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Background: Global myopia prevalence poses a substantial public health burden with vision-threatening complications, necessitating effective prevention and control strategies. Precise prediction of spherical equivalent (SE), myopia, and high myopia onset is vital for proactive clinical interventions. Methods: We reviewed electronic medical records of pediatric and adolescent patients who underwent cycloplegic refraction measurements at the Eye & Ear, Nose, and Throat Hospital of Fudan University between January 2005 and December 2019. Patients aged 3–18 years who met the inclusion criteria were enrolled in this study. To predict the SE and onset of myopia and high myopia in a specific year, two distinct models, random forest (RF) and the gradient boosted tree algorithm (XGBoost), were trained and validated based on variables such as age at baseline, and SE at various intervals. Outputs included SE, the onset of myopia, and high myopia up to 15 years post-initial examination. Age-stratified analyses and feature importance assessments were conducted to augment the clinical significance of the models. Results: The study enrolled 88,250 individuals with 408,255 refraction records. The XGBoost-based SE prediction model consistently demonstrated robust and better performance than RF over 15 years, maintaining an R2 exceeding 0.729, and a Mean Absolute Error ranging from 0.078 to 1.802 in the test set. Myopia onset prediction exhibited strong area under the curve (AUC) values between 0.845 and 0.953 over 15 years, and high myopia onset prediction showed robust AUC values (0.807–0.997 over 13 years, with the 14th year at 0.765), emphasizing the models' effectiveness across age groups and temporal dimensions on the test set. Additionally, our classification models exhibited excellent calibration, as evidenced by consistently low brier score values, all falling below 0.25. Moreover, our findings underscore the importance of commencing regular examinations at an early age to predict high myopia. Conclusions: The XGBoost predictive models exhibited high accuracy in predicting SE, onset of myopia, and high myopia among children and adolescents aged 3–18 years. Our findings emphasize the importance of early and regular examinations at a young age for predicting high myopia, thereby providing valuable insights for clinical practice. [ABSTRACT FROM AUTHOR]

Currently, more than 100 genotypes of human adenovirus (HAdV) are classified into seven species (A–G). The tissue tropism and virulence of HAdV are closely related to the genotypes, different genotypes of HAdV infection lead to different types and severity of the disease. Although traditional genotype identification techniques are widely used, they are time-consuming, complex and low sensitivity. Therefore, a rapid and accurate genotype recognition method for HAdV is necessary. In this study, silver nanoparticles incubated with iodine ions and aggregated with calcium ions were used as the enhanced substrate to form a good "hot spot" suitable for viral nucleic acid. The method could rapidly identify the seven common genotypes of respiratory HAdV characteristic fingerprints, combined with machine learning technology could classify and identify HAdV (500 copies/mL) within 5 min, with an accuracy rate of over 98%. In addition, the concentration dependence curve assessing the relationship between the intensity of the characteristic peak and the nucleic acid concentration showed a good linear relationship, the method can be used to quantitatively detect HAdV nucleic acid. This technique can enhance diagnosis and genotype identification of HAdV infection, thus providing a feasible method for the efficient response to respiratory human adenovirus outbreaks. • The Ag@ICNPs can be used to capture SERS signals of multiple genotypes of HAdV. • This method can be used to identify genotypes when concentration is 100 copies/mL. • CNN can identify the genotypes of HAdV with an accuracy rate of over 98%. • Compared with the existing genotype recognition method, this is faster and easier. [ABSTRACT FROM AUTHOR]