Incidence of Hospitalization due to Influenza-Associated Severe Acute Respiratory Infection During 2010-2019 in Bangladesh.

BACKGROUND: Global influenza-associated acute respiratory infections contribute to 3-5 million severe illnesses requiring hospitalization annually, with 90% of hospitalizations occurring among children < 5 years in developing countries. In Bangladesh, the inadequate availability of nationally representative, robust estimates of influenza-associated hospitalizations limits allocation of resources for prevention and control measures. METHODS: This study used data from the hospital-based influenza surveillance (HBIS) system in Bangladesh from 2010 to 2019 and healthcare utilization surveys to determine hospital utilization patterns in the catchment area. We estimated annual influenza-associated hospitalization numbers and rates for all age groups in Bangladesh using WHO methods, adjusted for a 6-day-a-week enrollment schedule, selective testing of specimens from children under five, and healthcare-seeking behavior, based on the proportion of symptomatic community participants seeking healthcare within the past week. We then estimated national hospitalization rates by multiplying age-specific hospitalization rates with the corresponding annual national census population. RESULTS: Annual influenza-associated hospitalization rates per 100,000 population for all ages ranged from 31 (95% CI: 27-36) in 2011 to 139 (95% CI: 130-149) in 2019. Children < 5 years old had the highest rates of influenza-associated hospitalization, ranging from 114 (95% CI: 90-138) in 2011 to 529 (95% CI: 481-578) in 2019, followed by adults aged ≥ 65 years with rates ranging from 46 (95% CI: 34-57) in 2012 to 252 (95% CI: 213-292) in 2019. The national hospitalization estimates for all ages during 2010-2019 ranged from 47,891 to 236,380 per year. CONCLUSIONS: The impact of influenza-associated hospitalizations in Bangladesh may be considerable, particularly for young children and older adults. Targeted interventions, such as influenza vaccination for these age groups, should be prioritized and evaluated.

Dataset of air pollutants (PM2.5, PM10, CO) concentrations in the export processing area of Dhaka, Bangladesh.

This study presents a valuable dataset on air quality in the densely populated Dhaka Export Processing Zone (DEPZ) of Bangladesh. It included a dataset of Particulate Matter (PM2.5, PM10) and CO concentrations with Air Quality Index (AQI) values. PM data was collected 24h, and CO data was collected 8h monthly from 2019 to 2023 using respirable dust sampler APS-113NL for PM2.5, APS-113BL for PM10, and LUTRON AQ9901SD Air Quality Monitor Data Logger used to measure CO concentration data. Data sampling locations are selected based on population density, and employment data for DEPZ is also included, highlighting a potential rise in population density. This article also forecasted pollutant concentrations, AQI values, and health hazards associated with air pollutants using the Auto Regressive Moving Average (ARIMA) model. The performance of the ARIMA model was also measured using root mean squared error (RMSE) and mean absolute error (MAE). However, this can be used to raise awareness among the public about the health hazards associated with air pollution and encourage them to take measures to reduce their exposure to air pollutants. In addition, this data can be instrumental for researchers and policymakers to assess air pollution risks, develop control strategies, and improve air quality in the DEPZ.

Post-discharge mortality among patients hospitalised with severe acute respiratory infection, Bangladesh, 2012-2019: a prospective observational study.

