Search

BackgroundAccurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data.MethodsWe derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound.ResultsSamples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).ConclusionsAlgorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.

Background: In the absence of international or national guidelines for the diagnosis and treatment of gestational diabetes mellitus (GDM), physicians’ current practice varies a great deal. This is particularly true for most developing countries, including Bangladesh. We have conducted a study to register the current practices related to the diagnosis and management of GDM in Bangladesh. Materials and Methods: A cross-sectional survey was conducted to obtain details regarding diagnostic criteria used, screening methods, management strategies, and postpartum follow-up of GDM using a structured questionnaire. A total of 756 physicians from 30 centers of the Diabetic Association of Bangladesh, Government and Private Hospitals located in both the capital Dhaka city and outside Dhaka participated. Results: The study found that diabetologists look after 42.5% of the GDM patients. Among the physicians participated, 44.4% preferred first antenatal care visit for the GDM screening, and 55.2% preferred two samples oral glucose tolerance test (OGTT) for screening. About 47.6% of the physicians followed the American Diabetic Association (ADA) guidelines for diagnosis. Half of the participated physicians preferred short-acting insulin and four-times home monitoring of blood glucose per day. Around 66.3% of the physicians advised OGTT 6 weeks after delivery. Conclusion: The majority of the participating physicians do not follow the recommendations for the diagnosis and management of GDM. This emphasizes the need for developing an evidence-based national guideline for GDM and necessary training to halt the rise of diabetes and safe mother and child health in Bangladesh.

Similar to many other countries around the world, Bangladesh is also suffering from a pandemic of diabetes. It makes the most significant contribution to morbidity and mortality in this country. Despite the high burden of diabetes, health care is still geared toward episodic care. The government has not yet invested substantial efforts into developing a national policy to detect, prevent, and control diabetes. Still, diabetes care is restricted to capital and other big cities. More than 60% of people with diabetes usually sought treatment and advice from private facilities, including the Diabetic Association of Bangladesh. For the past six decades, the Association has been trying to develop a proper organizational framework, health care, educational institutions, rehabilitation facilities for poor people with diabetes, appropriate diabetes prevention, and education programs. To address the pandemic, the country should focus on nationwide diabetes prevention and control programs, such as creating community awareness and changing lifestyle practices through well-designed public health programs. The country also needs public–private partnerships and multi-sectoral approaches to overcome the diabetes burden.

Prevalence of non-communicable diseases (NCDs) is high in rural Bangladesh. Given the complex multi-directional relationships between NCDs, COVID-19 infections and control measures, exploring pandemic impacts in this context is important. We conducted two cross-sectional surveys of adults ≥30-years in rural Faridpur district, Bangladesh, in February to March 2020 (survey 1, pre-COVID-19), and January to March 2021 (survey 2, post-lockdown). A new random sample of participants was taken at each survey. Anthropometric measures included: blood pressure, weight, height, hip and waist circumference and fasting and 2-hour post-glucose load blood glucose. An interviewer-administered questionnaire included: socio-demographics; lifestyle and behavioural risk factors; care seeking; self-rated health, depression and anxiety assessments. Differences in NCDs, diet and exercise were compared between surveys using chi2 tests, logistic and linear regression; sub-group analyses by gender, age and socio-economic tertiles were conducted. We recruited 950 (72.0%) participants in survey 1 and 1392 (87.9%) in survey 2. The percentage of the population with hypertension increased significantly from 34.5% (95% CI: 30.7, 38.5) to 41.5% (95% CI: 38.2, 45.0; p-value = 0.011); the increase was more pronounced in men. Across all measures of self-reported health and mental health, there was a significant improvement between survey 1 and 2. For self-rated health, we observed a 10-point increase (71.3 vs 81.2, p-value = 0.005). Depression reduced from 15.3% (95% CI: 8.4, 26.1) to 6.0% (95% CI: 2.7, 12.6; p-value = 0.044) and generalised anxiety from 17.9% (95% CI: 11.3, 27.3) to 4.0% (95% CI: 2.0, 7.6; p-value

