Understanding the socio-demographic and programmatic factors associated with adolescent motherhood and its association with child undernutrition in Bangladesh.

BACKGROUND: Worldwide, a significant number of girls become mothers during adolescence. In Bangladesh, adolescent childbirth is highly prevalent and has adverse effects on children's health and undernutrition. We aimed to identify the relationship between the undernutrition of children and adolescent motherhood, the factors associated with adolescent mothers' age at first birth, and to examine the programmatic factors and gaps influencing children's undernutrition in Bangladesh. METHODS: We analysed the 'Bangladesh Demographic and Health Survey' BDHS-17-18 data and desk review. To examine the factors associated with adolescent motherhood and its impact on child undernutrition, data from 7,643 mother-child pairs were selected. Child stunting, wasting, and underweight were measured according to the World Health Organisation (WHO) median growth guidelines based on z-scores - 2. Univariate, bivariate, simple, and multiple logistic regressions were used for analyse. We followed the systematic procedures for the literature review. RESULTS: Approximately, 89% of adolescents aged ≤ 19 years were married and 71% of them gave their first childbirth. Children of adolescent mothers (≤ 19 years) were significantly 1.68 times more wasted (aOR: 1.68; 95% CI: 1.08 to 2.64), 1.37 times more underweight (aOR: 1.37; 95% CI: 1.01 to 1.86) and either form 1.32 times more stunting, wasting or underweight (aOR:1.32; 95% Cl: 1.05 to 1.66) compared to the children of adult mothers (> 19 years) after adjusting potential confounders. The factors associated with mothers' first childbirth during adolescence were the age gap between husband and wife 5-10 years (aOR: 1.81; 95% Cl: 1.57-2.10) and age gap > 10 years (aOR: 2.41; 95% Cl: 1.96-2.97) compared with the age group < 5 years, and husbands' education (aOR: 1.29; 95% Cl: 1.04-1.61) compared with the uneducated husbands. In the literature review, we found potential gaps in focusing on the Adolescent Sexual and Reproductive Health (ASRH) program in Bangladesh, from thirty-two programmes only half of them focused on adolescents aged 10-19 years, and eleven programmes focused only on girls. CONCLUSION: Children of adolescent mothers are at risk of wasting, underweight, and any form of undernutrition. For effective policies and interventions in Bangladesh, it is important to emphasise delaying adolescent pregnancy and prioritising child undernutrition.

Contribution of socio-economic and demographic factors to maternal and child malnutrition in Bangladesh: Insights from a nationwide survey.

Background: Malnutrition is a major global public health issue, especially for under five children and their mothers. Objective: This study examined the relationships between socio-economic and demographic characteristics and under- and over-nutrition in mothers and children of Bangladesh. Method: Using the Nationally representative Bangladesh Demographic and Health Survey (2017-18) data, we performed the multiple logistic regression analysis to assess the relationships between key outcome variables and predictors. The analysis included 8321 children aged 0-59 months and 7800 mothers aged 15-49 years. Findings: The prevalence of stunting, wasting, and overweight among children under five was 30.72%, 8.44%, and 2.21% respectively. About 2.74% of the children were both stunted and wasted, while 0.56% of the children were both stunted and overweight. The prevalence of underweight and overweight among mother was 14.09% and 26.35% respectively. Children who are stunted are significantly associated with the mother's BMI status, mothers' education, fathers' education, and wealth index, while children who are wasted are significantly associated with the mother's BMI status and the child's sex. A child's sex and birth order are significantly associated with children being overweight. Mother's age, mother's education, father's education, place of residence and wealth index are significant determinants for mothers being underweight, whereas mother's age, mother's occupation, father's education, father's occupation, place of residence and wealth index are significant determinants for mothers being overweight. Conclusion: In addition to essential nutrition interventions, it is vital to address the basic social-economic and demographic determinants.

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim's blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.

Presence of toxic metals in rice with human health hazards in Tangail district of Bangladesh.

