Following the core outcome set standards for development(COS-STAD), this study aims to construct core outcome set(COS) for clinical research on traditional Chinese medicine(TCM) treatment of simple obesity. Firstly, a comprehensive review was conducted on the randomized controlled trial(RCT) and systematic review(SR) about TCM treatment of simple obesity that were published in Chinese and English databases to collect reported outcomes. Additional outcomes were obtained through semi-structured interviews with patients and open-ended questionnaire surveys for clinicians. All the collected outcomes were then merged and organized as an initial outcome pool, and then a preliminary list of outcomes was formed after discussion by the working group. Subsequently, two rounds of Delphi surveys were conducted with clinicians, methodology experts, and patients to score the importance of outcomes in the list. Finally, a consensus meeting was held to establish the COS for clinical research on TCM treatment of simple obesity. A total of 221 RCTs and 12 SRs were included, and after integration of supplementary outcomes, an initial outcome pool of 141 outcomes were formed. Following discussions in the steering advisory group meeting, a preliminary list of 33 outcomes was finalized, encompassing 9 domains. Through two rounds of Delphi surveys and a consensus meeting, the final COS for clinical research on TCM treatment of simple obesity was determined to include 8 outcomes: TCM symptom scores, body mass index(BMI), waist-hip ratio, waist circumference, visceral fat index, body fat rate, quality of life, and safety, which were classified into 4 domains: TCM-related outcomes, anthropometric measurements, quality of life, and safety. This study has preliminarily established a COS for clinical research on TCM treatment of simple obesity. It helps reduce the heterogeneity in the selection and reporting of outcomes in similar clinical studies, thereby improving the comparability of research results and the feasibility of meta-analysis and providing higher-level evidence support for clinical practice.
Humans ; Obesity/therapy* ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Drugs, Chinese Herbal/therapeutic use*

An electrochemical chemical oxygen demand(COD)sensor was proposed based on a FTO/TiO2/PbO2 electrode and a thin-layer electrochemical cell.The FTO/TiO2/PbO2 electrode was characterized by X-ray photoelectronic spectroscopy(XPS),X-ray diffraction(XRD)spectroscopy and electrochemical technique,and the results indicated that the rapid decrease in the output signals of the electrochemical COD sensor could be attributed to oxidation of PbSO4 occurring on the surface of FTO/TiO2/PbO2 electrode.The PbO2 deposition time and concentration of Na2SO4 were further optimized and then the electrochemical COD sensor was challenged by real samples including laker water sample,river water sample and wastewater sample.The evolution trend of signals of the electrochemical COD sensor in response to lake and river water samples was identical with that obtained with the standard method(HJ/T399-2007,Water quality-determination of the chemical oxygen demand-fast digestion-spectrophotometric method).The electrochemical COD sensor exhibited significant increase in the signal intensity after the samples were switched from lake water to wastewater sample,and a mean value of 32.5 mg/L with relative standard deviation(RSD)of 6.8%were obtained after measuring 45 times the wastewater with COD value of 30 mg/L under a sampling interval of 400 s.The as-prepared electrochemical COD sensor possessed good promise in regular monitoring of COD,discharge of wastewater and industrial process control,with advantages such as a small sampling interval,mild reaction conditions and no requirement of toxic and harmful chemical reagents.

Objective To understand the whole genome sequence characteristics of a Coxsackievirus A4(CVA4)isolated from Yunnan,China in 2022,and explore the phylogenetic characteristics of CVA4.Methods The whole genome sequence of CVA4 isolate 194R3/YN/CHN/2022 was amplified and sequenced,and the phylogenetic tree of CVA4 isolate was constructed by using Mega 7.0,Geneious 9.1.4 and Simplot 3.5.1 softwares.The whole ge-nome sequence characteristics were analyzed.Results The 194R3/YN/CHN/2022 isolate was identified as CVA4,belonging to the C2 gene subtype,which was consistent with the dominant gene subtype in recent years in China.Recombination analysis showed that recombination of CVA4 virus isolate with EVA114 prototype(V13-0285),CVA16 prototype(G-10),and CVA14 prototype(G-14)at the non-structural coding regions of P2 and P3 may have occurred.Conclusion The 194R3/YN/CHN/2022 isolated from Yunnan belongs to the C2 gene subtype,which is the prevalent CVA4 in China,but with certain mutations.

