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Human parechovirus (HPeV) is widely recognized as a severe viral infection affecting infants and neonates. Belonging to the Picornaviridae family, HPeV is categorized into 19 distinct genotypes. Among them, HPeV-1 is the most prevalent genotype, primarily associated with respiratory and digestive symptoms. Considering HPeV’s role as a leading cause of life-threatening viral infections in infants and the lack of effective antiviral therapies, our focus centered on developing two multi-epitope vaccines, namely HPeV-Vax-1 and HPeV-Vax-2, using advanced immunoinformatic techniques. Multi-epitope vaccines have the advantage of protecting against various virus strains and may be preferable to live attenuated vaccines. Using the NCBI database, three viral protein sequences (VP0, VP1, and VP3) from six HPeV strains were collected to construct consensus protein sequences. Then the antigenicity, toxicity, allergenicity, and stability were analyzed after discovering T-cell and linear B-cell epitopes from the protein sequences. The fundamental structures of the vaccines were produced by fusing the selected epitopes with appropriate linkers and adjuvants. Comprehensive physicochemical, antigenic, allergic assays, and disulfide engineering demonstrated the effectiveness of the vaccines. Further refinement of secondary and tertiary models for both vaccines revealed promising interactions with toll-like receptor 4 (TLR4) in molecular docking, further confirmed by molecular dynamics simulation. In silico immunological modeling was employed to assess the vaccine’s capacity to stimulate an immune reaction. In silico immunological simulations were employed to evaluate the vaccines’ ability to trigger an immune response. Codon optimization and in silico cloning analyses showed that Escherichia coli (E . coli) was most likely the host for the candidate vaccines. Our findings suggest that these multi-epitope vaccines could be the potential HPeV vaccines and are recommended for further wet-lab investigation.

Nipah virus is a highly virulent zoonotic pathogen. In this report from Bangladesh, which included 40% of the world’s known cases, the risk factors for human-to-human transmission were evaluated. No asymptomatic cases were identified. Increased respiratory symptoms in the patient and prolonged close contact from caregivers were associated with secondary transmission.

The increasing number and volume of genomic and metagenomic data necessitates scalable and robust computational models for precise analysis. Sketching techniques utilizing -mers from a biological sample have proven to be useful for large-scale analyses. In recent years, FracMinHash has emerged as a popular sketching technique and has been used in several useful applications. Recent studies on FracMinHash proved unbiased estimators for the containment and Jaccard indices. However, theoretical investigations for other metrics are still lacking. In this paper, we present a theoretical framework for estimating similarity/distance metrics by using FracMinHash sketches, when the metric is expressible in a certain form. We establish conditions under which such an estimation is sound and recommend a minimum scale factor s for accurate results. Experimental evidence supports our theoretical findings. We also present frac-kmc, a fast and efficient FracMinHash sketch generator program. frac-kmc is the fastest known FracMinHash sketch generator, delivering accurate and precise results for cosine similarity estimation on real data. frac-kmc is also the first parallel tool for this task, allowing for speeding up sketch generation using multiple CPU cores - an option lacking in existing serialized tools. We show that by computing FracMinHash sketches using frac-kmc, we can estimate pairwise similarity speedily and accurately on real data. frac-kmc is freely available here: https://github.com/KoslickiLab/frac-kmc/.

Depression is one of the most serious yet understudied issues among Bangladeshi nurses, bringing health dangers to this workforce. This study aimed to investigate how workplace violence (WPV), bullying, burnout, and job satisfaction are correlated with depression and identify the factors associated with depression among Bangladeshi nurses. For this cross-sectional study, data were collected between February 26, 2021, and July 10, 2021 from the Bangladeshi registered nurses. The Workplace Violence Scale (WPVS), the Short Negative Acts Questionnaire [S-NAQ], the Burnout Measure-Short version (BMS), the Short Index of Job Satisfaction (SIJS-5), and the Patient Health Questionnaire (PHQ-9) were used to measure WPV, bullying, burnout, job satisfaction, and depression, respectively. Inferential statistics include Pearson's correlation test, t-test, one-way ANOVA test, multiple linear regression, and multiple hierarchal regression analyses were performed. The study investigated 1,264 nurses (70.02% female) with an average age of 28.41 years (SD = 5.54). Depression was positively correlated with WPV, bullying, and burnout and negatively correlated with job satisfaction (p <0.001). According to the multiple linear regression model, depression was significantly lower among nurses with diploma degrees (β = -1.323, 95% CI = -2.149 to -0.497) and bachelor's degrees (β = -1.327, 95% CI = -2.131 to- 0.523) compared to the nurses with master's degree. The nurses who worked extended hours (>48 hours) had a significantly higher depression score (β = 1.490, 95% CI = 0.511 to 2.470) than those who worked ≤ 36 hours. Depression was found to be significantly higher among those who did not receive a timely salary (β = 2.136, 95% CI = 1.138 to 3.134), rewards for good works (β = 1.862, 95% CI = 1.117 to 2.607), and who had no training on WPV (β = 0.895, 95% CI = 0.092 to 1.698). Controlling burnout, bullying, and workplace violence, as well as improving the work environment for nurses and increasing job satisfaction, are the essential indicators of reducing depression. This can be accomplished with integrative support from hospital executives, policymakers, and government officials.

