Investigating the prevalence and associated factors of depression, anxiety, and loneliness among people with type-2 diabetes in Bangladesh: a community-based study.

Diabetes mellitus is a major worldwide health concern. Diabetes has been associated with a number of adverse mental health conditions including depression, anxiety, and loneliness that can negatively impact diabetes outcomes. This study aimed to investigate factors associated with depression, anxiety, and loneliness in people, in the community, suffering with diabetes in Bangladesh. A cross-sectional study was conducted with 600 people with type-2 diabetes (54.83% females; mean age: 52.70 ± 11.56 years) between July and September 2022. Purposive sampling method was used to recruit the participants. A validated semi-structured questionnaire was used to collect demographic and other data. Depression, anxiety, and loneliness were measured using the PHQ-9, GAD-7, and UCLA Loneliness scale, respectively. Bivariate and multivariable linear regression analyses were conducted to ascertain factors that were significantly associated with these mental health conditions. The prevalence of depression, anxiety, and loneliness was 31.17%, 21.83%, and 28.00%, respectively. A lack of formal education, and not taking part in physical activities were significantly associated with all three mental health states. Duration of diabetes and being on medication for high cholesterol were also associated with depression and anxiety. Older age and being widowed were significantly associated with loneliness. This study found that depression, anxiety, and loneliness are prevalent among Bangladeshi people with diabetes, with certain sociodemographic and diabetes-related factors associated with increased risk. The findings emphasize the need for targeted interventions to people within the communities, at grassroot levels in order to improve reduce health inequality, and improve the mental health of people living with diabetes.

An ultra-sensitive surface plasmon resonance biosensor with PtSe2 and BlueP/WS2 heterostructure.

This paper presents the design and simulation of a surface plasmon resonance (SPR) biosensor using a Platinum diselenide (PtSe2) and Blue Phosphorus/tungsten disulfide (BlueP/WS2) heterostructure for biosensing protocols. The simulation is done by using a finite element method (FEM) based COMSOL Multiphysics software. The performance of the SPR biosensor is then optimized for obtaining maximum sensitivity, quality factor, detection accuracy, and low limit of detection (LOD). The SPR biosensor demonstrates a maximum sensitivity of 234 deg/RIU, suggesting its ability to detect minute refractive index changes with remarkable precision. Furthermore, a quality factor of 390 RIU-1 demonstrates the biosensor's capacity to detect tiny fluctuations in target analyte concentration. The achieved detection accuracy of 7.8 deg-1 presents the biosensor's ability to detect target biomolecule solutions in the desired RI range. The remarkably low LOD of 4.26 × 10-6 ensures early and accurate detection. The significance of this research lies in five layered hetero-structure based combinations of BK7 prism, gold, PtSe2, BlueP/WS2 and sensing medium respectively. The introduction of transition metal dichalcogenides (TMDC) material of PtSe2 with a hybrid 2D nanomaterials heterostructure of BlueP and TMDCs offers a rapid, sensitive, label-free and reliable platform for early detection. Additionally, the FEM method allows for the investigation of physical phenomena as part of the work. In summary, the proposed senor outcomes effectively demonstrate the speedy capability of detecting any pathogens or analytes in the RI range of 1.330-1.350 with remarkable sensitivity and accuracy. The rapid detection without giving false results is the benefit of the proposed sensor structure.

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma.

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

Spatial and seasonal variability of chlorophyll-a, total suspended matter, and colored dissolved organic matter in the Sundarban mangrove forest using earth observation and field data.

