Objective:To analyze the learning curve of TiRobot-assisted lumbar pedicle screw fixation (LPSF) by cumulative sum (CUSUM) test method.Methods:The clinical data of 50 patients who underwent TiRobot-assisted LPSF from January 2020 to December 2022 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. CUSUM analysis and learning curve fitting were performed with robot usage time as the main indicator with the time for each step refined (robot registration time, path planning time and guide wire placement time), to select the best learning curve fitting model with the R2 value closest to 1. Using the turning point of the learning curve as the boundary, the learning curve was divided into two stages as learning stage and maturity stage, and then the observation indexes were compared between the two stages. Results:All 50 patients successfully completed the surgery without perioperative complications, with a total of 244 pedicle screws implanted. The total robot usage time and robot registration time showed a gradually decreasing trend with the increase of case number, and the learning curves were successfully fitted and reached their peaks at the seventeenth and thirteenth cases respectively. The entire learning process was divided into learning stage (17 cases) and maturity stage (33 cases) based on the turning point of the learning curve of total robot usage time. The path planning time and guide wire placement time did not show significant changes with the increase in the case number. The total robot usage time, robot registration time and the intraoperative blood loss in the learning stage were significantly higher than those in the maturity stage: (35.35 ± 1.58) min vs. (30.61 ± 0.43) min, (20.83 ± 1.56) min vs. (14.94 ± 0.29) min and 400 (150, 500) ml vs. 200 (110, 300) ml, the guide wire placement time of per screw was significantly lower than that in the maturity stage: 2.00 (1.83, 2.34) min/screw vs. 2.33 (2.13, 2.69) min/screw, and there were statistical differences ( P<0.05 or <0.01). There were no statistical difference in the path planning time, path planning time of per screw, guide wire placement time and the accuracy of screw placement between two stages ( P>0.05). Conclusions:TiRobot-assisted LPSF is a new technology with safety and effectiveness, and it has a relatively short learning curve. To achieve technological maturity, at least 17 surgeries are required with accumulated experience, and the robot registration is the main step of the learning process. After reaching maturity stage, the robot usage time is significantly shortened and intraoperative trauma is significantly reduced while the relatively high screw placement accuracy is ensured.

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Objective:To analyze the learning curve of TiRobot-assisted lumbar pedicle screw fixation (LPSF) by cumulative sum (CUSUM) test method.Methods:The clinical data of 50 patients who underwent TiRobot-assisted LPSF from January 2020 to December 2022 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. CUSUM analysis and learning curve fitting were performed with robot usage time as the main indicator with the time for each step refined (robot registration time, path planning time and guide wire placement time), to select the best learning curve fitting model with the R2 value closest to 1. Using the turning point of the learning curve as the boundary, the learning curve was divided into two stages as learning stage and maturity stage, and then the observation indexes were compared between the two stages. Results:All 50 patients successfully completed the surgery without perioperative complications, with a total of 244 pedicle screws implanted. The total robot usage time and robot registration time showed a gradually decreasing trend with the increase of case number, and the learning curves were successfully fitted and reached their peaks at the seventeenth and thirteenth cases respectively. The entire learning process was divided into learning stage (17 cases) and maturity stage (33 cases) based on the turning point of the learning curve of total robot usage time. The path planning time and guide wire placement time did not show significant changes with the increase in the case number. The total robot usage time, robot registration time and the intraoperative blood loss in the learning stage were significantly higher than those in the maturity stage: (35.35 ± 1.58) min vs. (30.61 ± 0.43) min, (20.83 ± 1.56) min vs. (14.94 ± 0.29) min and 400 (150, 500) ml vs. 200 (110, 300) ml, the guide wire placement time of per screw was significantly lower than that in the maturity stage: 2.00 (1.83, 2.34) min/screw vs. 2.33 (2.13, 2.69) min/screw, and there were statistical differences ( P<0.05 or <0.01). There were no statistical difference in the path planning time, path planning time of per screw, guide wire placement time and the accuracy of screw placement between two stages ( P>0.05). Conclusions:TiRobot-assisted LPSF is a new technology with safety and effectiveness, and it has a relatively short learning curve. To achieve technological maturity, at least 17 surgeries are required with accumulated experience, and the robot registration is the main step of the learning process. After reaching maturity stage, the robot usage time is significantly shortened and intraoperative trauma is significantly reduced while the relatively high screw placement accuracy is ensured.