Rice is the staple food of Bangladeshi people and is consumed at least twice a day. Thus, the presence of toxic metals in rice grains has become a major public health concern in Bangladesh. The present research was conducted to investigate the concentrations of toxic metals in rice grains and their possible human health risks in the Tangail district of Bangladesh. Toxic metals were measured by using an inductively coupled plasma mass spectrometer (ICP-MS), and the mean concentrations of toxic metals in rice samples were found in order of Cr > Pb > Ni > As > Cu > Cd. The concentrations of Cr, Pb, As, and Cd in the studied rice grain samples exceeded the FAO/WHO standard values for food samples by 100%, whereas the Ni concentrations by 10%. The principal component analysis (PCA) revealed significant anthropogenic contributions of Cr, Ni, As, and Pb concentrations in rice grains. The metal concentrations in rice grain samples showed strong significant correlations by forming primary clusters with each other. The estimated daily intake (EDI) values of Cr, Ni, As, Cd, and Pb from all samples were higher than the maximum tolerable daily intake (MTDI) allowed. The total targeted hazard quotient (TTHQ) values of Cu, Ni, As, Cd, and Pb also exceeded the threshold value of 1.00, indicating a potential non-carcinogenic risk. The estimated target carcinogenic risk of As was higher than the USEPA threshold level 10-4 (0.0001) indicating increased risk of cancer for adults and children in the study area.

The impact of the COVID-19 pandemic on the mental health of the adult population in Bangladesh: a nationwide cross-sectional study.

The recent COVID-19 pandemic has imposed threats on both physical and mental health since its outbreak. This study aimed to explore the impact of the COVID-19 pandemic on mental health among a representative sample of home-quarantined Bangladeshi adults. A cross-sectional design was used with an online survey completed by a convenience sample recruited via social media. A total of 1,427 respondents were recruited, and their mental health was assessed by the DASS-21 measure. The prevalence of anxiety symptoms and depressive symptoms was 33.7% and 57.9%, respectively, and 59.7% reported mild to extremely severe levels of stress. Perceptions that the pandemic disrupted life events, affected mental health, jobs, the economy and education, predictions of a worsening situation, and uncertainty of the health care system capacities were significantly associated with poor mental health outcomes. Multivariate logistic regressions showed that sociodemographic factors and perceptions of COVID-19 significantly predict mental health outcomes. These findings warrant the consideration of easily accessible low-intensity mental health interventions during and beyond this pandemic.

Determinants of household food security and dietary diversity during the COVID-19 pandemic in Bangladesh.

OBJECTIVES: The study aimed to determine the associated factors of household food security (HFS) and household dietary diversity (HDD) during the COVID-19 pandemic in Bangladesh. DESIGN: Both online survey and face-to-face interviews were employed in this cross-sectional study. The Household Food Security Scale and Household Dietary Diversity Score were used to access HFS and HDD, respectively. The HDD scores were derived from a 24-h recall of food intake from 12 groups. SETTING: Bangladesh. PARTICIPANTS: A total sample of 1876 households were recruited. RESULTS: The overall mean scores of HFS and HDD were 31·86 (sd 2·52) and 6·22 (sd 5·49), respectively. Being a rural resident, having no formal education, occupation of household head other than government job and low monthly income were potential determinants of lower HFS and HDD. Approximately 45 % and 61 % of Bangladeshi households did not get the same quantity and same type of food, respectively, as they got before the pandemic. Over 10 % of respondents reported that they lost their job or had to close their businesses, and income reduction was reported by over 70 % of household income earners during the COVID-19 pandemic, which in turn was negatively associated with HFS and HDD. CONCLUSION: Household socio-economic variables and COVID-19 effects on occupation and income are potential predictors of lower HFS and HDD scores. HFS and HDD deserve more attention during this pandemic particularly with reference to low-earning households and the households whose earning persons' occupation has been negatively impacted during the COVID-19 pandemic.

Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique.

The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does not require the memorization of predefined special gesture patterns. Rather, it is sensitive to the subject and language of interest. Traditional air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process. To make the signals consistent in length, researchers attempted various strategies including padding and truncating, but these procedures result in significant data loss. Interpolation is a statistical technique that can be employed for time-series signals to ensure minimum data loss. In this paper, we extensively investigated different interpolation techniques on seven publicly available air-writing datasets and developed a method to recognize air-written characters using a 2D-CNN model. In both user-dependent and user-independent principles, our method outperformed all the state-of-the-art methods by a clear margin for all datasets.

Mental health outcomes during the COVID-19 and perceptions towards the pandemic: Findings from a cross sectional study among Bangladeshi students.

