delivery, child age, respiratory illness, size at birth, measles vaccination, fever, diarrhoea, maternal BMI, maternal education, paternal occupation, wealth index and household toilet facilities were found to be highly significant (p < 0.05) in the analysis. Multinomial regression analysis revealed that residence in Barisal and Chittagong divisions, a smaller than average size at birth and low maternal BMI (≤18.50 kg/m2) were significant determinants of both moderate and severe acute undernutrition among under-5 children in Bangladesh.

Spontaneous bacterial peritonitis (SBP)is an acute bacterial infection of ascitic fluid and one of the commonest complication of patients with cirrhosis presented with ascitis. Routine analysis of ascetic fluid particularly for polymorphonuclear leukocyte is an important tool for detecting spontaneous bacterial peritonitis including clinically unsuspected and asymptomatic patients also. Thus it helps in reducing mortality and morbidity by early and effective detection of spontaneous bacterial peritonitis. Objective: This study was carried out to evaluate the role of ascitic fluid analysis in early detection of spontaneous bacterial peritonitis and to document the proportion of spontaneous bacterial peritonitis present asymptomatically. Study design: This was a cross sectional observational study prospective in nature on 50 cases of diagnosed chronic liver disease patients in indoor of department of medicine of Sir Salimullah medical college Hospital, Dhaka from 02/01/13 to 01/07/13, for a period of 6 months. Result: The study showed that age frequency 5(10%) were from 21-30 years, 15(30%) were from 31- 40 years, 15(30%) were from 40 -50years and 15(30%) were from >50 years of age. Out of 50 patients, 38(76%) were male and 12(24%) were female patients. The etiology of liver cirrhosis was hepatitis B virus in 22(44%), hepatitis C virus in 4(8%), alcohol in 1 (2%) and others in 23(46%) patients. Among 50 patients, 16(32%) were spontaneous bacterial peritonitis and 34(68%) were non spontaneous bacterial peritonitis. The symptoms of spontaneous bacterial peritonitis were upper GI bleeding in 8 patients (50%), fever in 12 patients (75%), abdominal pain in 10 patients (62.5%), abdominal tenderness in 7 patients (43.75%), hepatic encephalopathy in 9 patients (56.25%), jaundice in 10 patients (62.50%), splenomegaly in 16 patients (100%), ascites in 16 patients (100%) and 5 (10%) patients were asymptomatic spontaneous bacterial peritonitis. According to Child Pugh’s, 2(12.5%) patients were at stage-A, 5 (31.25%) patients were at stage-B and 9 (56.25%) patients were at stage- C. Ascitic fluid characteristics (mean +SD) of all patients were, Total cell counts/mm3 (571+499.9), Neutrophil counts/mm3 (144.8+445.1), Sugar (112+38.19) mg/dl, Protein (1.85+1.09). Among SBP patients, 7 (43.75%) have PMN cell counts 250-750/mm3, 6 (37.50%) have 750-1750/mm3 and 3 (18.75%) have >1750/mm3. Conclusion: Spontaneous bacterial peritonitis is a common complication of decompensated cirrhotic patients. The classical signs of SBP (e.g. abdominal tenderness or fever) may not be present. We observed the trend towards more frequent occurrence of the infection in patients suffered from severe liver disease (e.g. Child Pugh’s –C) and the role of ascitic fluid analysis is statically significant. Bangladesh J Medicine Jan 2019; 30(1) : 9-18

This research study utilized a light-sensitive drug, nifedipine (NFD), to understand the impact of processing parameters, and formulation composition on drug degradation, crystallinity, and quality attributes (dimensions, hardness, disintegration time) of selective laser sintering (SLS) based 3D printed dosage forms. Selective laser sintering (SLS), in most cases, uses an ultraviolet laser source, and drugs tend to absorb radiation at varying intensities around this wavelength (455 nm). This phenomenon may lead to chemical degradation, and solid-state transformation, which was assessed for nifedipine in formulations with varying amounts of vinyl pyrrolidone-vinyl acetate copolymer (Kollidon® VA 64) and potassium aluminum silicate-based pearlescent pigment (Candurin®), processed under different SLS conditions in the presented work. After preliminary screening Candurin®, surface temperature (ST), and laser speed (LS) were identified as the significant independent variables. Further, using the identified independent variables a 17-run, randomized, Box-Behnken design was developed to understand the correlation trends and quantify the impact on degradation (%), crystallinity, quality attributes (dimensions, hardness, disintegration time) employing qualitative and quantitative analytical tools. The design of experiments (DoE) and statistical analysis observed that LS and Candurin® (%wt) had a strong negative correlation on drug degradation, hardness, and weight, whereas ST had a strong positive correlation with, drug degradation, amorphous conversion, and hardness of the 3D printed dosage form. From this study, it can be concluded that formulation and processing parameters have a critical impact on stability and performance; hence these parameters should be evaluated and optimized before exposing light-sensitive drugs to the SLS processes.

