A 20-year-old male patient presented to the Department of Dermatology of Peking Union Medical College Hospital with complaints of an 8-year history of facial scarring, swelling of the lower limbs, and a 4-year history of scalp thickening. Physical examination showed thickening furrowing wrinkling of the skin on the face and behind the ears, ciliary body hirsutism, blepharoptosis, and cutis verticis gyrate. Both lower limbs were swollen, especially the knees and ankles. The skin of the palms and soles of the feet was keratinized and thickened. Laboratory examination using bone and joint X-ray showed periostosis of the proximal middle phalanges and metacarpals of both hands, distal ulna and radius, tibia and fibula, distal femurs, and metatarsals.Genetic testing revealed two variants in SLCO2A1, c.1658T > A and c.96+4A > C.Through multidisciplinary consultation, we confirmed the diagnosis of pachydermoperiostosis.

Talents are the main force for the development of traditional Chinese medicine（TCM）， and the construction of TCM talents and the reformation of talent evaluation system are essential to promote the inheritance and innovation of TCM. At present， we are still exploring and developing in the fields of the formulation， implementation and evaluation indicators of TCM talent evaluation system. However， there are shortcomings and difficulties. For instance， insufficient stratification in the evaluation， excessive emphasis on the quantity of achievements， neglecting the quality of the achievements and the actual contribution， imperfect assessment indicators， and the weak characteristics of TCM. Therefore， national ministries and commissions have jointly issued a document requesting to break the four only and set a new standard， in order to promote the construction of a scientific and technological talent evaluation system oriented by innovation value， ability and contribution. For the evaluation of TCM clinical talents， China Association for Science and Technology commissioned China Association of Chinese Medicine to build the China Clinical Cases Library of TCM（CCCL-TCM）， which aims at collecting the most authoritative and representative TCM clinical cases and exploring the advantages of applying clinical cases as masterpiece of achievement in TCM clinical talents evaluation. CCCL-TCM can promote the construction of a talent evaluation system that is more in line with the development characteristics of TCM industry， and to carry out relevant pilot in TCM colleges and institutions across the country in order to promote the reformation of TCM talent evaluation system.

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
Humans ; White Matter/diagnostic imaging* ; Diffusion Tensor Imaging/methods* ; Alzheimer Disease/complications* ; Reproducibility of Results ; Cognition ; Cognitive Dysfunction/complications* ; Brain/diagnostic imaging*

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.
Humans ; Male ; Female ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Deep Learning ; Reproducibility of Results ; Radiography ; Algorithms ; Cone-Beam Computed Tomography

Presynaptic dopaminergic PET imaging is a useful method for the diagnosis of parkinsonism. Based on the expert consensus on operation and clinical application of dopamine transporter brain PET imaging technology published in 2020, this paper further recommends the relevant elements of result interpretation of presynaptic dopaminergic PET imaging.

With the rapid development of clinical trials, the relevant medical research and molecular detection based on biological samples are closely related to the progress of clinical trials, making the role of biological samples in clinical trials increasingly obvious. The standardized supervision mode of biological samples is an important prerequisite for carrying out high-quality clinical trials. Although the laws and regulations related to clinical trials are becoming more and more perfect, there are still a large number of adverse events related to biological samples, which seriously affects the progress and results of clinical trials, and is one of the important challenges currently facing. Therefore, it is urgent to enhance the supervision of biological samples and improve the management methods of biological samples in clinical trials at this stage. Through in-depth discussion of the current status of biological sample management in clinical trials at home and abroad, this paper analyzed the issues existed during the supervision of biological samples, and supplemented the biological sample management methods by further combing the existing relevant laws and regulations and the Guidelines for the Ethical Management of Biological Samples in Clinical Trials, with a view to providing suggestions and ideas for optimizing the management mode of biological samples in clinical trials.