Objective: To analyze the clinical and genetic characteristics of pediatric patients with dual genetic diagnoses (DGD). Methods: Clinical and genetic data of pediatric patients with DGD from January 2021 to February 2022 in Peking University First Hospital were collected and analyzed retrospectively. Results: Among the 9 children, 6 were boys and 3 were girls. The age of last visit or follow-up was 5.0 (2.7,6.8) years. The main clinical manifestations included motor retardation, mental retardation, multiple malformations, and skeletal deformity. Cases 1-4 were all all boys, showed myopathic gait, poor running and jumping, and significantly increased level of serum creatine kinase. Disease-causing variations in Duchenne muscular dystrophy (DMD) gene were confirmed by genetic testing. The 4 children were diagnosed with DMD or Becker muscular dystrophy combined with a second genetic disease, including hypertrophic osteoarthropathy, spinal muscular atrophy, fragile X syndrome, and cerebral cavernous malformations type 3, respectively. Cases 5-9 were clinically and genetically diagnosed as COL9A1 gene-related multiple epiphyseal dysplasia type 6 combined with NF1 gene-related neurofibromatosis type 1, COL6A3 gene-related Bethlem myopathy with WNT1 gene-related osteogenesis imperfecta type XV, Turner syndrome (45, X0/46, XX chimera) with TH gene-related Segawa syndrome, Chromosome 22q11.2 microduplication syndrome with DYNC1H1 gene-related autosomal dominant lower extremity-predominant spinal muscular atrophy-1, and ANKRD11 gene-related KBG syndrome combined with IRF2BPL gene-related neurodevelopmental disorder with regression, abnormal movement, language loss and epilepsy. DMD was the most common, and there were 6 autosomal dominant diseases caused by de novo heterozygous pathogenic variations. Conclusions: Pediatric patients with coexistence of double genetic diagnoses show complex phenotypes. When the clinical manifestations and progression are not fully consistent with the diagnosed rare genetic disease, a second rare genetic disease should be considered, and autosomal dominant diseases caused by de novo heterozygous pathogenic variation should be paid attention to. Trio-based whole-exome sequencing combining a variety of molecular genetic tests would be helpful for precise diagnosis.
Humans ; Abnormalities, Multiple ; Retrospective Studies ; Intellectual Disability/genetics* ; Bone Diseases, Developmental/complications* ; Tooth Abnormalities/complications* ; Facies ; Muscular Dystrophy, Duchenne/complications* ; Muscular Atrophy, Spinal/complications* ; Carrier Proteins ; Nuclear Proteins

Metabolic reprogramming is a hallmark of cancer, including lung cancer. However, the exact underlying mechanism and therapeutic potential are largely unknown. Here we report that protein arginine methyltransferase 6 (PRMT6) is highly expressed in lung cancer and is required for cell metabolism, tumorigenicity, and cisplatin response of lung cancer. PRMT6 regulated the oxidative pentose phosphate pathway (PPP) flux and glycolysis pathway in human lung cancer by increasing the activity of 6-phospho-gluconate dehydrogenase (6PGD) and α-enolase (ENO1). Furthermore, PRMT6 methylated R324 of 6PGD to enhancing its activity; while methylation at R9 and R372 of ENO1 promotes formation of active ENO1 dimers and 2-phosphoglycerate (2-PG) binding to ENO1, respectively. Lastly, targeting PRMT6 blocked the oxidative PPP flux, glycolysis pathway, and tumor growth, as well as enhanced the anti-tumor effects of cisplatin in lung cancer. Together, this study demonstrates that PRMT6 acts as a post-translational modification (PTM) regulator of glucose metabolism, which leads to the pathogenesis of lung cancer. It was proven that the PRMT6-6PGD/ENO1 regulatory axis is an important determinant of carcinogenesis and may become a promising cancer therapeutic strategy.