Background and aims Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of hypertension among adults in Bangladesh using machine learning (ML) algorithms. Materials and methods The hypertension data was derived from Bangladesh demographic and health survey, 2017–18, which included 6965 people aged 35 and above. Two most promising risk factor identification methods, namely least absolute shrinkage operator (LASSO) and support vector machine recursive feature elimination (SVMRFE) are implemented to detect the critical risk factors of hypertension. Additionally, four well-known ML algorithms as artificial neural network, decision tree, random forest, and gradient boosting (GB) have been used to predict hypertension. Performance scores of these algorithms were evaluated by accuracy, precision, recall, F-measure, and area under the curve (AUC). Results The results clarify that age, BMI, wealth index, working status, and marital status for LASSO and age, BMI, marital status, diabetes and region for SVMRFE appear to be the top-most five significant risk factors for hypertension. Our findings reveal that the combination of SVMRFE-GB gives the maximum accuracy (66.98%), recall (97.92%), F-measure (78.99%), and AUC (0.669) compared to others. Conclusion GB-based algorithm confirms the best performer for prediction of hypertension, at an early stage in Bangladesh. Therefore, this study highly suggests that the policymakers make proper judgments for controlling hypertension using SVMRFE-GB-based combination to save time and reduce cost for Bangladeshi adults.

This study demonstrated the first case of combining novel continuous granulation with powder-based pharmaceutical 3-dimensional (3D) printing processes to enhance the dissolution rate and physical properties of a poorly water-soluble drug. Powder bed fusion (PBF) and binder jetting 3D printing processes have gained much attention in pharmaceutical dosage form manufacturing in recent times. Although powder bed-based 3D printing platforms have been known to face printing and uniformity problems due to the inherent poor flow properties of the pharmaceutical physical mixtures (feedstock). Moreover, techniques such as binder jetting currently do not provide any solubility benefits to active pharmaceutical ingredients (APIs) with poor aqueous solubility (>40% of marketed drugs). For this study, a hot-melt extrusion-based versatile granulation process equipped with UV-Vis process analytical technology (PAT) tools for the in-line monitoring of critical quality attributes (i.e., solid-state) of indomethacin was developed. The collected granules with enhanced flow properties were mixed with vinylpyrrolidone-vinyl acetate copolymer and a conductive excipient for efficient sintering. These mixtures were further characterized for their bulk properties observing an excellent flow and later subjected to a PBF-3D printing process. The physical mixtures, processed granules, and printed tablets were characterized using conventional as well as advanced solid-state characterization. These characterizations revealed the amorphous nature of the drug in the processed granules and printed tablets. Further, the in vitro release testing of the tablets with produced granules as a reference standard depicted a notable solubility advantage (100% drug released in 5 minutes at >pH 6.8) over the pure drug and the physical mixture. Our developed system known as DosePlus combines innovative continuous granulation and PBF-3D printing process which can potentially improve the physical properties of the bulk drug and formulations in comparison to when used in isolation. This process can further find application in continuous manufacturing of granules and additive manufacturing of pharmaceuticals to produce dosage forms with excellent uniformity and solubility advantage.Abstract Figure