Eggplant or brinjal (Solanum melongena) is a popular vegetable grown throughout Asia where it is attacked by brinjal fruit and shoot borer (BFSB) (Leucinodes orbonalis). Yield losses in Bangladesh have been reported up to 86% and farmers rely primarily on frequent insecticide applications to reduce injury. Bangladesh has developed and released four brinjal varieties producing Cry1Ac (Bt brinjal) and is the first country to do so. We report on the first replicated field trials comparing four Bt brinjal varieties to their non-Bt isolines, with and without standard insecticide spray regimes. Results of the two-year study (2016-17) indicated Bt varieties had increased fruit production and minimal BFSB fruit infestation compared with their respective non-Bt isolines. Fruit infestation for Bt varieties varied from 0-2.27% in 2016, 0% in 2017, and was not significantly affected by the spray regime in either year. In contrast, fruit infestation in non-Bt lines reached 36.70% in 2016 and 45.51% in 2017, even with weekly spraying. An economic analysis revealed that all Bt lines had higher gross returns than their non-Bt isolines. The non-sprayed non-Bt isolines resulted in negative returns in most cases. Maximum fruit yield was obtained from sprayed plots compared to non-sprayed plots, indicating that other insects such as whiteflies, thrips and mites can reduce plant vigor and subsequent fruit weight. Statistically similar densities of non-target arthropods, including beneficial arthropods, were generally observed in both Bt and non-Bt varieties. An additional trial that focused on a single Bt variety and its isoline provided similar results on infestation levels, with and without sprays, and similarly demonstrated higher gross returns and no significant effects on non-target arthropods. Together, these studies indicate that the four Bt brinjal lines are extremely effective at controlling BFSB in Bangladesh without affecting other arthropods, and provide greater economic returns than their non-Bt isolines.

Since their introduction in clinical practice in the 1950′s, Sulfonylureas (SUs) have remained the main-stay of pharmacotherapy in the management of type 2 diabetes. Despite their well-established benefits, their place in therapy is inappropriately being overshadowed by newer therapies. Many of the clinical issues associated with the use of SUs are agent-specific, and do not pertain to the class as such. Modern SUs (glimepiride, gliclazide MR) are backed by a large body of evidence, experience, and most importantly, outcome data, which supports their role in managing patients with diabetes. Person-centred care, i.e., careful choice of SU, appropriate dosage, timing of administration, and adequate patient counseling, will ensure that deserving patients are not deprived of the advantages of this well-established class of anti-diabetic agents. Considering their efficacy, safety, pleiotropic benefits, and low cost of therapy, SUs should be considered as recommended therapy for the treatment of diabetes in South Asia. This initiative by SAFES aims to encourage rational, safe and smart prescription of SUs, and includes appropriate medication counseling.

Bangladesh has a population with a low gastric cancer risk but high prevalence of Helicobacter pylori infection. Several studies have examined virulence genes in H. pylori from Bangladesh. We analyzed cagA and vacA subtypes and their association with severe histology phenotypes, and analyzed population types among Bangladeshi strains. We included patients who underwent endoscopy in Dhaka. Sequences of virulence genes and seven housekeeping genes were obtained by next generation sequencing and confirmed by Sanger sequencing. We isolated 56 H. pylori strains from 133 patients, of which 73.2% carried cagA, and all were considered Western-type. Patients infected with cagA-positive strains had more severe histological scores than patients infected with cagA-negative strains. Among vacA s1 and m1 genotypes, the s1a (97.8%, 43/44) and m1c (28/30, 93.3%) genotypes were predominant. All strains containing s1 and m1 (30/56, 53.6%) also had i1, d1, and c1. In contrast, all strains containing the less-virulent genotypes s2 and m2 (12/56, 21.4%) also possessed i2, d2, and c2. Multivariate analysis indicated that subjects infected with vacA m1-genotype strains only had a significantly higher risk of antrum atrophy than patients infected with m2-genotype strains. Of the two main H. pylori populations in this study, hpAsia2 strains were associated with higher activity and inflammation in the antrum compared to hpEurope strains; however, only vacA s1m1i1d1c1 strains, independent of population type, were significantly associated with inflammation in the antrum, unlike the s2m2i2d2c2 genotype. In conclusion, Bangladeshi strains were divided into two main populations of different genotypes. The low incidence of gastric cancer in Bangladesh might be attributable to the high proportion of less-virulent genotypes, which may be a better predictor of gastric cancer risk than the ancestral origin of the H. pylori strains. Finally, the vacA m region may be a better virulence marker than other regions.

A person with diabetes mellitus (DM) has every right to perform the ritual of fasting during Ramadan. In daily practice we come across both type 1 and type 2 DM who wish to fast. This Consensus Statement describes the pre-Ramadan assessment, planning, prescription, management, and monitoring of patients on insulin, who wish to fast.

