Prevalence of antibiotic use for diarrhea among 1.3 million under-five years children: A multicounty retrospective analysis from 2006-2018.

BACKGROUND: This paper aims to estimate the prevalence of antibiotic use for diarrhea among under-five children (u5c) in low- and middle-income countries (LMICs) using data from 112 Demographic Health Surveys (DHS) conducted between 2006 and 2018. The focus is on understanding the extent of antibiotic usage for managing diarrhea, a condition characterized by frequent loose or watery bowel movements that can lead to severe dehydration. METHODS AND FINDINGS: A cross-sectional study design was employed in the DHS. The prevalence of antibiotic use for diarrhea among under-five children was estimated by analyzing DHS data from 2006 to 2018 and using the R statistical programming language. Out of a total of 12,69,944 children under five included in this study, 1,80,067 children had diarrhea and 19,502 children had bloody diarrhea. The overall prevalence of diarrhea estimated at ~14% (prevalence = 0.142; 95% CI = 0.141, 0.142). Among the children with diarrhea, 47,755 child received antibiotic treatment, resulting a prevalence of ~27% (prevalence = 0.27, 95% CI = 0.26, 0.27) globally. Central Asia had the highest prevalence of antibiotic use at ~55% (prevalence = (967/1748) = 0.55, 95% CI = 0.52, 0.59), followed by the Europe region with a prevalence of ~44% (prevalence = (5483/12502) = 0.44, 95% CI = 0.43, 0.45). In the South East and Central Asia region, DHS conducted between 2006 and 2018, showed the highest prevalence of antibiotic use in DHS 2007 (~44%), DHS 2012 (~49%), DHS 2016 (~40%) and DHS 2017 (~65%). The linear trend analysis showed an upward trend for using antibiotic of diarrhea in the South East and Central Asia region. CONCLUSIONS: The Central Asia region had the highest proportion of antibiotic use, with an estimated prevalence of ~55% (95% CI = 0.52, 0.59). The Europe region followed closely with a prevalence of ~44% (95% CI = 0.43, 0.45). The South East Asia region had the lowest prevalence of antibiotic use estimated at ~23% (95% CI = 0.22, 0.24), with a gradual increasing trend.

Hematological Abnormalities and Comorbidities Are Associated With the Severity of Kidney Disease: A Hospital-Based Cross-Sectional Study in Bangladesh.

Background: Abnormalities in hematology and comorbidities might have a role in chronic kidney disease (CKD) patients. However, the exact relationships between hematological parameters and the severity of CKD are not well understood. Also, the underlying mechanisms remain under investigation. The present study aimed to evaluate the association of different blood parameters and comorbidities among hospitalized CKD patients in Bangladesh. Methods: The present study enrolled admitted CKD patients at Evercare Hospital Ltd, Dhaka, Bangladesh, from January 1, 2021, to August 1, 2021. For this study, the demographic and clinical information of the patients were collected. Then some routine blood tests for the hematological profile of CKD patients were performed. Finally, several statistical methods were performed and data interpretations were done to evaluate the role of hematological changes on CKD patients. Results: Among 300 patients, early-stage CKD patients (ESCKDP) and advanced-stage CKD patients (ASCKDP) were 153 and 147, respectively. The decreased levels of hemoglobin (Hb) and red blood cell (RBC) in ASCKDP were observed. However, the present study found increased levels of corpuscular Hb in ASCKDP than ESCKDP. Also, the present study noticed correlations between these changes and the severity of CKD. Also, we observed a significant difference in age and body mass index between ESCKDP and ASCKDP. Conclusions: Based on our results, lower Hb and RBC levels may use in assessing the severity and the treatment decisions of CKD patients in the hospital setting. Therefore, our findings may assist with developing a treatment protocol for hospitalized CKD patients.

Pattern of Antibiotic Dispensing at Pharmacies According to the WHO Access, Watch, Reserve (AWaRe) Classification in Bangladesh.