Selective laser sintering (SLS) 3D printing (3DP) offers novel opportunities for manufacturing various pharmaceutical dosage forms with a wide array of drug delivery systems. The purpose of this research was to introduce ferromagnetic nanoparticles, for the first time, as a multi-functional magnetic and heat conductive ingredient for 3DP tablet formulations, and further to analyze its effect on the drug release of the SLS printed tablets under a specially designed magnetic field. Optimization of tablet quality was performed by adjusting SLS printing parameters. The independent factors studied were laser scanning speed (2, 50, 100, and 200 mm/s), hatching space (13, 25, 50, 100, 300, and 2000 µm), and temperature. The responses measured were tablet weight, hardness, disintegration time (DT), and dissolution kinetics studied within the first hour. The content uniformity, chemical interaction, drug distribution, and surface morphology were tested for characterizing the printed dosage forms. It has been observed, for the drug formulations with carbonyl iron, due to its inherent heat conductivity, that sintering tablets required low energy input compared to that of other batches that contained no magnetic particles, to make the tablets of the same quality attributes. Also, under the magnetic field, printed tablets with carbonyl iron released 25% more drug as compared to those without. Therefore, we report for the first time the use of magnetic nanoparticles as a novel conductive excipient to sinter the particles in an SLS 3D printing process of pharmaceutical dosage forms and hence this finding opens up numerous opportunities for magnetically triggerable drug delivery systems.

This study demonstrated the first case of combining a novel continuous granulation technique with powder-bed fusion-based selective laser sintering (SLS) process to enhance the dissolution rate and physical properties of a poorly water-soluble drug. Selective laser sintering and binder jetting 3D printing processes have gained much attention in pharmaceutical dosage form manufacturing in recent times. These powder bed-based 3D printing platforms have been known to face printing and uniformity problems due to the inherent poor flow properties of the pharmaceutical physical mixtures. To address this issue a hot-melt extrusion-based versatile granulation process equipped with a process analytical technology (PAT) tool for the in-line monitoring of critical quality attributes (i.e., solid-state) of indomethacin was developed. The collected granules with enhanced flow properties were mixed with Kollidon® VA64 and a conductive excipient for efficient sintering. These mixtures were further characterized for their bulk properties observing an excellent flow and later subjected to an SLS-3D printing process. The physical mixtures, processed granules, and printed tablets were characterized using conventional as well as advanced solid-state characterizations. These characterizations revealed the amorphous nature of the drug in the processed granules and printed tablets. Further, the in vitro release testing of the tablets with produced granules as a reference standard depicted a notable dissolution advantage (100% drug released in 5 min at >pH 6.8) over the pure drug and the physical mixture. Our developed system known as DosePlus combines innovative continuous granulation and SLS-3D printing process which can potentially improve the physical properties of the bulk drug and formulations in comparison to when used in isolation. This process can further find application in continuous manufacturing of granules and additive manufacturing of pharmaceuticals to produce dosage forms with excellent uniformity and solubility advantage.

[This corrects the article DOI: 10.1371/journal.pone.0245923.].

The aim of this study was to fabricate polymeric microneedles, loaded with macrolides (erythromycin, azithromycin), using hyaluronic acid and polyvinyl pyrollidone. These microneedles were fabricated using a vacuum micromolding technique. The integrity of the microneedle patches was studied by recording their morphologic features, folding endurance, swelling and micro-piercing. Physicochemical characteristics were studied by differential scanning calorimetry, thermogravimetric analysis and fourier transform infrared spectroscopy. In-vitro drug release, antibiofilm and effect of microneedle patch on wound healing were also studied to confirm the efficacy of the formulations. Formulated patches displayed acceptable folding endurance (>100) and uniform distribution of microneedles (10 × 10) that can penetrate parafilm. Differential scanning calorimetry results depict a decrease in the crystallinity of macrolides following their incorporation in to a polymer matrix. Percentage release of azithromycin and erythromycin from the polymeric patch formulations (over 30 min) was 90% and 63% respectively. Broadly, the zone of bacterial growth inhibition follows the same order for Staphylococcus aureus, Escherichia coli and Salmonella enterica. After 5 days of treatment with azithromycin patches, the wound healing was complete and skin structure (e.g. hair follicles and dermis) was regenerated. It was concluded that azithromycin loaded microneedle patches can be used to treat biofilms in the infected wounds.

Magnetic resonance imaging is one of the best methods for detecting brain tumors. But the images captured by this method may contain different kinds of noises. So it is very essential to remove the noises for properly identifying the specific brain tumor. A filter is usually used to remove the noises. This paper illustrates different image filtering methods, such as low pass filter, high pass filter, and median filter, to improve the image quality by removing the noises from magnetic resonance images to identify the brain tumor. The MSE, RMSE, and the PSNR is used for understanding the quality of the filtered images. A graphical user interface is developed in MATLAB to implement all the filtering process and performance analysis for magnetic resonance images used to detect brain tumor.

