Opportunistic screening for osteoporosis can be performed using low‐dose computed tomography (LDCT) imaging obtained for other clinical indications. In this study we explored the CT‐derived bone mineral density (BMD) and prevalence of osteoporosis from thoracic LDCT in a large population cohort of Chinese men and women. A total of 69,095 adults (40,733 men and 28,362 women) received a thoracic LDCT scan for the purpose of lung cancer screening between 2018 and 2019, and data were obtained for analysis from the China Biobank Project, a prospective nationwide multicenter population study. Lumbar spine (L1–L2) trabecular volumetric bone mineral density (vBMD) was derived from these scans using quantitative computed tomography (QCT) software and the American College of Radiology QCT diagnostic criteria for osteoporosis were applied. Geographic regional differences in the prevalence of osteoporosis were assessed and the age‐standardized, population prevalence of osteoporosis in Chinese men and women was estimated from the 2010 China census. The prevalence of osteoporosis by QCT for the Chinese population aged >50 years was 29.0% for women and 13.5% for men, equating to 49.0 million and 22.8 million, respectively. In women, this rate is comparable to estimates from dual‐energy X‐ray absorptiometry (DXA), but in men, the prevalence is double. Prevalence varied geographically across China, with higher rates in the southwest and lower rates in the northeast. Trabecular vBMD decreased with age in both men and women. Women had higher peak trabecular vBMD (185.4 mg/cm3) than men (176.6 mg/cm3) at age 30 to 34 years, but older women had lower trabecular vBMD (62.4 mg/cm3) than men (92.1 mg/cm3) at age 80 years. We show that LDCT‐based opportunistic screening could identify large numbers of patients with low lumbar vBMD, and that future cohort studies are now required to evaluate the clinical utility of such screening in terms of fracture prevention and supporting national health economic analyses. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..

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Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists. Lung cancer screening has now been widely applied because of its effectiveness and ease of performance. More than 10 million chest CT scans were performed in the United States alone in 2012, highlighting the potential for this clinical scenario 1. Radiologists who evaluate a large number of low-dose CT (LDCT) screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. 2-5. Artificial intelligence (AI) is showing rapid advantages and exciting achievements in the fields of imaging diagnosis and/or evaluation 6-12. AI detection of lung nodules has long been expected to be an effective assistant in daily clinical practice, especially for LDCT lung nodule screening. Thus far, many novel deep neural networkbased systems have demonstrated the potential for use in the proposed technique for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness 13-18. However, the majority of the proposed systems were trained on CT scans from the Lung Image Database Consortium/Image Database Resource Initiative (LIDC-IDRI), the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database 19-24. The effectiveness in clinical practice has not been fully recognized or proven. From the previous study, the prevalence of pulmonary nodules (PNs) varies greatly in different populations, ranging from 13 to 58% 25-32. Different demographic features, selection criteria of participants, and the referral pattern of the study centre may explain such differences 27. In China, the incidence of non-c...

Objectives Alveolar bone osteoporosis has attracted more and more attention because of its profound impact on stomatognathic function and treatment, but current treatments have not been targeted to alveolar bone and might even cause severe side effects. Thus, identifying the effects of anti‐osteoporosis agents on alveolar bone is essential. Icariin ameliorates metabolic dysfunction of long bones, but its effects on alveolar bone remain unclarified. Materials and methods BMSCs were isolated from rat mandibles (mBMSCs). The osteogenic potential of mBMSCs and the signalling pathway involved under icariin treatment were measured by ALP and alizarin red staining, reverse transcription‐polymerase chain reaction (RT‐PCR), Western blotting and immunofluorescence. Dual‐luciferase assay, chromatin immunoprecipitation (ChIP) and co‐immunoprecipitation were used to investigate the molecular mechanism. Ovariectomized and sham‐operated rats treated with or without icariin were analysed by micro‐CT, TRAP staining and calcein double labelling. Results We found that icariin promoted osteoblast differentiation of mBMSCs. Furthermore, STAT3 was critical for icariin‐promoted osteoblast differentiation, as indicated by increased phosphorylation levels in icariin‐treated mBMSCs, while preventing STAT3 activation blocked icariin‐induced osteoblast differentiation. Mechanistically, icariin‐promoted transcription of the downstream osteogenic gene osteocalcin (Ocn) through STAT3 and STAT3 bound to the promoter of Ocn. Notably, icariin prevented the alveolar bone osteoporosis induced by oestrogen deficiency through promoting bone formation. Conclusions For the first time, our work provides evidence supporting the potential application of icariin in promoting osteogenesis and treating alveolar bone osteoporosis.