In many low- and middle-income countries (LMICs), including Bangladesh, socioeconomic inequalities in access to maternity care remain a substantial public health concern. Due to the paucity of research, we attempted to determine the factors affecting the facility delivery, quantify wealth-related inequality, and identify potential components that could explain the inequality. We used the latest Bangladesh Demographic and Health Survey (BDHS 2017-18) data in this study. We utilized logistic regression to investigate the associated factors of facility delivery. The concentration curves (CC), concentration index (CIX) and decomposition of CIX techniques were used to analyze the inequality in-facility delivery. Women living in the urban areas, age at first birth after (18-24 years ≥25 years), being overweight/obese, having secondary and higher-level education of the women and their husband, seeking four or more ANC, coming from more affluent households, and women with high enlightenment were significant determinants of facility delivery. The concentration curve was below the line of equality, and the relative concentration index (CIX) was 0.205 (p <0.001), indicating that women from wealthy groups were disproportionately more prevalent to facility delivery. The decomposition analysis reveals that wealth status of women (57.40%), age at first birth (10.24%), husband's education (8.96%), husband's occupation (7.35%), education of women (7.13%), women's enlightenment (6.15%), residence (8.64%) and ANC visit (6.84%) are the most major contributors to the inequalities in utilizing facility delivery. The study demonstrates a clear disparity in the use of facility delivery among Bangladeshi women; hence, immediate action is required to lower the inequalities, with a special emphasis on the contributing factors.

Commonly cultivated Brassicaceae mustards, namely garlic mustard (Alliaria petiolata), white mustard (Brassica alba), Ethiopian mustard (B. carinata), Asian mustard (B. juncea), oilseed rape (B. napus), black mustard (B. nigra), rapeseed (B. rapa), white ball mustard (Calepina irregularis), ball mustard (Neslia paniculata), treacle mustard (Erysimum repandum), hedge mustard (Sisymbrium officinale), Asian hedge mustard (S. orientale), smooth mustard (S. erysimoides) and canola are the major economically important oilseed crops in many countries. Mustards were naturalized to Australia and New Zealand and Australia is currently the second largest exporter of Brassicaceae oilseeds to meet the global demand for a healthy plant-derived oil, high in polyunsaturated fats. Apart from providing edible oil, various parts of these plants and many of their phytochemicals have been used traditionally for both agronomic as well as medicinal purposes, with evidence of their use by early Australian and New Zealand settlers and also the indigenous population. This review provides an overview of the current knowledge of traditional and agronomic uses of Brassicaceae oilseeds and mustards with a focus on their importance in Australia and New Zealand.

We identified predictors for bone-marrow [ 18 F]FDG uptake and MR signals among complete blood count, C-reactive protein (CRP), and anthropometric factors, and demonstrated the bone-marrow physiology using integrated [ 18 F]FDG-PET/MRI. 174 oncology patients without bone-marrow lesions underwent whole-body [ 18 F]FDG-PET/MRI. The standardized uptake value (SUV), apparent diffusion coefficient (ADC), proton density fat-fraction (PDFF), and a reciprocal of T2* relaxation time (R2*) were measured in lumbar vertebrae (L3–5) and bilateral ilia. Vertebrae, pelvis, and ribs were evaluated by 3-point visual scoring on DWI. The association of the PET/MR features with the predictors was examined. Multi-regression analyses identified CRP as the strongest predictor for lumbar and iliac SUVs (standardized coefficient: β = 0.31 and β = 0.38, respectively), and for lumbar and iliac R2* (β = 0.31 and β = 0.46, respectively). In contrast, age was the strongest factor influencing lumbar and iliac ADCs (β = 0.23 and β = 0.21, respectively), and lumbar and iliac PDFFs (β = 0.53 and β = 0.54, respectively). Regarding DWI-visual scores, age was the strongest predictor for vertebrae (β = − 0.47), and the red cell distribution width (RDW) was the strongest predictor for pelvis and ribs (β = 0.33 and β = 0.47, respectively). The bone-marrow [ 18 F]FDG uptake and R2* reflect anemia of inflammation (increased granulopoiesis and reduced iron metabolism), whereas bone-marrow DWI and PDFF reflect age and anemia-responsive erythropoiesis.

