Sensitive and specific assay of microRNAs (miRNAs) is beneficial to early disease screening. Herein, we for the first time proposed clustered regularly interspaced short palindromic repeats (CRISPR)/Cas13a-mediated photoelectrochemical biosensors for the direct assay of miRNA-21. In this study, compared with traditional nucleic acid-based signal amplification strategies, the CRISPR/Cas13a system can greatly improve the specificity and sensitivity of target determination due to its accurate recognition and high-efficient trans-cleavage capability without complex nucleic acid sequence design. Moreover, compared with the CRISPR/Cas12a-based biosensing platform, the developed CRISPR/Cas13a-mediated biosensor can directly detect RNA targets without signal transduction from RNA to DNA, thereby avoiding signal leakage and distortion. Generally, the proposed biosensor reveals excellent analysis capability with a wider linear range from 1 fM to 5 nM and a lower detection limit of 1 fM. Additionally, it also shows satisfactory stability in the detection of human serum samples and cell lysates, manifesting that it has great application prospects in the areas of early disease diagnosis and biomedical research.

INTRODUC TIONMyopia, a common eye disease globally, often manifests in children and adolescents. 1 The prevalence of myopia is estimated to rise to 49.8% of the global population by 2050, most dramatically among younger people in East and Southeast Asia. 2 Myopia is attributable to the interaction of genetic and environmental factors, leading to excessive axial elongation. Furthermore, the increasing rates of early onset and rapid progression of myopia may result in a dramatic increase in the number of people with high myopia, ultimately increasing the risks of complications such as myopic maculopathy, retinal detachment and glaucoma, possibly leading to incurable visual impairment or blindness. 3,4 Thus, delaying myopia onset and retarding its progression are recognised as priorities for myopia-related public health concerns.

BACKGROUND Patients with high myopia have an increased lifetime risk of complications. The prevalence patterns of high myopia in children and adolescents in southern China are unclear. Early identification of high-risk individuals is critical for reducing the occurrence and development of high myopia and avoiding the resulting complications. OBJECTIVE This study aimed to determine the prevalence of high myopia in children and adolescents in southern China via real-world screening data and to predict its onset by studying the risk factors for high myopia based on machine learning. METHODS This retrospective school-based study was conducted in 13 cities with different gross domestic products in southern China. Through data acquisition and filtering, we analyzed the prevalence of high myopia and its association with age, school stage, gross domestic product, and risk factors. A random forest algorithm was used to predict high myopia among schoolchildren and then assessed in an independent hold-out group. RESULTS There were 1,285,609 participants (mean age 11.80, SD 3.07, range 6-20 years), of whom 658,516 (51.2%) were male. The overall prevalence of high myopia was 4.48% (2019), 4.88% (2020), and 3.17% (2021), with an increasing trend from the age of 11 to 17 years. The rates of high myopia increased from elementary schools to high schools but decreased at all school stages from 2019 to 2021. The coastal and southern cities had a higher proportion of high myopia, with an overall prevalence between 2.60% and 5.83%. Age, uncorrected distance visual acuity, and spherical equivalents were predictive factors for high myopia onset in schoolchildren. The random forest algorithm achieved a high accuracy of 0.948. The area under the receiver operator characteristic curve (AUC) was 0.975. Both indicated sufficient model efficacy. The performance of the model was validated in an external test with high accuracy (0.971) and a high AUC (0.957). CONCLUSIONS High myopia had a high incidence in Guangdong Province. Its onset in children and adolescents was well predicted with the random forest algorithm. Efficient use of real-world data can contribute to the prevention and early diagnosis of high myopia.

In this paper, we consider an abstract non-autonomous evolution equation with multiple delays in a Hilbert space H: ′ 1 () () ((), , ()) (),

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