Background: Enhancing outcomes post-hospitalisation requires an understanding of predictive factors for adverse events. This study aimed to estimate post-discharge mortality rates among patients with severe acute respiratory infection (SARI) in Bangladesh, identify associated factors, and document reported causes of death. Methods: From January 2012 to December 2019, we conducted follow-up calls to patients or their families 30 days after discharge to assess the status of patients with SARI. Proportions of deaths within 30 days of discharge were estimated, and a comparative analysis of demographics, clinical characteristics, and influenza illness between decedents and survivors was performed using multivariable Cox regression models. Findings: Among 23,360 patients with SARI (median age: 20 years, IQR: 1.5-48, 65% male), 351 (1.5%) died during hospitalisation. Of 23,009 patients alive at discharge, 20,044 (87%) were followed, with 633 (3.2%) deaths within 30 days of discharge. In children (<18 years), difficulty breathing (adjusted hazard ratio [aHR] 1.8; 95% CI 1.1-3.0), longer hospital stay (aHR 1.1; 95% CI 1.1-1.1), and heart diseases (aHR 8.5; 95% CI 3.2-23.1) were associated with higher post-discharge death risk. Among adults (≥18 years), difficulty breathing (aHR 2.3; 95% CI 1.7-3.0), chronic obstructive pulmonary disease (aHR 1.7; 95% CI 1.4-2.2), and intensive care unit admission (aHR 5.2; 95% CI 1.9-14.0) were linked to elevated post-discharge death risk. Influenza virus was detected in 13% (46/351) of in-hospital SARI deaths and 10% (65/633) of post-discharge SARI deaths. Interpretation: Nearly one in twenty patients with SARI died during hospitalisation or within 1 month of discharge, with two-thirds of deaths occurring post-discharge. Seasonal influenza vaccination is recommended to mitigate influenza-associated mortality. To enhance post-discharge outcomes, hospitals should consider developing safe-discharge algorithms, reinforcing post-discharge care plans, and establishing outpatient monitoring for recently discharged patients. Funding: Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA [U01GH002259].

Safety and effectiveness of a recombinant hepatitis E vaccine in women of childbearing age in rural Bangladesh: a phase 4, double-blind, cluster-randomised, controlled trial.

BACKGROUND: Hepatitis E virus (HEV) leads to high mortality in pregnant women in low-income countries. We aimed to evaluate the safety of a HEV vaccine and its effectiveness in preventing hepatitis E during pregnancy. METHODS: In this phase 4, double-blind, cluster-randomised trial, 67 villages in Matlab, Bangladesh, were randomised 1:1 to receive HEV239 (a recombinant HEV vaccine) or a control vaccine (Hepa-B, a hepatitis B vaccine), using block randomisation with random number tables and blocks of size eight, stratified by cluster population size. Eligible non-pregnant women (aged 16-39 years) were vaccinated intramuscularly on day 0, at 1 month, and at 6 months, and followed up for 2 years after the last immunisation. The primary endpoint was hepatitis E in the pregnant, per-protocol population (those who received all three doses within 2 days of the scheduled dates), while safety was a secondary endpoint, assessed in the intention-to-treat (ITT) population (participants who received at least one dose). Solicited adverse events were recorded for the first 7 days after each dose, and unsolicited events until 2 years after a participant's final dose. Pregnancy-related safety outcomes were assessed in the pregnant ITT population. This study is registered with ClinicalTrials.gov (NCT02759991). FINDINGS: Between Oct 2, 2017, and Feb 28, 2019, 19 460 participants were enrolled and received either HEV239 (9478 [48·7%] participants, 33 clusters) or Hepa-B (9982 [51·3%] participants, 34 clusters), of whom 17 937 (92·2%) participants received three doses and 17 613 (90·5%) were vaccinated according to protocol (8524 [48·4%] in the HEV239 group and 9089 [51·6%] in the control group). No pregnant participants were confirmed to have hepatitis E in either treatment group. HEV239 showed a mild safety profile, similar to Hepa-B, with no difference in the proportion of solicited adverse events between groups and no severe solicited events. Pain was the most common local symptom (1215 [12·8%] HEV239 recipients and 1218 [12·2%] Hepa-B recipients) and fever the most common systemic symptom (141 [1·5%] HEV239 recipients and 145 [1·5%] Hepa-B recipients). None of the serious adverse events or deaths were vaccine related. Among pregnant participants, the HEV239 group had a higher risk of miscarriage (136 [5·7%] of 2407 pregnant participants) compared with the control group (102 [3·9%] of 2604; adjusted odds ratio 1·54 [95% CI 1·15-2·08]). INTERPRETATION: The effectiveness of HEV239 in pregnant women remains uncertain. HEV239 was safe and well tolerated in non-pregnant women, but findings regarding miscarriage warrant further investigation. FUNDING: Research Council of Norway; Innovax.

Emergence of the novel sixth Candida auris Clade VI in Bangladesh.

