Background Nutritional status influences the growth and development of the eyes. However, there are few studies on the association between diet, especially whole grains (WG) consumption, and myopia. The study aimed to evaluate the association between WG intake and myopia prevalence among primary school-age children in China. Methods This cross-sectional epidemiological study conducted between November 2019 and December 2019 included 586 children, aged 6–12 years, attending primary school in Binhai district, Tianjin, China. Ophthalmologic examinations and optometric cycloplegic refraction measurements were conducted. Information was collected on known risks and protective factors for myopia and the consumption of WGs, vegetables, and fruits. This association between the probability of myopia and the proportion of WG consumption (WG proportion was calculated as the mean intake from WG sources divided by total grain intake), adjusted for protective and risk factors, was analysed using crude and multivariable logistic regression. Results Among the study participants, 226/586 (38.57%) children had myopia in at least one eye. WG intake was inversely correlated with the prevalence of myopia. Furthermore, in the multivariate analysis, WG intake of > 50% was identified as a protective factor against myopia after subsequent adjustment for children’s age, sex, parental myopia, near-work activity, screen time, reading and writing habits, visual fatigue, outdoor time, and classroom light environment (all P < 0.05). Conclusion WG intake (> 50%) was an independent protective factor against myopia. Modifying the form of grains consumed (whole versus refined) could be one of the targets of future public health measures.

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AIM: To evaluate the prevalence of refractive errors and ocular biometry in 3573 freshman students at Tianjin Medical University for 4 consecutive years. METHODS: In this university-based, cross-sectional study, comprising 3573 students, visual acuity (VA), slit-lamp examination, non-cycloplegic auto-refraction, and ocular biometry were recorded. RESULTS: The prevalence of myopia increased annually, from 2017 to 2020 were 93.5%, 94.5%, 95.9%, and 96.2%, respectively (P=0.03), and the prevalence of high myopia was 25.7%, 26.9%, 28.6%, and 28.6%, respectively. Males tended to have a higher percentage of total astigmatism than females, with astigmatism ≥0.75 and ≥1.0 D criteria. The percentage of with-the-rule astigmatism, against-the-rule astigmatism, and oblique astigmatism was 90.3%, 5.8%, and 3.9%, respectively, with astigmatism ≥1.00 D criteria. The mean spherical equivalent, axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT), corneal radius (CR), and lens position (LP) were 4.37±2.52 D, 25.28±1.24 mm, 539.49±34.98 μm, 3.31±0.34 mm, 3.47±0.21 mm, 7.8±0.28 mm, and 5.04±0.32 mm, respectively. With diopter increase in myopia, the AL became longer, CR became steeper, ACD became deeper, LT became thinner, and LP became more posterior (all P<0.01). Females had a shorter AL, thinner CCT, smaller CR, shallower ACD, thicker lens, and more anterior LP than males (P<0.01). The 64% of high myopia had AL≥26 mm, meanwhile, 5.8% mild myopia and 21.1% moderate myopia had AL≥26 mm. With AL≥26 mm, mild and moderate myopia compared to high myopia, AL was shorter (26.51±0.46 vs 26.87±0.70 mm), CR was larger (8.10±0.3 vs 7.85±0.23 mm) and LT was thinner (3.39±0.19 vs 3.45±0.19 mm, P<0.001). CONCLUSION: The prevalence of myopia and high myopia is significantly high in freshman students. The majority of astigmatism is with-the-rule. Inconformity of refractive errors and ocular biometry existed in some students. Attention should be paid to the ocular biometry of myopia.

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ObjectivesThe study aims to investigate the value of a convolutional neural network (CNN) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting malignancy of breast lesions.MethodsWe developed a CNN model based on DCE-MRI to characterize breast lesions. Between November 2018 and October 2019, 6,165 slices of 364 lesions (234 malignant, 130 benign) in 364 patients were pooled in the training/validation set. Lesions were semi-automatically segmented by two breast radiologists using ITK-SNAP software. The standard of reference was histologic consequences. Algorithm performance was evaluated in an independent testing set of 1,560 slices of 127 lesions in 127 patients using weighted sums of the area under the curve (AUC) scores.ResultsThe area under the receiver operating characteristic (ROC) curve was 0.955 for breast cancer prediction while the accuracy, sensitivity, and specificity were 90.3, 96.2, and 79.0%, respectively, in the slice-based method. In the case-based method, the efficiency of the model changed by adjusting the standard for the number of positive slices. When a lesion with three or more positive slices was determined as malignant, the sensitivity was above 90%, with a specificity of nearly 60% and an accuracy higher than 80%.ConclusionThe CNN model based on DCE-MRI demonstrated high accuracy for predicting malignancy among the breast lesions. This method should be validated in a larger and independent cohort.