Objective To explore the current status of H.pylori infection in the natural population of Sanya City,analyze its influencing factors,and provide a reference basis for the prevention and control of H.pylori infection.Methods A total of 677 residents from four districts of Sanya City were selected by overall stratified random sampling method,and were subjected to urea 14C breath test and questionnaire survey to calculate the positive rate of H.pylori in the natural population and analyze the influencing factors of H.pylori infection.Results A total of 606 residents were included,and the number of H.pylori positive detections was 261,with a positive detection rate of 38.5％.Among them,different ethnicity,marital status,smoking,eating vegetables and fruits,and literacy level were associated with H.pylori infection(P＜0.05);gender,age,BMI,alcohol consumption,drinking water source,betel quid chewing,and the number of cohabitants were not significantly associated with H.pylori infection(P＞0.05).Family infection was an independent risk factor for H.pylori infection in the natural population of Sanya City,and Li ethnicity,frequent consumption of fruits and vegetables,and college and higher education level were independent protective factors for H.pylori infection in the natural population of Sanya City.Conclusion The rate of H.pylori infection in the natural population of Sanya City is lower than the national average.Consuming more fruits and vegetables and improving the awareness of hygiene protection are conducive to the prevention of H.pylori infection;and the promotion of the family and related members with the same examination and treatment is important to avoid aggregation of infection within the family.

Objective To investigate the structural characteristics of intestinal flora in children with sepsis and its association with inflammatory response.Methods A prospective cohort study was conducted.The children with sepsis who were admitted from December 2021 to January 2023 were enrolled as the sepsis group,and the children with non-sepsis who were admitted during the same period were enrolled as the non-sepsis group.The two groups were compared in terms of the distribution characteristics of intestinal flora,peripheral white blood cell count(WBC),C reactive protein(CRP),and cytokines,and the correlation of the relative abundance of fecal flora with WBC,CRP,and cytokines was analyzed.Results At the genus level,compared with the non-sepsis group,the sepsis group had significantly lower relative abundance of Akkermansia,Ruminococcus,and Alistipes and significantly higher relative abundance of Enterococcus,Streptococcus,and Staphylococcus(P<0.05).At the phylum level,Proteobacteria was the dominant phylum(37.46%)in the group of children with a score of≤70 from the Pediatric Critical Illness Score(PICS),and Firmicutes was the dominant phylum in the group of children with a score of 71-80 or 81-90 from the PICS(72.20%and 43.88%,respectively).At the genus level,among the 18 specimens,5 had a relative abundance of>50%for a single flora.Compared with the non-sepsis group,the sepsis group had significant higher levels of WBC,CRP,interleukin-6(IL-6),interleukin-10(IL-10),and tumor necrosis factor-α(P<0.05).The Spearman's rank correlation analysis showed that at the genus level,the relative abundance of Ruminococcus,Alistipes,and Parasutterella in the sepsis group was negatively correlated with the levels of WBC,CRP,and IL-6(P<0.05);the relative abundance of Enterococcus was positively correlated with the CRP level(P<0.01);the relative abundance of Streptococcus and Staphylococcus was positively correlated with the levels of CRP and IL-6(P<0.05);the relative abundance of Streptococcus was positively correlated with WBC(P<0.05).Conclusions Intestinal flora disturbance is observed in children with sepsis,and its characteristics vary with the severity of the disease.The structural changes of intestinal flora are correlated with inflammatory response in children with sepsis.

Objectives: To investigated the safety and efficacy of treating patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) and elevated levels of N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP) with levosimendan within 24 hours of first medical contact (FMC). Methods: This multicenter, open-label, block-randomized controlled trial (NCT03189901) investigated the safety and efficacy of levosimendan as an early management strategy of acute heart failure (EMS-AHF) for patients with NSTEMI and high NT-proBNP levels. This study included 255 patients with NSTEMI and elevated NT-proBNP levels, including 142 males and 113 females with a median age of 65 (58-70) years, and were admitted in the emergency or outpatient departments at 14 medical centers in China between October 2017 and October 2021. The patients were randomly divided into a levosimendan group (n=129) and a control group (n=126). The primary outcome measure was NT-proBNP levels on day 3 of treatment and changes in the NT-proBNP levels from baseline on day 5 after randomization. The secondary outcome measures included the proportion of patients with more than 30% reduction in NT-proBNP levels from baseline, major adverse cardiovascular events (MACE) during hospitalization and at 6 months after hospitalization, safety during the treatment, and health economics indices. The measurement data parameters between groups were compared using the t-test or the non-parametric test. The count data parameters were compared between groups using the χ² test. Results: On day 3, the NT-proBNP levels in the levosimendan group were lower than the control group but were statistically insignificant [866 (455, 1 960) vs. 1 118 (459, 2 417) ng/L, Z=-1.25,P=0.21]. However, on day 5, changes in the NT-proBNP levels from baseline in the levosimendan group were significantly higher than the control group [67.6% (33.8%,82.5%)vs.54.8% (7.3%,77.9%), Z=-2.14, P=0.03]. There were no significant differences in the proportion of patients with more than 30% reduction in the NT-proBNP levels on day 5 between the levosimendan and the control groups [77.5% (100/129) vs. 69.0% (87/126), χ²=2.34, P=0.13]. Furthermore, incidences of MACE did not show any significant differences between the two groups during hospitalization [4.7% (6/129) vs. 7.1% (9/126), χ²=0.72, P=0.40] and at 6 months [14.7% (19/129) vs. 12.7% (16/126), χ²=0.22, P=0.64]. Four cardiac deaths were reported in the control group during hospitalization [0 (0/129) vs. 3.2% (4/126), P=0.06]. However, 6-month survival rates were comparable between the two groups (log-rank test, P=0.18). Moreover, adverse events or serious adverse events such as shock, ventricular fibrillation, and ventricular tachycardia were not reported in both the groups during levosimendan treatment (days 0-1). The total cost of hospitalization [34 591.00(15 527.46,59 324.80) vs. 37 144.65(16 066.90,63 919.00)yuan, Z=-0.26, P=0.80] and the total length of hospitalization [9 (8, 12) vs. 10 (7, 13) days, Z=0.72, P=0.72] were lower for patients in the levosimendan group compared to those in the control group, but did not show statistically significant differences. Conclusions: Early administration of levosimendan reduced NT-proBNP levels in NSTEMI patients with elevated NT-proBNP and did not increase the total cost and length of hospitalization, but did not significantly improve MACE during hospitalization or at 6 months.
Male ; Female ; Humans ; Aged ; Natriuretic Peptide, Brain ; Simendan/therapeutic use* ; Non-ST Elevated Myocardial Infarction ; Heart Failure/drug therapy* ; Peptide Fragments ; Arrhythmias, Cardiac ; Biomarkers ; Prognosis

