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Abstract Cardioprotective Unani drug, Saad Kufi (Cyperus scariosus R.Br.), mentioned by the intellectual colossus Ibn Sina (Avicenna) a thousand years back in his book, “Kitab al-Adviya al-Qalbia” and still widely used by Unani physicians, was selected for experimental study. The main objective of the study was to explore the trial drug ex vivo for validation, hemodynamic elucidation, and molecular mechanism identification for the improvement of Unani therapeutics and to identify a safe and effective novel cardiovascular drug for mainstream medicine. Five doses of 50% ethanolic extract of the physiochemically standardized root were studied in a normal rat heart, on a semi-automated Langendorff apparatus supported with an advanced data acquisition system, perfused with carbogenated non-recirculating Kreb’s-Hensleit solution, at constant flow mode, by continuous recording. The effect of 6 repetitions (n = 6) of each dose was studied with the following parameters on the Lab chart pro version: heart rate, left ventricular developed pressure, cardiac work, and coronary pressure. The significance of the difference was determined by the student’s “t” test. Results of all doses of Saad Kufi showed a significant increase in the above parameters. The maximum increase was observed by Dose III. It could be concluded that Unani’s clinical use of Saad Kufi (C. scariosus R.Br) as cardioprotective was validated and higher doses were indicated to be clinically optimal. Hemodynamically and molecular mechanism-wise, it is indicated to be a sympathomimetic or inotropic-like agent and possibly a direct vasodilator. Since existing long-term treatment of heart failure is not done by cardiac stimulants or positive inotropic and chronotropic agents, the possible future clinical demonstration of therapeutic improvement in heart failure will bring Saad Kufi as a novel drug in mainstream medicine.

Migratory birds are a natural reservoir for major respiratory viruses such as the avian influenza virus (AIV) and the avian coronavirus (AvCoV). Transmission of these viruses from migratory birds to domestic birds increases the prevalence of those diseases that cause severe economic and public health concerns in Bangladesh. The study focused on active surveillance of major respiratory viral pathogens in migratory birds, molecular identification of the viruses, and their phylogenetic origin. To conduct this study, 850 environmental samples (830 fecal samples, 10 soil samples, and 10 water samples) were collected during three consecutive winter seasons from three divisions (Dhaka, Sylhet, and Mymensingh) and pooled according to the year of collection and locations, resulting in a total of 184 tested samples. Using gene-specific primers and probes in TaqMan-and SYBR Green-based RT-qPCR assays, the samples were screened for AIV and AvCoV, respectively. Out of the 184 pooled samples, 37 were found to be positive for these respiratory pathogens. Furthermore, out of the 37 (20.11%) positive respiratory pathogens, 11.96% were AIV (n = 22) and 8.15% were AvCoV (n = 15). For the first time in Bangladesh, AIV H4N2, H4N6, and AvCoVs have been found in fecal samples from migratory birds through surveillance. Phylogenetic analyses of the HA and NA genes of AIV and the polymerase gene (Orf 1) of AvCoV revealed that these strains share a close phylogenetic relationship with the isolates from wild birds in Europe and Asia. The Bangladeshi strains with Eurasian ancestry might pose a significant threat to migratory birds flying through the Asian flyways. They might also be a potential source of virus introduction and spread to poultry raised on land. These findings emphasize the significance of ongoing AIV and AvCoV surveillance in migratory birds in Bangladesh.

Abstract A regulated stress response is essential for healthy child growth and development trajectories. We conducted a cluster-randomized trial in rural Bangladesh (funded by the Bill & Melinda Gates Foundation, ClinicalTrials.gov NCT01590095) to assess the effects of an integrated nutritional, water, sanitation, and handwashing intervention on child health. We previously reported on the primary outcomes of the trial, linear growth and caregiver-reported diarrhea. Here, we assessed additional prespecified outcomes: physiological stress response, oxidative stress, and DNA methylation (N = 759, ages 1–2 years). Eight neighboring pregnant women were grouped into a study cluster. Eight geographically adjacent clusters were block-randomized into the control or the combined nutrition, water, sanitation, and handwashing (N + WSH) intervention group (receiving nutritional counseling and lipid-based nutrient supplements, chlorinated drinking water, upgraded sanitation, and handwashing with soap). Participants and data collectors were not masked, but analyses were masked. There were 358 children (68 clusters) in the control group and 401 children (63 clusters) in the intervention group. We measured four F2-isoprostanes isomers (iPF(2α)-III; 2,3-dinor-iPF(2α)-III; iPF(2α)-VI; 8,12-iso-iPF(2α)-VI), salivary alpha-amylase and cortisol, and methylation of the glucocorticoid receptor (NR3C1) exon 1F promoter including the NGFI-A binding site. Compared with control, the N + WSH group had lower concentrations of F2-isoprostanes isomers (differences ranging from −0.16 to −0.19 log ng/mg of creatinine, P

High pathogenicity avian influenza (HPAI) virus H5N1 first emerged in Bangladesh in 2007. Despite the use of vaccines in chickens since 2012 to control HPAI, HPAI H5Nx viruses have continued to infect poultry, and wild birds, resulting in notable mass mortalities in house crows (Corvus splendens). The first HPAI H5Nx viruses in Bangladesh belonged to clade 2.2.2, followed by clade 2.3.4.2 and 2.3.2.1 viruses in 2011. After the implementation of chicken vaccination in 2012, these viruses were mostly replaced by clade 2.3.2.1a viruses and more recently clade 2.3.4.4b and h viruses. In this study, we reconstruct the phylogenetic history of HPAI H5Nx viruses in Bangladesh to evaluate the role of major host species in the maintenance and evolution of HPAI H5Nx virus in Bangladesh and reveal the role of heavily impacted crows in virus epidemiology. Epizootic waves caused by HPAI H5N1 and H5N6 viruses amongst house crows occurred annually in winter. Bayesian phylodynamic analysis of clade 2.3.2.1a revealed frequent bidirectional viral transitions between domestic ducks, chickens, and house crows that was markedly skewed towards ducks; domestic ducks might be the source, or reservoir, of HPAI H5Nx in Bangladesh, as the number of viral transitions from ducks to chickens and house crows was by far more numerous than the other transitions. Our results suggest viral circulation in domestic birds despite vaccination, with crow epizootics acting as a sentinel. The vaccination strategy needs to be updated to use more effective vaccinations, assess vaccine efficacy, and extension of vaccination to domestic ducks, the key reservoir.