BackgroundMultimodal PET/MRI image data simultaneously obtained from patients with early-stage of Alzheimer’s disease (eAD) were assessed in order to observe pathophysiologic and functional changes, as well as alterations of morphology and connectivity in the brain. Fifty-eight patients with mild cognitive impairment and early dementia (29 males, 69 ± 12 years) underwent [11C]Pittsburgh compound-B (PiB) PET/MRI with 70-min PET and MRI scans. Sixteen age-matched healthy controls (CTL) (9 males, 68 ± 11 years) were also studied with the same scanning protocol. Cerebral blood flow (CBF) was calculated from the early phase PET images using the image-derived input function method. A standardized uptake value ratio (SUVr) was calculated from 50 to 70 min PET data with a reference region of the cerebellar cortex. MR images such as 3D-T1WI, resting-state functional MRI (RS-fMRI), diffusion tensor image (DTI), and perfusion MRI acquired during the dynamic PET scan were also analyzed to evaluate various brain functions on MRI.ResultsTwenty-seven of the 58 patients were determined as eAD based on the results of PiB-PET and clinical findings, and a total of 43 subjects’ data including CTL were analyzed in this study. PiB SUVr values in all cortical regions of eAD were significantly greater than those of CTL. The PiB accumulation intensity was negatively correlated with cognitive scores. The regional PET-CBF values of eAD were significantly lower in the bilateral parietal lobes and right temporal lobe compared with CTL, but not in MRI perfusion; however, SPM showed regional differences on both PET- and MRI-CBF. SPM analysis of RS-fMRI delineated regional differences between the groups in the anterior cingulate cortex and the left precuneus. VBM analysis showed atrophic changes in the AD group in a part of the bilateral hippocampus; however, analysis of fractional anisotropy calculated from DTI data did not show differences between the two groups.ConclusionMultimodal analysis conducted with various image data from PiB-PET/MRI scans showed differences in regional CBF, cortical volume, and neuronal networks in different regions, indicating that pathophysiologic and functional changes in the AD brain can be observed from various aspects of neurophysiologic parameters. Application of multimodal brain images using PET/MRI would be ideal for investigating pathophysiologic changes in patients with dementia and other neurodegenerative diseases.

Mobile phones have a large spectrum of applications, aiding in risk prevention and improving health and wellbeing of their owners. So far, however, they have not been used for direct assessment of personal exposure to air pollution. In this study, we comprehensively evaluated the first, and the only available, mobile phone-BROAD Life-equipped with air pollution sensors (PM2.5 and VOC), to answer the question whether this technology is a viable option in the quest of reducing the burden of disease to air pollution. We tested its performance, applicability and suitability for the purpose by subjecting it to varied concentrations of different types of aerosol particles (cigarette smoke, petrol exhaust and concrete dust) and formaldehyde under controlled laboratory conditions, as well as to ambient particles during field measurements. Six reference instruments were used in the study: AEROTRAK Optical Particle Counter (OPC model number 9306), DustTrak, Aerodynamic Particle Counter (APS), Scanning Mobility Particle Sizer (SMPS), Tapered Element Oscillating Microbalance (TEOM) and Formaldehyde Analyser. Overall, we found that the phone's response was linear at higher particle number concentrations in the chamber, above 5 and 10 μg m-3, for combustion and concrete dust particles, respectively, and for higher formaldehyde concentrations, making it potentially suitable for applications in polluted environments. At lower ambient concentrations of particles around 10 ug m-3 and 20 μg m-3 for PM2.5 and PM10, respectively, the phone's response was below its noise level, suggesting that it is not suitable for ambient monitoring under relatively clean urban conditions. This mobile phone has a number of limitations that may hinder its use in personal exposure and for continuous monitoring. Despite these limitations, it may be used for comparative assessments, for example when comparing outcomes of intervention measures or local impacts of air pollution sources. It should be kept in mind, however, that a mobile phone measuring air quality alone cannot as such 'reduce the burden of disease to air pollution, as knowing ambient concentrations is only one of the building block in this quest. As long as individuals cannot avoid exposure e.g. in urban areas, knowing concentrations is not sufficient to reduce potential adverse effects. Yet, there are many situations and microenvironments, which individuals could avoid knowing the concentrations and also being aware of the risk caused by exposure to them. This includes for example to proximity to vehicle emissions, either for social purposes (e.g. street cafes) or exercising (e.g. walking or jogging along busy roads)or indoor environments affected by combustion emissions (smoking, candle burning, open fire).

Whether an individual becomes infected in an infectious disease outbreak depends on many interconnected risk factors, which may relate to characteristics of the individual (e.g., age, sex), his or her close relatives (e.g., household members), or the wider community. Studies monitoring individuals in households or schools have helped elucidate the determinants of transmission in small social structures due to advances in statistical modeling; but such an approach has so far largely failed to consider individuals in the wider context they live in. Here, we used an outbreak of chikungunya in a rural community in Bangladesh as a case study to obtain a more comprehensive characterization of risk factors in disease spread. We developed Bayesian data augmentation approaches to account for uncertainty in the source of infection, recall uncertainty, and unobserved infection dates. We found that the probability of chikungunya transmission was 12% [95% credible interval (CI): 8–17%] between household members but dropped to 0.3% for those living 50 m away (95% CI: 0.2–0.5%). Overall, the mean transmission distance was 95 m (95% CI: 77–113 m). Females were 1.5 times more likely to become infected than males (95% CI: 1.2–1.8), which was virtually identical to the relative risk of being at home estimated from an independent human movement study in the country. Reported daily use of antimosquito coils had no detectable impact on transmission. This study shows how the complex interplay between the characteristics of an individual and his or her close and wider environment contributes to the shaping of infectious disease epidemics.