It is obligatory for all adult Muslims to observe fast during the holy month of Ramadan, but sick individuals including those with diabetes mellitus are exempted from the duty of fasting. Specific medical advice must be provided to individual patients concerning the potential risks they must accept if they decide to fast. Any alteration in medications deemed necessary to provide an effective and safe antidiabetic regimen should be instituted well before the start of Ramadan. Diet-controlled patients and those well controlled on insulin sensitizers have low risk of hypoglycemia and may safely fast with some modification in the timing of the doses. Newer generation sulfonylureas (gliclazide MR and glimepiride) have reasonable safety profile during Ramadan fasting and are economical options for a large number of diabetics worldwide, especially in the developing countries; older, long acting sulfonylureas like glibenclamide and chlorpropamide should be avoided during fasting. Oral DPP-IV inhibitors are important substitutes to sulfonylureas for patients with diabetes mellitus during fasting owing to their glucose-dependent mechanism of action, efficacy, and tolerability. This group of drugs causes a moderate A1c reduction, are weight neutral, and have a very low risk of hypoglycemia. Short-acting insulin secretagogues are an option in the subset of fasting diabetic patients who have predominantly post-prandial hyperglycemia.

Majority of physicians are of the opinion that Ramadan fasting is acceptable for well-balanced type 2 patients conscious of their disease and compliant with their diet and drug intake. Fasting during Ramadan for patients with diabetes carries a risk of an assortment of complications. Islamic rules allow patients not to fast. However, if patient with diabetes wish to fast, it is necessary to advice them to undertake regular monitoring of blood glucose levels several times a day, to reduce the risk of hypoglycemia during day time fasting or hyperglycemia during the night. Patient with type 1 diabetes who fast during Ramadan may be better managed with fast-acting insulin. They should have basic knowledge of carbohydrate metabolism, the standard principles of diabetes care, and pharmacology of various antidiabetic drugs. This Consensus Statement describes the management of the various diabetic emergencies that may occur during Ramadan.

Abstract Coxiella burnetii, the causative agent of Q fever, is an intracellular pathogen posing a significant global public health threat. There is a pressing need for dependable and effective treatments, alongside an urgency for further research into the molecular characterization of its genome. Within the genomic landscape of Coxiella burnetii, numerous hypothetical proteins remain unidentified, underscoring the necessity for in-depth study. In this study, we conducted comprehensive in silico analyses to identify and prioritize potential hypothetical protein of Coxiella burnetii, aiming to elucidate the structure and function of uncharacterized protein. Furthermore, we delved into the physicochemical properties, localization, and molecular dynamics and simulations, and assessed the primary, secondary, and tertiary structures employing a variety of bioinformatics tools. The in-silico analysis revealed that the uncharacterized protein contains a conserved Mth938-like domain, suggesting a role in preadipocyte differentiation and adipogenesis. Subcellular localization predictions indicated its presence in the cytoplasm, implicating a significant role in cellular processes. Virtual screening identified ligands with high binding affinities, suggesting the protein’s potential as a drug target against Q fever. Molecular dynamics simulations confirmed the stability of these complexes, indicating their therapeutic relevance. The findings provide a structural and functional overview of an uncharacterized protein from C. burnetii, implicating it in adipogenesis. This study underscores the power of in-silico approaches in uncovering the biological roles of uncharacterized proteins and facilitating the discovery of new therapeutic strategies. The findings provide valuable preliminary data for further investigation into the protein’s role in adipogenesis.

Adrenal insufficiency is a life-threatening event, so it is recommended for patients with known adrenal insufficiency to be properly educated regarding sick-day management. In the month of Ramadan, people refrain from eating and drinking during daylight hours. It is very important for patients with adrenal insufficiency, who wish to keep a fast, to be well aware of the disease, the suitable drug to be used for that particular period, warning signs, sick-day management, physical activity, and dietary limits. This article describes guidelines for the sick-day management of patients with adrenal insufficiency, in the month of Ramadan.

Ramadan is a lunar based month, during which Muslims across the world observe the ritual fast. This provides a challenge not only to the diabetic patient who wishes to observe the fast but also to the health care professional managing his diabetes. The challenge is to use therapies which are effective in maintaining good glycemic control and at the same time have a low propensity to cause hypoglycemia during the several hours of no calorie intake. The GLP-1 analogues are unique agents which are effective in providing glycemic reduction with a very low risk of hypoglycemia and hence find an important place in the management of diabetes during Ramadan. This Consensus Statement describes the pre-Ramadan assessment, planning, prescription and management and monitoring of patients who are on GLP-1 analogues, with or without other antidiabetic therapies.

In the month of Ramadan, patients with thyroid diseases, most of the time, do not need treatment adjustments and can fast safely without any health hazards. Patients with hypothyroidism taking thyroxine can take their tablets on an empty stomach at bedtime instead of half an hour before Sehr. Patients with hyperthyroidism, on methimazole/carbimazole can continue their dose in once or twice daily regimes, while those on propylthiouracil need to be switched. Hyperthyroid patients with severe symptoms should start treatment immediately and can avoid fast for few days after a consultation with their religious scholar.

