Background: Myopia, especially high myopia, would cause damage in the choroid, retina and sclera, thereby leading to vision loss. Although refractive error correction can help improve visual acuity, the pathology of myopia, a global issue, remains unclear and myopia progression, as well as concomitant fundus progression, remains uncontrolled. Under such circumstances, prevention of myopia is of great significance and thus should be prioritized. Objective: To explore whether outdoor time has positive significance for myopia prevention. Search Methods: Databases of Pubmed, Science Direct, the Cochrane Library, the Chinese National Knowledge Infrastructure and the Wanfang Database were searched. The following terms or their combinations were used: myopia, prevention, control, random, randomized, randomization, intervention, outdoor. The full search strategy was shown in the Appendix below. The databases were last searched on ­October 24, 2018. Selection Criteria: Randomized controlled trials (RCTs) that participants accepted outdoor activity as an intervention measure for myopia prevention were included. Data Collection and Analysis: Two review authors independently extracted data and assessed the risk of bias for included studies. A fixed-effects model was applied, given that the heterogeneity among included studies was small. Main Results: Five RCTs with 3,014 subjects were included. Subjects’ age ranged from 6 to 12 years, and the follow-up duration ranged from 9 to 36 months. Spherical equivalent error (SER) of the outdoor group was larger than that of the control group, and the pooled mean difference (MD) was 0.15 (95% CI 0.06–0.23) diopter (D). The change in SER of the outdoor group was smaller than that of the control group, with a pooled MD of 0.17 (95% CI 0.16–0.18) D. New myopia cases in the outdoor group were fewer than that of the control group, and the pooled risk ratio was 0.76 (95% CI 0.67–0.87). The change in axial length of the outdoor group was smaller than that of the control group, and the pooled MD was –0.03 (95% CI –0.03 to –0.03) mm. For all analyzed outcomes, there was no heterogeneity across included studies (I² = 0%) and there was no publication bias either. Conclusion: Outdoor time helps slow down the change of axial length and reduce the risk of myopia.

Objective: To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis. Methods: Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian framework. The optimum model was selected according to the deviance information criterion (DIC) value. Coefficients of climate variables were then estimated using the best fitting model. Results: The model containing spatial-temporal interaction parameter was the best fitting one, with the smallest DIC value (−4,508,660). Ecological analysis results showed the relative risks (RRs) of average temperature, rainfall, wind speed, humidity, and air pressure were 1.00324 (95% CI, 1.00150–1.00550), 1.01010 (95% CI, 1.01007–1.01013), 0.83518 (95% CI, 0.93732–0.96138), 0.97496 (95% CI, 0.97181–1.01386), and 1.01007 (95% CI, 1.01003–1.01011), respectively. Conclusions: The spatial-temporal interaction was statistically meaningful and the prevalence of tuberculosis was influenced by the time and space interaction effect. Average temperature, rainfall, wind speed, and air pressure influenced tuberculosis. Average humidity had no influence on tuberculosis.

Bacterial keratitis (BK) is the most common type of infectious keratitis. The spectrum of pathogenic bacteria and their susceptibility to antibiotics varied with the different regions. A meta-analysis was conducted to review the global culture rate, distribution, current trends, and drug susceptibility of isolates from BK over the past 20 years (2000–2020). Four databases were searched, and published date was limited between 2000 and 2020. Main key words were “bacterial keratitis”, “culture results” and “drug resistance”. Forty-two studies from twenty-one countries (35 cities) were included for meta-analysis. The overall positive culture rate was 47% (95%CI, 42–52%). Gram-positive cocci were the major type of bacteria (62%), followed by Gram-negative bacilli (30%), Gram-positive bacilli (5%), and Gram-negative cocci (5%). Staphylococcus spp. (41.4%), Pseudomonas spp. (17.0%), Streptococcus spp. (13.1%), Corynebacterium spp. (6.6%) and Moraxella spp. (4.1%) were the most common bacterial organism. The antibiotic resistance pattern analysis revealed that most Gram-positive cocci were susceptive to aminoglycoside (86%), followed by fluoroquinolone (81%) and cephalosporin (79%). Gram-negative bacilli were most sensitive to cephalosporin (96%) and fluoroquinolones (96%), followed by aminoglycoside (92%). In Gram-positive cocci, the susceptibility trends of fluoroquinolones were decreasing since 2010. Clinics should pay attention to the changing trends of pathogen distribution and their drug resistance pattern and should diagnose and choose sensitive antibiotics based on local data.