Nipah virus (NiV) is an emerging zoonotic RNA virus that can cause fatal respiratory and neurological diseases in animals and humans. Accurate NiV diagnostics and surveillance tools are crucial for the identification of acute and resolved infections and to improve our understanding of NiV transmission and circulation. Here, we have developed and validated a split NanoLuc luciferase NiV glycoprotein (G) biosensor for detecting antibodies in clinical and animal samples. This assay is performed by simply mixing reagents and measuring luminescence, which depends on the complementation of the split NanoLuc luciferase G biosensor following its binding to antibodies. This anti-NiV-G “mix-and-read” assay was validated using the WHO's first international standard for anti-NiV antibodies and more than 700 serum samples from the NiV-endemic country of Bangladesh. Anti-NiV antibodies from survivors persisted for at least 8 years according to both ⍺NiV-G mix-and-read and NiV neutralization assays. The ⍺NiV-G mix-and-read assay sensitivity (98.6%) and specificity (100%) were comparable to anti-NiV IgG ELISA performance but failed to detect anti-NiV antibodies in samples collected less than a week following the appearance of symptoms. Overall, the anti-NiV-G biosensor represents a simple, fast, and reliable tool that could support the expansion of NiV surveillance and retrospective outbreak investigations.

Avian influenza virus (AIV) is of major concern to livestock, wildlife, and human health. In many countries in the world, including Bangladesh, AIV is endemic in poultry, requiring improving biosecurity. In Bangladesh, we investigated how variation in biosecurity practices in commercial chicken farms affected their AIV infection status to help guide AIV mitigation strategies. We collected pooled fecal swabs from 225 farms and tested the samples for the AIV matrix gene followed by H5, H7, and H9 subtyping using rRT-PCR. We found that 39.6% of chicken farms were AIV positive, with 13% and 14% being positive for subtypes H5 and H9, respectively. Using a generalized linear mixed effects model, we identified as many as 12 significant AIV risk factors. Two major factors promoting AIV risk that cannot be easily addressed in the short term were farm size and the proximity of the farm to a live bird market. However, the other ten significant determinants of AIV risk can be more readily addressed, of which the most important ones were limiting access by visitors (reducing predicted AIV risk from 42 to 6%), isolation and treatment of sick birds (42 to 7%), prohibiting access of vehicles to poultry sheds (38 to 8%), improving hand hygiene (from 42 to 9%), not sharing farm workers across farms (37 to 8%), and limiting access by wild birds to poultry sheds (37 to 8%). Our findings can be applied to developing practical and cost-effective measures that significantly decrease the prevalence of AIV in chicken farms. Notably, in settings with limited resources, such as Bangladesh, these measures can help governments strengthen biosecurity practices in their poultry industry to limit and possibly prevent the spread of AIV.

Quantum computing is emerging as a significant threat to information protected by widely used cryptographic systems. Cryptographic methods, once deemed secure for decades, are now at risk of being compromised, posing a massive threat to the security of sensitive data and communications across enterprises worldwide. As a result, there is an urgent need to migrate to quantum-resistant cryptographic systems. This is no simple task. Migrating to a quantum-safe state is a complex process, and many organisations lack the in-house expertise to navigate this transition without guidance. In this paper, we present a comprehensive framework designed to assist enterprises with this migration. Our framework outlines essential steps involved in the cryptographic migration process, and leverages existing organisational inventories. The framework facilitates the efficient identification of cryptographic assets and can be integrated with other enterprise frameworks smoothly. To underscore its practicality and effectiveness, we have incorporated case studies that utilise graph-theoretic techniques to pinpoint and assess cryptographic dependencies. This is useful in prioritising crypto-systems for replacement.

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models.

Early fire detection is the key to saving lives and limiting property damage. Advanced technology can detect fires in high-risk zones with minimal human presence before they escalate beyond control. This study focuses on providing a more advanced model structure based on the YOLOv8 architecture to enhance early recognition of fire. Although YOLOv8 is excellent at real-time object detection, it can still be better adjusted to the nuances of fire detection. We achieved this advancement by incorporating an additional context-to-flow layer, enabling the YOLOv8 model to more effectively capture both local and global contextual information. The context-to-flow layer enhances the model’s ability to recognize complex patterns like smoke and flames, leading to more effective feature extraction. This extra layer helps the model better detect fires and smoke by improving its ability to focus on fine-grained details and minor variation, which is crucial in challenging environments with low visibility, dynamic fire behavior, and complex backgrounds. Our proposed model achieved a 2.9% greater precision rate, 4.7% more recall rate, and 4% more F1-score in comparison to the YOLOv8 default model. This study discovered that the architecture modification increases information flow and improves fire detection at all fire sizes, from tiny sparks to massive flames. We also included explainable AI strategies to explain the model’s decision-making, thus adding more transparency and improving trust in its predictions. Ultimately, this enhanced system demonstrates remarkable efficacy and accuracy, which allows additional improvements in autonomous fire detection systems.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that presents significant diagnostic challenges due to its varied symptoms and nature. This study aims to improve ASD classification using advanced deep learning techniques applied to neuroimaging data. We developed an automated system leveraging the ABIDE-1 dataset and a novel lightweight quantized one-dimensional (1D) Convolutional Neural Network (Q-CNN) model to analyze fMRI data. Our approach employs the NIAK pipeline with multiple brain atlases and filtering methods. Initially, the Regions of Interest (ROIs) are converted into feature vectors using tangent space embedding to feed into the Q-CNN model. The proposed 1D-CNN is quantized through Quantize Aware Training (QAT). As the quantization method, int8 quantization is utilized, which makes it both robust and lightweight. We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. Our findings indicate that the CC200 brain atlas, within the NIAK pipeline’s filt-global filtering methods, provides the best results for ASD classification. Notably, the ASD classification outcomes have achieved a significant test accuracy of 98% using the CC200 and filt-global filtering techniques. To the best of our knowledge, this performance surpasses previous studies in the field, highlighting a notable enhancement in ASD detection from fMRI data. Furthermore, the FL-based Q-CNN model demonstrated robust performance and high efficiency on a Raspberry Pi 4, underscoring its potential for real-world applications. We exhibit the efficacy of the Q-CNN model by comparing its inference time, power consumption, and storage requirements with those of the 1D-CNN, quantized CNN, and the proposed int8 Q-CNN models. This research has made several key contributions, including the development of a lightweight int8 Q-CNN model, the application of FL for data privacy, and the evaluation of the proposed model in real-world settings. By identifying optimal brain atlases and filtering methods, this study provides valuable insights for future research in the field of neurodevelopmental disorders.