The Sundarbans, the world's largest mangrove forest, confronts potential threats from various anthropogenic activities leading to degradation of its aquatic ecosystem. To examine the current status of the aquatic ecosystem, this study aimed to evaluate the spatial and seasonal fluctuation of three principal water quality attributes namely Chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and Colored Dissolved Organic Matter (CDOM) in the complex tidal river systems of the Sundarban mangroves forest using earth observation and in-situ data. A set of two bio-optical algorithms, Ocean color-2 (OC-2) and Ocean color-3 (OC-3), were applied to measure Chl-a concentration, Green/NIR and the Red/NIR band ratio algorithms were used for TSM and the Case-2 Regional Coast Color (C2RCC) processor in the SNAP software was applied to obtain CDOM concentration in study area. A total of 50 in-situ samples were collected during post-monsoon and pre-monsoon to validate the results. Our results clearly demonstrated seasonal variability with higher Chl-a concentrations in post-monsoon than pre-monsoon. This was due to the OC-2 algorithm which produced better results with R2 = 0.73, RMSE = 0.27 for post-monsoon and R2 = 0.55, RMSE = 0.32 for pre-monsoon. Whilst, TSM concentration performed the best with R2 = 0.77; RMSE = 15.82 and R2 = 0.65; RMSE = 33.96 in post-monsoon and pre-monsoon according to the Green/NIR band ratio method. The nearshore and narrow waterway regions had the highest concentrations of TSM and Chl-a, whereas the offshore regions had the lowest. Strong association were observed between the in-situ and satellite derive absorption coefficient, aCDOM (m-1). The R2 for a CDOM during pre-monsoon was 0.65 and throughout the post-monsoon, it was 0.74. Pre-monsoon concentrations were found to be higher due to marine sources and higher wind speeds, possibly due to sediment resuspension. This kind of baseline evaluation will help to detect threats, direct preventive measures for the protection of biodiversity, and deepen our knowledge of these distinct ecosystems. The results will help develop flexible management and preservation plans that can adjust to both natural and man-made changes.

Trace element contamination in water and sediment in an estuarine ecosystem connected to the Bay of Bengal: A preliminary assessment of ecological and human health risks.

The research, focusing on the analysis of nine trace elements, namely As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, completely analyzed their quantities in both water and sediment inside the Rabnabad Channel. Samples were collected during the post-monsoon and analyzed by ICP-OES following acid digestion. The mean concentrations of elements in water and sediments are as follows: Fe > Mn > Pb > Cu > Ni > Zn > Cr > As>Cd, and Zn > Fe > Pb > Mn > As>Cu > Cr > Ni > Cd. To understand the state of ecological and human health risk, several indices were incorporated. Health risk assessment revealed that children posed higher risk than adults. PERI, TRI, and Igeo indices for water sediment indicate a significant ecological risk. Moreover, Mn and Pb exhibit elevated HPI values and contribute substantially to contamination factors. Correlation and PCA implicate both anthropogenic and geogenic sources, such as agricultural practices, coal-based power plants, and the Payra seaport, in the elevated concentrations of Cd, Cr, Mn, and Fe in both water and sediment samples.

Toxicity assessment of heavy metals translocation in maize grown in the Ganges delta floodplain soils around the Payra power plant in Bangladesh.

As a cereal crop, maize ranked third place after wheat and rice in terms of land area coverage for its cultivation, and in Bangladesh, it ranked second place after rice in its production. As the substitution of wheat products, maize has been used widely in baking for human consumption and animal fodder. However, maize grown in this soil around the coal-burning power plant may cause heavy metals uptake that poses a risk to humans. The study was conducted at the maize fields in the Ganges delta floodplain soils of Bangladesh to know the concentration of eight heavy metals (Ni, Cr, Cd, Mn, As, Cu, Zn, and Pb) in soil and maize samples using an inductively coupled plasma mass spectrometer (ICP-MS) and to estimate the risk of heavy metals in maize grains. Mean concentrations of heavy metals (mg/kg) in soil were in decreasing order of Zn (10.12) > Cu (10.02) > Mn (5.48) > Ni (4.95) > Cr (3.72) > As (0.51) > Pb (0.27) > Cd (0.23). The plant tissues showed the descending order of heavy metal concentration as roots > grains > stems > leaves. BCF values for As, Cd, Pb, and Mn in roots were higher than 1.0, indicating considerable accumulation of these elements in maize via roots. Total hazard quotient (Æ©THQ) of heavy metals through maize grain consumption was 3.7E+00 and 3.9E+00 for adults and children, respectively, indicating non-cancer risk to the consumers. Anthropogenic influences contributed to the heavy metals enrichment in the Ganges delta floodplain soils around the thermal plant, and potential risks (non-carcinogenic and carcinogenic) were observed due to the consumption of maize grain cultivated in the study area.

Prevalence of inappropriate antibiotic doses among pediatric patients of inpatient, outpatient, and emergency care units in Bangladesh: A cross-sectional study.