A brain tumor is one kind of illness or clutter of the human brain that emerges within the brain of humans for the unusual development of cells that have increment quickly in numbers in a liberal way. Medical image handling is the foremost vital and inventive range, significantly magnetic resonance imaging modalities. The complete work methods presented in this paper are pre-processing, segmentation, feature extraction as well as classification, which are applied to distinguish the brain tumor from magnetic resonance images. By using a support vector machine classifier, we identify at which specific region in the human brain is growing or hold a tumor, and by observing their features we find out which one is benign and which one is malignant. The rates of accuracy, sensitivity, and specificity obtained in this work are 98.30%, 98%, and 100%, respectively.

Epileptic seizure is the abnormal electrical activity of EEG signal. As EEG signal is a non-stationary signal, DWT is a powerful tool to interpret such kind of signals both in time and frequency domains. In this study, DWT is performed using the number of mother wavelets to measure their classification performance. Characteristic features extraction from the sub-bands, as a consequence of DWT, plays the key role to classify the signal accurately. A new feature set, consists of four nonlinear statistical features obtained from each subbands, has been fed to the input of ANN. To evaluate the performance of our study a famous publicly available dataset is used. This study is conducted on four classification problems mixture of normal and seizure segments. 100%, 99.33%, 97.33% and 98.4% classification accuracy are achieved for Class 1, Class 2, Class 3 and Class 4 classification problems, respectively.

Activated Carbon (AC) is an adsorbent having high surface area which makes the process of removing heavy metals from wastewater (such as landfill leachate) very effective. This study explored the utilization of three methods of modification of AC produced from coconut shell by treating it with nitric acid (HNO3), potassium permanganate (KMnO4) and heating at 600°C to improve the adsorption capacity. The AC can remove multi-pollutants in the filtration process which was used to treat landfill leachate. The water quality parameters such as pH, TSS, Ammonia-Nitrogen and a few heavy metals were considered in the present study. Results showed that the removal of these parameters was proportional with the increase of contact time and the bed depth of AC. The isotherm analysis of the adsorption of modified AC showed the best Removal Efficiency (RE) can be achieved when AC treated with KMnO4 for NH3-N, zinc, TSS and sulphide. The morphology of the AC was studied through Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX) pattern analysis and Fourier Transform Infrared (FTIR) analysis. It was found that various types of oxygen functional groups were introduced onto the surface of coconut shell derived AC through oxidation using HNO3. FTIR was used to characterize the surface oxygen functional groups. The surface functional groups such as N-H and C-H stretching played a significant role in heavy metals adsorption. Hence, it can be concluded that the hybrid technique by using electrolysis process with AC adsorption be an effective way to remove the suspended solids and heavy metals from landfill leachate and thus able to reduce environmental pollution.

On the basis of their large specific surface areas, high adsorption and cation exchange capacities, swelling potential and low toxicity, natural smectite clays are attractive substrates for the gastric protection of neutral and cationic drugs. Theophylline is an amphoteric xanthine derivative that is widely used as a bronchodilator in the treatment of asthma and chronic obstructive pulmonary disease. This study considers the in vitro uptake and release characteristics of the binary theophylline-smectite system.\ud The cationic form of theophylline was readily ion exchanged into smectite clay at pH 1.2 with a maximum uptake of 67 ± 2 mg g−1. Characterisation of the drug-clay hybrid system by powder X-ray diffraction analysis, Fourier transform infrared spectroscopy, differential scanning calorimetry and scanning electron microscopy confirmed that the theophylline had been\ud exclusively intercalated into the clay system in an amorphous form. The drug remained bound within the clay under simulated gastric conditions at pH 1.2; and the prolonged release of approximately 40% of the drug was observed in simulated intestinal fluid at pH 6.8 and 7.4 within a 2-h timeframe. The incomplete reversibility of the intercalation process was attributed to chemisorption of the drug within the clay lattice. These findings indicate that smectite clay is a potentially suitable vehicle for the safe passage of theophylline into the duodenum. Protection from absorption in the stomach and subsequent prolonged release in the small intestine are advantageous in reducing fluctuations in serum concentration which may impact therapeutic effect and\ud toxicity.

Aims Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction. Methods This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy. Results The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight. Conclusion This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh’s U5 children.

