Osteoporosis （OP）， a common systemic skeletal disease in the elderly， is characterised by bone loss and bone microstructural degeneration. Its clinical manifestations include increased bone fragility and bone pain. Furthermore， OP increases the risk of fracture due to the high bone fragility， which leads to lifelong disability or death， imposing a heavy economic and psychological burden on the patients and their families. The pathogenesis of OP is extremely complex and associated with a variety of factors such as proliferation and differentiation of osteoblasts， impairment of osteoclast activity and function， and abnormalities in autophagy activation. Recent studies have found that mammalian target of rapamycin （mTOR） signaing pathway is involved in the regulation of bone homeostasis， which can promote bone formation and improve bone metabolism and bone microstructure by regulating osteoblast proliferation and differentiation and osteoclast function and activating cellular autophagy， thus playing a crucial role in the prevention and treatment of OP. The prevention and treatment of OP with Chinese medicine has a long history， clear efficacy， multiple targets of action， low adverse effects， and wide medicine sources. Therefore， this paper briefly describes the role of mTOR signaling pathway in the development of OP by reviewing the latest research reports and summarizes in detail the latest research results on the treatment of OP with Chinese medicine extracts and prescriptions via the mTOR signaling pathway. This review aims to provide a basis for the in-depth research on the relationship between mTOR signaling pathway and OP and the clinical application of traditional Chinese medicine in the prevention and treatment of OP.

Objective: To investigate the association between sleep duration and frailty among people aged 50 years and over. Methods: Cross-sectional data was collected from the first wave of World Health Organization Study on global AGEing and adult health in China. Frailty index was constructed on the proportion of deficits, out of the 40 variables. A two-level (individual level and community level) linear model was performed to identify the related factors on frailty. All the models were stratified by age, gender, residence (urban/rural). Restricted cubic spline was performed to graphically evaluate the dose-response association between self-reported sleep duration and frailty. Results: A total of 13 175 individuals aged 50 years and over participated in this study. Without adjusting on any confounding factors, shorter or longer sleep duration significantly increased the risk of weakness compared with normal sleep time (OR=2.05, 95%CI: 1.71-2.44; OR=1.35, 95%CI: 1.12-1.63). After adjusting for confounding factors such as gender, age, residence, education, family assets, vegetable, smoking, drinking and physical activity, a positive association between short sleep duration and frailty was noticed compared with normal sleep time (aOR=1.60, 95%CI: 1.27-2.01). The results of stratified analysis on sex, age and urban and rural areas showed that, after adjusting for gender, age, residence, education level, family assets, intake of vegetables and fruits, smoking, drinking and physical activity, only shorter sleep duration was positively correlated with the risk of weakness. In addition, among people aged 65 years and over, adjusted for confounding factors, the risk of weakness increased by 91%, compared with normal sleep time (aOR=1.91, 95%CI: 1.46-2.49). The dose-response curve also showed that the sleep duration and frailty present an approximate "U" shaped relationship. Conclusion Short sleep duration might be associated with frailty.

Background Air pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages. Objective To construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution. Methods Data on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO₂, NO₂, CO, PM_2.5, PM₁₀, and maximum 8 h O₃ concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model. Results PM_2.5, NO₂, SO₂, and O₃ were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM_2.5, NO₂, SO₂ and O₃ were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI . Conclusion The J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.