Abstract Background Wastewater-based epidemiological surveillance has been considered a powerful tool for early detection and monitoring of the dynamics of SARS-CoV-2 and its lineages circulating in a community. This study is aimed to investigate the complexity of SARS-CoV-2 infection dynamics in Dhaka city by examining its genetic variants in wastewater. Also, the study seeks to determine a connection between the SARS-CoV-2 variations detected in clinical testing and those found in wastewater samples. Results Out of 504 samples tested in RT-qPCR, 185 (36.7%) tested positive for SARS-CoV-2 viral RNA. The median log10 concentration of SARS-CoV-2 N gene copies/Liter of wastewater (gc/L) was 5.2, and the median log10 concentration of ORF1ab was 4.9. To further reveal the genetic diversity of SARS-CoV-2, ten samples with ORF1ab real-time RT-PCR cycle threshold (Ct) values ranging from 28.78 to 32.13 were subjected to whole genome sequencing using nanopore technology. According to clade classification, sequences from wastewater samples were grouped into 4 clades: 20A, 20B, 21A, 21J, and the Pango lineage, B.1, B.1.1, B.1.1.25, and B.1.617.2, with coverage ranging from 94.2 to 99.8%. Of them, 70% belonged to clade 20B, followed by 10% to clade 20A, 21A, and 21J. Lineage B.1.1.25 was predominant in Bangladesh and phylogenetically related to the sequences from India, the USA, Canada, the UK, and Italy. The Delta variant (B.1.617.2) was first identified in clinical samples at the beginning of May 2021. In contrast, we found that it was circulating in the community and was detected in wastewater in September 2020. Conclusion Environmental surveillance is useful for monitoring temporal and spatial trends of existing and emerging infectious diseases and supports evidence-based public health measures. The findings of this study supported the use of wastewater-based epidemiology and provided the baseline data for the dynamics of SARS-CoV-2 variants in the wastewater environment in Dhaka, Bangladesh.

BackgroundA number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change.ObjectivesOur objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change.MethodsWe matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters.ResultsComparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null.DiscussionHigher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

High pathogenicity avian influenza (HPAI) H5N1 outbreaks pose a significant threat to the health of livestock, wildlife, and humans. Avian influenza viruses (AIVs) are enzootic in poultry in many countries, including Bangladesh, necessitating improved farm biosecurity measures. However, the comprehension of biosecurity and hygiene practices, as well as the infection of AIV in turkey farms, are poorly understood in Bangladesh. Therefore, we conducted this study to determine the prevalence of AIV subtypes and their association with biosecurity and hygiene practices in turkey farms. We collected oropharyngeal and cloacal swabs from individual turkeys from 197 farms across 9 districts in Bangladesh from March to August 2019. We tested the swab samples for the AIV matrix gene (M gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We found 24.68% (95% CI:21.54–28.04) of turkey samples were AIV positive, followed by 5.95% (95% CI: 4.33–7.97) for H5, 6.81% (95% CI: 5.06–8.93) for H9 subtype and no A/H7 was found. Using a generalized linear mixed model, we determined 10 significant risk factors associated with AIV circulation in turkey farms. We found that the absence of sick turkeys, the presence of footbaths, the absence of nearby poultry farms, concrete flooring, and the avoidance of mixing newly purchased turkeys with existing stock can substantially reduce the risk of AIV circulation in turkey farms (odds ratio ranging from 0.02 to 0.08). Furthermore, the absence of nearby live bird markets, limiting wild bird access, no visitor access, improved floor cleaning frequency, and equipment disinfection practices also had a substantial impact on lowering the AIV risk in the farms (odds ratio ranging from 0.10 to 0.13). The results of our study underscore the importance of implementing feasible and cost-effective biosecurity measures aimed at reducing AIV transmission in turkey farms. Particularly in resource-constrained environments such as Bangladesh, such findings might assist governmental entities in enhancing biosecurity protocols within their poultry sector, hence mitigating and potentially averting the transmission of AIV and spillover to humans.

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.

