Aims The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581.

The dynamics model is established in view of the self-designed, two-wheeled, and self-balancing robot. This paper uses the particle swarm algorithm to optimize the parameter matrix of LQR controller based on the LQR control method to make the two-wheeled and self-balancing robot realize the stable control and reduce the overshoot amount and the oscillation frequency of the system at the same time. The simulation experiments prove that the LQR controller improves the system stability, obtains the good control effect, and has higher application value through using the particle swarm optimization algorithm.

The gas gain and energy resolution of single and double THGEM detectors (5×5cm 2 effective area) with mini-rims (rim is less than 10µm) were studied. The maximum gain can reach 5×10 3 and 2×10 5for single and double THGEM respectively, while the energy resolution of 5.9 keV X-ray varied from 18% to 28% for both single and double THGEM detectors of different hole sizes and thicknesses.All the experiments were investigated in mixture of noble gases(argon,neon) and small content of other gases(iso-butane,methane) at atmospheric pressure.

Acute pulmonary embolism (APE) is one of the prominent causes of death in patients with cardiovascular disease. Currently, reliable biomarkers to predict the prognosis of patients with APE are limited. The present study aimed to investigate the association of blood urea nitrogen to serum albumin (B/A) ratio and intensive care unit (ICU) mortality in critically ill patients with APE. A retrospective cohort study was performed using data extracted from a freely accessible critical care database (MIMIC-III). Adult (≥18 years) patients of first ICU admission with a primary diagnosis of APE in the database were enrolled in the study. The primary endpoint was the ICU mortality rate while the 28-day mortality after ICU admission was the secondary endpoint. The data of survivors and non-survivors were compared. A total of 1048 patients with APE were enrolled in this study, of which 131 patients died in ICU and 169 patients died within 28 days after ICU admission. The B/A ratio in the non-survivors group was significantly higher compared to the survivors group ( P < 0.001). The multivariate analysis revealed that the B/A ratio was an independent predictor of ICU mortality (odds ratio [OR] 1.10, 95% CI 1.07-1.14, P < 0.001) and all-cause mortality within 28 days after ICU admission (hazard ratio [HR] 1.07, 95% CI 1.05-1.09, P < 0.001) in APE patients. The B/A ratio showed a greater area under the curve (AUC) of ICU mortality prediction (0.80; P < 0.001) than simplified acute physiology score II (SAPSII) (0.79), systemic inflammatory response syndrome score (SIRS) (0.62), acute physiology score III (APSIII) (0.76) and sequential organ failure assessment (SOFA) score (0.71). The B/A ratio could be a simple and useful prognostic tool to predict mortality in critically ill patients with APE.

Osteoarthritis (OA) is a kind of degenerative disease, which is caused by many factors such as aging, obesity, strain, trauma, congenital joint abnormalities, joint deformities. Exosomes are mainly derived from the invagination of intracellular lysosomes, which are released into the extracellular matrix after fusion of the outer membrane of multi vesicles with the cell membrane. Exosomes mediate intercellular communication and regulate the biological activity of receptor cells by carrying non-coding RNA, long noncoding RNAs (lncRNAs), microRNAs (miRNAs), proteins and lipids. Evidences show that exosomes are involved in the pathogenesis of OA. In view of the important roles of exosomes in OA, this paper systematically reviewed the roles of exosomes in the pathogenesis of OA, including the roles of exosomes in OA diagnosis, the regulatory mechanisms of exosomes in the pathogenesis, and the intervention roles of exosomes in the treatment of OA. Reviewing the roles of exosomes in OA will help to clarify the pathogenesis of OA and explore new diagnostic biomarkers and therapeutic targets.