BIRDEM is the largest referral center of diabetes in the world. It registered more than 300,000 diabetic patients from 1956 to 2005. This retrospective study compared the biophysical characteristics of diabetic patients registered in 1995 to those registered in 2005. Information on social (income, education), clinical (height, weight, blood pressure) and oral glucose tolerance (OGTT) of patients registered in 1995 and 2005 were retrieved from the BIRDEM registry. The age group ³ 20y was considered eligible. Overall, there were 11489 patients for 1995 and 19580 for 2005. Compared with the registry of 1995, a significant increase of registry for female patients were observed (39.5 vs. 46.7%, p

Abstract As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.

In the modern manufacturing system, the industrial automation plays an important role in improving the efficiency, productivity, and overall performance of the system by increasing throughput, reducing downtime and more. However, challenges arise in implementation of real-time continuous data streaming and the integration of diverse data types, posing significant track events. To overcome the challenges, the present paper proposes a methodological approach that employs linear mixed models for data integration and online stochastic gradient descent with the Adaptive Gradient algorithm for processing streaming data in real-time. The use of linear mixed models proves advantageous, allowing for the effective integration of data from diverse sources. The methodology captures complex relationships within the data, offering a comprehensive understanding of the industrial process. A case study of predictive maintenance systems for industrial machinery is demonstrated for empirical validation for the online stochastic gradient descent with Adaptive Gradient facilitates real-time processing of streaming data. This adaptive algorithm ensures continuous adjustment to evolving data patterns, and providing a holistic view of industrial processes. Through empirical results, the demonstration of the system’s efficiency in increasing throughput and in-built system visualization is achieved. This paper contributes to the advancement of industrial automation by presenting an innovative solution for handling disparate data types in a unified framework. The findings cover the way for improved operational efficiency and reliability in complex industrial environments.

Recently, we proposed a new method, based on protein profiles derived from physicochemical dynamic time warping (PCDTW), to functionally/structurally classify coronavirus spike protein receptor binding domains (RBD). Our method, as used herein, uses waveforms derived from two physicochemical properties of amino acids (molecular weight and hydrophobicity (MWHP)) and is designed to reach into the twilight zone of homology, and therefore, has the potential to reveal structural/functional relationships and potentially homologous relationships over greater evolutionary time spans than standard primary sequence alignment-based techniques. One potential application of our method is inferring deep evolutionary relationships such as those between the RBD of the spike protein of betacoronaviruses and functionally similar proteins found in other families of viruses, a task that is extremely difficult, if not impossible, using standard multiple alignment-based techniques. Here, we applied PCDTW to compare members of four divergent families of viruses to betacoronaviruses in terms of MWHP physicochemical similarity of their RBDs. We hypothesized that some members of the families Arteriviridae, Astroviridae, Reoviridae (both from the genera rotavirus and orthoreovirus considered separately), and Toroviridae would show greater physicochemical similarity to betacoronaviruses in protein regions similar to the RBD of the betacoronavirus spike protein than they do to other members of their respective taxonomic groups. This was confirmed to varying degrees in each of our analyses. Three arteriviruses (the glycoprotein-2 sequences) clustered more closely with ACE2-binding betacoronaviruses than to other arteriviruses, and a clade of 33 toroviruses was found embedded within a clade of non-ACE2-binding betacoronaviruses, indicating potentially shared structure/function of RBDs between betacoronaviruses and members of other virus clades.

IntroductionCOVID-19 pandemic hit Bangladesh with relatively low intensity, unlike its neighbors India and European countries and USA.MethodsThe present report included data of 8,480 individuals tested for COVID-19 RT-PCR of the workers and officials from readymade garments (RMG) industry in Chandra area in Gazipur. The present data looked into the clinic-demographic factors associated with the susceptibility of the condition.ResultThe data elucidated the susceptibility of the individuals to SARS-CoV-2 based on age, gender, pre-existing health conditions, and the presence of symptoms. It was observed that individuals aged over 60 had the highest rate of COVID-19 positivity, and men exhibited a higher infection rate compared to women. Regardless of age, fever and cough were the most frequently reported symptoms. Two-thirds of the individuals included in this report appeared to be asymptomatic carriers. The prevalence of comorbidities among individuals who tested positive for COVID-19 was notably higher, and this exhibited a gender-specific pattern.DiscussionAlthough our study provides important epidemiological insights into the initial year of the pandemic among Bangladeshi populations, it can also add value for future drug and vaccine development. However, it is essential to acknowledge the limitations like - restriction of public movement, unavailability of vehicle yielding a selection bias, due to the lockdown conditions imposed owing to the pandemic and the diverse characteristics of the participants. The report emphasizes the significance of figuring out how age, gender, and underlying health conditions impact susceptibility to and transmission of COVID-19, thereby providing valuable insights for public health strategies and future research initiatives.