Although the COVID-19 pandemic triggered commination on both physical and mental wellbeing since its outbreak, the impact of the pandemic on mental health difficulties among Bangladeshi students is still lack in substantial evidence. The study aimed to explore such an impact on mental health among Bangladeshi students and their perception towards the COVID-19 pandemic. A web-based cross-sectional study was conducted among 589 students from Bangladesh in between April 29th to 7th May 2020. Data was collected by using an online questionnaire on demographic status, perceptions towards COVID-19, and mental health symptoms by using the Depression, Anxiety and Stress Scale (DASS 21). About 26.66% and 61.97% of students reported mild to extremely severe anxiety symptoms, and depressive symptoms, respectively, and 57.05% reported mild to extremely severe levels of stress. Multivariate logistic regression reported that students' age, gender, family income, residence, and family size are associated with mental health difficulties. Negative perceptions on the effect of the pandemic on life events, mental health, disruptions in education, and health care system, existing physical health conditions, and COVID-19 like symptoms were significantly associated with poor mental outcomes. It is suggested that students' mental health difficulties should be monitored to provide adequate support and services during this ongoing pandemic.

Mental Health Outcomes of Adults with Comorbidity and Chronic Diseases during the COVID-19 Pandemic: A Matched Case-Control Study.

BACKGROUND: Individuals with certain pre-existing chronic health conditions have been identified as a high-risk group for fatalities of COVID-19. Therefore, it is likely that individuals with chronic diseases may worry during this pandemic to the detriment of their mental health. This study compares the mental health of Bangladeshi adults affected by chronic disease to a healthy, matched control group during the COVID-19 pandemic. SUBJECTS AND METHODS: A matched case-control analysis was performed with data collected from 395 respondents with chronic diseases and 395 controls matched for age, gender, and residence. Inclusion criteria for cases were respondents who self-reported having asthma, cardiovascular disease symptoms and/or diabetes. Respondents were recruited using an online survey, which included the DASS-21 measure to assess symptoms of stress, anxiety, and depression. Chi-square test, t-test, Fisher's exact test and a conditional logistic regression were performed to examine associations among variables. RESULTS: The prevalence of anxiety symptoms and depression symptoms and the level of stress were significantly higher among cases (59%; 71.6%; 73.7%, respectively) than among controls (25.6%; 31.1%; 43.3%, respectively). Chi-square and t-test showed significant associations and differences between having chronic diseases and mental health outcomes. A conditional logistic regression showed that respondents with asthma, diabetes, cardiovascular disease symptoms, or any combination of these diseases had higher odds of exhibiting symptoms of stress, anxiety, and depression than healthy individuals. CONCLUSION: These results underscore a subpopulation vulnerable to mental health consequences during this pandemic and indicate the need for additional mental health resources to be available to those with chronic diseases.

Effect of low-cost kitchen with improved cookstove on birthweight of neonates in Shahjadpur, Bangladesh: a cluster-randomised controlled trial.

Background: Smoke from biomass fuels used for cooking in traditional cookstoves contains a variety of health-damaging pollutants. Inhalation of these pollutants by pregnant women has been linked to abnormal foetal development and adverse pregnancy outcomes, including low birthweight (LBW). There is a dearth of data on environmental interventions that have the potential to reduce exposure to biomass fuel during pregnancy and improve birth outcomes. International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) therefore, designed a low-cost kitchen with an improved cookstove and examined the impact of this intervention on the birthweight of neonates. Methods: icddr,b conducted a cluster-randomised controlled trial of a 'low-cost kitchen with improved cookstove' intervention among 1,267 pregnant women who used traditional cookstoves in a rural sub-district of Bangladesh. All participants were enrolled during the first trimester of pregnancy among 104 randomly selected clusters after obtaining informed consent. The model kitchens were installed in 628 participants' households of the intervention group, and 639 participants continued to use traditional cookstoves as the control group. The primary outcome was the proportion of LBW neonates between the intervention and control groups. The study also examined if the intervention would reduce CO exposure, measured by the differences in maternal blood carbon monoxide saturation (SpCO) levels and prevalence of LBW in neonates. We performed a generalized structural equation model for jointly assessing the simultaneous relationships of biomass fuel exposure to LBW of neonates and the relationships of LBW of neonates to maternal blood SpCO level. This trial was registered with ClinicalTrials.gov (NCT02923882). Findings: We found that in the intervention group using 'low-cost kitchen with improved cookstove', the risk of LBW reduced by 37% (adjusted risk ratio: 0.63, 95% CI [0.45, 0.89]). Between the second and third trimester, the mean maternal blood SpCO level was significantly reduced from 10.4% to 8.9% (p-value <0.01) in the intervention group but remained unchanged in the control group (11.6% and 11.5%). Of the total effects of the intervention on the risk of LBW, 48.3% was mediated through maternal blood SpCO level. Interpretation: The risk of LBW among rural neonates was reduced in the intervention group using 'low-cost kitchen with improved cookstove', which may be attributed to the reduction in maternal blood SpCO level. Additional research is needed to identify other mechanisms through which biomass fuel exposure might lead to adverse pregnancy outcomes. Funding: Grand Challenges Canada: Rising Stars in Global Health Programme.

