Prevalence of Being Obese, Overweight, and Underweight Among Jordanian Children and Adolescents Based on International Growth Standards

Objectives: The rise of obesity and other nutrition-related conditions among children and adolescents is a global challenge, particularly in the Middle East. This study aimed to determine the prevalence of being underweight, overweight, and obese among Jordanian children and adolescents using the body mass index (BMI) percentiles of the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) standards. Methods: This retrospective cross-sectional/longitudinal study analyzed 58,474 (42.6% males; 57.4% females) height, weight, and BMI-for-age records from 31508 healthy Jordanian children and adolescents aged 2–19 years. The data were retrieved from the Ministry of Health’s nationwide electronic database (2017–2023) and assessed using the CDC and WHO growth standards. Logistic regression was performed to assess the variables associated with overweight/obese status. Results: The prevalence of being underweight, overweight, and obese varied by the reference used, as more cases of being obese and underweight were reported when applying the CDC standards. The regression models showed the males had significantly lower odds of being overweight and obese than the females. Increased age was associated with higher odds of being overweight and obese, with annual increases observed across all age groups. Conclusions: Using the WHO and CDC standards, the prevalence of being underweight was higher in the males aged 6 years and older, while being overweight and obese was more prevalent in the females. The observed annual increase in the prevalence of being overweight and obese underscores the need for targeted strategies. Growth references tailored to regional profiles may improve national nutrition policies for Jordanian children and adolescents

Spatiotemporal diversity in molecular and functional abnormalities in the mdx dystrophic brain

Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration and neuropsychiatric abnormalities. Loss of full-length dystrophins is both necessary and sufficient to initiate DMD. These isoforms are expressed in the hippocampus, cerebral cortex (Dp427c), and cerebellar Purkinje cells (Dp427p). However, our understanding of the consequences of their absence, which is crucial for developing targeted interventions, remains inadequate. We combined RNA sequencing with genome-scale metabolic modelling (GSMM), immunodetection, and mitochondrial assays to investigate dystrophic alterations in the brains of the mdx mouse model of DMD. The cerebra and cerebella were analysed separately to discern the roles of Dp427c and Dp427p, respectively. Investigating these regions at 10 days (10d) and 10 weeks (10w) followed the evolution of abnormalities from development to early adulthood. These time points also encompass periods before onset and during muscle inflammation, enabling assessment of the potential damage caused by inflammatory mediators crossing the dystrophic blood-brain barrier. For the first time, we demonstrated that transcriptomic and functional dystrophic alterations are unique to the cerebra and cerebella and vary substantially between 10d and 10w. The common anomalies involved altered numbers of retained introns and spliced exons across mdx transcripts, corresponding with alterations in the mRNA processing pathways. Abnormalities in the cerebra were significantly more pronounced in younger mice. The top enriched pathways included those related to metabolism, mRNA processing, and neuronal development. GSMM indicated dysregulation of glucose metabolism, which corresponded with GLUT1 protein downregulation. The cerebellar dystrophic transcriptome, while significantly altered, showed an opposite trajectory to that of the cerebra, with few changes identified at 10 days. These late defects are specific and indicate an impact on the functional maturation of the cerebella that occurs postnatally. Although no classical neuroinflammation markers or microglial activation were detected at 10 weeks, specific differences indicate that inflammation impacts DMD brains. Importantly, some dystrophic alterations occur late and may therefore be amenable to therapeutic intervention, offering potential avenues for mitigating DMD-related neuropsychiatric defects.</p

Android Malware Classification and Optimisation Based on BM25 Score of Android API

