Strengthening health monitoring: Intention and adoption of Internet of Things-enabled wearable healthcare devices.

Objective: Health self-monitoring technologies are gaining popularity worldwide, but they face low adoption rates in emerging countries. There is a deficiency in studies that have applied the value-belief-norm (VBN) model to understand the adoption of IoT-enabled wearable healthcare devices (WHDs). This study investigates the adoption of IoT-enabled WHDs among older adults in China, using the VBN model as a theoretical framework. Methods: Using a convenience sampling method and a web-based cross-sectional survey method, we collected data from 476 respondents, which we analyzed using partial least squares structural equation modeling using Smart PLS version 3.3.5. Results: The findings highlight the significance of health values and motivation in shaping personal health beliefs, which, in turn, influence personal norms and awareness of consequences. Particularly, awareness of consequences and attributions of responsibility significantly impact personal norms. Personal and social norms, in turn, strongly affect the intention to adopt IoT-enabled WHDs, ultimately driving their actual adoption. Conclusion: This research contributes novel insights into the behavioral dynamics surrounding the adoption of IoT-enabled WHDs, providing valuable guidance for marketers and policymakers. Marketers can leverage these insights to develop tailored marketing strategies within the IoT-enabled WHD industry. Additionally, policymakers are urged to prioritize campaigns aimed at enhancing awareness and understanding of self-healthcare monitoring, with a focus on promoting the unique health benefits of IoT-enabled WHDs.

Ageing well with tech: Exploring the determinants of e-healthcare services adoption in an emerging economy.

The adoption of e-healthcare services is critical for improving healthcare accessibility and efficiency, particularly in regions with diverse populations, such as Malaysia. Although e-healthcare services offer numerous advantages, their adoption is considerably low and requires a thorough understanding of the key factors that influence their use. This study investigated the determinants and dynamics of e-healthcare adoption among adults over 40 years by extending the unified theory of acceptance and use of technology. We employed a quantitative research approach, specifically a cross-sectional design. Data were collected from 393 Malaysian respondents through a structured survey questionnaire, using convenience sampling. They were analyzed using partial least-squares-structural equation modeling. The findings revealed that performance expectancy, effort expectancy, social influence, and perceived product value significantly influenced individuals' intentions to use e-healthcare services. Meanwhile, perceived risk had an insignificant negative effect. Facilitating conditions significantly influenced individuals' intentions and actual usage of e-healthcare services. Furthermore, individuals' intentions to use e-healthcare services significantly affected their actual use of these services. Additionally, the intention to use e-healthcare services mediated the relationship between the factors and usage of e-healthcare services, except for perceived risk. Surprisingly, perceived service accuracy had no significant moderating effect on the relationship between individuals' intention to use and their actual use of e-healthcare services. This study offers valuable insights for educators, practitioners, and policymakers, enriching the scholarly discourse in this field. For education, integrating e-healthcare topics into curricula can enhance digital health literacy. In practice, healthcare providers should focus on improving user experience and addressing barriers to technology adoption. For policy making, developing supportive policies, and infrastructure to facilitate e-healthcare adoption is crucial to enhancing public health outcomes.

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest.

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Chemico-pharmacological evaluations of the dwarf elephant ear (Colocasia affinis Schott) plant metabolites and extracts: health benefits from vegetable source.