Coronary perforation is when a contrast agent or blood flows outside a blood vessel through a tear in a coronary artery.In this case,we reported a case of percutaneous coronary intervention for coronary calcified lesions,which led to iatrogenic coronary perforation and cardiac tamponade after the use of Shockwave balloon to treat intracoronary calcified nodules,and the management of PCI-related CAP was systematically reviewed through the literature.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

OBJECTIVE To provide reference for the normalization of drug volume-based procurement （hereinafter referred to as “VBP”） and the establishment of local emergency management mechanism. METHODS Based on the equilibrium theory of the whole process of emergency management with Chinese conditions， referring to the “6+1” stage model， the emergency management mechanism of VBP was constructed； 21 provincial-level procurement projects in China from 2019 to 2022 were evaluated accordingly. RESULTS The emergency management mechanism of VBP including 14 measures in 7 stages of preparation， prevention， mitigation， response， recovery， learning and monitoring was successfully constructed. The total score of the 21 provincial-level procurement projects was 3 to 11 points， and the average score of each project in the past 4 years fluctuated slightly within 6 to 7 points. Among the 5 evaluated stages， the scores of the prevention and response stage were relatively high， followed by the mitigation and preparation stage， and the lowest in the recovery stage. Among the 11 rated emergency management measures， more than half had an average score greater than 0.5， but the average score of “determining alternative enterprise selection methods” was only 0.19. CONCLUSIONS Some emergency management measures of drug VBP are lacking， and the emergency management mechanism still needs to be perfected， which is not conducive to the development of emergency management work. The procurement organizer should focus on building a complete emergency management mechanism covering all stages， and clarify the specific measures at each stage， so as to ensure procurement effective and good in operation.

The development of chemoresistance which results in a poor prognosis often renders current treatments for colorectal cancer(CRC).In this study,we identified reduced microvessel density(MVD)and vascular immaturity resulting from endothelial apoptosis as therapeutic targets for overcoming chemoresistance.We focused on the effect of metformin on MVD,vascular maturity,and endothelial apoptosis of CRCs with a non-angiogenic phenotype,and further investigated its effect in overcoming chemoresistance.In situ transplanted cancer models were established to compare MVD,endothelial apoptosis and vascular maturity,and function in tumors from metformin-and vehicle-treated mice.An in vitro co-culture system was used to observe the effects of metformin on tumor cell-induced endothelial apoptosis.Transcriptome sequencing was performed for genetic screening.Non-angiogenic CRC developed inde-pendently of angiogenesis and was characterized by vascular leakage,immaturity,reduced MVD,and non-hypoxia.This phenomenon had also been observed in human CRC.Furthermore,non-angiogenic CRCs showed a worse response to chemotherapeutic drugs in vivo than in vitro.By suppressing endo-thelial apoptosis,metformin sensitized non-angiogenic CRCs to chemo-drugs via elevation of MVD and improvement of vascular maturity.Further results showed that endothelial apoptosis was induced by tumor cells via activation of caspase signaling,which was abrogated by metformin administration.These findings provide pre-clinical evidence for the involvement of endothelial apoptosis and subsequent vascular immaturity in the chemoresistance of non-angiogenic CRC.By suppressing endothelial apoptosis,metformin restores vascular maturity and function and sensitizes CRC to chemotherapeutic drugs via a vascular mechanism.

