Cellular redox disruption and apoptosis: Differential effects of RFR frequencies on Leydig cells.

Is It Time to Reconsider Chronic Electromagnetic Field Exposure as a Possible Risk Factor in Oral Cancer?

Analysis of electric bicycle riders’ visual strategies and riding performances when using a mobile phone

Mobile phone addiction and social anxiety among nursing students during internship: The longitudinal mediating role of physical activity and social adaptability in mental health promotion.

Fear of Missing Out (FoMO) is one of the risk factors for problematic mobile phone use (PMPU) among adolescents. However, previous findings have been inconsistent and have not comprehensively considered the roles of cognition and emotion. Grounded in the I-PACE model, this study examines the role of desire thinking and craving in the predictive relationship between FoMO and PMPU among adolescents through path analysis, while employing network analysis to identify the most central and influential nodes within this mechanism. This study focused on adolescents and employed the FoMO scale, the Desire Thinking Questionnaire, the Psychological Craving Assessment Scale, and the Smartphone Application-Based Addiction Scale to conduct a four-wave longitudinal survey of 509 adolescents. The results of the path model showed that desire thinking and craving played a role of chain mediation in this relationship. Network analysis revealed that the "irresistible longing" node was the strongest bridge node of the network. Among the associations between nodes of different communities, the strongest association was between the "difficulty stopping" node in Desire Thinking and the "irresistible longing" node in Craving, followed by the "stress relief" node in Craving and the "mood modification" node in PMPU. These findings provide empirical evidence for the I-PACE model and underscore the critical roles of desire thinking and craving. They also offer valuable insights for future research and clinical interventions targeting PMPU among adolescents.

Mobility resilience to compounding disruptions: Power outages and social events

Cannabis use among adolescents is an increasing public health concern, particularly in the context of the digital era, where social and behavioral influences are rapidly evolving. However, the mechanisms linking social media exposure to cannabis use remain unclear. This study investigates the association between social media use and cannabis use in late adolescence, examining the mediating roles of behavioral problems using an instrumental variable (IV) approach to address endogeneity. We analyzed data from 1766 participants in the Longitudinal Study of Australian Children (aged 18-19years at Wave 8) using a control-function approach, with mobile phone ownership with home internet access as instrumental variables for social media use. We evaluated adolescent cannabis use using self-reported lifetime cannabis use from longitudinal surveys of children aged 18-19years at Wave 8. Generalized structural equation modeling (GSEM) was applied in the second stage to estimate IV-based direct, indirect, and total effects through behavioral problems, with bias-corrected bootstrapped confidence intervals. By late adolescence, 34.6% reported cannabis use. Instrumental mediation analysis showed instrument-induced frequent social media use was associated with nearly threefold higher odds of cannabis use (OR=2.85; 95% CI: 1.99-4.10). Externalizing behaviors significantly mediated this IV-based relationship, accounting for a 22% increase in cannabis use odds via this pathway (OR=1.22; 95% CI: 1.10-1.37). Internalizing behaviors did not mediate the association. The total IV based indirect effect through behavioral problems was significant (OR=1.20; 95% CI: 1.07-1.36), confirming behavioral problems as key mechanisms linking social media use and cannabis use. IV-based frequent social media use is robustly associated with increased cannabis use in adolescents, primarily through elevated externalizing behaviors. These findings highlighted the need for integrated digital and behavioral interventions targeting externalizing symptoms to mitigate substance use risk in the digital era. Enhanced parental engagement and digital literacy may further buffer against adverse outcomes associated with social media exposure.

A modular scalable electrification network for Sub-Saharan Africa: Real-world deployment of autonomous DC picogrids for Tier 2 access and communal refrigeration

Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long-term health risks for mothers and offspring. Multiple micronutrient supplementation (MMS) during pregnancy has demonstrated benefits, including reduced risks of low birth weight, small-for-gestational-age births, and neonatal mortality, when compared with standard iron-folic acid supplementation. Current MMS strategies, however, often follow a standard MMS, overlooking variations in nutritional status, health profiles, and context. Advances in artificial intelligence (AI), particularly deep learning and natural language processing, provide opportunities to strengthen maternal nutrition programs by integrating diverse data sources. Rather than promising fully individualized recommendations, AI could help stratify women by risk of insufficiencies or deficiencies, highlight groups most likely to benefit from additional support, and inform the design of more responsive supplementation strategies during preconception and pregnancy. We outline a conceptual model in which multimodal health data-including electronic health records (EHRs), wearable sensor outputs, nutrition and fertility app logs, genomic markers, and sociodemographic information-are aggregated and analyzed by AI systems to inform personalized MMS plans. The framework introduces the concept of a "nutritional digital twin," a virtual profile of the patient's nutritional and metabolic state. This digital twin can simulate micronutrient needs and predict maternal-fetal outcomes under different supplementation scenarios, enabling clinicians to test scenario-based options (e.g. standard MMS ± targeted add-ons) for individuals. We describe how deep learning models can identify complex patterns (e.g. diet-genome interactions or behavioral trends) while natural language processing (NLP) algorithms extract clinically relevant insights from unstructured data (such as medical notes or patient queries). In addition, we discuss the role of digital maternal health tools, such as mobile apps and wearable trackers, in supplying real-time data to the AI models and in engaging women to improve adherence to supplementation regimens. Harnessing AI for MMS could transform maternal nutrition care in both high- and low-resource settings. In high-income contexts, rich data (comprehensive EHRs, genetic tests, continuous monitoring devices) could feed advanced predictive models to support risk-stratified care with protocolized supplementation options, under clinical oversight. In low- and middle-income countries, where maternal undernutrition and micronutrient gaps are most prevalent, AI-driven approaches can help stratify risk groups and optimize limited resources. Ubiquitous mobile phone access and digital health tools in many such settings provide avenues for data collection and intervention delivery. We highlight examples where machine learning on population data revealed "hidden hunger" patterns and key predictors of low supplement uptake (e.g. low education, minimal antenatal visits)-insights that policymakers can use to target nutrition programs. A nutritional digital twin could further allow scenario-testing (e.g. predicting the impact of adding a vitamin D supplement for a specific patient) before clinical decisions are made. To realize this vision, the key concerns are ethics, credibility, and fairness. Ethical frameworks must guide development so that sensitive reproductive health data are protected and clinician oversight remains central. The credibility of AI-generated recommendations depends on transparency about the assumptions used to translate nutritional and health data into supplement type and dose, and on prospective validation against maternal and neonatal outcomes. This requires a continuous feedback loop in which recommendations are tested in real-world settings and recalibrated using outcomes data, ensuring that the system learns from observed benefits and harms, rather than relying solely on theoretical modeling. Fairness demands that training data sets represent diverse populations and that solutions are tailored to local contexts to reduce bias and avoid widening disparities. Critically, the approach must be fed by data streams that extend beyond initial demographics and clinical baselines to include biomarkers, adherence patterns, and pregnancy outcomes, so that the models can be refined and dosing rules adjusted over time. If these safeguards are embedded, AI-enhanced personalized MMS can move beyond proof of concept towards a credible, equitable, and empirically grounded contribution to global maternal health. AI-driven personalized nutrition support represents a frontier in obstetric care. By combining clinical knowledge with data-driven intelligence, we can move beyond generalized prenatal supplements towards precision maternal nutrition. The integration of deep learning models and digital health innovations into antenatal care pathways has the potential to better nourish pregnancies, save lives, and ensure healthier futures for mothers and children worldwide.

Nigeria, a key agricultural nation in West Africa, faces persistent food insecurity due to limited access to improved farming practices, with cassava production playing a pivotal role in addressing hunger and supporting rural economies. In Ekiti State, mobile phone technologies offer a potentially impactful solution to enhance cassava production and food security, yet their utilization remains underexplored. This study investigates the socio-economic characteristics of cassava farmers in Ekiti State, Nigeria, and identifies factors influencing their use of mobile phone technologies to improve agricultural productivity and food security. Conducted across three agricultural zones (Aramoko, Ikere, and Isan), the study employed a multistage sampling technique to select 376 cassava farmers from nine local government areas. Data were collected using structured questionnaires via the Open Data Kit (ODK) and focus group discussions (FGD) with a Multifunctional Rechargeable Device (MRD), a software app for data collection. Factor analysis, chi-square tests, and linear regression were applied to analyze quantitative data, while qualitative data were processed using Atlas. Ti. Four key factors emerged from factor analysis influencing mobile phone technology utilization: economic factors (27.267%), perceived benefits (20.517%), technology enablers (15.033%), and constraints (10.921%). Significant relationships were found between utilization and technology enablers, alongside socio-economic variables like education, farming experience, and farmland size. Qualitative data from FGDs revealed inadequate electricity and poor network coverage as major barriers to mobile phone technology utilization. The study underscores that the mobile technology adoption by cassava farmers is driven by economic factors and perceived benefits but hindered by infrastructural constraints. To improve utilization and boost productivity, it is recommended to invest in rural electricity and internet, design inclusive applications with local languages, and enhance farmers' access to affordable credit.

High-throughput image-based pupal sex classification in Aedes aegypti using convolutional neural network models for sterile insect technique applications.