Abstract Introduction Endoscopic sinus surgery is often challenging because of bleeding and pain. A variety of techniques have been explored to reduce surgical function and pain, with the sphenopalatine ganglion block showing the most promising results. All of these researches, however, had methodological flaws since saline was used as a placebo injection, which could have irritated the ganglion and produced less-than-ideal results, in accordance with our theory. Aim and objective To determine the effect of sphenopalatine ganglion block with bupivacaine on intraoperative endoscopic surgical field and postoperative pain. Patients and methods A prospective double blind, randomized study was conducted in 50 subjects undergoing endoscopic sinus surgery for chronic rhinosinusitis with or without polyp. The case group received 1.5 ml of 0.5% bupivacaine in sphenopalatine ganglion block while control group didn’t receive any drug in ganglion. The intra-operative surgical field grade and postoperative pain score was recorded to analyze the effect of the block. Results We discovered a statistically significant difference in the endoscopic surgical field between the case and control groups. In comparison to the control group, the surgical field was more visible in the sphenopalatine ganglion block group. The case group significantly outperformed the control group in terms of pain visual analogue score throughout the observation period, with the exception of the first day following surgery. Conclusion Sphenopalatine ganglion block with bupivacaine 0.5% is a straightforward, efficient, and secure way to improve the endoscopic surgical field and lessen postoperative pain in patients undergoing endoscopic sinus surgery.

ABSTRACT Hard-wired within the genome of a pathogen is the information regarding factors responsible for its pathogenicity. Advances in functional genomics, particularly gene expression analysis, have made possible genome-wide interrogation of a pathogen to decipher pathogenicity-associated genes. Standard protocols assess differential expression of genes during pathogenesis; however, often a conservative approach is taken, which requires expression fold change above an arbitrarily defined threshold and a statistical significance test to infer a gene as differentially expressing. This renders a high-confidence set of differentially expressed genes in pathogenesis, however, at the cost of numerous false negatives. To circumvent this problem and comprehensively catalog pathogenicity-associated genes, we have developed a novel pipeline that uses standard protocol in combination with gene co-expression network of a pathogen constructed using publicly available RNA-Seq data sets. We assessed the efficacy of this pipeline on Pseudomonas aeruginosa PAO1, a model bacterial pathogen, highlighting the power of our network-based approach in discovering novel genes or pathways associated with the pathogenesis, or antibiotic resistance of this strain. Complementing standard protocol with a gene network-based method thus elevated the ability to identify pathogenicity-associated genes in P. aeruginosa PAO1.IMPORTANCEWe present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

COVID-19, caused by SARS-CoV-2, results in respiratory and cardiopulmonary infections. There is an urgent need to understand not just the pathogenic mechanisms of this disease but also its impact on the physiology of different organs and microbiomes. Multiple studies have reported the effects of COVID-19 on the gastrointestinal microbiota, such as promoting dysbiosis (imbalances in the microbiome) following the disease’s progression. Deconstructing the dynamic changes in microbiome composition that are specifically correlated with COVID-19 patients remains a challenge. Motivated by this problem, we implemented a biomarker discovery pipeline to identify candidate microbes specific to COVID-19. This involved a meta-analysis of large-scale COVID-19 metagenomic data to decipher the impact of COVID-19 on the human gut and respiratory microbiomes. Metagenomic studies of the gut and respiratory microbiomes of COVID-19 patients and of microbiomes from other respiratory diseases with symptoms similar to or overlapping with COVID-19 revealed 1169 and 131 differentially abundant microbes in the human gut and respiratory microbiomes, respectively, that uniquely associate with COVID-19. Furthermore, by utilizing machine learning models (LASSO and XGBoost), we demonstrated the power of microbial features in separating COVID-19 samples from metagenomic samples representing other respiratory diseases and controls (healthy individuals), achieving an overall accuracy of over 80%. Overall, our study provides insights into the microbiome shifts occurring in COVID-19 patients, shining a new light on the compositional changes.

The automobile industry is shifting from internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs) or electric vehicles (EVs) extremely fast. Our calculation regarding the most popular private car brand in Bangladesh, Toyota, shows that the life cycle cost (LCC) of a Toyota BZ3 (EV), USD 43,409, is more expensive than a Toyota Aqua (HEV) and Toyota Prius (HEV), but cheaper than a Toyota Axio (ICEV) and Toyota Allion (ICEV). It has been found that about a 25% reduction in the acquisition cost of a Toyota BZ3 would lower its LCC to below others. EVs can be a good choice for those who travel a lot. Changes in electricity prices have little effect upon the LCC of EVs. With the expected decline in the annual price for batteries, which is between 6 and 9%, and the improvement of their capacities, EVs will be more competitive with other vehicles by 2030 or even earlier. EVs will dominate the market since demand for alternative fuel-powered vehicles is growing due to their environmental and economic advantages.