Objective: Compare and analyze the results of the domestic Lanyi AH600 glycated hemoglobin analyzer and other different detection systems to understand the comparability of the detection results of different detectors, and establish the best cut point of Lanyi AH600 determination of haemoglobin A1c (HbA1c) in the diagnosis of diabetes. Methods: Multi center cohort study was adopted. The clinical laboratory departments of 18 medical institutions independently collected test samples from their respective hospitals from March to April 2022, and independently completed comparative analysis of the evaluated instrument (Lanyi AH600) and the reference instrument HbA1c. The reference instruments include four different brands of glycosylated hemoglobin meters, including Arkray, Bio-Rad, DOSOH, and Huizhong. Scatter plot was used to calculate the correlation between the results of different detection systems, and the regression equation was calculated. The consistency analysis between the results of different detection systems was evaluated by Bland Altman method. Consistency judgment principles: (1) When the 95% limits of agreement (95% LoA) of the measurement difference was within 0.4% HbA1c and the measurement score was≥80 points, the comparison consistency was good; (2) When the measurement difference of 95% LoA exceeded 0.4% HbA1c, and the measurement score was≥80 points, the comparison consistency was relatively good; (3) The measurement score was less than 80 points, the comparison consistency was poor. The difference between the results of different detection systems was tested by paired sample T test or Wilcoxon paired sign rank sum test; The best cut-off point of diabetes was analyzed by receiver operating characteristic curve (ROC). Results: The correlation coefficient R² of results between Lanyi AH600 and the reference instrument in 16 hospitals is≥0.99; The Bland Altman consistency analysis showed that the difference of 95% LoA in Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180) was -0.486%-0.325%, and the measurement score was 94.6 points (473/500); The difference of 95% LoA in the Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant II) was -0.727%-0.612%, and the measurement score was 89.8 points; The difference of 95% LoA in the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT) was -0.231%-0.461%, and the measurement score was 96.6 points; The difference of 95% LoA in the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT) was -0.469%-0.479%, and the measurement score was 91.9 points. The other 14 hospitals, Lanyi AH600, were compared with 4 reference instrument brands, the difference of 95% LoA was less than 0.4% HbA1c, and the scores were all greater than 95 points. The results of paired sample T test or Wilcoxon paired sign rank sum test showed that there was no statistically significant difference between Lanyi AH600 and the reference instrument Arkray HA8180 (Z=1.665,P=0.096), with no statistical difference. The mean difference between the measured values of the two instruments was 0.004%. The comparison data of Lanyi AH600 and the reference instrument of all other institutions had significant differences (all P<0.001), however, it was necessary to consider whether it was within the clinical acceptable range in combination with the results of the Bland-Altman consistency analysis. The ROC curve of HbA1c detected by Lanyi AH600 in 985 patients with diabetes and 3 423 patients with non-diabetes was analyzed, the area under curve (AUC) was 0.877, the standard error was 0.007, and the 95% confidence interval 95%CI was (0.864, 0.891), which was statistically significant (P<0.001). The maximum value of Youden index was 0.634, and the corresponding HbA1c cut point was 6.235%. The sensitivity and specificity of diabetes diagnosis were 76.2% and 87.2%, respectively. Conclusion: Among the hospitals and instruments currently included in this study, among these four hospitals included Nanjing Maternity and Child Health Care Hospital in Jiangsu Province (reference instrument: Arkray HA8180), Tibetan Traditional Medical Hospital of TAR (reference instrument: Bio-Rad Variant Ⅱ), the People's Hospital of Chongqing Liang Jiang New Area (reference instrument: Huizhong MQ-2000PT), and the Taihe Hospital of traditional Chinese Medicine in Anhui Province (reference instrument: Huizhong MQ-2000PT), the comparison between Lanyi AH600 and the reference instruments showed relatively good consistency, while the other 14 hospitals involved four different brands of reference instruments: Arkray, Bio-Rad, DOSOH, and Huizhong, Lanyi AH600 had good consistency with its comparison. The best cut point of the domestic Lanyi AH600 for detecting HbA1c in the diagnosis of diabetes is 6.235%.
Pregnancy ; Child ; Humans ; Female ; Glycated Hemoglobin ; Cohort Studies ; Diabetes Mellitus/diagnosis* ; Sensitivity and Specificity ; ROC Curve

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