With the growth of Android devices, there is a rise in malware applications affecting these networked devices. Android malware classification is an important task in ensuring the security and privacy of Android devices. One promising approach to this problem is to capture the difference in the usage of API in benign and malware applications through the BM25 (Best Matching 25) scoring function by calculating the BM25 score of each API (Application Program Interface). A linear regression model is fitted using the BM25 score to select the 1000 most important APIs using the feature importance weight of the linear regression model. The selected API's BM25 score and the Permission and Intents of an application are used to train Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, and CNN (Convolutional Neural Network) for classification. To illustrate the effectiveness of using the BM25 score of APIs for malware classification, we train the optimised Particle Swarm Optimisation (PSO) based Machine learning and Deep Learning algorithms using Permission and Intents features with and without the BM25 score. Experiments show that the BM25 score improves the result. Overall, this study demonstrates the potential of using the BM25 score of API calls, in combination with Permissions and Intents, as a valuable tool for Android malware classification.</p

Towards Long-Term Operational and Geo-Mechanical Stability of Underground Hydrogen Storage in Salt Caverns

Hydrogen is rapidly gaining momentum as a cornerstone of the global transition to cleaner energy systems, providing a versatile and low-carbon fuel alternative across a range of industries, including power generation and transportation. However, large-scale hydrogen storage poses significant challenges, as effective storage solutions are critical for balancing supply and demand, especially for renewable energy sources. Underground salt caverns present a promising option for hydrogen storage due to their unique mechanical properties, such as high impermeability and self-sealing behaviour under stress. These characteristics make salt caverns particularly suited for long-term, safe, and high-pressure hydrogen storage.To ensure the reliable performance of hydrogen storage systems, it is essential to thoroughly understand the behaviour of salt caverns under operational conditions. This study adopts a comprehensive geo-mechanical modelling approach to assess the performance of salt caverns repurposed for hydrogen storage. By employing finite difference modelling, we simulate the stress, deformation, and displacement responses of the cavern structure. The geological model is developed based on real-world data from the Zechstein Group in East Yorkshire, United Kingdom, a region with favourable geological conditions for underground storage. The study incorporates detailed sensitivity analyses to evaluate the influence of varying operational parameters, such as injection pressures and cycle frequencies, on the stability of the caverns. These analyses provide a clear understanding of how operational stresses affect the long-term behaviour of the storage system, including potential risks related to creep, subsidence, and deformation.The results offer crucial insights into the optimization of design and operational parameters for hydrogen storage caverns. This work contributes to the development of safe, efficient, and scalable hydrogen storage infrastructure, which is a critical enabler for the widespread adoption of hydrogen as a clean energy vector

High soil bacterial diversity increases the stability of the community under grazing and nitrogen

Grasslands are one of the major terrestrial ecosystems facing severe degradation due to climatic changes and anthropogenic activities. In northwest China, the Typical steppe and alpine meadows are the major grasslands with diverse ecosystems. These grasslands are facing degradation due to excessive livestock grazing and nitrogen (N) deposition that can alter the overall grassland ecosystem, along with the soil bacterial communities and their role in the ecosystem. The bacterial community is vital for the sustainability of grassland ecosystems as it plays a crucial role in decomposing the dead organic matter and nutrient cycling. This study conducted a grazing and N addition experiment in alpine meadows and typical steppe. The impact of short-term N application and grazing on both grasslands' soil, plant, and bacterial communities was explored. Alpine meadows had higher bacterial richness (OTUs&gt;2000) and diversity (Shannon index&gt;6) than the typical steppe (OTUs&lt;900; Shannon index&lt;5.5) due to changes in climate and ecosystem. The alpha diversity (Shannon index) of the bacterial community was observed to increase under low grazing without N addition while adding medium N (100 kg/ha) without grazing increased the diversity. The combination of medium N (100 kg/ha) addition and low grazing resulted in the highest bacterial diversity in both grasslands. In contrast, the combination of N and high grazing decreased bacterial richness and diversity. The N addition and grazing affected the bacterial community composition in the typical steppe. The co-occurrence networks revealed that the network complexity in bacterial communities of alpine meadows was higher than that of typical steppe. The rich bacterial community and high soil nutrients in alpine meadows might have led to diverse microbial functionality, which provided stability to the bacterial network. The low nutrients and water availability in typical steppe lead to a lower bacterial richness, making the bacterial community vulnerable to the changes due to grazing and N. Climate is a significant factor in shaping the grassland ecosystem and its bacterial community. The changes in the grassland's ecosystem due to high grazing and N deposition would highly affect the distressed microbial communities in arid and semiarid regions. Further, in-depth studies are required to understand the fate of these vulnerable grasslands and design management strategies for their protection.</p