Candida auris, initially identified in 2009, has rapidly become a critical concern due to its antifungal resistance and significant mortality rates in healthcare-associated outbreaks. To date, whole-genome sequencing (WGS) has identified five unique clades of C. auris, with some strains displaying resistance to all primary antifungal drug classes. In this study, we presented the first WGS analysis of C. auris from Bangladesh, describing its origins, transmission dynamics, and antifungal susceptibility testing (AFST) profile. Ten C. auris isolates collected from hospital settings in Bangladesh were initially identified by CHROMagar Candida Plus, followed by VITEK2 system, and later sequenced using Illumina NextSeq 550 system. Reference-based phylogenetic analysis and variant calling pipelines were used to classify the isolates in different clades. All isolates aligned ~90% with the Clade I C. auris B11205 reference genome. Of the 10 isolates, 8 were clustered with Clade I isolates, highlighting a South Asian lineage prevalent in Bangladesh. Remarkably, the remaining two isolates formed a distinct cluster, exhibiting >42,447 single-nucleotide polymorphism differences compared to their closest Clade IV counterparts. This significant variation corroborates the emergence of a sixth clade (Clade VI) of C. auris in Bangladesh, with potential for international transmission. AFST results showed that 80% of the C. auris isolates were resistant to fluconazole and voriconazole, whereas Clade VI isolates were susceptible to azoles, echinocandins, and pyrimidine analogue. Genomic sequencing revealed ERG11_Y132F mutation conferring azole resistance while FCY1_S70R mutation found inconsequential in describing 5-flucytosine resistance. Our study underscores the pressing need for comprehensive genomic surveillance in Bangladesh to better understand the emergence, transmission dynamics, and resistance profiles of C. auris infections. Unveiling the discovery of a sixth clade (Clade VI) accentuates the indispensable role of advanced sequencing methodologies.IMPORTANCECandida auris is a nosocomial fungal pathogen that is commonly misidentified as other Candida species. Since its emergence in 2009, this multidrug-resistant fungus has become one of the five urgent antimicrobial threats by 2019. Whole-genome sequencing (WGS) has proven to be the most accurate identification technique of C. auris which also played a crucial role in the initial discovery of this pathogen. WGS analysis of C. auris has revealed five distinct clades where isolates of each clade differ among themselves based on pathogenicity, colonization, infection mechanism, as well as other phenotypic characteristics. In Bangladesh, C. auris was first reported in 2019 from clinical samples of a large hospital in Dhaka city. To understand the origin, transmission dynamics, and antifungal-resistance profile of C. auris isolates circulating in Bangladesh, we conducted a WGS-based surveillance study on two of the largest hospital settings in Dhaka, Bangladesh.

Application of extreme learning machine (ELM) forecasting model on CO2 emission dataset of a natural gas-fired power plant in Dhaka, Bangladesh.

Understanding and predicting CO2 emissions from individual power plants is crucial for developing effective mitigation strategies. This study analyzes and forecasts CO2 emissions from an engine-based natural gas-fired power plant in Dhaka Export Processing Zone (DEPZ), Bangladesh. This study also presents a rich dataset and ELM-based prediction model for a natural gas-fired plant in Bangladesh. Utilizing a rich dataset of Electricity generation and Gas Consumption, CO2 emissions in tons are estimated based on the measured energy use, and the ELM models were trained on CO2 emissions data from January 2015 to December 2022 and used to forecast CO2 emissions until December 2026. This study aims to improve the understanding and prediction of CO2 emissions from natural gas-fired power plants. While the specific operational strategy of the studied plant is not available, the provided data can serve as a valuable baseline or benchmark for comparison with similar facilities and the development of future research on optimizing operations and CO2 mitigation strategies. The Extreme Learning Machine (ELM) modeling method was employed due to its efficiency and accuracy in prediction. The ELM models achieved performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE), values respectively 3494.46 (<5000), 2013.42 (<2500), and 0.93 close to 1, which falls within the acceptable range. Although natural gas is a cleaner alternative, emission reduction remains essential. This data-driven approach using a Bangladeshi case study provides a replicable framework for optimizing plant operations and measuring and forecasting CO2 emissions from similar facilities, contributing to global climate change.

Rotavirus trends and distribution of genotypes before and during COVID-19 pandemic era: Bangladesh, 2017-2021.