A prescription - a written instruction of doctors to pharmacist to supply drugs in particular form to a patient and the directions to the patients regarding the use of medicines.This study was undertaken to observe the prescribing patterns of the practitioners in Bangladesh, 500 prescriptions were collected randomly from Dhaka city and analyzed using WHO/INRUD indicators. There were average 4.40drugs per prescription.Drugs were prescribed in generic name only in 0.30%. About 48% drugs were prescribed from the essential drug list, only prescriptions were complete. In respect to patient medication information, antibiotics were prescribed in 72% of the prescriptions; injections were prescribed in about 10% of the prescriptions.J Dhaka Medical College, Vol. 26, No.1, April, 2017, Page 48-51

The ongoing need to provide greater therapeutic efficacy and reduced side effects has accelerated the interest in modified-release oral dosage formulations. Orally-taken solid dosage forms continue to play a central role in the treatment of various medical conditions. Modified-release formulations are one of the most common systems for oral delivery due to improved compliance and reduction in the frequency of dosing. Based on the needs and challenges of modified-release formulations, a variety of novel modified-release platforms have been developed. This review aims to enhance the knowledge and understanding of properties of modified-release formulations and explains how these products release their active ingredients. The review also emphasises the types of modified-release dosage forms with reference to the commercial excipients and polymers currently used during the formulation development.

Arsenic is a major toxicant impairing in diverse metabolic alterations of the organisms and the mechanism of adaptive response is yet to be identified. In the present study, effects of different doses of arsenic in liver of Channa punctata on the regulation of metabolic activities were done. C. punctata, a variety of fresh water fish were exposed to 1, 10 and 1000 ?M concentration of Na2HAsO4 for 1 h. The amount of protein, in response to 1, 10 and 1000 ?M concentration of arsenic were augmented by 184.47% (2.84-folds), 202.82% (3.0- folds) and 317.49% (4.17-folds), respectively and was found to be higher for 1000 ?M dose. Cholesterol contents in liver were similarly exaggerated by 517.45% (6.17-folds), 308.13% (4.1-folds) and 286.41% (3.86-folds), respectively. However, the higher response was found for 1 ?M dose of Na2HAsO4. Similar stimulatory effects on triglyceride level were observed in response to arsenic. Na2HAsO4 causes 443.74% (5.43-folds), 533.11% (6.33-folds) and 548.48% (6.48-folds) enhanced triglyceride level in liver respectively and the effects were pronounced for 1000 ?M concentration. Our findings conclude that arsenic is involved in impairment of metabolic activities in liver of the species of fish and gives an impact to the environment for survival.Int. J. Agril. Res. Innov. & Tech. 6 (2): 87-94, December, 2016

Ibuprofen is a Biopharmaceutical classification system class II drug that exhibits poor dissolution rate in the gastrointestinal tract. The aim of the present study is to enhance the dissolution of ibuprofen in presence of glucosamine. To this end, different ratios of ibuprofen:glucosamine were dissolved in various organic solvents to obtain the solid dispersions of ibuprofen–glucosamine mixtures. The solid state analysis of the samples (differential scanning calorimetry, Fourier infrared spectroscopy and X-ray powder diffraction) and the morphology (scanning electron microscope) were investigated. Particle size analysis and in vitro dissolution studies showed that the type of solvent has a significant influence on dissolution rate. Ibuprofen–glucosamine solid dispersions obtained from acetone produced better dissolution compared to that of other organic solvents. The effect of water in binary mixtures of either acetone or ethanol was also explored and the results showed that when the ratio of acetone to water was 75:25, the highest dissolution was obtained. Solid state analysis ruled out any chemical interaction between the drug and carrier even in the presence of various organic solvents which indicates a good stability of the solid dispersions to enhance the dissolution rate of ibuprofen. It was also investigated via XRPD analysis that the ibuprofen retained its crystallinity without any adverse effect on the dissolution rates. In conclusion, the present study showed that a manipulation of the organic solvent:water ratio and a change in the type of organic solvent impacted on ibuprofen–glucosamine achieving very high dissolution rate without compromising its crystallinity and physical stability.