Elucidating the impact of laser shock peening on the biocompatibility and corrosion behaviour of wire arc additive manufactured SS316L bone staples

This study investigates the impact of Laser Shock Peening (LSP) on the biocompatibility and corrosion resistance of SS316L bone staples built using Wire Arc Additive Manufacturing (WAAM). Corrosion tests reveal substantial improvements, with a decrease in corrosion current density from 32.137 × 10− 4 mA/cm² to 3.50864 × 10− 4 mA/cm², a reduction in corrosion rate from 3.66754 × 10− 2 mm/year to 0.400415 × 10− 2 mm/year. Surface hydrophobicity evaluated through contact angle measurements, demonstrates an increase to 98.85° at the highest LSP intensity of 15.0 GW/cm², indicating improved surface properties critical for biomedical applications. The cytotoxicity analysis and surface morphology indicate that the survival, morphology, and adherence of L929 fibroblast cells improve with increasing LSP intensity

From fear to empowerment: the impact of employees AI awareness on workplace well-being – a new insight from the JD–R model

PurposeThe primary purpose of the study was to explore the impact of health workers’ awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD–R) model, the study aimed to provide insights into how health workers’ perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study’s findings could inform strategies for supporting healthcare workers during technological transformations.Design/methodology/approachThe study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling.FindingsThe study findings revealed that health workers’ AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers’ workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers’ AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour.Research limitations/implicationsWhile the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables.Practical implicationsThe study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers’ AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives.Originality/valueThe study contributes to the existing literature by addressing a relatively unexplored area – the impact of AI awareness on healthcare workers’ well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers’ perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD–R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations

Reimagining Higher Education Learning Spaces:Assembling Theory, Methods, and Practice

The Higher Education (HE) sector faces an increasingly challenging environment with key themes identified as: the economic and social fallout from the Covid-19 pandemic,shifting politics, changing expectations around education, and technological advances(Marshall et al., 2024). There are particular concerns about aspects including: a mismatch between tuition fee caps and inflation, reduced government grants (Atherton et al., 2024),decreasing numbers of international students (Bolton et al., 2024), concerns over pensions, pay, and working conditions (University and College Union, 2022), new thresholds for student outcomes (Office for Students, 2022), and gaps around research funding(Butland, 2022)

Probing and manipulating the gut microbiome with chemistry and chemical tools

The human gut microbiome represents an extended “second genome” harbouring about 1015 microbes containing &gt;100 times the number of genes as the host. States of health and disease are largely mediated by host-microbial metabolic interplay, and the microbiome composition also underlies the differential responses to chemotherapeutic agents between people. Chemical information will be the key in order to tackle this complexity and discover specific gut microbiome metabolism for creating more personalised interventions. Additionally, rising antibiotic resistance and growing awareness of gut microbiome effects iscreating a need for non-microbicidal therapeutic interventions. We classify chemical interventions for the gut microbiome into categories like molecular decoys, bacterial conjugation inhibitors, colonization resistance-stimulating molecules, “prebiotics” to promote the growth of beneficial microbes and inhibitors of specific gut microbial enzymes. Moreover, small molecule probes including click chemistry probes, artificial substrates for assaying gut bacterial enzymes and receptor agonists/antagonists which engage host receptors interacting with the microbiome, are some other promising developments in the expanding chemical toolkit for probing and modulating the gut microbiome. This review explicitly excludes ‘biologics’ such as probiotics, bacteriophages, and CRISPR to concentrate on chemistry and chemical tools like chemoproteomics in the gut-microbiome context