Objective:To explore the differences of risk stratification of very high-risk or extreme high-risk atherosclerotic cardiovascular diseases (ASCVD) and the attainment rates of low-density lipoprotein cholesterol (LDL-C) management targets evaluated by three different criteria, and the causal attributions of these differences.Methods:Patients with ASCVD were consecutively enrolled from January 1 to December 31 in 2019, and were evaluated for very high-risk or extreme high-risk and LDL-C goal attainment rates with 2018 American guideline on the management of blood cholesterol (2018AG), 2019 China Cholesterol Education Program (CCEP) Expert Advice for the management of dyslipidemias (2019EA) and 2020 Chinese expert consensus on lipid management of very high-risk ASCVD patients(2020EC), respectively. The causal attributions of the differences in attainment rates were analyzed as well.Results:A total of 1 864 ASCVD patients were included in this study. According to 2018AG, 2019EA and 2020EC, the proportions of the patients with very high-risk or extreme high-risk were 59.4%, 90.7%, and 65.6%, respectively. The absolute LDL-C target attainment rates were 37.2%, 15.7%, and 13.7%, respectively, the differences between each two rates were statistically significant (all P<0.001). As to the differences in attainment rates between 2020EC and 2018AG, 61.5% were due to the different LDL-C goal attainment values and 38.5% were caused by the different risk stratifications, while for the differences between 2020EC and 2019EA attainment rates, different LDL-C goal attainment values were responsible for 13.2%, and different risk stratifications were responsible for 86.8% of the differences. Conclusions:There are significant differences in the proportions and LDL-C attainment rates among the three different criteria for very high-risk or extreme high-risk ASCVD. 2020EC showed a moderate proportion of patients with extreme high-risk, and had the lowest LDL-C attainment rate. The differences between 2020EC and 2018AG are mainly due to the LDL-C target values, and the differences between 2020EC and 2019EA are mainly caused by the risk stratifications.

Multi-centre clinical trials on PET/CT brain imaging are complex to organize and require careful co-ordination and management. This article describes considerations, which are necessary when designing and starting a multi-centre clinical trial on PET/CT brain imaging, based on guidelines and multi-center clinical brain imaging studies, providing references for further studies.

Due to the availability of 18F-FDG in PET centers, this article aims to advocate and promote the standardization of 18F-FDG PET brain imaging in dementia in order to improve the reliability, repeatability and comparison of the imaging process and results. It is also provided to guide the PET imaging operation standard and to give suggestions on image interpretation.

Objective: To investigate the economic burden of bacillus Calmette-Guérin (BCG) lymphadenitis in Shandong Province. Methods: From May 2011 to December 2019, 304 patients applying for the province-level compensation of BCG lymphadenitis was selected from Shandong Province in this study. The basic situation, vaccination, outpatient (inpatient) records, cost and relevant information of those patients were collected to calculate the direct economic burden (including direct medical costs and direct non-medical costs), indirect economic burden and total economic burden. Comparison of the difference of economic burden of cases with different characteristics was taken. Results: The M(Q₁,Q₃) of age of BCG lymphadenitis patients was 3 (2, 4) months, among which 239 cases (78.6%) were male, 71 cases (23.4%) had lymphadenopathy, and 227 cases (74.7%) underwent surgery.The number of outpatient only, inpatient only and outpatient then inpatient was 25.7% (78 cases), 7.2% (22 cases) and 67.1% (204 cases), respectively. The M(Q₁,Q₃) of direct, indirect and total economic burden of single case after discount was 9 910 (5 713, 16 074), 2 081 (1 547, 3 122) and 12 262 (7 694, 18 571) yuan, respectively.The direct medical expenses accounted for 89.4% of the direct economic burden, the direct economic burden accounted for 84.9% of the total economic burden, the total economic burden of 80.0% cases accounted for only about 20.0% of the compensation amount, and the total economic burden of only 2.3% cases accounted for more than 60.0% of the compensation amount.The direct, indirect and total economic burden of patients with inpatient only and outpatient then inpatient was higher than that of patients with outpatient only; the direct, indirect and total economic burden of patients with operation was higher than that of patients with non-operation; the direct and total economic burden of patients with unulcerated lymph node was higher than that of patients with ulcerated lymph node(all P values<0.05). Conclusion: The economic burden of BCG lymphadenitis cases in Shandong Province is influenced by the mode of diagnosis and treatment, with direct medical expenses as the predominant component.
BCG Vaccine ; Cost of Illness ; Financial Stress ; Humans ; Infant ; Lymphadenitis/epidemiology* ; Male ; Vaccination