Factors influencing delivery-related complications and their consequences in hard-to-reach areas of Bangladesh.

BACKGROUND AND OBJECTIVES: Bangladesh's high maternal mortality ratio is exacerbated by delivery-related complications, particularly in hard-to-reach (HtR) areas with limited healthcare access. Despite this, few studies have explored delivery-related complications and factors contributing to these complications among the disadvantaged population. This study aimed to investigate the factors contributing to delivery-related complications and their consequences among the mothers residing in the HtR areas of Bangladesh. METHODS: Data were collected using a cross-sectional study design from 13 HtR sub-districts of Bangladesh between September 2019 and October 2019. Data from 1,290 recently delivered mothers were analysed. RESULTS: Around 32% (95% CI: 29.7-34.8) of the mothers reported at least one delivery-related complication. Prolonged labour pain (21%) was the highest reported complication during the delivery, followed by obstructive labour (20%), fever (14%), severe headache (14%). Mothers with higher education, a higher number of antenatal care (ANC) visits, complications during ANC, employed, and first-time mothers had higher odds of reporting delivery-related complications. More than one-half (51%) of these mothers had normal vaginal delivery. Nearly one-fifth (20%) of mothers who reported delivery-related complications were delivered by unskilled health workers at homes. On the other hand, about one-fifth (19%) of the mothers without any complications during delivery had a caesarean delivery. Nine out of ten of these caesarean deliveries were done at the private facilities. CONCLUSION: Delivery-related complications are significantly related to a woman's reproductive history and other background characteristics. Unnecessary caesarean delivery is prominent at private facilities.

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.

Investigating problematic uses of Facebook and other Internet activities among university students in Bangladesh during the COVID-19 pandemic.

Problematic use of Internet (PUI) and problematic use of Facebook (PUF) has been linked to escalating behavioral health issues among university students and has increased during the COVID-19 pandemic. This study estimated the prevalence of and explored associated factors for PUI and PUF among Bangladeshi university students during the COVID-19 pandemic. A cross-sectional online survey was undertaken among 1101 Bangladeshi university students between November and December 2020. The Internet Addiction Test and Facebook Addiction Scale were used to assess PIU and PUF, respectively. A multiple linear regression analysis was performed to adjust for confounders. Among the participants, PUI and PUF were found in 39.3% and 37.1%, respectively. The multiple linear regression model indicated PUI was significantly associated with participants residing in a village, arts majors, those unsatisfied with their major, having mediocre parental relationships, failure in romantic relationships, physical comorbidities, longer use of the Internet, using the Internet for purposes other than education, using social media, and downloading movies/TV series. PUF was significantly associated with village residence, lower income, arts majors, failure in romantic relationships, longer use of the Internet, using the Internet for purposes other than education, and downloading movies/TV series. Both PUI and PUF have been prevalent among Bangladeshi university students during the COVID-19 pandemic. Longitudinal & exploratory studies are warranted in the future to identify causal factors for PUI and PUF and appropriate interventions should be designed quickly for this population.

Prevalence and associated factors of depression among Bangladeshi university students: A cross-sectional study.