The first case of COVID-19 was detected in Bangladesh on 8 March 2020. Since then, the Government of Bangladesh (GoB) has implemented various measures to limit the transmission of COVID-19, including widespread testing facilities across the nation through a laboratory network for COVID-19 molecular testing. This study aimed to analyze the dynamics of SARS-CoV-2 in Bangladesh by conducting COVID-19 testing and genomic surveillance of the virus variants throughout the pandemic. Nasopharyngeal swabs were collected from authorized GoB collection centers between April 2020 and June 2023. The viral RNA was extracted and subjected to real-time PCR analysis in icddr,b’s Virology laboratory. A subset of positive samples underwent whole-genome sequencing to track the evolutionary footprint of SARS-CoV-2 variants. We tested 149,270 suspected COVID-19 cases from Dhaka (n = 81,782) and other districts (n = 67,488). Of these, 63% were male. The highest positivity rate, 27%, was found in the >60 years age group, followed by 26%, 51–60 years, 25% in 41–50 years, and the lowest, 9% in under five children. Notably, the sequencing of 2742 SARS-CoV-2 genomes displayed a pattern of globally circulating variants, Alpha, Beta, Delta, and Omicron, successively replacing each other over time and causing peaks of COVID-19 infection. Regarding the risk of SARS-CoV-2 infection, it was observed that the positivity rate increased with age compared to the under-5 age group in 2020 and 2021. However, these trends did not remain consistent in 2022, where older age groups, particularly those over 60, had a lower positivity rate compared to other age groups due to vaccination. The study findings generated data on the real-time circulation of different SARS-CoV-2 variants and the upsurge of COVID-19 cases in Bangladesh, which impacted identifying hotspots and restricting the virus from further transmission. Even though there is currently a low circulation of SARS-CoV-2 in Bangladesh, similar approaches of genomic surveillance remain essential for monitoring the emergence of new SARS-CoV-2 variants or other potential pathogens that could lead to future pandemics.

Abstract Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.

ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) continues to spread and evolve, giving rise to new surges in cases and deaths. Although nasopharyngeal swab (NPS) samples are the gold standard for diagnosing SARS-CoV2, NPS sampling is invasive and requires skilled staff, specialized transport medium, and nucleic acid extraction. The SalivaDirect method uses saliva samples and a simplified, flexible approach to overcome these drawbacks. In our study conducted at the COVID-19 Screening Unit of the Dhaka Hospital of icddr,b (International Centre for Diarrheal Disease Research, Bangladesh), the diagnostic performance of SalivaDirect and NPS was compared using paired samples. SARS-CoV2 was detected by performing RT-qPCR using RNA extracted from saliva samples with the SalivaDirect method and from the NPS with extracted RNA. A total of 200 participants were enrolled from February to March 2021, among whom 78 (39.0%) tested positive for SAR-CoV2 from at least one sample. Among the 78 participants testing positive, 65 (83.3%) tested positive in both specimens, eight (10.3%) tested positive only in NPS, and five (6.4%) tested positive only in saliva. SalivaDirect had a sensitivity of 89.0% and specificity of 96.1%, with NPS as the reference test. The sensitivity of NPS and SalivaDirect was 93.6% and 89.7% respectively, with a composite reference standard (where patients are defined as positive if tested positive in either method) as the reference. Our findings demonstrated that the SalivaDirect method can be used as an alternative to NPS in our clinical setting and also supports the use of SalivaDirect in other settings. 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.

Highly pathogenic avian influenza virus subtype H5N1 endangers poultry, wildlife, and human health and is enzootic in large parts of Asia, with live bird markets (LBMs) as putative hotspots for their maintenance, amplification, and spread. To mitigate the extent of these and other avian influenza viruses (AIV) of concern, we aimed to increase our quantitative understanding of the factors determining the presence of avian influenza virus in LBM stalls. Between 2016 and 2017, we collected fecal or offal samples from 1008 stalls in 113 LBMs across the Dhaka and Rajshahi districts in Bangladesh. For each stall, samples were pooled and tested for the AIV matrix gene, followed by H5 and H9 subtyping using rRT-PCR. We detected Influenza A viral RNA in 49% of the stalls. Of the AIV positive samples, 52% and 24% were determined to be H5 and H9 viruses, respectively, which are both subtypes of considerable health concern. We used generalized linear mixed effect modelling to study AIV presence in individual stalls within LBMs as a function of 13 out of the 20 risk factors identified by FAO. We found that small and feasible improvements in cleaning and disinfection frequency, installing running water in stalls, and not mixing different breeds of chicken in the same cages had large impacts on the presence of AIV in stalls (Odds ratios 0.03–0.05). Next, cleaning vehicles used in poultry transport, not selling waterfowl with chickens in the same stall, buying stock directly from commercial farms, separating sick birds from healthy ones, and avoiding access by wild birds like house crows, also had major effects on lowering the risk of stalls having AIV (Odds ratios 0.16–0.33). These findings can be directly used in developing practical and affordable measures to reduce the prevalence of AIV in LBMs. Also, in settings with limited resources like Bangladesh, such mitigation may significantly contribute to reducing AIV circulation amongst poultry and spillover to wildlife and humans.

Live bird markets (LBMs) are critical for poultry trade in many developing countries that are regarded as hotspots for the prevalence and contamination of avian influenza viruses (AIV). Therefore, we conducted weekly longitudinal environmental surveillance in LBMs to determine annual cyclic patterns of AIV subtypes, environmental risk zones, and the role of climatic factors on the AIV presence and persistence in the environment of LBM in Bangladesh. From January 2018 to March 2020, we collected weekly fecal and offal swab samples from each LBM and tested using rRT-PCR for the M gene and subtyped for H5, H7, and H9. We used Generalized Estimating Equations (GEE) approaches to account for repeated observations over time to correlate the AIV prevalence and potential risk factors and the negative binomial and Poisson model to investigate the role of climatic factors on environmental contamination of AIV at the LBM. Over the study period, 37.8% of samples tested AIV positive, 18.8% for A/H5, and A/H9 was, for 15.4%. We found the circulation of H5, H9, and co-circulation of H5 and H9 in the environmental surfaces year-round. The Generalized Estimating Equations (GEE) model reveals a distinct seasonal pattern in transmitting AIV and H5. Specifically, certain summer months exhibited a substantial reduction of risk up to 70–90% and 93–94% for AIV and H5 contamination, respectively. The slaughtering zone showed a significantly higher risk of contamination with H5, with a three-fold increase in risk compared to bird-holding zones. From the negative binomial model, we found that climatic factors like temperature and relative humidity were also significantly associated with weekly AIV circulation. An increase in temperature and relative humidity decreases the risk of AIV circulation. Our study underscores the significance of longitudinal environmental surveillance for identifying potential risk zones to detect H5 and H9 virus co-circulation and seasonal transmission, as well as the imperative for immediate interventions to reduce AIV at LBMs in Bangladesh. We recommend adopting a One Health approach to integrated AIV surveillance across animal, human, and environmental interfaces in order to prevent the epidemic and pandemic of AIV.

