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Background: With the popularity of the internet, short videos have become an indispensable tool to obtain health information. However, avoiding health disinformation owing to the openness of the Internet is difficult for users. Disinformation may endanger the health and lives of users. Objective: With a focus on the process of identifying short videos' health disinformation and the factors affecting the accuracy of identification, this study aimed to investigate the identification methods, coping strategies, and the impact of short videos’ health disinformation on undergraduate nursing students. The findings will provide guidance to users on obtaining high-quality and healthy information, in addition to reducing health risks. Methods: Semi-structured in-depth interviews were conducted with 22 undergraduate nursing students in October 2022, and data were collected for collation and content analyses. Results: The techniques used to identify short videos that include health disinformation as well as how undergraduate nursing students perceived these videos' features are among the study's findings. The failure factors in identification, coping paths, and adverse impacts of short videos on health disinformation were analyzed. The platform, the material itself, and the students' individual characteristics all have an impact on their identifying behavior. Conclusions: Medical students continue to face many obstacles in identifying and responding to health disinformation through short videos. Preventing and stopping health disinformation not only requires individual efforts to improve health literacy and maintain rational thinking, it also requires the joint efforts of short video producers, relevant departments, and platforms.

Background: Outdoor aeroallergens, such as pollens and molds, are known triggers of asthma exacerbation; however, few studies have examined children’s aeroallergen response based on sensitization. Objective: Our aim was to compare the relative impact of aeroallergen levels on asthma exacerbation between pediatric patients with asthma who tested positive or negative for sensitization to particular allergens. Methods: A case-crossover design study was conducted to examine associations between outdoor aeroallergen levels and asthma exacerbation events among children living in Philadelphia, Pennsylvania, who were treated within a large pediatric care network. Sensitization to common allergens was characterized in a subset of patients with asthma exacerbation who had undergone skin prick testing (5.5%). Odds ratios (ORs) and 95% CIs were estimated in all patients with asthma exacerbation and in those sensitized or not sensitized to aeroallergens. Results: Children who were sensitized to a particular allergen had higher odds of asthma exacerbation with exposure to the allergen (ie, early-season tree pollen, oak tree pollen, early-season weed pollen, and late-season molds) than did all patients with asthma or nonsensitized patients. For example, the association between early-season tree pollen and asthma exacerbation among sensitized children (>90th percentile vs ≤25th, OR = 2.28 [95% CI = 1.23-4.22]) was considerably stronger than that estimated among all patients (OR = 1.34 [95% CI = 1.19-1.50]), and it was also substantially different from the lack of association seen among nonsensitized children (OR = 0.89 [95% CI = 0.51-1.55] [P value for heterogeneity = .03]). Conclusion: More prevalent allergy testing may be useful for prevention of asthma exacerbation by informing interventions targeted to sensitized children and tailored for particular aeroallergens.

ObjectivesThis study utilizes bibliometric analysis to map the current research landscape and forecast emerging trends within the domain of Burning Mouth Syndrome (BMS).Materials and methodsA comprehensive review of literature related to BMS was conducted, drawing from the Web of Science Core Collection (WoSCC) from 2008 to 2023. The analysis included both publication types “Article” and “Review Article.” Advanced quantitative techniques and visual analytics tools, including CiteSpace, VOSviewer, Tableau, and the Map Equation online platform were utilized to analyze the academic publications within this domain.ResultsOur analysis incorporated 497 articles on BMS. The data exhibit a progressive increase in the annual volume of publications from 2008 to 2023. In terms of geographic and institutional contributions, the United States of America (with 80 publications) and Nihon University (with 26 publications) emerged as leading entities in BMS research, while the Netherlands and England were identified as central to international collaboration efforts. Prominent researchers in this field include Adamo Daniela (18 publications) and Sun Andy (16 publications). Furthermore, the most cited works were authored by Jääskeläinen SK. An examination of the journals in which these articles were published showed a dominance of dental journals, highlighting significant interest and research efforts in BMS within the dental research community.ConclusionThe steady growth in BMS research signifies the formation of a robust core of researchers and demonstrates the maturation of the field. Despite this progress, the findings highlight a notable deficiency in cross-institutional and cross-regional collaborative efforts. Keyword cluster analysis has revealed “management” as a persistently relevant theme, with “pain modulation” emerging as the current focal interest. Additionally, “blood profile,” “pernicious anemia,” and “folate” have been identified as prospective areas of growing interest, suggesting important directions for future investigations.Clinical relevanceThis bibliometric analysis reveals the research landscape of BMS, aiming to highlight potential collaborative opportunities and define future research directions. These insights are invaluable for guiding subsequent investigations and carving new paths in the exploration of BMS.