Phyllanthus emblica L. belonging to the family Euphorbiaceae is a common medicinal plant in Bangladesh. In order to evaluate the phytoconstituents and bioactivity of various parts of P. emblica, both the wild type (i.e., small) and cultivated (i.e., big) fruits were collected from the local market of Bangladesh and six ethanolic extracts were prepared from various parts of the collected fruits for this current study. A comparative analysis of the phytochemical compositions and various bioactivities especially antibacterial, antifungal, and antioxidant activity of the six ethanolic extracts were accomplished. The qualitative phytochemical analysis of plant extracts revealed the presence of large amounts of proteins, carbohydrates, phenolic compounds, glycosides, alkaloids, coumarins, flavonoids, tannins, saponins and resins etc. The maximum antioxidant activity was observed for wild type P. emblica seed extracts and the minimum activity was observed for cultivated flesh extracts. Moreover, among the samples the wild type P. emblica extracts have excellent amount of total phenol contents and the highest free radical scavenging activity. It is also found that P. emblica samples were active against both gram-positive (i.e., B. subtilis) and gram-negative (i.e., E. coli, and S. typhi) bacteria. The highest antibacterial activity was achieved in the wild type P. emblica seed extracts against both pathogenic (S. typhi) and non-pathogenic bacterial strains (E. coli, and B. subtilis). These samples showed almost no activity against Fusarium sp. fungus.

Hydrophilic matrix systems are currently some of the most widely used drug delivery systems for controlled-release oral dosage forms. Amongst a variety of polymers, polyethylene oxide (PEO) is considered an important material used in pharmaceutical formulations. As PEO is sensitive to thermal oxidation, it is susceptible to free radical oxidative attack. The aim of this study was to investigate the stability of PEO based formulations containing different model drugs with different water solubility, namely propranolol HCl, theophylline and zonisamide. Both polyox matrices 750 and 303 grade were used as model carriers for the manufacture of tablets stored at 40 °C. The results of the present study suggest that the drug release from the matrix was affected by the length of storage conditions, solubility of drugs and the molecular weight of the polymers. Generally, increased drug release rates were prevalent in soluble drug formulations (propranolol) when stored at the elevated temperature (40 °C). In contrast, it was not observed with semi soluble (theophylline) and poorly soluble (zonisamide) drugs especially when formulated with PEO 303 polymer. This indicates that the main parameters controlling the drug release from fresh polyox matrices are the solubility of the drug in the dissolution medium and the molecular weight of the polymer. DSC traces indicated that that there was a big difference in the enthalpy and melting points of fresh and aged PEO samples containing propranolol, whereas the melting point of the aged polyox samples containing theophylline and zonisamide was unaffected. Graphical abstractᅟ Electronic supplementary material The online version of this article (doi:10.1007/s13346-016-0344-5) contains supplementary material, which is available to authorized users.

Carbon dioxide (CO2) emitted from different sources into ambient air is a major contributor to air pollution. In addition, long-term exposure in humans contributes to symptoms such as tiredness, weakness, memory loss, confusion, nausea, and loss of appetite. The separation and storage of CO2 are important parts of utilizing low-carbon energy, which is needed to cope with climate change. Cost-effectiveness is an important factor for determining an optimal carbon separation strategy. Several review papers on CO2 capture and storage are available, but in most of these publications only the theoretical and operational aspects appear, while cost and techno-economic evaluations are missing. In this paper, different techniques for CO2 capture and separation are thoroughly reviewed with a focus on the associated economic costs. Presently, chemical and physical methods are widely used for CO2 separation; however, the hope is to switch to more environmentally friendly methods. This review reveals that, although biological separation processes are regarded as safe and sustainable, these methods are still time-consuming and involve high bioreactor management costs for. Another important finding is that chemical membrane technology is promising due to its low cost and simple operation.

BACKGROUND AND OBJECTIVES: Diabetes is a chronic disease characterized by high blood sugar. It may cause many complicated disease like stroke, kidney failure, heart attack, etc. About 422 million people were affected by diabetes disease in worldwide in 2014. The figure will be reached 642 million in 2040. The main objective of this study is to develop a machine learning (ML)-based system for predicting diabetic patients. MATERIALS AND METHODS: Logistic regression (LR) is used to identify the risk factors for diabetes disease based on p value and odds ratio (OR). We have adopted four classifiers like naïve Bayes (NB), decision tree (DT), Adaboost (AB), and random forest (RF) to predict the diabetic patients. Three types of partition protocols (K2, K5, and K10) have also adopted and repeated these protocols into 20 trails. Performances of these classifiers are evaluated using accuracy (ACC) and area under the curve (AUC). RESULTS: We have used diabetes dataset, conducted in 2009–2012, derived from the National Health and Nutrition Examination Survey. The dataset consists of 6561 respondents with 657 diabetic and 5904 controls. LR model demonstrates that 7 factors out of 14 as age, education, BMI, systolic BP, diastolic BP, direct cholesterol, and total cholesterol are the risk factors for diabetes. The overall ACC of ML-based system is 90.62%. The combination of LR-based feature selection and RF-based classifier gives 94.25% ACC and 0.95 AUC for K10 protocol. CONCLUSION: The combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.

