Objective To investigate the distribution of the preferred retinal locus (PRL) of eccentric fixation in eyes with high myopia. Methods A total of 40 highly myopic patients (54 eyes) with eccentric fixation were examined by MP-1 microperimeter to identify the PRL. The position of PRL relative to the fovea was estimated using the 90% confidence ellipse of normal adult foveal fixation. The differences of visual acuity between "desirable" and "undesirable" PRL were tested by analysis of variance. Results In 54 eyes with high myopia, 24 eyes (44.44%) had PRL of eccentric fixation below the scotoma after loss of central vision; 19 eyes (35.19%) had a left-field PRL; 6 eyes (11.11%) had an upper-field PRL; and 5 eyes (9.26%) had right-field PRL. In 14 patients who had binocular eccentric fixation, 13 had the same fixation pattern in both eyes, including lower-field PRL in 7 (50.00 %), left-field PRL in 5 (35.71 %), and upper-field PRL in 1 patient (7.14%). The difference of visual acuity between lower- and left-field PRL group and right- and upper-field undesirable PRL group was not statistically significant (F=0.144, P > 0.05). Conclusions The eccentric fixation in eyes with high myopia is usually situated as near as possible to the fovea. The optimal PRL is inferior visual field.

Objective To search for starfishes with hopoglycemic activity and orientate the bioactive fractions .Methods Firstly, the crude extracts of four starfishes were subjected to oral glucose tolerance test on normal and diabetic mice .And then the in vitroα-glucosidase activity assay was introduced to confirm the bioactive fractions .Results The crude extract of Asterias rollestoni could lower the blood glucose of normal and diabetic mice after 0.5 h and 1 h.Furthermore, its n-BuOH fraction exhibited potent α-glucosidase inhibitoary activity .Conclusion The n-BuOH extract of Asterias rollestoni had a potent α-glucosidase inhibitoary activity .

{L-End}Objective To analyze the correlation between occupational burnout and sleep quality among steelworkers. {L-End}Methods A total of 11 491 steelworkers from a large steel enterprise in Gansu Province were selected as the research subjects using convenient sampling method. The Burnout Questionnaire and the Pittsburgh Sleep Quality Index Scale were used to investigate their occupational burnout and sleep quality. Hierarchical regression analysis was used to analyze the effects of occupational burnout on the sleep quality. {L-End}Results The detection rate of occupational burnout and sleep disorder were 50.4% and 39.0%, respectively. There was a positive correlation between the level of occupational burnout and the total score of sleep quality (Spearman correlation coefficient=0.454, P<0.05). The results of hierarchical regression analysis, adjusting for confounding factors such as gender, age, marital status, education level, alcohol consumption, exercise, weekly working hours, seniority, work shift, noise exposure, dust exposure, and high-temperature work, showed that the score of occupational burnout was positively related to the score of sleep quality(P<0.01), explaining 16.0% of the variance in the score of sleep quality among these steelworkers. {L-End}Conclusion The detection rate of occupational burnout and sleep disorders are relatively high among the steelworkers in this enterprise. Higher levels of occupational burnout are associated with poorer sleep quality. Alleviating occupational burnout among steelworkers may contribute to improving their sleep quality.

Background Steel workers are exposed to occupational hazardous factors such as dust, noise, and heat, and often work in shifts, making them prone to sleep disorders. Objective To explore potential influencing factors of sleep disorders among workers in a steel enterprise in Gansu Province, and provide a basis for reducing the risk of sleep disorders among them. Methods From January to March 2022, a self-made questionnaire combined with the Pittsburgh Sleep Quality Index (PSQI) were used to investigate the employees of a steel enterprise in Gansu Province. According to their PSQI scores, they were divided into a normal sleep group and a sleep disorder group. The general demographic variables of the two groups were balanced by 1∶1 propensity score matching (PSM). Multiple logistic regression was used to analyze the contributing factors of sleep disorders. Restricted cubic spline (RCS) model was used to analyze potential dose-response relationship between weekly working hours and sleep disorders. Results The prevalence of sleep disorders in the steel workers was 48.06% (6029/12544). After PSM, 5847 pairs were successfully matched, and the distributions of matched variables were well balanced between the two groups. The results of multiple logistic regression showed that hypertension (OR=1.39, 95%CI: 1.24, 1.56), diabetes mellitus (OR=1.34, 95%CI: 1.07, 1.66), three-shift system (OR=1.26, 95%CI: 1.12, 1.41), dust exposure (OR=1.14, 95%CI: 1.01, 1.29), noise exposure (OR=1.23, 95%CI: 1.09, 1.39), heat exposure (OR=1.16, 95%CI: 1.04, 1.29), and work injury (OR=1.22, 95%CI: 1.02, 1.46) increased the risk of sleep disorders. Compared with workers with < 10 years of service, those with 10-20 years (OR=1.31, 95%CI: 1.19, 1.44), 20-30 years (OR=1.34, 95%CI: 1.19, 1.52), and ≥30 years of service (OR=1.35, 95%CI: 1.19, 1.53) had a higher risk of sleep disorders. Compared with non-exercise workers, the risk of developing sleep disorders was lower in workers with occasional exercise (OR=0.61, 95%CI: 0.56, 0.66) and regular exercise (OR=0.55, 95%CI: 0.49, 0.62). The RCS model showed that the weekly working hours and sleep disorders in the steel workers showed a nonlinear dose-response relationship (P<0.05 for overall trend, P<0.05 for nonlinear test). The relationship between weekly working hours and sleep disorders showed a "U" shaped distribution, with a significant increase in the risk of sleep disorders when the weekly working hours exceeded 49 h. Conclusion The non-occupational influencing factors of sleep disorders of employees in the steel enterprise include hypertension, diabetes, physical exercise, and occupational influencing factors include length of service, weekly working hours, shifts, dust exposure, noise exposure, heat exposure, and work injuries. It is recommended to consider both occupational and non-occupational factors to formulate appropriate sleep disorder prevention and control measures for steel employees to reduce the risk of sleep disorders.