Introduction: Colocasia affinis Schott (Family: Araceae), found in the Asian region, is a traditional root vegetable consumed by the locals and well-known as Dwarf Elephant Ear. Methods: For the pharmacological exploration of this root vegetable, four kupchan fractions (i.e. HSF, DCMSF, EASF, and AQSF) from ethanolic extract of C. affinis were employed to in vitro i.e. antioxidant, cytotoxicity, and antimicrobial and in vivo i.e. antidiarrheal and analgesic assays, followed by phytochemical screening and GC-MS protocol. Result and Discussion: In the antioxidant assay, the AQSF showed promising potential with an IC50 value of 29.4 µg/mL and additionally, it exhibited the greatest overall phenolic content, measuring 57.23 mg GAE/gm. of extract among other fractions. The AQSF also revealed promising cytotoxic activity in brine shrimp lethality assay with an LC50 value of 1.36 µg/mL. Both AQSF and EASF exhibited substantial antimicrobial efficacy against both gram-positive and gram-negative bacteria as well as various fungus species with a remarkable zone of inhibitions compared to standards. Whereas, during both the castor oil-induced antidiarrheal and acetic acid-induced writhing assay, the DCMSF at 400 mg/kg dose exhibited the highest 51.16% reduction of diarrhea and 52.33% reduction of writhing. Phytochemical screening revealed several chemical groups while GC-MS study of different fractions of dwarf elephant ear ethanolic extract revealed 48 different bioactive phytochemicals in total. Several targets such as KAS, DHFR for anti-microbial activities, GLR, URO for antioxidant activities, EGFR, BCL-2 for cytotoxicity, KOR, DOR for antidiarrheal activities and COX-2, TNF-α for analgesic activities are considered for molecular docking against identified phytocompounds and standards along with ADME/T studies to ascertain their safety, efficacy and drug likeliness profiles. Conclusion: To recapitulate, our study revealed that vegetables such as dwarf elephant ear can be considered as a prospective source of therapeutics and drug development besides their nutritive food values.

Financial and digital financial literacy through social media use towards financial inclusion among batik small enterprises in Indonesia.

Batik, an Indonesian textile art form, holds immense economic and cultural importance. Small and medium enterprises (SMEs) specialising in batik play a crucial role in Indonesia's economic growth and cultural preservation, contributing significantly to the gross domestic product (GDP) and preserving the nation's heritage. Nevertheless, these enterprises face several challenges, such as slow growth and limited access to credit. The batik industry also lags in financial literacy and the adoption of digital marketing strategies, hindering its development. This quantitative study aims to investigate the relationship between financial literacy, digital financial literacy, and financial inclusion in batik SMEs and also examined the moderating effect of online social networks. A survey was conducted involving 535 managers, owners, and financial officers of small batik enterprises. Subsequently, the SmartPLS statistical analysis method was employed for data analysis. The results demonstrate that financial literacy and digital financial literacy play a significant role in accessing financial inclusion for batik small enterprises. Moreover, the utilisation of social media was found to moderate these relationships, amplifying the impact of financial and digital literacy on financial inclusion. The findings contribute to the existing knowledge, provide insights for enhancing batik small enterprises, and propose a digital financial model to promote financial inclusion.

Genome mining for ribosomally synthesized and post-translationally modified peptides (RiPPs) in Streptomyces bacteria.

Ribosomally synthesized post-translationally modified peptides (RiPPs) are a novel category of bioactive natural products (NPs). Streptomyces bacteria are a potential source of many bioactive NPs. Limited opportunities are available to characterize all the bioactive NP gene clusters. In this study, 410 sequences of Streptomyces were analyzed for RiPPs through genome mining using the National Center for Biotechnology Information (NCBI), by combining BAGEL and anti-SMASH. A total of 4098 RiPPs were found; including both classified (lanthipeptide, RiPP-like, bacteriocin, LAPs, lassopeptide, thiopeptides) and nonclassified RiPPs. Soil was identified as a rich habitat for RiPPs. These data may offer alternative future remedies for various health issues.

The need for active and integrated involvement of the community and health professionals in the prevention and control of dengue hemorrhagic fever in Indonesia.

Successful control and prevention of dengue fever requires active involvement from all parties. For this reason, three innovative programs are needed, namely: i) increasing knowledge, attitude and practice (KAP) of the community and health professionals as capital in controlling dengue fever in a sustainable manner; ii) application of "3M Plus" to suppress vector breeding in household settings; iii) promotion of the "Jumantik" program as an effective community empowerment approach to prevent and control dengue fever based on community independence. It was concluded that successful control of dengue fever requires integration of the community and health workers through various innovative programs.