Brain computer interface (BCI) is an interconnecting scheme between human brain and computer which entitles humans to interact with their environs by their brain waves instead of muscle activities. In the age of new argumentative technology, EEG signals are extensively employed in the involvement of BCI. The key motive of the work is to investigate EEG activities during imagination of hand movements. BCI competition IV dataset 2a were studied as well as analyzed in this research. We have detected the event related desynchronization and synchronization of mu and beta rythms during hand imagery movements by Power Spectral Analysis (PSA), Event Related Potential (ERP) plots, ERP comparison, Time-Frequency Analysis. Mainly, the EEG rhythms recorded at channels C3 and C4 are investigated. Results of this paper show that imagery movement of hands is usually associated with decrement in mu and beta rhythms, which is typically adversary to the moving activities. Our results are compatible with previous motor imagery researches and are of practical interest for the evolvement of BCI for motor reclamation.

PURPOSE: Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators. METHODS: Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0. RESULTS: The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV10yr were the most influential carotid phenotypes for left CCA (OR = 250, P

The North-West (NW) region of Bangladesh is pivotal for the country’s agricultural development, mainly in producing irrigated Boro rice. However, increasing cost of irrigation water, fertilizers, labour and other inputs, and the spatio-temporal variation in actual yield, market price and profitability of rice, have added uncertainty to the sustainability of Boro rice cultivation. In this study, we evaluated the productivity, profitability, and prospect of Boro rice production using comprehensive field data collected directly from 420 farmers’ fields over two consecutive seasons (2015–16 and 2016–17), across seven geographically distributed locations in the NW region. We also analyzed the risk and return trade of popular Boro rice cultivars using Monte-Carlo simulation. The results show that there were significant (p≤0.05) variations in rice yield between sites, irrigation pump-types, and rice varieties, with Hybrid rice and BRRI dhan29 producing highest yields (6.0–7.5 t/ha). Due to different pricing systems, the cost of irrigation water varied from site to site and from year to year, but always comprised the highest input cost (20–25% of total production). The total paid-out cost, gross benefit, and gross income of rice significantly (p≤0.05) differed between sites, type of irrigation pumps, rice varieties, transplanting dates, and two cropping years. The variations in observed yield and profitability reveal considerable scope to improve rice production systems. Market variation in the price of rice affected overall profitability significantly. Probability and risk analysis results show that Minikit and BRRI dhan29 are the most stable varieties for yield and profitability. Hybrid rice, which has the maximum attainable yield among the cultivated rice varieties, also has the risk of negative net income. Based on the analysis, we discussed ways to improve yield and profitability and the prospect of Boro rice cultivation in the region. The study provides valuable information for policy-makers to sustain irrigated rice cultivation in both the NW region and nationally.

Cyclic lipopeptides (CLPs) derived from plant growth-promoting rhizobacteria (PGPR) are well known as potential antifungal compounds. However, the physiological function of CLPs from Bacillus valli...

Introduction: Low birth weight (LBW) is an essential component for child mortality, and it also has dangerous effects on the mother's health. This study attempted to estimate the prevalence of the LBW among Nepalese children as well as to identify its socioeconomic and demographic determinants. Methods: For this study, 2016 Nepal Demographic and Health Survey data was used; 2,618 women having child were considered as respondents under precise specifications. The LBW of children was defined as birth weight