The WHO Essential Medicines List Access, Watch, and Reserve (AWaRe) classification could facilitate antibiotic stewardship and optimal use. In Bangladesh, data on antibiotic dispensing in pharmacies according to the AWaRe classification are scarce. We aimed to explore antibiotic dispensing pattern in pharmacies according to the WHO AWaRe classification to aid pharmacy-targeted national antibiotic stewardship program (ASP). From January to July 2021, we interviewed drug-sellers from randomly selected pharmacies and randomly selected customers attending the pharmacies. We collected data on demographics and medicines purchased. We classified the purchased antibiotics into the Access, Watch, and Reserve groups among 128 pharmacies surveyed, 98 (76.6%) were licensed; 61 (47.7%) drug-sellers had pharmacy training. Of 2686 customers interviewed; 580 (21.6%) purchased antibiotics. Among the 580 customers, 523 purchased one, 52 purchased two, and 5 purchased three courses of antibiotics (total 642 courses). Of the antibiotic courses, the Watch group accounted for the majority (344, 53.6%), followed by the Access (234, 36.4%) and Reserve (64, 10.0%) groups. Approximately half of the antibiotics (327/642, 50.9%) were purchased without a registered physician's prescription. Dispensing of non-prescribed antibiotics was higher in the Access group (139/234, 59.4%), followed by Watch (160/344, 46.5%) and Reserve (28/64, 43.8%) groups. These findings highlight the need to implement strict policies and enforce existing laws, and pharmacy-targeted ASP focusing on proper dispensing practices to mitigate antimicrobial resistance in Bangladesh.

Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh.

The general population has been excessively using antibiotics during the COVID-19 pandemic. Therefore, the use of antibiotics for any reported illnesses in the preceding four weeks and knowledge of antibiotics among the general population in the community were assessed for possible interventions. A mobile phone survey among a general population across eight administrative divisions of Bangladesh was conducted during January-March 2021. Reported illness episodes irrespective of COVID-19 in the preceding four weeks of the interview, use of antibiotics for the illnesses, and knowledge on antibiotics among the general population were recorded. Descriptive analyses were performed. We randomly interviewed 1854 participants, with a mean age of 28.5 years (range: 18-75 years); 60.6% were male. Among all participants, 86.3% (95% CI: 84.7-87.8) heard names of antibiotics, but only 12.1% reported unspecified harmful effects, and 3.5% reported antimicrobial resistance when antibiotics were taken without a physician's prescription. Among 257 (13.9%) participants, who consumed medicines for their recent illness episode, 32.7% (95% CI: 27.2-38.6) reported using antibiotics. Of those who could recall the names of antibiotics prescribed (n = 36), the most frequently used was azithromycin (22.2%) followed by cefixime (11.1%) and ciprofloxacin (5.6%). Our findings show an increased antibiotic use for illnesses reported in the preceding four weeks and an elevated knowledge at the community level during the COVID-19 pandemic compared with the pre-pandemic period.

Estimating Catastrophic Costs due to Pulmonary Tuberculosis in Bangladesh.

To eliminate TB from the country by the year 2030, the Bangladesh National Tuberculosis (TB) Program is providing free treatment to the TB patients since 1993. However, the patients are still to make Out-of-their Pocket (OOP) payment, particularly before their enrollment Directly Observed Treatment Short-course (DOTS). This places a significant economic burden on poor-households. We, therefore, aimed to estimate the Catastrophic Health Expenditure (CHE) due to TB as well as understand associated difficulties faced by the families when a productive family member age (15-55) suffers from TB. The majority of the OOP expenditures occur before enrolling in. We conducted a cross-sectional study using multistage sampling in the areas of Bangladesh where Building Resources Across Communities (BRAC) provided TB treatment during June 2016. In total, 900 new TB patients, aged 15-55 years, were randomly selected from a list collected from BRAC program. CHE was defined as the OOP payments that exceeded 10% of total consumption expenditure of the family and 40% of total non-food expenditure/capacity-to-pay. Regular and Bayesian simulation techniques with 10,000 replications of re-sampling with replacement were used to examine robustness of the study findings. We also used linear regression and logit model to identify the drivers of OOP payments and CHE, respectively. The average total cost-of-illness per patient was 124 US$, of which 68% was indirect cost. The average CHE was 4.3% of the total consumption and 3.1% of non-food expenditure among the surveyed households. The poorest quintile of the households experienced higher CHE than their richest counterpart, 5% vs. 1%. Multiple regression model showed that the risk of CHE increased among male patients with smear-negative TB and delayed enrolling in the DOTS. Findings suggested that specific groups are more vulnerable to CHE who needs to be brought under innovative safety-net schemes.

Assessment of physico-chemical characteristics of river water emphasizing tannery industrial park: a case study of Dhaleshwari River, Bangladesh.