MRI Radiomics Combined with Clinicopathological Factors for Predicting 3-Year Overall Survival of Hepatocellular Carcinoma After Hepatectomy.

Background: A limited number of studies have examined the use of radiomics to predict 3-year overall survival (OS) after hepatectomy in patients with hepatocellular carcinoma (HCC). This study develops 3-year OS prediction models for HCC patients after liver resection using MRI radiomics and clinicopathological factors. Materials and Methods: A retrospective analysis of 141 patients who underwent surgical resection of HCC was performed. Patients were randomized into two set: the training set (n=98) and the validation set (n=43) including the survival groups (n=111) and non-survival groups (n=30) based on 3-year survival after hepatectomy. Furthermore, x2 or Fisher's exact test, univariate and multivariate logistic regression analyses were conducted to determine independent clinicopathological risk factors associated with 3-year OS. 1688 quantitative imaging features were extracted from preoperative T2-weighted imaging (T2WI) and contrast-enhanced magnetic resonance imaging (CE-MRI) of arterial phase (AP), portal venous phases (PVP)and delay period (DP). The features were selected using the variance threshold method, the select K best method and the least absolute shrinkage and selection operator (LASSO) algorithm. By using Bernoulli Naive Bayes (BernoulliNB) and Multinomial Naive Bayes (MultinomialNB) classifiers, we constructed models based on the independent clinicopathological factors and Rad-scores. To determine the best model, receiver operating characteristics (ROC) and Delong's test were used. Moreover, calibration curves were used to determine the calibration ability of the model, while decision curve analysis (DCA) was implemented to evaluate its clinical benefit. Results: The fusion model showed excellent prediction precision with AUC of 0.910 and 0.846 in training and validation set and revealed significant diagnostic accuracy and value in the calibration curve and DCA analysis. Conclusion: Nomograms based on MRI radiomics and clinicopathological factors have significant predictive value for 3-year OS after hepatectomy and can be used for risk classification.

Phytoremediation: a transgenic perspective in omics era.

Phytoremediation is an environmental safety strategy that might serve as a viable preventative approach to reduce soil contamination in a cost-effective manner. Using plants to remediate pollution from the environment is referred to as phytoremediation. In the past few decades, plants have undergone genetic manipulation to overcome inherent limitations by using genetically modified plants. This review illustrates the eco-friendly process of cleaning the environment using transgenic strategies combined with omics technologies. Herbicides tolerance and phytoremediation abilities have been established in genetically modified plants. Transgenic plants have eliminated the pesticides atrazine and metolachlor from the soil. To expand the application of genetically engineered plants for phytoremediation process, it is essential to test strategies in the field and have contingency planning. Omics techniques were used for understanding various genetic, hormonal, and metabolic pathways responsible for phytoremediation in soil. Transcriptomics and metabolomics provide useful information as resources to understand the mechanisms behind phytoremediation. This review aims to highlight the integration of transgenic strategies and omics technologies to enhance phytoremediation efficiency, emphasizing the need for field testing and comprehensive planning for successful implementation.

Pyroptosis in Diabetic Peripheral Neuropathy and its Therapeutic Regulation.