Rotavirus gastroenteritis is accountable for an estimated 128 500 deaths among children younger than 5 years worldwide, and the majority occur in low-income countries. Although the clinical trials of rotavirus vaccines in Bangladesh revealed a significant reduction of severe rotavirus disease by around 50%, the vaccines are not yet included in the routine immunization program. The present study was designed to provide data on rotavirus diarrhea with clinical profiles and genotypes before (2017-2019) and during the COVID-19 pandemic period (2020-2021). Fecal samples were collected from 2% of the diarrheal patients at icddr,b Dhaka hospital of all ages between January 2017 and December 2021 and were tested for VP6 rotavirus antigen using ELISA. The clinical manifestations such as fever, duration of diarrhea and hospitalization, number of stools, and dehydration and so on were collected from the surveillance database (n = 3127). Of the positive samples, 10% were randomly selected for genotyping using Sanger sequencing method. A total of 12 705 fecal samples were screened for rotavirus A antigen by enzyme immunoassay. Overall, 3369 (27%) were rotavirus antigen-positive, of whom children <2 years had the highest prevalence (88.6%). The risk of rotavirus A infection was 4.2 times higher in winter than in summer. Overall, G3P[8] was the most prominent genotype (45.3%), followed by G1P[8] (32.1%), G9P[8] (6.8%), and G2P[4] (6.1%). The other unusual combinations, such as G1P[4], G1P[6], G2P[6], G3P[4], G3P[6], and G9P[6], were also present. Genetic analysis on Bangladeshi strains revealed that the selection pressure (dN/dS) was estimated as <1. The number of hospital visits showed a 37% drop during the COVID-19 pandemic relative to the years before the pandemic. Conversely, there was a notable increase in the rate of rotavirus positivity during the pandemic (34%, p < 0.00) compared to the period before COVID-19 (23%). Among the various clinical symptoms, only the occurrence of watery stool significantly increased during the pandemic. The G2P[4] strain showed a sudden rise (19%) in 2020, which then declined in 2021. In the same year, G1P[8] was more prevalent than G3P[8] (40% vs. 38%, respectively). The remaining genotypes were negligible and did not exhibit much fluctuation. This study reveals that the rotavirus burden remained high during the COVID-19 prepandemic and pandemic in Bangladesh. Considering the lack of antigenic variations between the circulating and vaccine-targeted strains, integrating the vaccine into the national immunization program could reduce the prevalence of the disease, the number of hospitalizations, and the severity of cases.

Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health.

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.

Emergence of SARS-CoV-2 Omicron sub-lineage JN.1 in Bangladesh.

We report complete genome sequences of 14 severe acute respiratory syndrome coronavirus 2 Omicron sub-lineage JN.1 obtained from Bangladeshi individuals between 19 December 2023 and 21 January 2024. All sequence data were generated by Oxford Nanopore Sequencing Technology using the amplicon sequencing approach developed by the ARTIC network.

Coding-complete genomes of 18 SARS-CoV-2 Omicron JN.1, JN.1.4, and JN.1.11 sub-lineages in Bangladesh.

We report 18 coding-complete genome sequences of emerging SARS-CoV-2 Omicron sub-lineages JN.1, JN.1.4, and JN.1.11 from Bangladesh. Nasopharyngeal swab samples were obtained from individuals with COVID-19 symptoms between December 2023 and January 2024. Whole genome sequencing was performed following the ARTIC Network-based protocol using Oxford Nanopore Technology.

Standardised quantitative assays for anti-SARS-CoV-2 immune response used in vaccine clinical trials by the CEPI Centralized Laboratory Network: a qualification analysis.