Abstract Background Maternal pre-pregnancy obesity is an established risk factor for childhood obesity. Investigating epigenetic alterations induced by maternal obesity during fetal development could gain mechanistic insight into the developmental origins of childhood obesity. While obesity disproportionately affects underrepresented racial and ethnic mothers and children in the USA, few studies investigated the role of prenatal epigenetic programming in intergenerational obesity of these high-risk populations. Methods This study included 903 mother–child pairs from the Boston Birth Cohort, a predominantly urban, low-income minority birth cohort. Mother-infant dyads were enrolled at birth and the children were followed prospectively to age 18 years. Infinium Methylation EPIC BeadChip was used to measure epigenome-wide methylation level of cord blood. We performed an epigenome-wide association study of maternal pre-pregnancy body mass index (BMI) and cord blood DNA methylation (DNAm). To quantify the degree to which cord blood DNAm mediates the maternal BMI-childhood obesity, we further investigated whether maternal BMI-associated DNAm sites impact birthweight or childhood overweight or obesity (OWO) from age 1 to age 18 and performed corresponding mediation analyses. Results The study sample contained 52.8% maternal pre-pregnancy OWO and 63.2% offspring OWO at age 1–18 years. Maternal BMI was associated with cord blood DNAm at 8 CpG sites (genome-wide false discovery rate [FDR]

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model’s overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model’s validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction.

Industrial solid waste is characterized by complex mineral phases and various components. Low-carbon cementitious materials can be prepared through precise regulation based on the material composition and properties of various industrial solid wastes. In this study, electrolytic manganese residue (EMR), carbide slag (CS), and granulated blast-furnace slag (GBFS) were used as alternatives to cement to prepare multicomponent solid waste cementitious materials. The effects of the proportions of EMR and CS on the cementitious activity of GBFS and the activation mechanism of alkali and sulfur were studied. The results showed that with increasing EMR content, the strength first increased and then decreased. At a GBFS content of 20%, CS content of 2%, and EMR content of 8%, the compressive strength was highest, reaching 45.5 MPa after 28 days of curing, mainly because the OH− in CS and SO42− in EMR synergistically stimulated the active components in GBFS. Hydrated products such as ettringite and hydrated calcium silicate (C–S–H gel) were generated and interlaced with each other to improve the densification of the mortar. Overall, the proposed system provides an avenue to reduce or replace the production of cement clinker and achieve the high-value-added utilization of industrial solid waste.

Summary: Background: DNA methylation (DNAm) in cord blood has been associated with various prenatal factors and birth outcomes. This study sought to fill an important knowledge gap: the link of cord DNAm with child postnatal growth trajectories from birth to age 18 years (y). Methods: Using data from a US predominantly urban, low-income, multi-ethnic birth cohort (N = 831), we first applied non-parametric methods to identify body-mass-index percentile (BMIPCT) trajectories from birth to age 18 y (the outcome); then, conducted epigenome-wide association study (EWAS) of the outcome, interrogating over 700,000 CpG sites profiled by the Illumina Infinium MethylationEPIC BeadChip. Multivariate linear regression models and likelihood ratio tests (LRT) were applied to examine the DNAm-outcome association in the overall sample and sex strata. Findings: We identified four distinct patterns of BMIPCT trajectories: normal weight (NW), Early overweight or obesity (OWO), Late OWO, and normal to very late OWO. DNAm at CpG18582997 annotated to TPGS1, CpG15241084 of TLR7, and cg24350936 of RAB31 were associated with BMIPCT at birth-to-3 y, 10 y, and 14 y, respectively (LRT FDR < 0.05 for all). Interpretation: In this prospective birth cohort study, we identified 4 distinct and robust patterns of growth trajectories from birth to 18 y, which were associated with variations in cord blood DNAm at genes implicated in inflammation induction pathways. These findings, if further replicated, raise the possibility that these DNAm markers along with early assessment of BMIPCT trajectories may help identify young children at high-risk for obesity later in life. Funding: Detailed in the Acknowledgements section.

Background: Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this article is to explain how agent-based modeling could fulfill this role. Methods: We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. Results: For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers, and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. Conclusion: In this article, we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components. Plain Language Summary: The goal of this paper is to explain how agent-based modeling could be used as a supplementary tool to support the components of complex implementation processes. Such models have not yet been widely used in implementation science, partly because they are not straightforward to develop. To promote the use of agent-based modeling we provide a stepwise procedure using non-technical language and emphasizing the relationships between the model and implementation processes. We used two detailed examples to demonstrate our proposed approach. In the first example, we simulate the observed smoking behaviors of persons with serious mental illness in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with Serious Mental Illness). In the second example, we illustrate how agent-based models that include interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. For this example, we show how the visual user interface of an agent-based model can be in the form of a dashboard with levers for simulating what-if scenarios that can be used to guide implementation decisions. In summary, this paper shows how agent-based models can provide insights into the processes in complex interventions, and guide implementation decisions for improving delivery of evidence-based practices in community mental health clinics.