Pyroptosis is a pro-inflammatory form of cell death resulting from the activation of gasdermins (GSDMs) pore-forming proteins and the release of several pro-inflammatory factors. However, inflammasomes are the intracellular protein complexes that cleave gasdermin D (GSDMD), leading to the formation of robust cell membrane pores and the initiation of pyroptosis. Inflammasome activation and gasdermin-mediated membrane pore formation are the important intrinsic processes in the classical pyroptotic signaling pathway. Overactivation of the NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome triggers pyroptosis and amplifies inflammation. Current evidence suggests that the overactivation of inflammasomes and pyroptosis may further induce the progression of cancers, nerve injury, inflammatory disorders and metabolic dysfunctions. Current evidence also indicates that pyroptosis-dependent cell death accelerates the progression of diabetes and its frequent consequences including diabetic peripheral neuropathy (DPN). Pyroptosis-mediated inflammatory reaction further exacerbates DPN-mediated CNS injury. Accumulating evidence shows that several molecular signaling mechanisms trigger pyroptosis in insulin-producing cells, further leading to the development of DPN. Numerous studies have suggested that certain natural compounds or drugs may possess promising pharmacological properties by modulating inflammasomes and pyroptosis, thereby offering potential preventive and practical therapeutic approaches for the treatment and management of DPN. This review elaborates on the underlying molecular mechanisms of pyroptosis and explores possible therapeutic strategies for regulating pyroptosis-regulated cell death in the pharmacological treatment of DPN.

Modelling the significance of value-belief-norm framework to predict mass adoption potentials of internet of things-enabled wearable fitness devices.

Self-health monitoring technologies have become increasingly popular in averting unanticipated health complications. However, the adoption rate of such technologies in developing countries is surprisingly low. Furthermore, empirical studies on the application of the value-belief-norm (VBN) model to elucidate intention to use IoT-enabled wearable fitness devices (IoT-enabled WFDs) are scarce. This study aimed to expand the VBN model by integrating health values, health consciousness, health knowledge-seeking, and social norms as influencing constructs. The proposed holistic framework was empirically tested to examine these constructs on users' decision-making process of adopting IoT-enabled WFDs. A web-based survey involving 866 adults in China aged 18-30 years was conducted. The gathered data were analysed using partial least squares-structural equation modeling. The results revealed the significant influence of health consciousness and health knowledge-seeking on personal health beliefs, as well as the favourable impact of personal health beliefs on personal norms and awareness of consequences. The results further demonstrated the substantial influence of awareness of consequences and ascription of responsibilities on personal norms. Besides that, personal norms and societal norms were found to have strong influence on the intention to adopt IoT-enabled WFDs, which was revealed to have significant influence on the actual usage. This study's findings offer novel theoretical insights on the behavioural characteristics of adopting IoT-enabled WFDs and serve as a practical guideline for industry experts and marketers to establish appropriate marketing strategies to support the IoT-enabled wearable sector. The findings also benefit policymakers in their efforts of developing strategies that emphasise the unique benefits of self-healthcare monitoring to encourage active lifestyle and decrease obesity and overweight-related health risks.

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies.

BACKGROUND: Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications. METHODS: Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis. RESULTS: Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs. CONCLUSION: Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.

Nitrogen and sulfur cycling and their coupling mechanisms in eutrophic lake sediment microbiomes.

Microorganisms play important roles in the biogeochemical cycles of lake sediment. However, the integrated metabolic mechanisms governing nitrogen (N) and sulfur (S) cycling in eutrophic lakes remain poorly understood. Here, metagenomic analysis of field and bioreactor enriched sediment samples from a typical eutrophic lake were applied to elucidate the metabolic coupling of N and S cycling. Our results showed significant diverse genes involved in the pathways of dissimilatory sulfur metabolism, denitrification and dissimilatory nitrate reduction to ammonium (DNRA). The N and S associated functional genes and microbial groups generally showed significant correlation with the concentrations of NH4+, NO2- and SO42, while with relatively low effects from other environmental factors. The gene-based co-occurrence network indicated clear cooperative interactions between N and S cycling in the sediment. Additionally, our analysis identified key metabolic processes, including the coupled dissimilatory sulfur oxidation (DSO) and DNRA as well as the association of thiosulfate oxidation complex (SOX systems) with denitrification pathway. However, the enriched N removal microorganisms in the bioreactor ecosystem demonstrated an additional electron donor, incorporating both the SOX systems and DSO processes. Metagenome-assembled genomes-based ecological model indicated that carbohydrate metabolism is the key linking factor for the coupling of N and S cycling. Our findings uncover the coupling mechanisms of microbial N and S metabolism, involving both inorganic and organic respiration pathways in lake sediment. This study will enhance our understanding of coupled biogeochemical cycles in lake ecosystems.