Handan Eye Study (HES), a large population-based cohort study in rural area of northern China, is one of the few studies focusing on the major eye diseases of rural Chinese population. The aim of this study was to introduce the design, methodology and to assess the data quality of the follow-up phase of HES. All participants were recruited in Yongnian county of Handan city between 2012 and 2013. Main outcomes were measured by visual quality scales and ocular examinations. We performed the Chi-square test to make comparison of categorical data among groups, one-way analysis of variance and Kruskal-Wallis test was applied to make comparison of continuous data among groups, a post-hoc test was done to make further pairwise comparison. Inter-class correlation coefficient (ICC) and Kappa coefficients were used to evaluate the consistency between different operators. Logistic regression was used to explore the influence factors of death, odds ratio (OR) and 95% confidence interval (CI) was used to estimate the effect size of each influence factor. The follow-up rate was 85.3%. Subjects were classified into three groups: the follow-up group (n = 5394), the loss to follow-up group (n = 929), and the dead group (n = 507), comparison of their baseline information was done. Compared with the other two groups, age of the dead group (66.52 ± 10.31 years) was the oldest (Z = 651.293, P < 0.001), male proportion was the highest (59.0%) (χ 2 = 42.351, P < 0.001), only 65.9% of the dead finished middle school education (Z = 205.354, P < 0.001). The marriage percentage, body mass index (BMI), best-corrected visual acuity (BCVA), and intra-ocular pressure of the dead group was the lowest either. Spherical equivalent error (SER) of the dead group was the highest. Besides, history of smoking, hypertension, diabetic, and heart disease was more common in the dead group. Multivariate analysis showed that age (OR = 1.901, 95% CI: 1.074–1.108), gender (OR = 0.317, 95% CI: 0.224–0.448), and BCVA (OR = 0.282, 95% CI: 0.158–0.503) were associated with death. While between the follow-up group and the loss to follow-up group, there was only difference on age, gender, BMI, systolic blood pressure and SER. The Cronbach coefficients of all scales used in the follow-up were ≥0.63 and the cumulative variances were ≥0.61, indicating of good reliability and validity. The ICCs and Kappa coefficients between different operators were ≥0.69. HES has a high follow-up rate and a low risk of loss to follow-up bias. Age, gender, and BCVA are influence factors of death. Specifically, male subjects are at a higher risk of death than female, age is a risk factor of death while BCVA is a protective factor for death.

To develop an automatic tool on screening diabetic retinopathy (DR) from diabetic patients. We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic (ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model. A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%. Textures extracted by grey level co-occurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

High-density lipoprotein (HDL) is associated with the incidence of metabolic syndrome (MetS). It is unclear whether subjects with different HDL levels develop different components of MetS over time. Our study aimed to determine what MetS components tend to emerge and change relative to different levels of HDL. During the period 2007 to 2012, 4,905 adults in Tongren and Xiaotangshan Hospitals in Beijing were included with no MetS, self-reported type 2 diabetes, or cardiovascular disease at baseline. An association rule was used to determine the changes of MetS components over time. The incidence of MetS at follow-up was 3.40% for men and 1.50% for women in the high-normal HDL group; 6.65% and 4.55%, respectively, in the normal HDL group; and 11.05% and 6.45%, respectively, in the low HDL group. The most common transition was from healthy to healthy in normal-high or normal HDL groups (47.2% to 63.8%), whereas 11.7% to 39.9% of subjects with low HDL returned to healthy status or stayed unchanged in the low HDL group. The most common new-onset components were elevated blood pressure (9.2 to 10.0%), elevated high-density lipoprotein (5.5 to 11.0%), and raised fasting glucose (5.4 to 5.5%) in the groups with normal-high or normal HDL. The incidence of MetS increased in parallel with the decrease in HDL. Adults with a low HDL level were more susceptible to developing MetS over time. Low HDL seemed to be a pre-existing phase of MetS and may be a crucial status for MetS prevention.