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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.

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.

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.