An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.

Impulse buying behavior during livestreaming: Moderating effects of scarcity persuasion and price perception.

This research aimed to identify the factors that influence impulse buying behavior during livestreaming and advance the existing literature based on a proposed conceptual framework grounded in the stimulus-organism-response (S-O-R) model. We also tested the moderating effects of price perception and scarcity persuasion. An online self-administered questionnaire was used to collect data from 837 Chinese participants aged over 18 years. The data were analyzed using partial least squares structural equation modeling using Smart-PLS version 4.0. The findings showed that susceptibility to social influence, impulse buying tendency, cognitive reactions, affective reactions, and the urge to buy impulsively are statistically significant predictors of impulse buying during livestreaming, with price perception and scarcity persuasion as moderators. The study expands the S-O-R model for livestreaming impulse buying in e-commerce context, highlighting its multifaceted nature and revealing the mediating role of Urge to Buy Impulsively in translating cognitive and emotional factors into impulse buying behavior. These insights offer practical guidance for marketers to design tailored strategies that leverage psychological triggers and external cues to enhance consumer engagement and encourage desired behaviors, ultimately leading to more effective marketing campaigns and improved consumer experiences.

Envisaging the intention and adoption of electronic health applications among middle-aged and older adults: Evidence from an emerging economy.

Technology plays a substantial role in our daily lives, and the spread of COVID-19 paves the way for the use of healthcare technologies to manage health in the comfort of our homes. Middle-aged and older adults face health issues and require instant healthcare advice and support. Electronic healthcare (EH) applications have emerged to facilitate middle-aged and older adults receiving healthcare support instantly while remaining in their homes. The present study empirically evaluates the intention and adoption of electronic health applications with the technological attributes of perceived compatibility, cost, product value, technology accuracy, privacy issues and health motivation among middle-aged and older adults from Malaysia. Moreover, this research examines the mediating effect of the intention to adopt EH applications between technology attributes and the adoption of EH applications. This study uses a cross-sectional method and employs an online survey to assemble quantitative data from 298 middle-aged and older Malaysian adults. It utilizes partial least squares structural equation modelling for data analysis. The data analysis reveals that perceived compatibility, cost, privacy, product value and health motivation significantly influenced the intention to use EH apps. Furthermore, the analysis shows that the intention to adopt EH apps significantly mediates the relationship between the perception of cost, personal privacy, product value, health motivation and adoption of EH apps. However, the intention to use EH apps insignificantly instigates their adoption. Finally, the study presents its implications, limitations and future research directions.

Understanding vermicompost and organic manure interactions: impact on toxic elements, nitrification activity, comammox Nitrospira inopinata, and archaea/bacteria.