BACKGROUND: Accurate quantitation of immune markers is crucial for ensuring reliable assessment of vaccine efficacy against infectious diseases. This study was designed to confirm standardised performance of SARS-CoV-2 assays used to evaluate COVID-19 vaccine candidates at the initial seven laboratories (in North America, Europe, and Asia) of the Coalition for Epidemic Preparedness Innovations (CEPI) Centralized Laboratory Network (CLN). METHODS: Three ELISAs (pre-spike protein, receptor binding domain, and nucleocapsid), a microneutralisation assay (MNA), a pseudotyped virus-based neutralisation assay (PNA), and an IFN-γ T-cell ELISpot assay were developed, validated or qualified, and transferred to participating laboratories. Immune responses were measured in ELISA laboratory units (ELU) for ELISA, 50% neuralisation dilution (ND50) for MNA, 50% neutralisation titre (NT50) for PNA, and spot-forming units for the ELISpot assay. Replicate assay results of well characterised panels and controls of blood samples from individuals with or without SARS-CoV-2 infection were evaluated by geometric mean ratios, standard deviation, linear regression, and Spearman correlation analysis for consistency, accuracy, and linearity of quantitative measurements across all laboratories. FINDINGS: High reproducibility of results across all laboratories was demonstrated, with interlaboratory precision of 4·1-7·7% coefficient of variation for all three ELISAs, 3·8-19·5% for PNA, and 17·1-24·1% for MNA, over a linear range of 11-30 760 ELU per mL for the three ELISAs, 14-7876 NT50 per mL for PNA, and 21-25 587 ND50 per mL for MNA. The MNA was also adapted for detection of neutralising antibodies against the major SARS-CoV-2 variants of concern. The results of PNA and MNA (r=0·864) and of ELISA and PNA (r=0·928) were highly correlated. The IFN-γ ELISpot interlaboratory variability was 15·9-49·9% coefficient of variation. Sensitivity and specificity were close to 100% for all assays. INTERPRETATION: The CEPI CLN provides accurate quantitation of anti-SARS-CoV-2 immune response across laboratories to allow direct comparisons of different vaccine formulations in different geographical areas. Lessons learned from this programme will serve as a model for faster responses to future pandemic threats and roll-out of effective vaccines. FUNDING: CEPI.

SalivaDirect: an alternative to a conventional RNA extraction protocol for molecular detection of SARS-CoV-2 in a clinical setting.

IMPORTANCE: Affordable and accessible tests for COVID-19 allow for timely disease treatment and pandemic management. SalivaDirect is a faster and easier method to implement than NPS sampling. Patients can self-collect saliva samples at home or in other non-clinical settings without the help of a healthcare professional. Sample processing in SalivaDirect is less complex and more adaptable than in conventional nucleic acid extraction methods. We found that SalivaDirect has good diagnostic performance and is ideal for large-scale testing in settings where supplies may be limited or trained healthcare professionals are unavailable.

Complete genome sequence of Escherichia phage iGC_PHA_EC001.

Antimicrobial resistance (AMR) in bacteria poses a global health emergency due to limited treatment options. Here, we report a lytic bacteriophage belonging to Stephanstirmvirinae family against an AMR Escherichia coli (ST2089). Escherichia phage iGC_PHA_EC001 is of genus Phapecoctavirus and 148,445 bp in length, encoding 269 predicted protein-coding sequences and 10 tRNAs. The phage encodes two lytic proteins containing phage_lysozyme (PF00959.22) and cell wall hydrolase_2 (PF07486.15) as catalytic domains, respectively.

Detection of Rotavirus in Respiratory Specimens From Bangladeshi Children Aged <2 Years Hospitalized for Acute Gastroenteritis.

To examine the potential for respiratory transmission of rotavirus, we systematically assessed if rotavirus RNA is detectable by real-time quantitative reverse transcription-polymerase chain reaction from nasal and oropharyngeal swab specimens of Bangladeshi children with acute rotavirus gastroenteritis. Forehead swabs were collected to assess skin contamination. Among 399 children aged <2 years hospitalized for gastroenteritis during peak rotavirus season, rotavirus RNA was detected in stool, oral, nasal and forehead swab specimens of 354 (89%). A subset was genotyped; genotype was concordant within a child's specimen set and several different genotypes were detected across children. These findings support possible respiratory transmission of rotavirus and warrant further investigation.

Coding-complete genomes of XBB.1.16, XBB.2.3, FL.4 (alias of XBB.1.9.1.4), and XBB.3 of SARS-CoV-2 Omicron isolated from Bangladesh.

We announce the coding-complete genomes of four different strains of SARS-CoV-2 Omicron lineages, XBB.1.16, XBB.2.3, FL.4 (alias of XBB.1.9.1.4), and XBB.3. These strains were obtained between October 2022 and May 2023 from nasopharyngeal swabs of four Bangladeshi individuals, while one of them had a travel history. Genomic data were produced by implementing ARTIC Network-based amplicon sequencing using the Oxford Nanopore Technology.