This study examines the association between six types of carotid artery disease image-based phenotypes and HbA1c in diabetes patients. Six phenotypes (intima-media thickness measurements (cIMT (ave.), cIMT (max.), cIMT (min.)), bidirectional wall variability (cIMTV), morphology-based total plaque area (mTPA), and composite risk score (CRS)) were measured in an automated setting using AtheroEdge™ (AtheroPoint, CA, USA).Consecutive 199 patients (157 M, age: 68.96 ± 10.98 years), L/R common carotid artery (CCA; 398 US scans) who underwent a carotid ultrasound (L/R) were retrospectively analyzed using AtheroEdge™ system. Two operators (novice and experienced) manually calibrated all the US scans using AtheroEdge™. Logistic regression (LR) and Odds ratio (OR) was computed and phenotypes were ranked.The baseline results showed 150 low-risk patients (HbA1c 6.50 mg/dl) and 49 high-risk patients (HbA1c ≥ 6.50 mg/dl). The fasting blood sugar (FBS) was highly associated with HbA1c (P 0.001). Except for cIMTV, all phenotypes showed an OR1.0 (P 0.001) for left common carotid artery (LCCA), right carotid artery (RCCA), and mean of left and right common carotid artery (MCCA). After adjusting the FBS, the OR for mTPA showed a higher risk for LCCA, RCCA, and MCCA. The coefficient of correlation (CC) between phenotypes and HbA1c were strong and inter-CC between cIMT and mTPA/CRS was above 0.9 (P 0.001). The statistical tests showed that phenotypes were significantly associated with diabetes (P-value0.0001).All phenotypes using AtheroEdge™, except cIMTV, showed a strong association with HbA1c. mTPA and CRS were equally strong phenotypes as cIMT. The CRS phenotype showed the strongest relationship to HbA1c.

Background Child and neonatal mortality is a serious problem in Bangladesh. The main objective of this study was to determine the most significant socio-economic factors (covariates) between the years 2011 and 2014 that influences on neonatal and child mortality and to further suggest the plausible policy proposals. Methods We modeled the neonatal and child mortality as categorical dependent variable (alive vs death of the child) while 16 covariates are used as independent variables using χ2 statistic and multiple logistic regression (MLR) based on maximum likelihood estimate. Findings Using the MLR, for neonatal mortality, diarrhea showed the highest positive coefficient (β = 1.130; P

This study characterises the microstructural properties of Hot Rolled Asphalt (HRA60/20) modified with different percentages and sizes of crumb rubber using dry process method. A number of specimens from five different mixtures were scanned using X-ray Computed Tomography (CT) together with image analysis technique to evaluate the effects of rubber size and concentration on the voids formation and rubber distribution within the gyratory compacted specimens. A set of procedures for processing and extracting the voids and crumb rubber was established in thresholding the region of interest. Results showed that the increase in the rubber content and rubber sizes affect the rubber distribution and air voids properties within the compacted asphalt mixture. The rubber particles affect the effectiveness of compaction and tend to cause the large air voids to scatter randomly into smaller sizes during compaction. This finding is more pronounced in specimen with fine rubber, whereas for the control mixture, the air voids are more localised and large sized.

Experiments were carried out to find out the bio-efficacy of four Trichoderma species, viz. Trichoderma harzianum , T.koningii , T.viride (green strain), T.viride (yellow strain) against canker pathogen Lasiodiplodia theobromae. Bioassay of antagonist against test pathogens conducted by dual culture techniques at different temperatures; volatile, non volatile and naturally untreated metabolites of isolates were examined. T. koningii and T.viride (yellow strain) exhibited maximum inhibition in controlling the pathogens. Fungicides, viz. Bavistin and Dithane M-45 used where Bavistin found little effective but Dithane M-45 showed no effects on pathogen. Trichoderma viride showed better performance to control Lasiodiplodia theobromae than commercial fungicides used during present investigation. DOI: http://dx.doi.org/10.3329/bjsir.v49i2.22008 Bangladesh J. Sci. Ind. Res. 49(2) , 125-130, 2014

The purpose of this study was to evaluate the performance of Neusilin® (NEU) a synthetic magnesium aluminometasilicate as an inorganic drug carrier co-processed with the hydrophilic surfactants Labrasol and Labrafil to develop Tranilast (TLT)-based solid dispersions using continuous melt extrusion (HME) processing. Twin-screw extrusion was optimized to develop various TLT/excipient/surfactant formulations followed by continuous capsule filling in the absence of any downstream equipment. Physicochemical characterization showed the existence of TLT in partially crystalline state in the porous network of inorganic NEU for all extruded formulations. Furthermore, in-line NIR studies revealed a possible intermolecular H-bonding formation between the drug and the carrier resulting in the increase of TLT dissolution rates. The capsules containing TLT-extruded solid dispersions showed enhanced dissolution rates and compared with the marketed Rizaben® product.