Objective:  The study aimed to assess the prevalence of depression and its associated factors among university students in Bangladesh. Participants: A total of 403 undergraduate students from Patuakhali Science and Technology University, and Barisal University participated in the study. Method: A cross-sectional study was conducted using Beck's Depression Inventory (BDI). Result: The prevalence of depression (BDI ≥ 14) was 47.14%. Depression was 3.4 times (95% CI: 1.6-7.1), 3.8 times (95% CI: 1.7-8.6), and 3.9 times (95% CI: 1.5-8.9) higher among 2nd, 3rd, and 4th-year students, respectively than 1st-year students. Students with a history of stressful life events (aOR = 2.7, 95% CI: 1.7-4.4), suicidal attempts (aOR = 3.0, 95% CI: 1.2-7.9), and who received inadequate monthly allowance from their family (aOR = 0.53 95% CI: 0.3-0.9) were more likely to develop depressive symptoms. Conclusion: This study reports a high level of depression among university students which needs further discussion, exploration, and calls for designing appropriate interventions.

Effect of an integrated maternal and neonatal health intervention on maternal healthcare utilisation addressing inequity in Rural Bangladesh.

BACKGROUND: Although Bangladesh has made significant improvements in maternal, neonatal, and child health, the disparity between rich and poor remains a matter of concern. OBJECTIVE: The study aimed to increase coverage of skilled maternal healthcare services while minimising the inequity gap among mothers in different socioeconomic groups. METHODS: We implemented an integrated maternal and neonatal health (MNH) intervention between 2009 and 2012, in Shahjadpur sub-district of Sirajganj district, Bangladesh. The study was quasi-experimental in design for the evaluation. Socioeconomic status was derived from household assets using principal component analysis. Inequity in maternal healthcare utilisation was calculated using rich-poor ratio and concentration index to determine the changes in inequity between the baseline and the endline time period. RESULT: The baseline and endline surveys included 3,158 (mean age 23.5 years) and 3,540 (mean age 24.3 years) recently delivered mothers respectively. Reduction in the rich-poor ratio was observed in the utilisation of skilled 4+ antenatal care (ANC) (2.4:1 to 1.1:1) and related concentration index decreased from 0.220 to 0.013 (p < 0.001). The rich-poor ratio for skilled childbirth reduced from 1.7:1 to 1.0:1 and the related concentration index declined from 0.161 to -0.021 (p < 0.001). A similar reduction was also observed in the utilisation of skilled postnatal care (PNC); where the rich-poor gap decreased from 2.5:1 to 1.0:1 and the related concentration index declined from 0.197 to -0.004 (p < 0.001). CONCLUSION: The MNH intervention was successful in reducing inequity in receiving skilled 4+ ANC, delivery, and PNC in rural Bangladesh.

Community-based integrated intervention for skilled maternal health care utilization in riverine remote areas, Bangladesh.

INTRODUCTION: Despite the decrease in maternal mortality ratio, many women in Bangladesh are still at high-risk of death due to pregnancy-related morbidities. Increasing the rate of skilled maternal healthcare service utilization is effective to reduce maternal mortality rate. This paper examines the intervention effect of an integrated community-based maternal healthcare project implemented by a non-government organization, Friendship, aiming to provide maternal health services to women living in the remote riverine regions of Bangladesh. METHODS: We examined the skilled maternal healthcare service utilization before and after project implementation of the mothers with birth experience of 0-6 months from the intervention (N = 1,304) and comparison areas (N = 1,304). A difference-in-differences logistic model measured the effect of the intervention. RESULTS: After the intervention, mothers were three times more likely to receive ≥ 4 ANC visits from skilled providers (AOR: 2.9; 95 % CI: 2.1-4.2), 1.5 times more likely to have skilled birth attendants during deliveries (AOR: 1.5; 95 % CI: 1.1-2.1) and 1.5 times more likely to seek at least one PNC within 42 days after delivery (AOR: 1.5; 95 % CI: 1.1-2.2) as compared to the comparison group. CONCLUSION: The intervention showed positive effect on improving the ANC coverage, skilled delivery, and PNC among the mothers residing the remote riverine areas. Therefore, it opens up the opportunity for adaptation of such integrated community and facility-based interventions by other LMICs.

A population-based serological survey of Vibrio cholerae antibody titers in Ouest Department, Haiti in the year prior to the 2022 cholera outbreak.