The heterogeneous pediatric populations, their physiological differences, along with the necessity of performing additional dose calculation, make the pediatric population more vulnerable to the incidences of inappropriate antibiotic doses. This study was conducted to examine and evaluate the appropriateness of antibiotic doses. A cross-sectional study with a quantitative approach was conducted in three hospitals located in Savar from January 06, 2021 to October 17, 2022. This study had used a convenient sampling method to collect 405 filled prescription orders from heterogeneous pediatric patients prescribed by physicians from emergency, inpatient, and outpatient care units of various clinical settings. The Harriet Lane Handbook was used as reference to investigate inappropriate doses of antibiotics. Subsequently, all analyses were conducted using the RStudio 1.3.959 software. Binary logistic regression was used to assess the risk of inappropriate antibiotic prescription in pediatrics. The overall prevalence of inappropriate antibiotic dosing in pediatrics was 335 out of 545 (61.5%). Overdosing (36.3%) and oral antibiotic prescriptions (64%) were more common than underdosing (20.4%) and parenteral antibiotics (36%). The majority (230 out of 405, 56.8%) of pediatric patients had prescriptions with inappropriate antibiotic doses, with prevalence rates of 33.8% for inpatients, 86.7% for outpatients, and 50% for emergency pediatrics. The results also indicated that pediatric patients in outdoor and emergency care units, infants, toddlers, and early childhood, those prescribed two antibiotics simultaneously, and those receiving parenteral antibiotics, were less likely to have inappropriate antibiotic dosages in their prescriptions. This study demonstrated that about one out of every two prescriptions had inappropriate antibiotic doses; in particular, prescriptions containing only one antibiotic exhibited a substantial proportion of inappropriate antibiotic doses. Inappropriate antibiotic doses may result in therapeutic failure, patient harm, and antibiotic resistance. Good clinical pharmacy practice and careful adherence to pediatric dosing standards may minimize inappropriate antibiotic doses.

Toxicity factors, ecological and health risk assessments of heavy metal in the urban soil: a case study of an agro-machinery area in a developing country.

The contribution of heavy metals in surface soils by the influences of agro-machinery factories is a significant growing concern. Heavy metals were analyzed by inductively coupled plasma mass spectrometry technique to assess human and ecological risks. The concentrations of Fe, Cd, Cr, Cu, As, Pb, Mn, Ni, and Zn in soil ranged from 18,274-22,652, 2.06-4.92, 24.8-41.9, 126.8-137.5, 9.20-25.2, 17.8-46.1, 114.4-183.1, 86.9-118.1, and 101.6-159.6 mg/kg, respectively. The enrichment factor values of heavy metals were greater than 1.5, suggesting severe anthropogenic activities such as untreated waste discharging, burning of metallic wastes, wear, and tear, and dismantling of old batteries for heavy metals enrichment in studied soil. The contamination factor indicates considerable to very high contamination of heavy metals in soil. Moderate to high ecological risk was observed for analyzed metals which mainly originated from the maintenance and repairing of various engines in the workshop and welding and soldering of metallic substances. The target hazard quotient (THQ) was ranged from 6.99E-04 to 2.21E-01 for adults and 5.59E-03 to 1.82E + 00 for children, respectively; indicating children were more sensitive to heavy metals exposure from soil dust. The carcinogenic risk of As (1.72E-05) exceeded the USEPA acceptable limits indicating cancer risk to the residence. The current emphasized the significance of intensive heavy metals monitoring in surface soils around the agro-machinery areas due to their potential health risks associated with children.

Understanding antibiotic purchasing practices in community pharmacies: A potential driver of emerging antimicrobial resistance.