Vermicompost is a substantial source of nutrients, promotes soil fertility, and maintains or increases soil organic matter levels. Potentially toxic elements (PTEs) in vermicompost impact on nitrification activity. However, it is yet unknown how vermicompost affects nitrifying bacteria and archaea, comammox Nitrospira inopinata (complete ammonia oxidizers), net nitrification rates (NNRs), and PTEs. The effects of vermicompost application on NNRs, potential nitrification rates (NPs), PTEs, and the abundances of comammox N. inopinata bacteria, nitrite-oxidizing bacteria (NOB), and ammonia-oxidizing bacteria (AOB)/archaea (AOA) were studied. NNRs and NPs were significantly higher (p < 0.05) in fresh cow-dung vermicompost (stored for 40 days) as compared with other organic manure. The level of PTEs (Cu2+, Fe2+, Pb2+, Cd2+, and Zn2+) was significantly lower (p < 0.05) in vermicompost as compared with compost of waste material with Trichoderma and cow dung. Comammox N. inopinata, NOB, AOB, and AOA were significantly higher (p < 0.05) in stored cow-dung vermicompost (more than 1 year) as compared with other organic manure. The results of the scatterplot matrix analysis suggested that Fe2+, total nitrogen (TN), soil organic carbon (SOC), and total carbon (TC) were linearly correlated (p < 0.001) with NNRs and NPs in vermicompost and organic manure. Similarly, comammox N. inopinata bacteria, NOB, AOB, and AOA were linearly correlated (p < 0.001) with NNR and NP. These results indicated that vermicompost promoted nitrification activity by increasing microbial diversity and abundance, supplying nutrients and organic matter for microbial growth, and facilitating complex microbial interactions. It may be concluded that the influence of vermicompost, which played a great role in PTE concentration reduction, increased chemical, and biological properties, increased the growth rate of nitrifying bacteria/archaea and the nitrogen cycle.

The Prevalence, Risk Factors, and Antimicrobial Resistance Determinants of Helicobacter pylori Detected in Dyspeptic Patients in North-Central Bangladesh.

Chronic infection of Helicobacter pylori represents a key factor in the etiology of gastrointestinal diseases, with high endemicity in South Asia. The present study aimed to determine the prevalence of H. pylori among dyspeptic patients in north-central Bangladesh (Mymensingh) and analyze risk factors of infection and antimicrobial resistance (AMR) determinants in the pathogen. Endoscopic gastrointestinal biopsy samples were collected from dyspeptic patients for a one-year period from March 2022 and were checked for the presence of H. pylori via the rapid urease test and PCR and further analyzed for the status of virulence factors vacA/cagA and genetic determinants related to AMR via PCR with direct sequencing or RFLP. Among a total of 221 samples collected, 80 (36%) were positive for H. pylori, with the vacA+/cagA+ genotype being detected in almost half of them. H. pylori was most prevalent in the age group of 41-50-year-olds, with it being more common in males and rural residents with a lower economic status and using nonfiltered water, though the rates of these factors were not significantly different from those of the H. pylori-negative group. Relatively higher frequency was noted for the A2147G mutation in 23S rRNA, related to clarithromycin resistance (18%, 7/39). Amino acid substitutions in PBP-1A (T556S) and GyrA (N87K and D91N) and a 200 bp deletion in rdxA were detected in samples from some patients with recurrence after treatment with amoxicillin, levofloxacin, and metronidazole, respectively. The present study describes the epidemiological features of H. pylori infection in the area outside the capital in Bangladesh, revealing the spread of AMR-associated mutations.

Social entrepreneurial intention among university students in China.

The issues of employment, social inequality, and resource scarcity can be effectively addressed through social entrepreneurship, contributing to the growing research interest on the formation of social entrepreneurial intention. Using the theory of planned behaviour, the current study examined the influence of selected key factors on social entrepreneurial intention among university students. Based on the cross-sectional quantitative research design, online survey was conducted, which involved 684 students and graduates from five universities in Southern China. The obtained results demonstrated the significant and positive influence of perceived values on sustainability, opportunity recognition competency, attitude towards entrepreneurship, subjective norms, and perceived behavioural control on social entrepreneurial intention. Additionally, attitude towards entrepreneurship partially mediated the relationships of risk-taking propensity, self-efficacy, and need for achievement on social entrepreneurial intention. The multi-group analysis results also showed that need for achievement and social entrepreneurial intention differed significantly between genders, which provides new ideas for future investigations into the impact of gender on social entrepreneurial intentions. Based on the findings of this study, it is crucial that university students are exposed to relevant courses or training to develop social entrepreneurship competencies and promote sustainable values. The findings of this study will provide policymakers with relevant policy guidelines and more effective theoretical support to achieve the goal of promoting social entrepreneurship among university students in a more resource-efficient and effective manner.