Amantadine(AMD)residue can accumulate in organisms through the food chain and cause serious harm to human body.AMD can specifically bind to AMD specific aptamer and cause its conformation to change from a random single strand to a stem-loop structure.To avoid the influence of excess nucleotides on binding of aptamer to AMD,the truncation of the AMD original aptamer J was optimized by retaining an appropriate stem-loop structure,and a new type of truncation aptamers was developed in this work.By comparing the truncated aptamer with the original aptamer,it was found that the truncated aptamer J-7 had better affinity and specificity with AMD.The detection limit of AMD was 0.11 ng/mL by using J-7 as specific recognition element and molybdenum disulfide nanosheet(MoS2Ns)as signal amplification element.The developed method base on truncated aptamer J-7 was used for detection of AMD in milk,yogurt and SD rat serum samples for the first time with recoveries of 86.6%-108.2%.This study provided a reference for truncating other long sequence aptamers and provided a more sensitive detection method for monitoring AMD residues in food.

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/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

Aim To investigate the protective effect of mGluR5 activated by VU0360172 on germinal matrix hemorrhage in neonatal rats.Methods Seven day- old SD rats were randomly divided into Sham, GMH, and low-, medium-, and high-dose groups.The model was established by intracerebral injection of collagenase W-S.Then three doses of VU0360172 were injected intraperitoneal^ 3 h after surgery.Sham and GMH group were given the same amount of solvent.Neurobe- havioral tests were performed 24 h after surgery.Then the brain tissues were collected for evaluation of brain water content, brain hemoglobin content and HE stai¬ning.The expressions of Bcl-2 and cleaved-caspase-3 were determined by Western blot.Results Compared with Sham, GMH group had pooler behaviors in neuro- functional tests with increased brain water content and brain hemoglobin content (P < 0.01 ).And brain tis¬sues were destroyed significantly.WB results showed the expression level of Bcl-2 decreased ( P < 0.05 ) , while cleaved-caspase-3 being up-regulated ( P < 0.01).However, the administration of VU0360172 improved neurological function and ameliorated brain edema and hemorrhage ( P <0.01 ).Brain pathologi¬cal damage was reduced.Moreover, the stimulation of mGluR5 up-regulated Bcl-2 protein expression ( P < 0.05 ) and decreased the level of cleaved-caspase-3 ( P <0.01 ).Conclusion Activation of mGluR5 by VIJ0360172 protects against germinal matrix hemor¬rhage in neonatal rats.

This study aims to acetylate Rehmannia glutinosa polysaccharides by acetic anhydride method, optimize process parameters and evaluate their antioxidant activity. With the degree of substitution(D_s) as a criterion, the effects of reaction time, acetic anhydride-to-polysaccharides ratio and temperature were investigated. Process parameters were optimized by single-factor experiment and response surface methodology. The infrared spectroscopy(IR) and scanning electron microscopy(SEM) proved the successful acetylation and were employed to preliminarily analyze the structural characteristics of acetylated derivatives. The results showed that the D_s was 0.327 under the optimal technological conditions, including m(acetic anhydride):m(R. glutinosa polysaccharides)=2.70, reaction time 3.0 h and temperature 48 ℃. Further, the antioxidant properties of acetylated derivatives were investigated in vitro and acetylation was found effective to improve the antioxidant activity of R. glutinosa polysaccharides. This study provides a reference for the further development and application of R. glutinosa polysaccharides.
Acetylation ; Antioxidants/pharmacology* ; Polysaccharides/pharmacology* ; Rehmannia/chemistry*

Objective: To investigate the current situation of influenza vaccination, vaccination willingness, recommended behavior and influencing factors of health care workers (HCWs) under the policy of free vaccination. Methods: A cross-sectional survey was conducted among 3 167 medical staff from 8 hospitals in Nanshan district of Shenzhen city based on a web-based questionnaire platform. The logistic regression was used to analyze the data. Results: The influenza vaccination rate in HCWs was 23.97%, and the recommendation rate was 25.69% from 2019 to 2020. Staff with high professional titles, high academic qualifications, and positive awareness about influenza vaccine had a higher vaccination rate. The main reasons for not recommending influenza vaccine were the fear of patients' misunderstanding of commercial benefits, fear of possible disputes caused by recommended vaccination, lack of national or institutional requirements for recommended influenza vaccine, and fear of adverse reactions of influenza vaccines. Conclusion: Under the free policy, the influenza vaccination rate and recommendation rate of HCWs in Nanshan district of Shenzhen city are relatively low. Strengthening health education on influenza and related knowledge, publicizing the policy of free influenza vaccination, providing convenient vaccination services and promoting the construction of relevant policies and regulations are the key to improve the influenza vaccination rate and recommendation rate among HCWs.
Humans ; Influenza Vaccines ; Influenza, Human/prevention & control* ; Vaccination Coverage ; Cross-Sectional Studies ; Attitude of Health Personnel ; Vaccination ; Health Personnel ; Surveys and Questionnaires ; Policy