Introduction: Antimicrobial resistance (AMR), a transboundary health issue, critically impacting low- and middle-income countries (LMICs) where 80% of antibiotics are used in the community, with 20-50% being inappropriate. Southeast-Asia, including Bangladesh, faces heightened AMR risk due to suboptimal healthcare standard and unregulated antibiotic sales. This study aimed to audit antibiotic dispensing patterns from community pharmacies, identifying factors influencing purchasing behaviors. Methods: A cross-sectional survey of 385 antibiotic customers and structured observations of 1000 pharmacy dispensing events were conducted in four urban and rural areas in Bangladesh. Descriptive analysis defined antibiotic use, while Poisson regression examined how patients' demographics and health symptoms influenced prescription behaviors. Results: Among 1000 observed medicine dispensing events, 25.9% were antibiotics. Commonly purchased antibiotics included macrolides (22.8%), third-generation-cephalosporins (20.8%), and second-generation-cephalosporins (16.9%). Following WHO-AWaRe classifications, 73.5% of antibiotics were categorized as Watch, and 23.1% as Access. From the survey, 56.6% antibiotics were purchased without a prescription from drug-sellers and informal healthcare providers, primarily for "non-severe" health-symptoms such as upper-respiratory-tract infections (37.4%), fever (31.7%), uncomplicated skin infections (20%), gastrointestinal-infections (11.2%), and urinary-tract infections (7.9%). The likelihood of presenting a prescription while purchasing antibiotics was 27% lower for individuals aged 6-59 compared to those ≤5 or ≥ 60. Lower-respiratory-tract infections and enteric-fever had higher prescription rates, with adjusted prevalence ratios of 1.78 (95% CI: 1.04, 3.03) and 1.87 (95% CI: 1.07, 3.29), respectively. After adjusting for confounders, sex, urban-rural locations, income, education, and number of health-symptoms exhibited no significant influence on prescription likelihood. Conclusion: This study underscores unregulated antibiotic sales without prescriptions, urging tailored interventions considering prevailing health-seeking practices in diverse healthcare settings in LMICs. Enforcing prescription-only regulations is hindered by easy access through community pharmacies and conflicts of interest. Future strategies should consider how stewardship impacts the financial interests of pharmacy personnel in settings lacking clear authority to ensure optimal compliance.

First identification and genomic features of multidrug-resistant Citrobacter freundii ST669 strain isolated from a domesticated duck in Bangladesh.

Objectives: Citrobacter freundii is a prevalent source of nosocomial infections and a well-known cause of diarrheal diseases. In recent years, it has also become increasingly resistant to various antimicrobials. In this study, we screened and characterized a multidrug-resistant (MDR) C. freundii isolate obtained from a domesticated diseased duck to better understand the genetic features, molecular epidemiology, and underlying factors linked to the antimicrobial resistance genes (ARGs) and virulence factor genes (VFGs) of the isolate. Methods: The C. freundii BAU_TM8 strain was isolated using culturing, staining, biochemical, polymerase chain reaction, and Matrix-assisted laser desorption/ionization-time of flight methods. The MDR properties of the strain were determined by a disk diffusion test. The genomic sequence of C. freundii BAU_TM8 was performed using the Illumina NextSeq2000 platform. The ARGs, VFGs, and genomic functional characteristics of the C. freundii BAU_TM8 strain were identified using several open-source databases. Results: The sequence type of this strain was ST669, and the pathogenicity index of the strain was 0.919. Moreover, the strain had an estimated genome length of 5,797,806 bp, harboring 62 contigs, a G + C content of 54.32 %, and five contig L50s with an N50 value of 443,947 bp. Using phylogenetic analysis, this strain was closely related to two strains isolated from human and environmental samples in the USA and China despite huge geographical distances. The C. freundii BAU_TM8 strain consisted of 40 AGRs encoding resistance to 19 antimicrobial categories, e.g., fluoroquinolones, macrolides, folate pathway antagonists, aminoglycosides, tetracyclines, cephalosporins, and others. According to the phenotypic assay and genome sequence, the sensitivity and specificity of resistance profiles of the strain were 100 % and 20 %, respectively. Moreover, the virulence factor database detected 66 VFGs in this strain. This strain contained 1581 subsystems, having 33 % subsystem coverage and 2275 genes encoding amino acid derivatives, carbohydrate metabolism, protein metabolism, cofactors, vitamins, prosthetic groups, pigments, respiration, motility and chemotaxis, stress response, DNA metabolism, nucleosides and nucleotides, and others. Conclusions: To the best of our knowledge, this is the first WGS report of C. freundii from a domesticated duck in Bangladesh. The ubiquitous occurrence of ARGs and VFGs in the C. freundii BAU_TM8 strain detected in this study highlights the growing concern about antimicrobial resistance in humans, animals, and environments.

Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach.

The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+Na+pH, Ca2+Mg2+NO3-, and HCO3-SO42-PO43-F-. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43- content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43- distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.

Solar power conversion: CuI hole transport layer and Ba3NCl3 absorber enable advanced solar cell technology boosting efficiency over 30.