This study investigates the influence of built environment factors on pedestrian route choices in an urban context using passively collected mobile phone trajectory data from Sydney, Australia. We estimate and compare multiple discrete choice models including C-Logit, Path Size Logit (PSL), and Error Component (EC) models to quantify associations between pedestrian route choices and route characteristics such as distance, slope, turns, crossings, amenities, and greenery. The models are applied to a high-resolution sidewalk network to simulate pedestrian flows across the city. Our findings are broadly consistent with existing literature, highlighting the importance of route simplicity, directness, and terrain in walking behavior. A key contribution of this study is the integration of passively collected GPS trajectories with route choice modeling and network-level flow assignment, demonstrating a scalable framework for understanding and forecasting pedestrian behavior. The approach enables city-scale assessments of pedestrian infrastructure and offers valuable insights for data-driven planning of walkable urban environments.

Digital mindfulness and the laboring self: A discourse analysis of burnout in Headspace.

Circumferential neck burns in children from mobile phone chargers and metal Necklaces: A case series

Reconstruction of OD demand using mobile phone data: A regression model with spatially varying coefficients

Bioelectricity generation and remission glyphosate stress for wheat by microbial fuel cell with a high-performance hollow Fe/Fe5C2-SNC@PNC cathode derived from mother-child "MOF on MOF".

Advocating for Our Youth: The End of Cell Phone Use in Schools

The global increase in mobile phone usage, specifically in low- and middle-income countries (LMICs) provides opportunities for making non-communicable disease (NCD) data collection more efficient and economical. We investigated the feasibility of WhatsApp surveys and compared their population representativeness and NCD risk factor estimates with interactive voice response (IVR) surveys in Colombia. Participants were randomized to receive the NCD survey either via IVR or WhatsApp as the survey mode. Adults aged 18 years and over with a working mobile phone were sampled using random digit dialing (RDD). The survey included questions on demographics, tobacco use, alcohol consumption, and diet, with the primary difference in study arms being that visual examples (e.g., showcards) were used in the WhatsApp survey. We compared the demographic representativeness and NCD indicator estimates of the two surveys using log-binomial regression. The study was conducted between March 8 and March 12, 2023. Approximately 627 completed IVR surveys and 602 completed WhatsApp surveys were analyzed from 127,592 IVR and 2,632 WhatsApp calls (from 245,620 IVR enrollment calls). Overall, the WhatsApp arm had a higher proportion of young (18-44-year-olds: 81.9% vs. 74.2%), high-school educated (90.0% vs. 80.9%), and urban residents (80.4% vs. 73.4%) than IVR respondents. The overall prevalence/mean of most studied indicators was similar. For instance, with an adjusted risk ratio of 1.08 (95% CI: 0.83-1.40), the prevalence of tobacco smoking was 14.4% (95% CI: 11.7-17.3) and 16.4% (95% CI: 13.6-19.7) among IVR (ref.) and WhatsApp arms, respectively. The prevalence/mean differed for only two indicators: "number of vegetable servings consumed per day" and "<5 servings of fruits and/or vegetables consumed per day." Despite the inclusion of images in WhatsApp surveys, only minor differences in NCD indicators were found compared to IVR. Persistent gaps in rural and older populations must be addressed to ensure equitable monitoring in Colombia.

Mobile phone cases as hotspots for perfluoroalkyl substances and pathogenic bacteria: An analysis based on user habits.

IntroductionTechnologies to help patients with type 2 diabetes mellitus (T2DM) have increased and have shown promising results in supporting self-management. However, not all applications consider healthcare providers’ knowledge in designing and developing these tools, which may impact adoption and effectiveness. This study examined healthcare providers’ perspectives on how mobile phone technologies can support patients with T2DM in self-managing their condition and inform user-centered design.MethodsWe conducted two online focus group sessions with nine healthcare providers involved in T2DM care. A semistructured guide was used to explore the participants’ perspectives on adopting mobile apps. Transcribed narratives were transcribed verbatim and analyzed using thematic analysis. The report followed COREQ guidelines for qualitative studies.ResultsFive main themes emerged: (1) diabetes—challenges and self-care; (2) the use of technologies in managing T2DM; (3) the market for diabetes management tools; (4) suggestions for ideal app features; and (5) the role of healthcare providers. Healthcare providers acknowledged the benefits of mobile apps in enhancing patient engagement to manage type 2 diabetes. Participants also pointed out the barriers to full implementation, such as usability challenges, patient digital literacy, and integration with clinical workflows. They also considered that patients with T2DM should provide feedback on digital health designs.ConclusionHealthcare providers’ involvement in developing an app ensures alignment with clinical practices and patients’ needs. The study's findings support the user-centered design of digital tools tailored to managing T2DM and may inform future digital health design and evaluation for T2DM or other chronic conditions.