Pollution of industry-adjacent surface water bodies become a major environmental concern in Bangladesh recently. Therefore, this study aimed to elaborate assessment of physico-chemical characteristics of the Dhaleshwari River (the adjacent river of newly shifted tannery industrial park) and also the discharged effluent from the central effluent treatment plant (CETP) considering both seasonal and spatial variations. Among the examined 30 water quality parameters (including 11 heavy metals), only TDS, Cl-, NO3-N, EC, Cu, Zn, Cd, and Ni met the standards for discharged wastewater from industrial units into inland surface water set by Bangladesh government. This indicated that the CETP is not fully and/or effectively functioning. Out of 19 parameters (excluding heavy metal), 14 parameters exceeded the Bangladesh standard for drinking water quality either spatially or seasonally. The river water quality indicated slowly biodegradable capacity with algal bloom risk. The concentrations of the studied heavy metals in the river water followed a decreasing order of Ca > K > Mg > Na > Fe > Cr > Pb > Ni > Cu > Zn > Cd, and Cr concentration in the winter season exceeded the standard values. The concentrations of other heavy metals met the quality standard that indicated still the river is capable to self-purify. Furthermore, strong (p < 0.01) and significant (p < 0.05) correlation were observed among the quality parameters in river water. This study suggested that the efficiency of the existing CETP needs to be enhanced, and the proper operation and maintenance are required to meet the desired discharge standard quality. Also, all the unauthorized wastewater outfalls from tanneries must be closed to save the Dhaleshwari River.

Risk assessment and evaluation of heavy metals concentrations in blood samples of plastic industry workers in Dhaka, Bangladesh.

To assess the potential health risk caused by heavy metals twenty-six blood samples were collected from plastic industry workers based on ages and smoking status in Dhaka, Bangladesh. Heavy metals were analyzed with an atomic absorption spectrometer. The mean concentrations of Lead (Pb), Cadmium (Cd), Nickel (Ni), and Zinc (Zn) found in blood samples of the exposed workers were 32.78 ± 9.47, 1.08 ± 0.47, 1.42 ± 1.01, and 9.08 ± 1.95 µgL-1, respectively. The average heavy metal concentrations in blood samples of smoking workers show a narrow range of fluctuation than that of non-smoking workers. A review of different age groups of industry workers shows the workers between the ages of 26 and 40 are more likely to contaminated with Pb (35.90 ± 8.06 µgL-1) and Ni (1.61 ± 1.31 µgL-1). The higher level of Cd (1.26 ± 0.46 µgL-1) and Zn (9.91 ± 2.80 µgL-1) was found in >40 years old workers. The mean concentration in indoor dust samples of different industrial subsections reported as 40.27 ± 10.33, 3.24 ± 0.83, 18.08 ± 3.61, and 103.64 ± 8.16 mg kg-1 for Pb, Cd, Ni, and Zn, respectively. Exposed workers have relatively less critical health implications concluded from the average daily intake (ADI), hazard quotient (HQs), and hazard index (HI) values. The HI values of Pb, Cd, Ni, and Zn were reported as 2.0 × 10-2, 4.64 × 10-4, 1.62 × 10-3, and 5.49 × 10-4, respectively, which have imparted minimal risks (as HI < 1) to the health of the workers. The cancer risks of Pb, Cd, and Ni were reported as 1.46 × 10-10, 1.77 × 10-9, and 1.31 × 10-9, respectively lower than the threshold values. Therefore, the result divulged a potentially lower cancer risk compared to EPA limit value of 1 × 10-6 to 1 × 10-4 for exposed industrial workers.

Genomic profiling of Nipah virus using NGS driven RNA-Seq expression data.

Nipah virus (NiV) is an ssRNA, enveloped paramyxovirus in the genus Henipaveridae with a case fatality rate >70%. We analyzed the NGS RNA-Seq gene expression data of NiV to detect differentially expressed genes (DEGs) using the statistical R package limma. We used the Cytoscape, Ensembl, and STRING tools to construct the gene-gene interaction tree, phylogenetic gene tree and protein-protein interaction networks towards functional annotation. We identified 2707 DEGs (p-value <0.05) among 54359 NiV genes. The top-up and down-regulated DEGs were EPST1, MX1, IFIT3, RSAD2, OAS1, OASL, CMPK2 and SLFN13, SPAC977.17 using log2FC criteria with optimum threshold 1.0. The top 20 up-regulated gene-gene interaction trees showed no significant association between Nipah and Tularemia virus. Similarly, the top 20 down-regulated genes of neither Ebola nor Tularemia virus showed an association with the Nipah virus. Hence, we document the top-up and down-regulated DEGs for further consideration as biomarkers and candidates for vaccine or drug design against Nipah virus to combat infection.

