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Background. Depression is a disorder that can have a serious impact on functioning and quality of life. Understanding the global, regional, and national epidemiology of depression in working‐age populations (15–49 years) is important for informing mental health policies and services. However, up‐to‐date data have been lacking, especially in developing regions. Methods. This study delved into the prevalence of depression among individuals in the working‐age group, specifically those between 15 and 49 years, by analyzing data from the 2019 Global Burden of Disease, Injuries, and Risk Factors Study (GBD). The comprehensive analysis aimed to determine the age‐standardized prevalence, incidence, and disability‐adjusted life years (DALYs) associated with depression across diverse groups. It considered factors such as geographical regions, age brackets, genders, and sociodemographic indices, spanning a time frame from 1990 to 2019. Results. Globally, the estimated prevalent cases, incident cases, and DALYs for depression increased from 1990 to 2019. Regionally, certain regions like Central Latin America saw rapid increases in age‐standardized prevalence and incidence rates over time. By sociodemographic regions, all tiers witnessed rises in incident cases, with high sociodemographic index (SDI) areas having the greatest burden in 2019. Nationally, countries such as India, China, and the United States had the highest total prevalence and incidence in 2019, while nations like Yemen and Angola reported exceptionally high age‐standardized rates (ASRs). Peak prevalence risk occurred in the mid‐to‐late 40s age range. Period effects showed declining risks over time. Earlier birth cohorts, especially before the mid‐1960s, faced higher risks than more recent generations. Population growth rather than epidemiological changes appeared to drive increases in disease burden. Conclusion. From 1990 to 2019, the overall trend of depression burden in working‐age individuals presents regional and national variations and differs by age, sex, period, and cohort. [ABSTRACT FROM AUTHOR]

Background: Depression is a significant mental health concern affecting the overall well-being of adolescents and young adults. Recently, the prevalence of depression has increased among young people. Nonetheless, there is little research delving into the longitudinal epidemiology of adolescent depression over time. Aims: To investigate the longitudinal epidemiology of depression among adolescents and young adults aged 10–24 years. Method: Our research focused on young people (aged 10–24 years) with depression, using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. We explored the age-standardised prevalence, incidence and disability-adjusted life-years (DALYs) of depression in different groups, including various regions, ages, genders and sociodemographic indices, from 1990 to 2019. Results: The prevalence, incidence and DALYs of depression in young people increased globally between 1990 and 2019. Regionally, higher-income regions like High-Income North America and Australasia recorded rising age-standardised prevalence and incidence rates, whereas low- or middle-income regions mostly saw reductions. Nationally, countries such as Greenland, the USA and Palestine reported the highest age-standardised prevalence and incidence rates in 2019, whereas Qatar witnessed the largest growth over time. The burden disproportionately affected females across age groups and world regions. The most prominent age effect on incidence and prevalence rates was in those aged 20–24 years. The depression burden showed an unfavourable trend in younger cohorts born after 1980, with females reporting a higher cohort risk than males. Conclusions: Between 1990 and 2019, the general pattern of depression among adolescents varied according to age, gender, time period and generational cohort, across regions and nations. [ABSTRACT FROM AUTHOR]

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To analyze the normal reference left common iliac vein (LCIV) area in chronic venous disease (CVD) patients with or without May–Thurner syndrome (MTS) and its relevant influencing factors. MATERIALS AND METHODS: A prospective analysis of patients with left lower extremity CVD was conducted in a single institution from January to August 2019. A total of 326 patients, 98 with MTS and 228 without MTS after computed tomography (CT) venography, were finally enrolled in this study, and their basic information was recorded. MTS cases were distinguished from non-MTS cases by calculating area stenosis rate with CT three-dimensional reconstruction. The reference area of LCIV was also defined and calculated to analyze its coefficient of variation and relationship with influencing factors in patients with and without MTS. RESULTS: Ninety-eight cases of MTS and 228 cases of non-MTS were finally analyzed. In the MTS group, the mean area of reference LCIV was significantly lower (116.29 ± 53.17 mm2 vs. 160.44 ± 52.99 mm2, P < 0.01) and showed greater variability (0.21 ± 0.13 vs. 0.16 ± 0.08, P = 0.04) compared with non-MTS group. Obesity radio was significantly higher in non-MTS group (28.5% vs. 16.3%, P = 0.03). In both MTS and non-MTS groups, the increase of body mass index (BMI) was generally associated with statistically significant increases in the LCIV area (P = 0.002 and P = 0.005), while other factors showed no statistically significant correlation. CONCLUSIONS: The reference area of LCIV was redefined in this study. We found that the increase of the LCIV area in both MTS and non-MTS groups was correlated with an increase in BMI. Furthermore, a smaller area of reference LCIV and more variability in the LCIV area were found in the MTS group in contrast to the non-MTS group.