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

Humans ; Aortic Valve/surgery* ; Aortic Valve Stenosis/surgery* ; Calcinosis/surgery* ; Transcatheter Aortic Valve Replacement ; Heart Valve Prosthesis

Humans ; Aortic Valve/surgery* ; Aortic Valve Stenosis/surgery* ; Calcinosis/surgery* ; Transcatheter Aortic Valve Replacement ; Heart Valve Prosthesis

OBJECTIVE: To explore the clinical significance of magnetic resonance imaging water-fat separation (Dixon) technique in patients with multiple myeloma. METHODS: A total of 41 newly diagnosed patients with multiple myeloma who underwent Dixon in The Affiliated Hospital of Qingdao University from April 2019 to April 2021 were included in this study. Patients were divided into observation group and control group according to whether Dixon performance was normal or not. The differences of clinical data and fat fraction (FF) between the two groups were compared. The correlation between FF and clinical data, disease stages and differences before and after treatment were also compared. The receiver operator characteristic curve of patients was drawn to analyze the diagnostic value of FF combined with serum alkaline phosphatase for bone destruction in patients with multiple myeloma. RESULTS: Among the 41 patients, there were 12 cases in the control group and 29 cases in the observation group. There was no significant difference in age and sex between the two groups. In the observation group, β2-microglobulin concentration and M protein were significantly higher than those in the control group, while serum alkaline phosphatase and FF were lower (P<0.05). In all 41 patients included in the study, there was a significant negative correlation between FF value and β2-microglobulin concentration (r=-0.57), and a significant positive correlation between FF value and serum alkaline phosphatase (r=0.31). After treatment, FF value increased, while myeloma cell percentage, β2-microglobulin concentration and M protein decreased in 11 patients who completed 4 cycles of chemotherapy, and the differences before and after treatment were statistically significant (P<0.05). The value of serum alkaline phosphatase combined with FF value in predicting bone destruction is higher than that of FF value or serum alkaline phosphatase alone. CONCLUSION Dixon's different imaging manifestations can reflect the severity of the disease. FF value is correlated with clinical examination results and R-ISS staging, and there is a significant difference before and after treatment. Serum alkaline phosphatase combined with FF value is better than two indicators alone in predicting bone destruction.
Humans ; Magnetic Resonance Imaging ; Multiple Myeloma/diagnostic imaging* ; Technology ; Water

BACKGROUND: MAVERIC (Mitral Valve Repair Clinical Trial) validates the safety and efficacy of the ARTO system. We here report the first two successful cases of utilizing the ARTO system in patients with symptomatic heart failure (HF) with functional mitral regurgitation (FMR) in Asia. METHODS: Two patients, aged 70 and 63, had severe HF with FMR. Transesophageal echocardiography confirmed that the left ventricular ejection fractions were less than 50% with severe mitral regurgitation (MR) in both patients. Optimizing drug treatment could not mitigate their symptoms. Therefore, we used the ARTO system to repair the mitral valve for these patients on March 5 and 6, 2019, respectively. RESULTS: Mitral valve repairs using the ARTO system were successfully performed under general anaesthesia for these two patients. MR was decreased immediately after the procedures in both patients. The 30-day and 3-month transthoracic echocardiography (TTE) revealed a moderate to severe MR in both patients, and the New York Heart Association (NYHA) scales were also partially improved. CONCLUSION The first two cases in Asia indicate that the ARTO system is feasible for patients with heart failure with FMR, and the patient selection appears to be crucial.