Structure modeling to function prediction of Uncharacterized Human Protein C15orf41.

The dyserythropoietic anemia disease is a genetic disorder of erythropoiesis characterized by morphological abnormalities of erythroblasts. This is caused by human gene C15orf41 mutation. The uncharacterized C15orf41 protein is involved in the formation of a functional complex structure. The uncharacterized C15orf41 protein is thermostable, unstable and acidic. This is associated with TPD (Treponema Pallidum) domain (135 to 265 residue position) and three PTM sites such as K50 (Acetylation), T114 (Phosphorylation) and K176 (Ubiquitination). C15orf41 is paralogous to isoform-1 (gi|194018542|) and open reading frame isoform-CRA_c (gi|119612744|) of Homo sapiens located at chromosome 15. It interacts with the human ATP (Adenosine Triphosphate) binding domain 4 (ATPBD4) having similarity score 0.725 as per protein-protein interaction (PPI) network analysis. This data provides valuable insights towards the functional characterization of human gene C15orf41.

Prediction of Protein Acetylation Sites using Kernel Naive Bayes Classifier Based on Protein Sequences Profiling.

Lysine acetylation is one of the decisive categories of protein post-translational modification (PTM), it is convoluted in many significant cellular developments and severe diseases in the biological system. The experimental identification of protein-acetylated sites is painstaking, time-consuming and expensive. Hence, there is significant interest in the development of computational approaches for consistent prediction of acetylation sites using protein sequences. Features selection from protein sequences plays a significant role for acetylation sites prediction. We describe an improved feature selection approach for acetylation sites prediction based on kernel naive Bayes classifier (KNBC). We have shown that KNBC generated from selected features by a new feature selection method outperforms than the existing methods for identification of acetylation sites. The sensitivity, specificity, ACC (Accuracy), MCC (Matthews Correlation Coefficient) and AUC (Area under Curve of ROC) in our proposed method are as follows 80.71%, 93.39%, 76.73%, 41.37% and 83.0% with the optimum window size is 47. Thus the kernel naive Bayes classifier finds application in acetylation site prediction.

Robust Feature Selection Approach for Patient Classification using Gene Expression Data.

Patient classification through feature selection (FS) based on gene expression data (GED) has already become popular to the research communities. T-test is the well-known statistical FS method in GED analysis. However, it produces higher false positives and lower accuracies for small sample sizes or in presence of outliers. To get rid from the shortcomings of t-test with small sample sizes, SAM has been applied in GED. But, it is highly sensitive to outliers. Recently, robust SAM using the minimum ß-divergence estimators has overcome all the problems of classical t-test & SAM and it has been successfully applied for identification of differentially expressed (DE) genes. But, it was not applied in classification. Therefore, in this paper, we employ robust SAM as a feature selection approach along with classifiers for patient classification. We demonstrate the performance of the robust SAM in a comparison of classical t-test and SAM along with four popular classifiers (LDA, KNN, SVM and naive Bayes) using both simulated and real gene expression datasets. The results obtained from simulation and real data analysis confirm that the performance of the four classifiers improve with robust SAM than the classical t-test and SAM. From a real Colon cancer dataset we identified 21 additional DE genes using robust SAM that were not identified by the classical t-test or SAM. To reveal the biological functions and pathways of these 21 genes, we perform KEGG pathway enrichment analysis and found that these genes are involved in some important pathways related to cancer disease.

Robust Significance Analysis of Microarrays by Minimum ß-Divergence Method.

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum ß-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition.

Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis.

The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression dataset is often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis. Therefore, in this paper, an attempt is made to robustify the Gaussian NBC by the minimum ß-divergence method. The role of minimum ß-divergence method in this article is to produce the robust estimators for the location and scale parameters based on the training dataset and outlier detection and modification in test dataset. The performance of the proposed method depends on the tuning parameter ß. It reduces to the traditional naïve Bayes classifier when ß â†’ 0. We investigated the performance of the proposed beta naïve Bayes classifier (ß-NBC) in a comparison with some popular existing classifiers (NBC, KNN, SVM, and AdaBoost) using both simulated and real gene expression datasets. We observed that the proposed method improved the performance over the others in presence of outliers. Otherwise, it keeps almost equal performance.