Cobalt isonicotinate tetrahydrate has been explored as the molecular precursor for the fabrication of both the bare and SBA-15 supported Co3O4. This method appears to be effective in minimizing the formation of oxide particles outside the mesopore volumes of SBA-15. Microstructural properties of the synthesized materials have been investigated by the PXRD, SEM, TEM, H2-TPR, N2-Physisorption, and DR. UV–Visible and Raman spectroscopic techniques. The SBA-15 supported nanocomposite has shown excellent catalytic activity giving 95% conversion in the epoxidation of styrene using TBHP as the oxidant. Deposition of trace amount of gold significantly enhances the epoxide selectivity of the catalysts up to 85%. The catalysts have shown excellent reusability.

Spillovers of Nipah virus (NiV) from Pteropus bats to humans occurs frequently in Bangladesh, but the risk for spillover into other animals is poorly understood. We detected NiV antibodies in cattle, dogs, and cats from 6 sites where spillover human NiV infection cases occurred during 2013–2015.

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.

SARS-CoV-2 has been highly susceptible to mutations since its emergence in Wuhan, China, and its subsequent propagation due to containing an RNA as its genome. The emergence of variants with improved transmissibility still poses a grave threat to global health. The spike protein mutation is mainly responsible for higher transmissibility and risk severity. This study retrieved SARS-CoV-2 variants structural and nonstructural proteins (NSPs) sequences from several geographic locations, including Africa, Asia, Europe, Oceania, and North and South America. First, multiple sequence alignments with BioEdit and protein homology modeling were performed using the SWISS Model. Then the structure visualization and structural analysis were performed by superimposing against the Wuhan sequence by Pymol to retrieve the RMSD values. Sequence alignment revealed familiar, uncommon regional among variants and, interestingly, a few unique mutations in Beta, Delta, and Omicron. Structural analysis of such unique mutations revealed that they caused structural deviations in Beta, Delta, and Omicron spike proteins. In addition, these variants were more severe in terms of hospitalization, sickness, and higher mortality, which have a substantial relationship with the structural deviations because of those unique mutations. Such evidence provides insight into the SARS-CoV-2 spike protein vulnerability toward mutation and their structural and functional deviations, particularly in Beta, Delta, and Omicron, which might be the cause of their broader coverage. This knowledge can help us with regional vaccine strain selection, virus pathogenicity testing, diagnosis, and treatment with more specific vaccines.

The Retinex Theory (RT) and its adaptations have gained significant popularity in the field of image processing. Nevertheless, traditional Retinex algorithms are generally customized to specific tasks. Besides, their use of logarithmic transformation (LT) to convert the multiplicative model to an additive one often results in the loss of texture information in the reflectance layer. In contrast to conventional methods, our approach involves direct decomposition of the observed image. This approach circumvents the necessity of intermediate transformations, thereby preserving essential texture features. In this study, we introduce a weight-aware ℓ1-ℓ2 technique based on the assumption that the reflectance layer is discontinuous and the illumination layer is spatially smooth. To preserve texture and structural information in the illumination layer, we introduce a weight-aware illumination component coefficient, ℓ1-norm, and estimate the reflectance component using ℓ2-norm. By utilizing weight-aware coefficients, the proposed technique is highly effective in addressing the issue of texture loss in the reflectance layer. Additionally, we employ ℓ2-norm to extract accurate information from the reflectance layer and implement a bright channel prior to prevent ambiguity during the decomposition process. We utilize an alternating minimization approach to obtain the optimal objective function solution and modify the illumination layer using gamma and non-linear stretching algorithms. Our proposed technique not only tackles the problem of texture duplication but also improves the quality of low-light images, and can be seamlessly integrated with various image and vision-based tasks. Our evaluation of eight benchmark datasets using 15 quality metrics, along with a variety of 22 conventional and modern algorithms, shows that the proposed algorithm is capable of delivering competitive qualitative and quantitative results without compromising its flexibility and scalability. Besides, the proposed model is evaluated on retinal images, and the results demonstrate a substantial improvement in the accuracy of learning-based models.

Waterfowl are considered to be natural reservoirs of the avian influenza virus (AIV). However, the dynamics of transmission and evolutionary patterns of AIV and its subtypes within duck farms in Bangladesh remain poorly documented. Hence, a cross-sectional study was conducted in nine districts of Bangladesh between 2019 and 2021, to determine the prevalence of AIV and its subtypes H5 and H9, as well as to identify risk factors and the phylodynamics of H5N1 clades circulating in domestic duck farms. The oropharyngeal and cloacal swab samples were tested for the AIV Matrix gene (M-gene) followed by H5, H7, and H9 subtypes using rRT-PCR. The exploratory analysis was performed to estimate AIV and its subtype prevalence in different production systems, and multivariable logistic regression model was used to identify the risk factors that influence AIV infection in ducks. Bayesian phylogenetic analysis was conducted to generate a maximum clade credibility (MCC) tree and the maximum likelihood method to determine the phylogenetic relationships of the H5N1 viruses isolated from ducks. AIV was detected in 40% (95% CI: 33.0–48.1) of the duck farms. The prevalence of AIV was highest in nomadic ducks (39.8%; 95% CI: 32.9–47.1), followed by commercial ducks (24.6%; 95% CI: 14.5–37.3) and backyard ducks (14.4%; 95% CI: 10.5–19.2). The H5 prevalence was also highest in nomadic ducks (19.4%; 95% CI: 14.0–25.7). The multivariable logistic regression model revealed that ducks from nomadic farms (AOR: 2.4; 95% CI: 1.45–3.93), juvenile (AOR: 2.2; 95% CI: 1.37–3.61), and sick ducks (AOR: 11.59; 95% CI: 4.82–32.44) had a higher risk of AIV. Similarly, the likelihood of H5 detection was higher in sick ducks (AOR: 40.8; 95% CI: 16.3–115.3). Bayesian phylogenetic analysis revealed that H5N1 viruses in ducks belong to two distinct clades, 2.3.2.1a, and 2.3.4.4b. The clade 2.3.2.1a (reassorted) has been evolving silently since 2015 and forming at least nine subgroups based on >90% posterior probability. Notably, clade 2.3.4.4b was introduced in ducks in Bangladesh by the end of the year 2020, which was genetically similar to viruses detected in wild birds in Japan, China, and Africa, indicating migration-associated transmission of an emerging panzootic clade. We recommend continuing AIV surveillance in the duck production system and preventing the intermingling of domestic ducks with migratory waterfowl in wetlands.