Lessons learned from identifying clusters of severe acute respiratory infections with influenza sentinel surveillance, Bangladesh, 2009-2020.

Background: We explored whether hospital-based surveillance is useful in detecting severe acute respiratory infection (SARI) clusters and how often these events result in outbreak investigation and community mitigation. Methods: During May 2009-December 2020, physicians at 14 sentinel hospitals prospectively identified SARI clusters (i.e., ≥2 SARI cases who developed symptoms ≤10 days of each other and lived <30 min walk or <3 km from each other). Oropharyngeal and nasopharyngeal swabs were tested for influenza and other respiratory viruses by real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We describe the demographic of persons within clusters, laboratory results, and outbreak investigations. Results: Field staff identified 464 clusters comprising 1427 SARI cases (range 0-13 clusters per month). Sixty percent of clusters had three, 23% had two, and 17% had ≥4 cases. Their median age was 2 years (inter-quartile range [IQR] 0.4-25) and 63% were male. Laboratory results were available for the 464 clusters with a median of 9 days (IQR = 6-13 days) after cluster identification. Less than one in five clusters had cases that tested positive for the same virus: respiratory syncytial virus (RSV) in 58 (13%), influenza viruses in 24 (5%), human metapneumovirus (HMPV) in five (1%), human parainfluenza virus (HPIV) in three (0.6%), adenovirus in two (0.4%). While 102/464 (22%) had poultry exposure, none tested positive for influenza A (H5N1) or A (H7N9). None of the 464 clusters led to field deployments for outbreak response. Conclusions: For 11 years, none of the hundreds of identified clusters led to an emergency response. The value of this event-based surveillance might be improved by seeking larger clusters, with stronger epidemiologic ties or decedents.

Natural selection shapes the evolution of SARS-CoV-2 Omicron in Bangladesh.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved to give rise to a highly transmissive and immune-escaping variant of concern, known as Omicron. Many aspects of the evolution of SARS-CoV-2 and the driving forces behind the ongoing Omicron outbreaks remain unclear. Substitution at the receptor-binding domain (RBD) in the spike protein is one of the primary strategies of SARS-CoV-2 Omicron to hinder recognition by the host angiotensin-converting enzyme 2 (ACE2) receptor and avoid antibody-dependent defense activation. Here, we scanned for adaptive evolution within the SARS-CoV-2 Omicron genomes reported from Bangladesh in the public database GISAID (www.gisaid.org; dated 2 April 2023). The ratio of the non-synonymous (Ka) to synonymous (Ks) nucleotide substitution rate, denoted as ω, is an indicator of the selection pressure acting on protein-coding genes. A higher proportion of non-synonymous to synonymous substitutions (Ka/Ks or ω > 1) indicates positive selection, while Ka/Ks or ω near zero indicates purifying selection. An equal amount of non-synonymous and synonymous substitutions (Ka/Ks or ω = 1) refers to neutrally evolving sites. We found evidence of adaptive evolution within the spike (S) gene of SARS-CoV-2 Omicron isolated from Bangladesh. In total, 22 codon sites of the S gene displayed a signature of positive selection. The data also highlighted that the receptor-binding motif within the RBD of the spike glycoprotein is a hotspot of adaptive evolution, where many of the codons had ω > 1. Some of these adaptive sites at the RBD of the spike protein are known to be associated with increased viral fitness. The M gene and ORF6 have also experienced positive selection. These results suggest that although purifying selection is the dominant evolutionary force, positive Darwinian selection also plays a vital role in shaping the evolution of SARS-CoV-2 Omicron in Bangladesh.

Interpretation of molecular detection of avian influenza A virus in respiratory specimens collected from live bird market workers in Dhaka, Bangladesh: infection or contamination?