This study explored the physical and clinical phenotype of Bangladeshi children with autism spectrum disorder (ASD). A totally of 283 children who were referred for screening and administered Module 1 of the Autism Diagnostic Observation Schedule (ADOS) were included. Overall, 209 met the ADOS algorithmic cutoff for ASD. A trend for greater weight and head circumference was observed in children with ASD versus non-ASD. Head circumference was significantly (p < 0.03) larger in ASD males compared with non-ASD males. A trend was also observed for symptom severity, higher in females than males (p = 0.068), with further analyses demonstrating that social reciprocity (p < 0.014) and functional play (p < 0.03) were significantly more impaired in ASD females than males. The findings help understand sex differences in ASD.

The utmost goal of selecting an RNA-Seq alignment software is to perform accurate alignments with a robust algorithm, which is capable of detecting the various intricacies underlying read-mapping procedures and beyond. Most alignment software tools are typically pre-tuned with human or prokaryotic data, and therefore may not be suitable for applications to other organisms, such as plants. The rapidly growing plant RNA-Seq databases call for the assessment of the alignment tools on curated plant data, which will aid the calibration of these tools for applications to plant transcriptomic data. We therefore focused here on benchmarking RNA-Seq read alignment tools, using simulated data derived from the model organism Arabidopsis thaliana. We assessed the performance of five popular RNA-Seq alignment tools that are currently available, based on their usage (citation count). By introducing annotated single nucleotide polymorphisms (SNPs) from The Arabidopsis Information Resource (TAIR), we recorded alignment accuracy at both base-level and junction base-level resolutions for each alignment tool. In addition to assessing the performance of the alignment tools at their default settings, accuracies were also recorded by varying the values of numerous parameters, including the confidence threshold and the level of SNP introduction. The performances of the aligners were found consistent under various testing conditions at the base-level accuracy; however, the junction base-level assessment produced varying results depending upon the applied algorithm. At the read base-level assessment, the overall performance of the aligner STAR was superior to other aligners, with the overall accuracy reaching over 90% under different test conditions. On the other hand, at the junction base-level assessment, SubRead emerged as the most promising aligner, with an overall accuracy over 80% under most test conditions.

In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.

Since the discovery of the second chromosome in the Rhodobacter sphaeroides 2.4.1 by Suwanto and Kaplan in 1989 and the revelation of gene sequences, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. This phenomenon shattered the dogma of a unipartite genome (a single circular chromosome) in bacteria. Recently, Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL) have emerged as powerful tools in the investigation of big data in a plethora of disciplines to decipher complex patterns in these data, including the large-scale analysis and interpretation of genomic data. An important inquiry in bacteriology pertains to the genetic factors that underlie the structural evolution of multipartite and unipartite bacterial species. Towards this goal, here we have attempted to leverage machine learning as a means to identify the genetic factors that underlie the differentiation of, in general, bacteria with multipartite genomes and bacteria with unipartite genomes. In this study, deploying ML algorithms yielded two gene lists of interest: one that contains 46 discriminatory genes obtained following an assessment on all gene sets, and another that contains 35 discriminatory genes obtained based on an investigation of genes that are differentially present (or absent) in the genomes of the multipartite bacteria and their respective close relatives. Our study revealed a small pool of genes that discriminate bacteria with multipartite genomes and their close relatives with single-chromosome genomes. Machine learning thus aided in uncovering the genetic factors that underlie the differentiation of bacterial multipartite and unipartite traits.

The depletion of fossil fuels in the current world has been a major concern due to their role as a primary source of energy for many countries. As non-renewable sources continue to deplete, there is a need for more research and initiatives to reduce reliance on these sources and explore better alternatives, such as renewable energy. Hydrogen is one of the most intriguing energy sources for producing power from fuel cells and heat engines without releasing carbon dioxide or other pollutants. The production of hydrogen via the electrolysis of water using renewable energy sources, such as solar energy, is one of the possible uses for solid oxide electrolysis cells (SOECs). SOECs can be classified as either oxygen-ion conducting or proton-conducting, depending on the electrolyte materials used. This article aims to highlight broad and important aspects of the hybrid SOEC-based solar hydrogen-generating technology, which utilizes a mixed-ion conductor capable of transporting both oxygen ions and protons simultaneously. In addition to providing useful information on the technological efficiency of hydrogen production in SOEC, this review aims to make hydrogen production more efficient than any other water electrolysis system.

