Water, Sanitation, Hygiene, and Nutrition in Bangladesh: Can Building Toilets Affect Children's Growth?

This report provides a systematic review of the evidence to date, both published and grey literature, on the relationship between water and sanitation and nutrition. It also examines the potential impact of improved water, sanitation, and hygiene (WASH) on undernutrition. This is the first report that undertakes a thorough review and discussion of WASH and nutrition in Bangladesh

Multisectoral Approaches to Addressing Malnutrition in Bangladesh: The Role of Agriculture and Microcredit

The objective of this study is to demonstrate how the interaction between sectors can be improved to increase the effectiveness of sectoral interventions, and how the interventions in the agricultural sector and microfinance can be used to improve nutritional outcomes. The study will examine what has been done to improve nutrition through interventions in the agriculture sector and microcredit programmes in Bangladesh and around the world, how they were implemented and to the extent possible, what the impact of those interventions was. The populations of primary concern for this study are infants, children and women of childbearing age, the group that is the target of many of the millennium development goals. The study will also pay special attention to the extent to which programmes and policies are successful at reaching poor and vulnerable groups in society and thus, reduce inequalities in nutrition. The introduction provides the background and rationale for this work. Chapter two assesses the status of malnutrition in Bangladesh, provides a brief history of policies and programmes to address malnutrition in the country and lays out the case for a multi-sectoral response to malnutrition. Chapter three reviews the potential role of interventions in the agriculture sector, including existing evidence on the impact of such interventions and institutional and other challenges to enhancing the impact. Chapter four provides a similar review of the role of microcredit programmes in improving nutrition outcomes. Recommendations on using multi-sectoral approaches to improve nutrition in Bangladesh are the subject of chapter five

Climate-related shocks and other stressors associated with depression and anxiety in Bangladesh: a nationally representative panel study

BACKGROUND: Climate change has major implications for common mental disorders including depression and anxiety in vulnerable nations such as Bangladesh. However, knowledge gaps exist around national estimations of depression and anxiety, and the associations between the prevalence of these disorders with climate-related and sociodemographic risk factors. To address these gaps, this study analysed data from a nationally representative panel study in Bangladesh that examined climate-related and sociodemographic correlates of depression and anxiety. METHODS: Two rounds of nationally representative household panel data were collected from urban and rural areas between August and September, 2019, and January and February, 2020. Households were selected for inclusion across 150 enumeration areas as the primary sampling units with use of a two-stage stratified random sampling design, and survey instruments were administered to the available adult member of the household. Depression and anxiety were measured with the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 scales, respectively, and weighted prevalence estimates were calculated on the basis of the 2011 national population census. Data on temperature and humidity were collected from 43 weather stations and constructed as mean values for the 2-month period preceding each round of the survey. Self-reported exposure to flooding was collected for a 12-month recall period. We applied a weighted population average logistic model on the pooled sample of both surveys to analyse the associations between ambient temperature, humidity, exposure to flooding, seasonality, sociodemographic variables, and three outcome conditions (depression, anxiety, and co-occurring depression and anxiety; at the level of p\u3c0·1). The models accounted for temporal and spatial heterogeneity. Standard errors were clustered at the level of each primary sampling unit. FINDINGS: 3606 individuals were included with 3·5% dropout in the second survey round (pooled sample n=7086; age range 15-90 years; 2898 [40·9%] men and 4188 [59·1%] women). National weighted prevalence estimates were 16·3% (95% CI 14·7-17·8) for depression, 6·0% (4·7-7·3) for anxiety, and 4·8% (3·7-5·9) for co-occurring depression and anxiety. We observed no significant associations between overall seasonality (summer vs winter) and the odds of depression (adjusted odds ratio 3·14 [95% CI 0·52-19·13], p=0·22), anxiety (0·16 [0·02-1·41], p=0·10), or co-occurring depression and anxiety (0·13 [0·01-1·49], p=0·10). An increase in mean temperature of 1°C within the 2 months preceding the surveys was associated with increased odds of anxiety (1·21 [1·00-1·47], p=0·046) and increased odds of co-occurring depression and anxiety (1·24 [1·00-1·53], p=0·045), whereas increased temperature was not associated with depression (0·90 [0·77-1·04], p=0·15). An increase in mean humidity of 1 g/m was not associated with depression (0·99 [0·96-1·02], p=0·60) or anxiety (1·04 [0·99-1·09], p=0·13), but was associated with co-occurring depression and anxiety (1·06 [1·00-1·12], p=0·064). Exposure to flooding within the 12 months preceding the survey rounds was associated with increased odds of all outcome conditions (depression, 1·31 [1·00-1·70], p=0·047; anxiety, 1·69 [1·21-2·36], p=0·0020; and co-occurring depression and anxiety, 1·87 [1·31-2·68], p=0·0006). INTERPRETATION: Climate-related shocks and other stressors have an important association with the burden of depression and anxiety in Bangladesh. Community-level interventions for common mental disorders need to be developed and assessed for safety, feasibility, and effectiveness in a Bangladeshi context. Further research on climate-related stressors is needed over different timespans and time intervals. FUNDING: The World Bank