The impacts of the avian influenza virus (AIV) on farmed poultry and wild birds affect human health, livelihoods, food security, and international trade. The movement patterns of turkey birds from farms to live bird markets (LBMs) and infection of AIV are poorly understood in Bangladesh. Thus, we conducted weekly longitudinal surveillance in LBMs to understand the trading patterns, temporal trends, and risk factors of AIV circulation in turkey birds. We sampled a total of 423 turkeys from two LBMs in Dhaka between May 2018 and September 2019. We tested the swab samples for the AIV matrix gene (M-gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We used exploratory analysis to investigate trading patterns, annual cyclic trends of AIV and its subtypes, and a generalized estimating equation (GEE) logistic model to determine the factors that influence the infection of H5 and H9 in turkeys. Furthermore, we conducted an observational study and informal interviews with traders and vendors to record turkey trading patterns, demand, and supply and turkey handling practices in LBM. We found that all trade routes of turkey birds to northern Dhaka are unidirectional and originate from the northwestern and southern regions of Bangladesh. The number of trades from the source district to Dhaka depends on the turkey density. The median distance that turkey was traded from its source district to Dhaka was 188 km (Q1 = 165, Q3 = 210, IQR = 45.5). We observed seasonal variation in the median and average distance of turkey. The qualitative findings revealed that turkey farming initially became reasonably profitable in 2018 and at the beginning of 2019. However, the fall in demand and production in the middle of 2019 may be related to unstable market pricing, high feed costs, a shortfall of adequate marketing facilities, poor consumer knowledge, and a lack of advertising. The overall prevalence of AIV, H5, and H9 subtypes in turkeys was 31% (95% CI: 26.6–35.4), 16.3% (95% CI: 12.8–19.8), and 10.2% (95% CI: 7.3–13.1) respectively. None of the samples were positive for H7. The circulation of AIV and H9 across the annual cycle showed no seasonality, whereas the circulation of H5 showed significant seasonality. The GEE revealed that detection of AIV increases in retail vendor business (OR: 1.71; 95% CI: 1.12–2.62) and the bird’s health status is sick (OR: 10.77; 95% CI: 4.31–26.94) or dead (OR: 11.33; 95% CI: 4.30–29.89). We also observed that winter season (OR: 5.83; 95% CI: 2.80–12.14) than summer season, dead birds (OR: 61.71; 95% CI: 25.78–147.75) and sick birds (OR 8.33; 95% CI: 3.36–20.64) compared to healthy birds has a higher risk of H5 infection in turkeys. This study revealed that the turkeys movements vary by time and season from the farm to the LBM. This surveillance indicated year-round circulation of AIV with H5 and H9 subtypes in turkey birds in LBMs. The seasonality and health condition of birds influence H5 infection in birds. The trading pattern of turkey may play a role in the transmission of AIV viruses in the birds. The selling of sick turkeys infected with H5 and H9 highlights the possibility of virus transmission to other species of birds sold at LBMs and to people.

The lack of hygiene in the preparation and packing of ready-to-eat foods becoming a serious public health concern especially in school-aged children due to the intervention of different pathogenic microorganisms which pose risk factors for foodborne disease outbreaks. The present study was designed to assess the bacterial contamination in different types of ready-to-eat branded and un-branded snacks purchased in various elementary schools of district Peshawar, Pakistan. A total of 20 samples were collected from various elementary schools located in Peshawar, Pakistan. The samples were analyzed using the pour plate method for total plate counts (TPC) and the multiple fermentation tube method for total coliforms (TC), and fecal coliforms (FC). The Escherichia coli isolates were identified using E. coli O157:H7 latex test reagent kit Pro Lab. Canada. The results revealed total plate counts of 6.67%, total coliforms bacteria 40%, fecal coliforms bacteria 33.33%, and E. coli 20% of samples, which were higher than the permissible limits. The conclusion of this study revealed that there is a practice gap in food safety knowledge among ready-to-eat foods. Ready-to-eat vendors are usually illiterate, and they lack knowledge of proper hygiene and food handling procedures. The government should pay special attention to improving public awareness of food safety and the quality of ready-to-eat foods sold in Peshawar, Pakistan.

ABSTRACT Whole-genome sequencing (WGS) of influenza A virus (IAV) is crucial for identifying diverse subtypes and newly evolved variants and for selecting vaccine strains. In developing countries, where facilities are often inadequate, WGS is challenging to perform using conventional next-generation sequencers. In this study, we established a culture-independent, high-throughput native barcode amplicon sequencing workflow that can sequence all influenza subtypes directly from a clinical specimen. All segments of IAV in 19 clinical specimens, irrespective of their subtypes, were amplified simultaneously using a two-step reverse transcriptase PCR (RT-PCR) system. First, the library was prepared using the ligation sequencing kit, barcoded individually using the native barcodes, and sequenced on the MinION MK 1C platform with real-time base-calling. Then, subsequent data analyses were performed with the appropriate tools. WGS of 19 IAV-positive clinical samples was carried out successfully with 100% coverage and 3,975-fold mean coverage for all segments. This easy-to-install and low-cost capacity-building protocol took only 24 h complete from extracting RNA to obtaining finished sequences. Overall, we developed a high-throughput portable sequencing workflow ideal for resource-limited clinical settings, aiding in real-time surveillance, outbreak investigation, and the detection of emerging viruses and genetic reassortment events. However, further evaluation is required to compare its accuracy with other high-throughput sequencing technologies to validate the widespread application of these findings, including WGS from environmental samples. IMPORTANCE The Nanopore MinION-based influenza sequencing approach we are proposing makes it possible to sequence the influenza A virus, irrespective of its diverse serotypes, directly from clinical and environmental swab samples, so that we are not limited to virus culture. This third-generation, portable, multiplexing, and real-time sequencing strategy is highly convenient for local sequencing, particularly in low- and middle-income countries like Bangladesh. Furthermore, the cost-efficient sequencing method could provide new opportunities to respond to the early phase of an influenza pandemic and enable the timely detection of the emerging subtypes in clinical samples. Here, we meticulously described the entire process that might help the researcher who will follow this methodology in the future. Our findings suggest that this proposed method is ideal for clinical and academic settings and will aid in real-time surveillance and in the detection of potential outbreak agents and newly evolved viruses.