Taxonomic profiling of ancient metagenomic samples is challenging due to the accumulation of specific damage patterns on DNA over time. Although a number of methods for metagenome profiling have been developed, most of them have been assessed on modern metagenomes or simulated metagenomes mimicking modern metagenomes. Further, a comparative assessment of metagenome profilers on simulated metagenomes representing a spectrum of degradation depth, from the extremity of ancient (most degraded) to current or modern (not degraded) metagenomes, has not yet been performed. To understand the strengths and weaknesses of different metagenome profilers, we performed their comprehensive evaluation on simulated metagenomes representing human dental calculus microbiome, with the level of DNA damage successively raised to mimic modern to ancient metagenomes. All classes of profilers, namely, DNA-to-DNA, DNA-to-protein, and DNA-to-marker comparison-based profilers were evaluated on metagenomes with varying levels of damage simulating deamination, fragmentation, and contamination. Our results revealed that, compared to deamination and fragmentation, human and environmental contamination of ancient DNA (with modern DNA) has the most pronounced effect on the performance of each profiler. Further, the DNA-to-DNA (e.g., Kraken2, Bracken) and DNA-to-marker (e.g., MetaPhlAn4) based profiling approaches showed complementary strengths, which can be leveraged to elevate the state-of-the-art of ancient metagenome profiling.

In the modern world, as the population rises and fossil fuel supplies decline, energy demands continue to rise. Moreover, the use of fossil fuels harms the ecology, contributing to pollution and global warming. In order to overcome these difficulties, several approaches are revealed, such as the utilization of biomass as a renewable source of energy. Studies revealed that biomass can be converted into bioenergy via several thermal conversion processes, like pyrolysis, gasification, and torrefaction. Pyrolysis is the most convenient process to obtain three different types of biofuels (biochar as a solid, bio-oil as a liquid, and syngas as a gas). The biofuels produced in this process are normally lower in quality and cannot be used directly as fuel because they contain many undesirable components. Catalytic pyrolysis is one of the best processes to upgrade the quality of biofuels. Several varieties of catalysts are used in the catalytic pyrolysis process (ex situ and in situ). Due to stable operating conditions, both catalytic and non-catalytic pyrolysis procedures produce biochar that has a consistent output. Meanwhile, the effects of catalysts in the catalytic pyrolysis process considerably enhance the quality and quantity of bio-oils and syngas. By removing the unwanted oxygenated and nitrogenous components, the bio-oils produced through the catalytic pyrolysis method have a higher calorific value, reduced viscosity, and improved stability. Many researchers have looked at ways to increase the rate of pyrolysis, whereas a few have focused on maximizing the effects of the factors in order to improve the efficiency of catalytic pyrolysis. This review addresses the impact of catalysts on the catalytic pyrolysis of biomass to enhance the quality of the bio-oils in great detail. Machine learning and techno-economic analysis were investigated, as well as the future potential of the catalytic pyrolysis method for the generation of bio-oil.

CSZM compounds were synthesized by dry chemistry route with 5, 10, and 15% dopant of Mg dopants in the Ce 0.8−2x Sm 0.2 Zrx Mgx O2−d, {x = 0.05, 0.1 & 0.15}. The newly investigated materials were physically, chemically, and electrochemically studied and have shown promising results. The CSZM was crystalized in a fluorite structure with a pure cubic phase in a space group Fm3m and cell parameter a = 5.401742 °A and theoretical density from 7.6 to 8.9 after firing in the air with a final temperature of 1400 °C. Characterization of the structure and indexing of electrolyte materials were made after X-ray diffraction (XRD) testing. A Scanning electron microscope (SEM) morphological analysis was used to examine the microstructure details. Electrochemical impedance spectroscopy (EIS) measurements were performed from 400 °C to 700 °C which show the highest conductivity value of 1.0461 × 10+1 S/cm at 700 °C. In comparison, the minimum value was 2.7329 × 10−2 S/cm at 400 °C, and the total activation energy (Ea°A) was found to be 0.6865 eV under 5% H2/Ar.

The global population has increased over time, therefore the need for sufficient energy has risen. However, many countries depend on nonrenewable resources for daily usage. Nonrenewable resources take years to produce and sources are limited for generations to come. Apart from that, storing and energy distribution from nonrenewable energy production has caused environmental degradation over the years. Hence, many researchers have been actively participating in the development of energy storage devices for renewable resources using batteries. For this purpose, the lithium-ion battery is one of the best known storage devices due to its properties such as high power and high energy density in comparison with other conventional batteries. In addition, for the fabrication of Li-ion batteries, there are different types of cell designs including cylindrical, prismatic, and pouch cells. The development of Li-ion battery technology, the different widely used cathode and anode materials, and the benefits and drawbacks of each in relation to the most appropriate application were all thoroughly studied in this work. The electrochemical processes that underlie battery technologies were presented in detail and substantiated by current safety concerns regarding batteries. Furthermore, this review collected the most recent and current LIB recycling technologies and covered the three main LIB recycling technologies. The three recycling techniques—pyrometallurgical, hydrometallurgical, and direct recycling—have been the subject of intense research and development. The recovery of valuable metals is the primary goal of most recycling processes. The growth in the number of used LIBs creates a business opportunity to recover and recycle different battery parts as daily LIB consumption rises dramatically.