An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning

Tai Chi has been proven effective in preventing falls in older adults, improving the joint function of knee osteoarthritis patients, and improving the balance of stroke survivors. However, the effect of Tai Chi on human gait dynamics is still less understood. Studies conducted in this domain only relied on statistical and clinical measurements on the time-series gait data. In recent years machine learning has proven its ability in recognizing complex patterns from time-series data. In this research work, we have evaluated the performance of several machine learning algorithms in classifying the walking gait of Tai Chi masters (people expert on Tai Chi) from the normal subjects. The study is designed in a longitudinal manner where the Tai Chi naive subjects received 6 months of Tai Chi training and the data was recorded during the initial and follow-up sessions. A total of 57 subjects participated in the experiment among which 27 were Tai Chi masters. We have introduced a gender, BMI-based scaling of the features to mitigate their effects from the gait parameters. A hybrid feature ranking technique has also been proposed for selecting the best features for classification. The research reports 88.17% accuracy and 93.10% ROC AUC values from subject-wise 5-fold cross-validation for the Tai Chi masters' vs normal subjects' walking gait classification for the "Single-task" walking scenarios. We have also got fairly good accuracy for the "Dual-task" walking scenarios (82.62% accuracy and 84.11% ROC AUC values). The results indicate that Tai Chi clearly has an effect on the walking gait dynamics. The findings and methodology of this study could provide preliminary guidance for applying machine learning-based approaches to similar gait kinematics analyses. 2022 Elsevier LtdThis work was supported in part by the Qatar National Research Fund under Grant NPRP12S-0227-190164 and in part by the International Research Collaboration Co-Fund ( IRCC ) through Qatar University under Grant IRCC-2021-001 . The statements made herein are solely the responsibility of the authors. Open access publication is supported by Qatar National Library.Scopu

NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern

Neurodegenerative diseases damage neuromuscular tissues and deteriorate motor neurons which affects the motor capacity of the patient. Particularly the walking gait is greatly influenced by the deterioration process. Early detection of anomalous gait patterns caused by neurodegenerative diseases can help the patient to prevent associated risks. Previous studies in this domain relied on either features extracted from gait parameters or the Ground Reaction Force (GRF) signal. In this work, we aim to combine both GRF signals and extracted features to provide a better analysis of walking gait patterns. For this, we designed NDDNet, a novel neural network architecture to process both of these data simultaneously to detect 3 different Neurodegenerative Diseases (NDDs). We have done several experiments on the data collected from 64 participants and got 96.75% accuracy on average in detecting 3 types of NDDs. The proposed method might provide a way to get the most out of the data in hand while working with GRF signals and help diagnose patients with an anomalous gait more effectively.This work was supported in part by the Qatar National Research Fund under Grant NPRP12S-0227-190164 and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021- 001. The statements made herein are solely the responsibility of the authors. This open-access publication is supported by Qatar National Library

A Multicentre Study Reveals Dysbiosis in the Microbial Co-Infection and Antimicrobial Resistance Gene Profile in the Nasopharynx of COVID-19 Patients

The impact of SARS-CoV-2 infection on the nasopharyngeal microbiome has not been well characterised. We sequenced genetic material extracted from nasopharyngeal swabs of SARS-CoV-2-positive individuals who were asymptomatic (n = 14), had mild (n = 64) or severe symptoms (n = 11), as well as from SARS-CoV-2-negative individuals who had never-been infected (n = 5) or had recovered from infection (n = 7). Using robust filters, we identified 1345 taxa with approximately 0.1% or greater read abundance. Overall, the severe cohort microbiome was least diverse. Bacterial pathogens were found in all cohorts, but fungal species identifications were rare. Few taxa were common between cohorts suggesting a limited human nasopharynx core microbiome. Genes encoding resistance mechanisms to 10 antimicrobial classes (\u3e 25% sequence coverages, 315 genes, 63 non-redundant) were identified, with β-lactam resistance genes near ubiquitous. Patients infected with SARS-CoV-2 (asymptomatic and mild) had a greater incidence of antibiotic resistance genes and a greater microbial burden than the SARS-CoV-2-negative individuals. This should be considered when deciding how to treat COVID-19 related bacterial infections

SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome with inclusion of pathobionts

The microbiota of the nasopharyngeal tract (NT) play a role in host immunity against respiratory infectious diseases. However, scant information is available on interactions of SARS-CoV-2 with the nasopharyngeal microbiome. This study characterizes the effects of SARS-CoV-2 infection on human nasopharyngeal microbiomes and their relevant metabolic functions. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 patients = 8, recovered humans = 7, and healthy people = 7) were collected, and underwent to RNAseq-based metagenomic investigation. Our RNAseq data mapped to 2281 bacterial species (including 1477, 919 and 676 in healthy, COVID-19 and recovered metagenomes, respectively) indicating a distinct microbiome dysbiosis. The COVID-19 and recovered samples included 67% and 77% opportunistic bacterial species, respectively compared to healthy controls. Notably, 79% commensal bacterial species found in healthy controls were not detected in COVID-19 and recovered people. Similar dysbiosis was also found in viral and archaeal fraction of the nasopharyngeal microbiomes. We also detected several altered metabolic pathways and functional genes in the progression and pathophysiology of COVID-19. The nasopharyngeal microbiome dysbiosis and their genomic features determined by our RNAseq analyses shed light on early interactions of SARS-CoV-2 with the nasopharyngeal resident microbiota that might be helpful for developing microbiome-based diagnostics and therapeutics for this novel pandemic disease