OBJECTIVES: Interpreting real-time reverse transcription-polymerase chain reaction (rRT-PCR) results for human avian influenza A virus (AIV) detection in contaminated settings like live bird markets (LBMs) without serology or viral culture poses a challenge. METHODS: During February-March 2012 and November 2012-February 2013, we screened workers at nine LBMs in Dhaka, Bangladesh, to confirm molecular detections of AIV RNA in respiratory specimens with serology. We tested nasopharyngeal (NP) and throat swabs from workers with influenza-like illness (ILI) and NP, throat, and arm swabs from asymptomatic workers for influenza virus by rRT-PCR and sera for seroconversion and antibodies against HPAI A(H5N1) and A(H9N2) viruses. RESULTS: Among 1273 ILI cases, 34 (2.6%) had A(H5), 56 (4%) had A(H9), and six (0.4%) had both A(H5) and A(H9) detected by rRT-PCR. Of 192 asymptomatic workers, A(H5) was detected in eight (4%) NP and 38 (20%) arm swabs. Of 28 ILI cases with A(H5) or A(H9) detected, none had evidence of seroconversion, but one (3.5%) and 12 (43%) were seropositive for A(H5) and A(H9), respectively. CONCLUSION: Detection of AIV RNA in respiratory specimens from symptomatic and asymptomatic LBM workers without evidence of seroconversion or virus isolation suggests environmental contamination, emphasizing caution in interpreting rRT-PCR results in high viral load settings.

Is women's household decision-making autonomy associated with their higher dietary diversity in Bangladesh? Evidence from nationally representative survey.

In Bangladesh, a low-quality repetitive diet characterized by starchy staple foods is typical, leading to disorders associated with micronutrient deficiencies, particularly among mothers and their children. The purpose of the study was to validate the link between women's decision-making autonomy and higher dietary diversity score. Participants were ever married women aged 15-49 years old with comprehensive dietary information (n = 17,842), selected from the Bangladesh Demographic and Health Survey, 2014. The dietary diversity score (DDS) was obtained from a 24-h recall of dietary intake from nine food groups, categorized into lower DDS (DDS ≤ 4) and higher DDS (DDS ≥ 5). Descriptive analysis, bivariate and multivariate logistic regression were conducted using STATA version 15. Almost all women consumed starchy foods, flesh (83.86%), and fruits (67.30%). Using logistic regression, the odds of achieving dietary diversity score were higher among women who participated in household purchases (OR 2.40; 95% CI: 1.52-3.83; p = 0.022). Women who had higher and secondary education were 2.72 (95% CI: 1.49-3.02; p = 0.025) and 1.31 (95% CI: 0.58-2.18; p = 0.029) times more likely to achieve higher DDS than women having no education, as well as women in the richest quintile (OR 6.49; 95% CI: 4.12-8.5; p = 0.037) compared to women in the lowest quintile. This study highlighted the association of several socioeconomic conditions of ever married women and their dietary diversity score in Bangladesh. Therefore, promoting the women's education status, improving the socioeconomic conditions, and prioritizing their decisions are recommended for the attainment of higher dietary diversity score.

Epidemiology and Risk Factors of Norovirus Infections Among Diarrhea Patients Admitted to Tertiary Care Hospitals in Bangladesh.

BACKGROUND: Norovirus is a major cause of endemic acute gastroenteritis (AGE) worldwide. We described the epidemiology, risk factors, and genotypic distribution of noroviruses among hospitalized patients of all ages in Bangladesh. METHODS: From March 2018 to October 2021, 1250 AGE case patients and controls (age, sex, season, and site matched) were enrolled at 10 hospitals. Demographic and clinical information was collected; real-time reverse-transcriptase polymerase chain reaction (RT-PCR) used to test stool specimens, and positive samples were genotyped. RESULTS: Norovirus was detected in 9% of cases (111 of 1250) and 15% (182 of 1250) of controls. Eighty-two percent of norovirus-positive cases were in children <5 years old. Norovirus-positive AGE hospitalizations occurred year-round, with peaks in April and October. Risk factors for norovirus included age <5 years (adjusted odds ratio, 3.1 [95% confidence interval, 1.9-5.2]) and exposure to a patient with AGE in the 10 days before enrollment (3.8 [1.9-7.2]). GII.3[P16] and GII.4 Sydney[P16] were the predominant genotypes. CONCLUSIONS: We highlight the burden of norovirus in hospital settings. Young age and recent exposure to a patient with AGE were risk factors for norovirus. A high prevalence of norovirus among controls might represent asymptomatic reinfections or prolonged shedding from a previous infection; carefully designed longitudinal studies are needed to improve our understanding of norovirus infections in Bangladesh.