After three years with no confirmed cholera cases in Haiti, an outbreak of Vibrio cholerae O1 emerged in October 2022. Levels of pre-existing antibodies provide an estimate of prior immunologic exposure, reveal potentially relevant immune responses, and set a baseline for future serosurveillance. We analyzed dried blood spots collected in 2021 from a population-weighted representative cross-sectional serosurvey in two communes in the Ouest Department of Haiti. We found lower levels of circulating IgG and IgA antibodies against V. cholerae lipopolysaccharide (LPS, IgG and IgA p<0.0001) in those below 5 years of age compared to those five years and older. Among a subset of patients with higher titers of antibodies, we were unable to detect any functional (vibriocidal) antibodies. In conclusion, the lack of detectable functional antibodies, and age-discordant levels of V. cholerae LPS IgG, suggest that populations in Haiti may be highly susceptible to cholera disease, especially among young children.

Trends and inequity in improved sanitation facility utilisation in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys.

Improved sanitation is indispensable to human health. However, lack of access to improved sanitation remains one of the most daunting public health challenges of the twenty-first century in Bangladesh. The aim of the study was to describe the trends in access to improved sanitation facilities following the inequity gap among households in different socioeconomic groups in Bangladesh. Data from the Bangladesh Demographic and Health Survey (BDHS) 2007, 2011, 2014, and 2017-18 were extracted for this study. Inequity in access to improved sanitation was calculated using rich-poor ratio and concentration index to determine the changes in inequity across the time period. In Bangladesh, the proportion of households with access to improved sanitation increased steadily from 25.4% to 45.4% between 2007 and 2014, but slightly decreased to 44.0% in 2017-18. Age, educational status, marital status of household head, household wealth index, household size, place of residence, division, and survey year were significantly associated with the utilisation of improved sanitation. There is a pro-rich situation, which means that utilisation of improved sanitation was more concentrated among the rich across all survey years (Concentration Index ranges: 0.40 to 0.27). The government and other relevant stakeholders should take initiatives considering inequity among different socioeconomic groups to ensure the use of improved sanitation facilities for all, hence achieving universal health coverage.

Factors associated with overweight and obesity among Bangladeshi university students: a case-control study.

Objectives: Overweight and obesity has an adverse effect on public health, and emerging adulthood (18-25 years) is recognized as a potential period for the onset of obesity, thus, this study aimed to explore risk factors associated with overweight and obesity in Bangladeshi university students. Participants: Bangladeshi university students (n = 280). Methods: A case-control study of 140 students with overweight and obesity and 140 students with normal weight. Results: Multiple logistic regression revealed having at least one overweight or obese parent (AOR = 4.2; 95% CI: 2.2-8.4) and participating in no physical activity (AOR = 3.2; 95% CI: 1.0-9.9) were potential risk factors. Reported consumption ≥4 meals in a day, junk food/fast food, and soft drinks for ≥3 days a week were potential determinants of overweight and obesity in this population. Conclusion: Increased awareness on the importance of regular physical activity and a healthy diet may reduce the risk of overweight and obesity among Bangladeshi university students.

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination.

RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis and the discovery of novel cancer biomarkers have extensively used RNA-Seq data. New biomarker identification is essential for moving cancer research forward, and early cancer diagnosis improves patients' chances of recovery and increases life expectancy. There is an urgency and scope of improvement in both sections. In this paper, we developed an autoencoder-based biomarker identification method by reversing the learning mechanism of the trained encoders. We devised an explainable post hoc methodology for identifying influential genes with a high likelihood of becoming biomarkers. We applied recursive feature elimination to shorten the list further and presented a list of 17 potential biomarkers that are 99.93% accurate in identifying cancer types using support vector machine for the UCI gene expression cancer RNA-Seq dataset consisting of five cancerous tumor types. Our methodology outperforms all of the state-of-the-art methods, confirming the potential of the newly identified biomarkers as well as the efficacy of the biomarker identification procedure. Moreover, we have evaluated the performance of our methodology using six independent RNA-Seq gene expression datasets for several tasks, i.e., classification of tumors from non-tumors, detecting the origin of circulating tumor cells (CTCs), and predicting if metastasis occurs or not. Our methodology achieved stimulating results for these tasks as well. The source code of this project is available at https://github.com/fuad021/biomarker-identification.