Malocclusion is one of the three most common oral diseases reported by World Health Organization(WHO). In China, its incidence rate is rising. Malocclusion seriously affects the dental and maxillofacial function, facial appearance and growth development of nearly 260 million children in China, and what is more, it affects their physical and mental health development. Malocclusion occurrence is related to genetic and environmental factors. Early treatment of malocclusion can create a good dental and maxillofacial development environment, correct abnormal growth and control the adverse effects of abnormal genetic factors. It can effectively reduce the prevalence of children's malocclusion and enhance their physical and mental health. This is an urgent need from the economic perspective of our society, so it has great practical and social significance. Experts from the project group "standard diagnose and treatment protocols for early orthodontic intervention of malocclusions of children" which initiated by China National Health Institute of Hospital Administration wrote the "China Experts' Consensus on Preventive and Interceptive Orthodontic Treatments of Malocclusions of Children", which aims to guide and popularize the clinical practice, improve the clinical theory and practice level, and accelerate the disciplinary development of early treatment of children's malocclusion in China. The consensus elaborates the harmfulness of malocclusion and the necessity of early treatment, and brings up the principles and fundamental contents. Based on the law of dental and maxillofacial development, this paper puts forward the guiding suggestions of preventive and interceptive treatments in different stages of dental development ranging from fetus to early permanent dentition. It is a systematic project to promote and standardize the early treatment of malocclusion. Through scientific and comprehensive stratified clinical practice and professional training, the clinical system of early treatment of malocclusion in China will eventually be perfected, so as to comprehensively care for children's dental and maxillofacial health, and improve their oral and physical health in China.
Child ; China/epidemiology* ; Consensus ; Dental Care ; Humans ; Malocclusion/prevention & control* ; Orthodontics, Interceptive

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.
Area Under Curve ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; China ; Deep Learning ; Humans ; ROC Curve ; Sensitivity and Specificity

Magnoliae Officinalis Cortex（MOC） is a commonly used traditional Chinese medicine（TCM） in China. It is spicy-warm in property and bitter in flavor. It has the effects in eliminating dampness， eliminating phlegm and removing fullness. It is commonly used for dampness obstruction to spleen and stomach， chest and epigastric distension， glutinous grains， nausea， vomiting， abdominal pain and abdominal distension. It has a good efficacy in treating gastrointestinal discomfort and anorexia in clinic. The results showed that MOC mainly contains phenolic compounds， alkaloids and volatile oil. Magnolol， honokiol and other phenolic compounds are the main active substances， with obvious pharmacological activities on digestive， nervous， cardiovascular and respiratory systems. In addition， it also has anti-inflammatory， analgesic， anti-bacterial， anti-tumor and anti-oxidation effects. Except for magnolol and honokiol and other active substances， MOC flowers also contain volatile oil， with a similar effect with MOC but a weaker function. It is mainly used for treating spleen and stomach dampness， fullness， chest and epigastric distension. In addition to magnolol and honokiol and other phenolic compounds， MOC leaves also contain volatile oil， flavonoids and polysaccharides and other chemical components， which have antibacterial， antioxidative， vasodilatory and other pharmacological effects. It can be used as medicine instead of MOC in clinic. In this paper， the pharmacology studies of MOC in recent 5 years was reviewed， in order to better develop and utilize magnolia bark and its waste flowers and leaves， and further develop relevant functional products with MOC as the main drug， while providing new ideas for expanding the resources of TCM.

We have developed a new method using HPLC-CAD (charged aerosol detector) for the quantitative analysis of cyclovirobuxine D and related substances in the API of Huangyangning tablets. The related substances were further studied by HPLC-Q-Exactive coupled with hybrid quadrupole-orbitrap mass spectrometry. A HILIC column of XBridge Amide (4.6 mm×250 mm, 5 μm) was used, and the mobile phase was composed of acetonitrile and 100 mmol·L^-1 ammonium formate (85∶15), which was adjusted to pH 2.8 with formic acid. Isocratic mode elution was adopted at a flow rate of 1.1 mL·min^-1. The column temperature was set at 30 ℃. For CAD, the temperature of atomization and gas pressure were respectively set at 35 ℃ and 62.2 psi. This method detected and quantified five related substances to cyclovirobuxine D. The results showed that the LOD and LOQ of cyclovirobuxine D was 12.588 ng and 28.323 ng, respectively with an average recovery of 95.74% (RSD = 1.79%, n = 6). The content of cyclovirobuxine D in 12 batches of API samples provided by three manufacturers was from 79.94% to 88.49%, with an average value of 82.20%. The total content of the five related substances was from 15.99% to 22.15% with an average value of 20.10%, using an external standard method with cyclovirobuxine D as the reference and according to the CAD uniform response to non-volatile substances. The newly developed HPLC-CAD method has advantages in terms of the comprehensiveness of signals from Buxus alkaloids without UV absorption and with high sensitivity to its trace-related substances; the method yields good separation between the components and is compatible with mass spectrometry. It is applicable for the accurate quantitative analysis of main components and related substances in the API of Huangyangning tablets.

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: = 125; validation dataset, = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
Carcinoma, Hepatocellular ; diagnostic imaging ; Humans ; Liver Neoplasms ; diagnostic imaging ; Magnetic Resonance Imaging ; Neoplasm Grading ; methods ; ROC Curve