Objective: The relationship between serum uric acid (SUA) levels and glycemic indices, including plasma glucose (FPG), 2-hour postload glucose (2h-PG), and glycated hemoglobin (HbA1c), remains inconclusive. We aimed to explore the associations between glycemic indices and SUA levels in the general Chinese population. Methods: The current study was a cross-sectional analysis using the first follow-up survey data from The China Cardiometabolic Disease and Cancer Cohort Study. A total of 105,922 community-dwelling adults aged ≥ 40 years underwent the oral glucose tolerance test and uric acid assessment. The nonlinear relationships between glycemic indices and SUA levels were explored using generalized additive models. Results: A total of 30,941 men and 62,361 women were eligible for the current analysis. Generalized additive models verified the inverted U-shaped association between glycemic indices and SUA levels, but with different inflection points in men and women. The thresholds for FPG, 2h-PG, and HbA1c for men and women were 6.5/8.0 mmol/L, 11.0/14.0 mmol/L, and 6.1/6.5, respectively (SUA levels increased with increasing glycemic indices before the inflection points and then eventually decreased with further increases in the glycemic indices). Conclusion An inverted U-shaped association was observed between major glycemic indices and uric acid levels in both sexes, while the inflection points were reached earlier in men than in women.
Aged ; Asian Continental Ancestry Group ; Blood Glucose/analysis* ; China/epidemiology* ; Cohort Studies ; Diabetes Mellitus/blood* ; Female ; Glucose Tolerance Test ; Glycated Hemoglobin A/analysis* ; Glycemic Index ; Humans ; Male ; Middle Aged ; Uric Acid/blood*

Objective:To identify 24 Rana species such as Rana dybowskii by mitochondrial cytochrome C oxidase subunit I （COⅠ） gene-based DNA barcoding and build the neighbour-joining （NJ） tree for hierarchical cluster analysis， so as to provide a basis for the identification and classification of Rana species as well as the discovery of new species. Method:R. dybowskii， R. chensinensis， R. amurensis， R. culaiensis， and R. huanrenesis， ten for each species， were collected for DNA extraction and polymerase chain reaction （PCR） amplification and sequencing. A total of 50 COⅠ gene sequences were obtained. Then 163 COⅠ gene sequences for 24 species of Rana and one COⅠ gene sequence for Pelophylax， Odorrana， Nidirana， Hylarana， and Amolops were harvested from GenBank. After sequence alignment by MEGA X， the parsimony-informative sites of COⅠ gene sequences were analyzed and the intraspecific and interspecific genetic distances were calculated， followed by the built of NJ tree and hierarchical cluster analysis. Result:The COⅠ gene sequences of 24 Rana species including R. dybowskii were 554 bp in length and there were 210 parsimony-informative sites in total. The intraspecific genetic distance of each species was smaller than 2%. Except that the interspecific genetic distance between R. sangzhiensis and R. zhengi was 0.004， the genetic distances between the other species ranged from 0.024 to 0.228. R. sangzhiensis and R. zhengi were clustered into one branch and some R. dybowskii and R. uenoi into one branch. There were two separate branches for R. chensinensis and the other species were all clustered independently. Conclusion:COⅠ-based DNA barcoding enabled the identification of 24 species of Rana including R.dybowskii. The findings supported that R. sangzhiensis， R. zhengi， R. coreana， and R. kunyuensis were the same species. One branch of R. chensinensis might be one of the four undownloaded species in Ranidae or a new species. The results have demonstrated that COⅠ-based DNA barcoding allows not only the identification of 24 species of Rana including R. dybowskii but also the classification of ranidae species and the discovery of new species or subspecies.

OBJECTIVE: To provide clinical basis for the diagnosis and treatment of chronic neutrophilic leukemia (CNL) and to provide possible molecular targets for the treatment. METHODS: By summarizing the clinical data of 14 patients with CNL, the clinical characteristics, gene mutation types and possible prognostic factors were analyzed. RESULTS: Among the 14 patients with CNL, males (9 cases) were more than females (5 cases), with a median age of 57 years old. The detection rate of CSF3R mutation was 92.86% (13/14), including 12 cases (85.71%) with T318I mutation and 1 case of Y799X mutation, and only 1 case was not detected for mutation of CSF3R. The ASXL1 mutation was detected in 42.86% (6/14) of the patients, all of which were nonsense mutations, including 4 cases with R693X and 2 cases with E705X, and 14.29% (2/14) of the patients was detected for SETBP1 mutation, all of which were with D868N mutation. No patients with simultaneous ASXL1 and SETBP1 mutations were found, and JAK2 and CALR mutations were not detected. All of the patients had normal karyotypes. These patients' median survival time was 30 months (95%CI 13.19-46.80), and the influence of age over 60 years old was statistically significant (21.83 months vs 35.35 months) (P＜0.05). CONCLUSION It is difficult to diagnose CNL. CSF3R T618I mutation is its specific mutation, and ASXL1 mutation and SETBP1 mutation have auxiliary diagnostic significance for CNL. The age＞60 years old at diagnosis is a factor of unfavourable prognosis.

Objective To analyze the correlation between echocardiography report narratives and the risk level of congenital heart disease in children,and to validate the feasibility and value of employing text mining technique in such task.Methods Echocardiography reports were retrospectively analyzed for 1 042 children with congenital heart disease.We adopted natural language processing (NLP) technique to generate features from the clinical narratives for machine learning algorithms.Decision trees were trained to predict the risk level of patients.Model performance was evaluated by means of classification accuracy and normalized mean absolute error (NMAE),which were averaged among 50 rounds of stratified 10-fold cross validation.By analyzing branches of the decision tree,we formulated the possible decision path of a clinician and identifyied the key information in the clinical narratives.Results Compared with the auto-generated 3-grams,the selected features yielded a better performance.After feature selection,the predict accuracy was improved from 32.82％ to 48.57％,while the NMAE reduced from 0.33 to 0.25.Conclusions Based on echocardiography report narratives,the risk levels of congenital heart disease in children can be evaluated by our model with an accuracy level of 75 ％.Echocardiographic terms that describe the lesion provide significant information to support the clinical decision making.