Researchers are becoming more interested in novel barium-nitride-chloride (Ba3NCl3) hybrid perovskite solar cells (HPSCs) due to their remarkable semiconductor properties. An electron transport layer (ETL) built from TiO2 and a hole transport layer (HTL) made of CuI have been studied in Ba3NCl3-based single junction photovoltaic cells in a variety of variations. Through extensive numerical analysis using SCAPS-1D simulation software, we investigated elements such as layer thickness, defect density, doping concentration, interface defect density, carrier concentration, generation, recombination, temperature, series and shunt resistance, open circuit voltage (V OC), short circuit current (J SC), fill factor (FF), and power conversion efficiency (PCE). The study found that the HTL CuI design reached the highest PCE at 30.47% with a V OC of 1.0649 V, a J SC of 38.2609 mA cm-2, and an FF of 74.78%. These findings offer useful data and a practical plan for producing inexpensive, Ba3NCl3-based thin-film solar cells.

Advancements in MAX phase materials: structure, properties, and novel applications.

The MAX phase represents a diverse class of nanolaminate materials with intriguing properties that have received incredible global research attention because they bridge the divide separating metals and ceramics. Despite the numerous potential applications of MAX phases, their complex structure leads to a scarcity of readily accessible pure MAX phases. As a result, in-depth research on synthesis methods, characteristics, and structure is frequently needed for appropriate application. This review provides a comprehensive understanding of the recent advancements and growth in MAX phases, focusing on their complex crystal structures, unique mechanical, thermal, electrical, crack healing, corrosion-resistant properties, as well as their synthesis methods and applications. The structure of MAX phases including single metal MAX, i-MAX and o-MAX was discussed. Moreover, recent advancements in understanding MAX phase behaviour under extreme conditions and their potential novel applications across various fields, including high-temperature coatings, energy storage, and electrical and thermal conductors, biomedical, nanocomposites, etc. were discussed. Moreover, the synthesis techniques, ranging from bottom-up to top-down methods are scrutinized for their efficacy in tailoring MAX phase properties. Furthermore, the review explores the challenges and opportunities associated with optimizing MAX phase materials for specific applications, such as enhancing their oxidation resistance, tuning their mechanical properties, and exploring their functionality in emerging technologies. Overall, this review aims to provide researchers and engineers with a comprehensive understanding of MAX phase materials and inspire further exploration into their versatile applications in materials science and engineering.

Characterization and genome mining of Bacillus subtilis BDSA1 isolated from river water in Bangladesh: A promising bacterium with diverse biotechnological applications.

The metabolic versatility of Bacillus subtilis makes it useful for a wide range of applications in biotechnology, from bioremediation to industrially important metabolite production. Understanding the molecular attributes of the biocontrol characteristics of B. subtilis is necessary for its tailored use in the environment and industry. Therefore, the present study aimed to conduct phenotypic characterization and whole genome analysis of the B. subtilis BDSA1 isolated from polluted river water from Dhaka, Bangladesh to explore its biotechnological potential. The chromium reduction capacity at 100 ppm Cr (VI) showed that B. subtilis BDSA1 reduced 40 % of Cr (VI) within 24hrs at 37 °C. Exposure of this bacterium to 200 ppm cadmium resulted in 43 % adsorption following one week of incubation at 37 °C. Molecular detection of chrA and czcC gene confirmed chromium and cadmium resistance characteristics of BDSA1. The size of the genome of the B. subtilis BDSA1 was 4.2 Mb with 43.4 % GC content. Genome annotation detected the presence of numerous genes involved in the degradation of xenobiotics, resistance to abiotic stress, production of lytic enzymes, siderophore formation, and plant growth promotion. The assembled genome also carried chromium, cadmium, copper, and arsenic resistance-related genes, notably cadA, czcD, czrA, arsB etc. Genome mining revealed six biosynthetic gene clusters for bacillaene, bacillibacin, bacilysin, subtilosin, fengycin and surfactin. Importantly, BDSA1 was predicted to be non-pathogenic to humans and had only two acquired antimicrobial resistance genes. The pan-genome analysis showed the openness of the B. subtilis pan-genome. Our findings suggested that B. subtilis BDSA1 might be a promising candidate for diverse biotechnological uses.

Analysis of Internet of things implementation barriers in the cold supply chain: An integrated ISM-MICMAC and DEMATEL approach.

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimize operating procedures, and increase productivity. The integration of the IoT in this complicated setting is hindered by specific barriers that require thorough examination. Prominent barriers to IoT implementation in a cold supply chain, which is the main objective, are identified using a two-stage model. After reviewing the available literature on IoT implementation, 13 barriers were identified. The survey data were cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This study applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among these barriers, "regulatory compliance" and "cold chain networks" are the key drivers of IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate barrier interactions. In the second phase of this study, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system benefits as a whole. The findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

Soil bacteriome diversity and composition of rooftop and surface gardens in urban and peri-urban areas of Bangladesh.