Polymyxins are last-line antibiotics for the treatment of Gram-negative ‘superbugs’. However, nephrotoxicity can occur in up to 60% of patients administered intravenous polymyxins. The mechanisms underpinning nephrotoxicity remain unclear. To understand polymyxin-induced nephrotoxicity, human renal proximal tubule cells were treated for 24 h with 0.1 mM polymyxin B or two new analogues, FADDI-251 or FADDI-287. Transcriptomic analysis was performed, and differentially expressed genes (DEGs) were identified using ANOVA (FDR < 0.2). Cell viability following treatment with polymyxin B, FADDI-251 or FADDI-287 was 66.0 ± 5.33%, 89.3 ± 3.96% and 90.4 ± 1.18%, respectively. Transcriptomics identified 430, 193 and 150 DEGs with polymyxin B, FADDI-251 and FADDI-287, respectively. Genes involved with metallothioneins and Toll-like receptor pathways were significantly perturbed by all polymyxins. Only polymyxin B induced perturbations in signal transduction, including FGFR2 and MAPK signaling. SIGNOR network analysis showed all treatments affected essential regulators in the immune system, autophagy, cell cycle, oxidative stress and apoptosis. All polymyxins caused significant perturbations of metal homeostasis and TLR signaling, while polymyxin B caused the most dramatic perturbations of the transcriptome. This study reveals the impact of polymyxin structure modifications on transcriptomic responses in human renal tubular cells and provides important information for designing safer new-generation polymyxins.

Protonic ceramic fuel cells (PCFCs) are one of the promising and emerging technologies for future energy generation. PCFCs are operated at intermediate temperatures (450–750 °C) and exhibit many advantages over traditional high-temperature oxygen-ion conducting solid oxide fuel cells (O-SOFCs) because they are simplified, have a longer life, and have faster startup times. A clear understanding/analysis of their specific working parameters/processes is required to enhance the performance of PCFCs further. Many physical processes, such as heat transfer, species transport, fluid flow, and electrochemical reactions, are involved in the operation of the PCFCs. These parameters are linked with each other along with internal velocity, temperature, and electric field. In real life, a complex non-linear relationship between these process parameters and their respective output cannot be validated only using an experimental setup. Hence, the computational fluid dynamics (CFD) method is an easier and more effective mathematical-based approach, which can easily change various geometric/process parameters of PCFCs and analyze their influence on its efficiency. This short review details the recent studies related to the application of CFD modeling in the PCFC system done by researchers to improve the electrochemical characteristics of the PCFC system. One of the crucial observations from this review is that the application of CFD modeling in PCFC design optimization is still much less than the traditional O-SOFC.

Global agricultural by‐products usually go to waste, especially in developing countries where agricultural products are usually exported as raw products. Such waste streams, once converted to "value‐added" products could be an additional source of revenue while simultaneously having positive impacts on the socio‐economic well‐being of local people. We highlight the utilization of thermochemical techniques to activate and convert agricultural waste streams such as rice and straw husk, coconut fiber, coffee wastes, and okara power wastes commonly found in the world into porous activated carbons and biofuels. Such activated carbons are suitable for various applications in environmental remediation, climate mitigation, energy storage, and conversions such as batteries and supercapacitors, in improving crop productivity and producing useful biofuels. [ABSTRACT FROM AUTHOR]

Keratin is a highly specialized fibrous protein, which is found in feathers, hair, wool and nails. Bioremediation of these waste materials is an issue requiring serious attention regarding environmental concern. In the present research work keratin protein is extracted from poultry chicken feather which is now treated as valueless product of poultry processing plants. Although it contains high keratin protein content, this byproduct is dumped continuously into the environment without further treatment. Millions of tons of chicken feathers are produced every year from poultry industry of Bangladesh which disposed off into environment without any industrial treatment. This protein rich valuable byproduct can be recycled and utilized effectively which has great economic and ecological importance. In this study, alkali-enzymatic hydrolysis was done for the extraction of keratin protein from feather. Desired result was found in reflux condensation system which yielded 76.2% protein hydrolysate compared to conventional hot plate hydrolysis which was yielded 52.63%. Detection and concentration of protein were determined by Biuret and Kjeldahl methods respectively.