Objective: To understand the unhealthy listening habits and related factors hearing on impairment among primary and middle school students in Jilin Province, so as to provide a scientific basis for the prevention of hearing impairment in children and adolescents. Methods: From September to November 2021, a stratified cluster random sampling method was employed to select 12 847 primary and middle school students in nine cities of Jilin Province who use headphones for more than 0.5 hours daily for a questionnaire survey. Data on unhealthy listening habits, lifestyle habits and hearing impairment were collected. The data were analyzed using the χ 2 test and Logistic regression. Results: Totally 1 702 students(13.25%) experienced hearing impairment within the last month. There were statistical differences between the sexes with the average daily headphone use, the times of using headphones ≥1 h every day for one week use in all environment or noisy environment ( χ 2=47.86, 57.60, 66.31, P <0.01). Logistic regression analysis results showed that factors related to the occurrence of hearing impairment among primary and secondary school students included:average daily headphone use of 1-2 h and more than 2 h ( OR=1.74, 95%CI =1.60-1.90; OR=1.73, 95%CI =1.59-1.90), times of using headphones ≥1 h every day for one week were 1-2 times and >2 times ( OR=1.71, 95%CI =1.59- 1.84 ; OR=1.83, 95%CI =1.71-1.97), the times of using headphones≥1 h every day for one week being 1-2 times and >2 times in noisy environment per week ( OR=1.48, 95%CI =1.40-1.56; OR=1.72, 95%CI =1.61-1.86), economic underdevelopment ( OR=1.85, 95%CI =1.76-1.96), boarding （OR=1.78, 95%CI =1.69-1.89), single parent family ( OR=1.72, 95%CI =1.60- 1.87 ), daily activity duration less than 1 h ( OR=1.71, 95%CI =1.63-1.81), sedentary behavior duration more than 6 h per day ( OR=1.88, 95%CI =1.79-1.98) ( P <0.05). Conclusions The behavior of ear protection among primary and middle school students in Jilin Province needs to be enhanced, focusing on students in economically underdeveloped areas, boarding schools and single parent families. It is necessary to guide primary and middle school students to improve their bad ear habits, increase outdoor activities and reduce the time of sitting.

Cataract surgery is one of the most common ophthalmologic procedures. Advances in technology and medical policies have made it more precise. Residual refractive errors and deviation of target diopters are a main cause of dissatisfaction among patients. Refractive enhancement after cataract surgery can correct or eliminate these errors, improving patients' visual quality of life. There are multiple options for correcting residual refractive errors. The best approach depends on factors like the cause of the error, degrees of residual refractive errors, type of intraocular lens, ocular comorbidities, and patient preference. This paper summarizes the incidence and types of residual refractive errors, advancements in refractive enhancement surgeries, and provides practical solutions for clinical practice.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.