Soil microbiome science, rapidly evolving, predominantly focuses on field crop soils. However, understanding garden soil microbiomes is essential for enhancing food production sustainability in garden environments. This study aimed to unveil the bacteriome diversity and composition in rooftop garden soils (RGS) and surface garden soils (SGS) across urban (Dhaka North and Dhaka South City Corporations) and peri-urban (Gazipur City Corporation) areas of Dhaka Division, Bangladesh. We analyzed 11 samples, including six RGS and five SGS samples from 11 individual gardens using 16S rRNA (V3-V4 region) gene-based amplicon sequencing. A total of 977 operational taxonomic units (OTUs), including 270 and 707 in RGS and SGS samples, respectively, were identified. The observed OTUs were represented by 21 phyla, 45 classes, 84 orders, 173 families, and 293 genera of bacteria. Alpha diversity indices revealed significantly higher bacterial diversity in SGS samples (p = 0.01), while beta diversity analyses indicated distinct bacteriome compositions between RGS and SGS samples (p = 0.028, PERMANOVA). Despite substantial taxonomic variability between sample categories, there was also a considerable presence of shared bacterial taxa. At the phylum level, Bacilliota (61.14%), Pseudomonadota (23.42%), Actinobacteria (6.33%), and Bacteroidota (3.32%) were the predominant bacterial phyla (comprising > 94.0% of the total abundances) in both types of garden soil samples. Of the identified genera, Bacillus (69.73%) and Brevibacillus (18.81%) in RGS and Bacillus (19.22%), Methylophaga (19.21%), Acinetobacter (6.27%), Corynebacterium (5.06%), Burkholderia (4.78%), Paracoccus (3.98%) and Lysobacter (2.07%) in SGS were the major bacterial genera. Importantly, we detected that 52.90% of genera were shared between RGS and SGS soil samples. Our data reveal unique and shared bacteriomes with probiotic potential in soil samples from both rooftop and surface gardens. Further studies should explore the functional roles of shared bacterial taxa in garden soils and how urban environmental factors affect microbiome composition to optimize soil health and sustainable food production.

Genomic Insights of a Methicillin-Resistant Biofilm-Producing Staphylococcus aureus Strain Isolated From Food Handlers.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important zoonotic pathogen associated with a wide range of infections in humans and animals. Thus, the emergence of MRSA clones poses an important threat to human and animal health. This study is aimed at elucidating the genomics insights of a strong biofilm-producing and multidrug-resistant (MDR) S. aureus MTR_BAU_H1 strain through whole-genome sequencing (WGS). The S. aureus MTR_BAU_H1 strain was isolated from food handlers' hand swabs in Bangladesh and phenotypically assessed for antimicrobial susceptibility and biofilm production assays. The isolate was further undergone to high throughput WGS and analysed using different bioinformatics tools to elucidate the genetic diversity, molecular epidemiology, sequence type (ST), antimicrobial resistance, and virulence gene distribution. Phenotypic analyses revealed that the S. aureus MTR_BAU_H1 strain is a strong biofilm-former and carries both antimicrobial resistance (e.g., methicillin resistance; mecA, beta-lactam resistance; blaZ and tetracycline resistance; tetC) and virulence (e.g., sea, tsst, and PVL) genes. The genome of the S. aureus MTR_BAU_H1 belonged to ST1930 that possessed three plasmid replicons (e.g., rep16, rep7c, and rep19), seven prophages, and two clustered regularly interspaced short palindromic repeat (CRISPR) arrays of varying sizes. Phylogenetic analysis showed a close evolutionary relationship between the MTR_BAU_H1 genome and other MRSA clones of diverse hosts and demographics. The MTR_BAU_H1 genome harbours 42 antimicrobial resistance genes (ARGs), 128 virulence genes, and 273 SEED subsystems coding for the metabolism of amino acids, carbohydrates, proteins, cofactors, vitamins, minerals, and lipids. This is the first-ever WGS-based study of a strong biofilm-producing and MDR S. aureus strain isolated from human hand swabs in Bangladesh that unveils new information on the resistomes (ARGs and correlated mechanisms) and virulence potentials that might be linked to staphylococcal pathogenesis in both humans and animals.

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.