Streptococcus mutans, the primary cause of dental caries, relies on its ability to create and sustain a biofilm (dental plaque) for survival and pathogenicity in the oral cavity. This study was focused on the antimicrobial biofilm formation control and biofilm dispersal potential of Coumaric acid (CA) against Streptococcus mutans on the dentin surface. The biofilm was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) viability assay, microtiter plate assay, production of extracellular polymeric substances (EPSs), florescence microscopy (surface coverage and biomass μm2) and three-dimensional (3D) surface plots. It was observed that CA at 0.01 mg/mL reduced bacterial growth by 5.51%, whereases at 1 mg/mL, a significant (p < 0.05) reduction (98.37%) was observed. However, at 1 mg/mL of CA, a 95.48% biofilm formation reduction was achieved, while a 73.45% biofilm dispersal (after 24 h. treatment) was achieved against the preformed biofilm. The MTT assay showed that at 1 mg/mL of CA, the viability of bacteria in the biofilm was markedly (p < 0.05) reduced to 73.44%. Moreover, polysaccharide (EPS) was reduced to 24.80 μg/mL and protein (EPS) to 41.47 μg/mL. ImageJ software (version 1.54 g) was used to process florescence images, and it was observed that the biofilm mass was reduced to 213 (μm2); the surface coverage was reduced to 0.079%. Furthermore, the 3D surface plots showed that the untreated biofilm was highly dense, with more fibril-like projections. Additionally, molecular docking predicted a possible interaction pattern of CA (ligand) with the receptor Competence Stimulating Peptide (UA159sp, PDB ID: 2I2J). Our findings suggest that CA has antibacterial and biofilm control efficacy against S. mutans associated with dental plaque under tested conditions.

Abstract This paper presents a new method for approximately modeling 2‐D ideal steady fluid flows with finite vorticity induced by actuator curves of arbitrary shape. An actuator curve is an infinitesimally thin region containing a body force density acting on the flow, while the rest of the flow is force‐free. The approach can be used for any flow satisfying this description, but, like other actuator methods, it is naturally suited for modeling turbines or propellers. We derive a weak formulation of the governing equations in the Lagrange streamfunction ψ and solve it using finite elements. Compared to related methods such as the actuator cylinder (AC) approach, our formulation is uniquely suited for computations involving wake–wake or wake–turbine interactions. We validate the method by computing the flow through a single Darrieus vertical‐axis wind turbine (VAWT) and comparing with previous work. To demonstrate the ability to simulate interacting actuators of arbitrary configuration, we simulate a three‐VAWT array. The turbines are modeled in a freestream, and the loading is chosen to represent ideal airfoils. The standard VAWT results are consistent with previous work, validating the method. The three‐VAWT array demonstrates a higher efficiency than the single VAWT (0.56 vs. 0.52), with differing optmal tip speed ratios for the upwind and downwind turbines (upwind: 3.9, downwind: 3.1. The optimum for a single turbine is 3.6). The flow field of the three‐VAWT array shows expected features such as an acceleration of flow between the two counter‐rotating upwind turbines.

Knowledge of the dynamics and genetic diversity of Nipah virus circulating in bats and at the human-animal interface is limited by current sampling efforts, which produce few detections of viral RNA. We report a series of investigations at Pteropus medius bat roosts identified near the locations of human Nipah cases in Bangladesh during 2012–2019. Pooled bat urine was collected from 23 roosts; 7 roosts (30%) had >1 sample in which Nipah RNA was detected from the first visit. In subsequent visits to these 7 roosts, RNA was detected in bat urine up to 52 days after the presumed exposure of the human case-patient, although the probability of detection declined rapidly with time. These results suggest that rapidly deployed investigations of Nipah virus shedding from bat roosts near human cases could increase the success of viral sequencing compared with background surveillance and could enhance understanding of Nipah virus ecology and evolution.

Abstract Background Root debridement procedures for the treatment of periodontal diseases, produces root surface irregularities and smear layer on the root surface that can adversely affect the healing of periodontal tissues. The objective of the present study was to evaluate the surface texture of root after hand instrumentation with Gracey curette, After Five curette, and Mini Five curette. Methods A randomised, controlled clinical trial was conducted with 120 participants clinically diagnosed with chronic periodontitis. Participants were equally randomised into four groups, with parallel treatment assignment of scaling and root planning using Gracey Curettes, After five and Mini five curette, and a control group with no instrumentation. Mobile teeth of these patients were then extracted atraumatically and analysed under a Scanning Electron Microscope and graded for "Roughness and Loss of Tooth substance index" and "Smear layer index." Cross Tabulation was made between the test groups (Control, Gracey Curette, After five, and Mini Five) versus "Roughness and Loss of Tooth substance Index" and "Smear Layer Index." A Chi-square test with Bonferroni correction was used to determine the graded distribution among the groups. Results In the control group, 73.3% of the teeth showed grade 1 roughness. In the Gracey group, 56.7% showed grade 2 roughness; in the After 5 group, 70% showed grade 3 roughness; in the Mini 5 group, 76.7% showed grade 3 roughness. A significant association was found between roughness scores and the use of individual instruments. Regarding smear layer formation, 46.7% of teeth showed a grade 4 smear layer in the control group. 50% of teeth showed grade 2 smear layer thickness in the Gracey group. In the After 5 group, 73.3% of teeth showed a grade 1 smear layer, while in the Mini 5 group, 80.0% showed a grade 1 smear layer. The use of individual instruments was significantly associated with the smear layer scores. Conclusion Gracey curettes produced relatively smoother root surfaces with less smear layer formation than After Five and Mini Five curettes, which produced relatively more roughened root surfaces with thicker smear layer formation. Trial registration ID: ClinicalTrials.gov Identifier: NCT04216966 Date of Registration: January 3, 2020.

