Background: Low bone mineral density (LBMD), including osteoporosis and low bone mass, has becoming a serious public health concern. We aimed to estimate the disease burden of LBMD and its related fractures in 204 countries and territories over the past 30 years.Methods: We collected detailed information and performed a secondary analysis for LBMD and its related fractures from the Global Burden of Disease Study 2019. Numbers and age-standardized rates related to LBMD of disability-adjusted life-years (DALYs) and deaths in 204 countries and territories were compared by age, gender, socio-demographic index (SDI), and location.Results: Global deaths and DALYs number attributable to LBMD increased from 207 367 and 8 588 936 in 1990 to 437 884 and 16 647 466 in 2019, with a raise of 111.16% and 93.82%, respectively. DALYs and deaths number of LBMD-related fractures increased 121.07% and 148.65% from 4 436 789 and 121248 in 1990 to 9 808 464 and 301 482 in 2019. In 2019, the five countries with the highest disease burden of DALYs number in LBMD-related fractures were India (2 510 288), China (1 839 375), United States of America (819 445), Japan (323 094), and Germany (297 944), accounting for 25.59%, 18.75%, 8.35%, 3.29%, and 3.04%. There was a quadratic correlation between socio-demographic index (SDI) and burden of LBMD-related fractures: DALYs rate was 179.985-420.435SDI+417.936SDI²(R^{2 =} 0.188, p<0.001); Deaths rate was 7.879-13.416SDI+8.839 SDI²(R^{2 =} 0.101, p<0.001).Conclusions: The global burden of DALYs and deaths associated with LBMD and its related fractures has increased significantly since 1990. There were differences in disease burden between regions and countries. These estimations could be useful in priority setting, policy-making, and resource allocation in osteoporosis prevention and treatment.

Background: Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. Methods: In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. Results: The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. Conclusion: The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

Data sources: The Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Background: To describe burden, and to explore cross-country inequalities according to socio-demographic index (SDI) for stroke and subtypes attributable to diet. Methods: Death and years lived with disability (YLDs) data and corresponding estimated annual percentage changes (EAPCs) were estimated by year, age, gender, location and SDI. Pearson correlation analysis was performed to evaluate the connections between age-standardized rates (ASRs) of death, YLDs, their EAPCs and SDI. We used ARIMA model to predict the trend. Slope index of inequality (SII) and relative concentration index (RCI) were utilized to quantify the distributive inequalities in the burden of stroke. Results: A total of 1.74 million deaths (56.17% male) and 5.52 million YLDs (55.27% female) attributable to diet were included in the analysis in 2019.Between 1990 and 2019, the number of global stroke deaths and YLDs related to poor diet increased by 25.96% and 74.76% while ASRs for death and YLDs decreased by 42.29% and 11.34% respectively. The disease burden generally increased with age. The trends varied among stroke subtypes, with ischemic stroke (IS) being the primary cause of YLDs and intracerebral hemorrhage (ICH) being the leading cause of death. Mortality is inversely proportional to SDI (R = -0.45, p < 0.001). In terms of YLDs, countries with different SDIs exhibited no significant difference (p = 0.15), but the SII changed from 38.35 in 1990 to 45.18 in 2019 and the RCI showed 18.27 in 1990 and 24.98 in 2019 for stroke. The highest ASRs for death and YLDs appeared in Mongolia and Vanuatu while the lowest of them appeared in Israel and Belize, respectively. High sodium diets, high red meat consumption, and low fruit diets were the top three contributors to stroke YLDs in 2019. Discussion: The burden of diet-related stroke and subtypes varied significantly concerning year, age, gender, location and SDI. Countries with higher SDIs exhibited a disproportionately greater burden of stroke and its subtypes in terms of YLDs, and these disparities were found to intensify over time. To reduce disease burden, it is critical to enforce improved dietary practices, with a special emphasis on mortality drop in lower SDI countries and incidence decline in higher SDI countries.

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal’s method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Primary ovarian insufficiency (POI), also known as premature ovarian failure, is an ovarian defect in humans characterized by the premature depletion of ovarian follicles before the age of 40. However, the mechanisms underlying POI remain largely unknown. Here, we show that knockout of Epg5 (ectopic P-granules autophagy protein 5 homolog (C. elegans)) results in subfertility in female mice, which exhibit a POI-like phenotype. Single-cell RNA sequencing analysis revealed that the knockout of Epg5 affected the differentiation of granulosa cells (GCs). Further investigation demonstrated that knockout of Epg5 blocks macroautophagic/autophagic flux, resulting in the accumulation of WT1 (WT1 transcription factor), an essential transcription factor for GCs, suggesting WT1 needs to be selectively degraded by the autophagy pathway. We found that the insufficient degradation of WT1 in the antral follicular stage contributes to reduced expression of steroidogenesis-related genes, thereby disrupting GC differentiation. Collectively, our studies show that EPG5 promotes WT1 degradation in GCs, indicating that the dysregulation of Epg5 in GCs can trigger POI pathogenesis. Abbreviations: 3-MA, 3-methyladenine; CHX, cycloheximide; CQ, chloroquine; EPG5, ectopic P-granules autophagy protein 5 homolog (C. elegans); GC, granulosa cell; MAP1LC3/LC3, microtubule-associated protein 1 light chain 3; MII, metaphase II; POI, primary ovarian insufficiency; PB1, polar body 1; SQSTM1/p62, sequestosome 1; WT1, WT1 transcription factor