Abstract Background and Aims Saliva samples are less invasive and more convenient for patients than naso‐ and/or oropharynx swabs (NOS). However, there is no US Food and Drug Administration‐approved severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) rapid antigen test kit, which can be useful in a prolonged pandemic to reduce transmission by allowing suspected individuals to self‐sampling. We evaluated the performances of High sensitive AQ+ Rapid SARS‐CoV‐2 Antigen Test (AQ+ kit) using nasopharyngeal swabs (NPs) and saliva specimens from the same patients in laboratory conditions. Methods The real‐time reverse transcription‐polymerase chain reaction (rRT‐PCR) test result was used for screening the inrolled individuals and compared as the gold standard. NP and saliva samples were collected from 100 rRT‐PCR positives and 100 negative individuals and tested with an AQ+ kit. Results The AQ+ kit showed good performances in both NP and saliva samples with an overall accuracy of 98.5% and 94.0%, and sensitivity of 97.0% and 88.0%, respectively. In both cases, specificity was 100%. AQ+ kit performance with saliva was in the range of the World Health Organization recommended value. Conclusion xOur findings indicate that the saliva specimen can be used as an alternative and less invasive to NPs for quick and reliable SARS‐CoV‐2 antigen detection.

Abstract Background and Aims The coronavirus disease 2019 (COVID‐19) has brought serious threats to public health worldwide. Nasopharyngeal, nasal swabs, and saliva specimens are used to detect severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). However, limited data are available on the performance of less invasive nasal swab for testing COVID‐19. This study aimed to compare the diagnostic performance of nasal swabs with nasopharyngeal swabs using real‐time reverse transcription polymerase chain reaction (RT‐PCR) considering viral load, onset of symptoms, and disease severity. Methods A total of 449 suspected COVIDCOVID‐19 individuals were recruited. Both nasopharyngeal and nasal swabs were collected from the same individual. Viral RNA was extracted and tested by real‐time RT‐PCR. Metadata were collected using structured questionnaire and analyzed by SPSS and MedCalc software. Results The overall sensitivity of the nasopharyngeal swab was 96.6%, and the nasal swab was 83.4%. The sensitivity of nasal swabs was more than 97.7% for low and moderate Ct values. Moreover, the performance of nasal swab was very high (>87%) for hospitalized patients and at the later stage >7 days of onset of symptoms. Conclusion Less invasive nasal swab sampling with adequate sensitivity can be used as an alternative to nasopharyngeal swabs for the detection of SARS‐CoV‐2 by real‐time RT‐PCR.

Avian influenza viruses (AIV) have been frequently detected in live bird markets (LBMs) around the world, primarily in urban areas, and have the ability to spillover to other species, including humans. Despite frequent detection of AIV in urban LBMs, the contamination of AIV on environmental surfaces in rural and peri-urban LBMs in Bangladesh is poorly documented. Therefore, we conducted this study to determine the prevalence of AIV subtypes within a subset of peri-urban and rural LBMs in Bangladesh and to further identify associated risk factors. Between 2017 and 2018, we collected faecal and offal samples from 200 stalls in 63 LBMs across four sub-districts. We tested the samples for the AIV matrix gene (M-gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We performed a descriptive analysis of market cleanliness and sanitation practices in order to further elucidate the relationship between LBM biosecurity and AIV subtypes by species, sample types, and landscape. Subsequently, we conducted a univariate analysis and a generalized linear mixed model (GLMM) to determine the risk factors associated with AIV contamination at individual stalls within LBMs. Our findings indicate that practices related to hygiene and the circulation of AIV significantly differed between rural and peri-urban live bird markets. 42.5% (95% CI: 35.56–49.67) of stalls were positive for AIV. A/H5, A/H9, and A HA/Untyped were detected in 10.5% (95% CI: 6.62–15.60), 9% (95% CI: 5.42–13.85), and 24.0% (95% CI: 18.26–30.53) of stalls respectively, with no detection of A/H7. Significantly higher levels of AIV were found in the Sonali chicken strain compared to the exotic broiler, and in offal samples compared to fecal samples. In the GLMM analysis, we identified several significant risk factors associated with AIV contamination in LBMs at the stall level. These include: landscape (AOR: 3.02; 95% CI: 1.18–7.72), the number of chicken breeds present (AOR: 2.4; 95% CI: 1.01–5.67), source of birds (AOR: 2.35; 95% CI: 1.0–5.53), separation of sick birds (AOR: 3.04; 95% CI: 1.34–6.92), disposal of waste/dead birds (AOR: 3.16; 95% CI: 1.41–7.05), cleaning agent (AOR: 5.99; 95% CI: 2.26–15.82), access of dogs (AOR: 2.52; 95% CI: 1.12–5.7), wild birds observed on site (AOR: 2.31; 95% CI: 1.01–5.3). The study further revealed a substantial prevalence of AIV with H5 and H9 subtypes in peri-urban and rural LBMs. The inadequate biosecurity measures at poultry stalls in Bangladesh increase the risk of AIV transmission from poultry to humans. To prevent the spread of AIV to humans and wild birds, we suggest implementing regular surveillance at live bird markets and enhancing biosecurity practices in peri-urban and rural areas in Bangladesh.