BACKGROUND: The mechanisms distinguishing metabolically healthy from unhealthy phenotypes within the same BMI categories remain unclear. This study aimed to investigate the associations between regional fat distribution and metabolically unhealthy phenotypes in Chinese adults across different BMI categories. METHODS: This cross-sectional study involving 11833 Chinese adults aged 20 years and older. Covariance analysis, adjusted for age, compared the percentage of regional fat (trunk, leg, or arm fat divided by whole-body fat) between metabolically healthy and unhealthy participants. Trends in regional fat percentage with the number of metabolic abnormalities were assessed by the Jonckheere-Terpstra test. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated by logistic regression models. All analyses were performed separately by sex. RESULTS: In non-obese individuals, metabolically unhealthy participants exhibited higher percent trunk fat and lower percent leg fat compared to healthy participants. Additionally, percent trunk fat increased and percent leg fat decreased with the number of metabolic abnormalities. After adjustment for demographic and lifestyle factors, as well as BMI, higher percent trunk fat was associated with increased odds of being metabolically unhealthy [highest vs. lowest quartile: ORs (95%CI) of 1.64 (1.35, 2.00) for men and 2.00 (1.63, 2.46) for women]. Conversely, compared with the lowest quartile, the ORs (95%CI) of metabolically unhealthy phenotype in the highest quartile for percent arm and leg fat were 0.64 (0.53, 0.78) and 0.60 (0.49, 0.74) for men, and 0.72 (0.56, 0.93) and 0.46 (0.36, 0.59) for women, respectively. Significant interactions between BMI and percentage of trunk and leg fat were observed in both sexes, with stronger associations found in individuals with normal weight and overweight. CONCLUSIONS Trunk fat is associated with a higher risk of metabolically unhealthy phenotype, while leg and arm fat are protective factors. Regional fat distribution assessments are crucial for identifying metabolically unhealthy phenotypes, particularly in non-obese individuals.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Adipose Tissue ; Body Fat Distribution ; Body Mass Index ; China/epidemiology* ; Cross-Sectional Studies ; Health Surveys ; Phenotype

Patients with periodontal disease often require combined periodontal-orthodontic interventions to restore periodontal health, function, and aesthetics, ensuring both patient satisfaction and long-term stability. Managing these patients involving orthodontic tooth movement can be particularly challenging due to compromised periodontal soft and hard tissues, especially in severe cases. Therefore, close collaboration between orthodontists and periodontists for comprehensive diagnosis and sequential treatment, along with diligent patient compliance throughout the entire process, is crucial for achieving favorable treatment outcomes. Moreover, long-term orthodontic retention and periodontal follow-up are essential to sustain treatment success. This expert consensus, informed by the latest clinical research and practical experience, addresses clinical considerations for orthodontic treatment of periodontal patients, delineating indications, objectives, procedures, and principles with the aim of providing clear and practical guidance for clinical practitioners.
Humans ; Consensus ; Orthodontics, Corrective/standards* ; Periodontal Diseases/complications* ; Tooth Movement Techniques/methods* ; Practice Guidelines as Topic

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

Acute respiratory distress syndrome (ARDS) is a common respiratory emergency, but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures. Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS, but the application of hydrogen has flammable and explosive safety concerns. Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance, thus improving ARDS in patients and animal models. Coral calcium hydrogenation (CCH) is a new solid molecular hydrogen carrier prepared from coral calcium (CC). Whether and how CCH affects acute lung injury in ARDS remains unstudied. In this study, we observed the therapeutic effect of CCH on lipopolysaccharide (LPS) induced acute lung injury in ARDS mice. The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable, demonstrating a significant improvement compared to the untreated ARDS model group. CCH treatment significantly reduced pulmonary hemorrhage and edema, and improved pulmonary function and local microcirculation in ARDS mice. CCH promoted mitochondrial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2 (Trx2), improved lung mitochondrial dysfunction induced by LPS, and reduced oxidative stress damage. The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Comfortable supragingival scaling uses ultrasonic cleaning with sedation and minimally invasive techniques to minimize the negative emotions of patients and improve patient compliance. At present, there is still much room for optimization of the environment, equipment, and operation steps for the development of comfortable supragingival scaling. On this basis, the Department of Periodontology, College of Stomatology, Xi'an Jiaotong University proposed a 16-step new model of comfortable supragingival scaling. The new model incorporates adjustments to the previous model concerning the environment, equipment, operational procedures, process links and services, comprising 16 steps for optimization and innovation. Clinical practice has confirmed that the 16-step new model of comfortable supra-gingival scaling can significantly improve patient satisfaction and adherence to medical treatment, and it has good promotional value.
Humans ; Dental Scaling/instrumentation* ; Patient Satisfaction ; Patient Compliance ; Gingiva

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To analyze three-dimensional imaging characteristics and advantages for severe portal vein stenosis after liver transplantation, and to evaluate clinical efficacy of portal vein stent implantation. Methods Clinical data of 10 patients who received portal vein stent implantation for severe portal vein stenosis after liver transplantation were retrospectively analyzed. Imaging characteristics of severe portal vein stenosis, and advantages of three-dimensional reconstruction imaging and interventional treatment efficacy for severe portal vein stenosis were analyzed. Results Among 10 patients, 3 cases were diagnosed with centripetal stenosis, tortuosity angulation-induced stenosis in 2 cases, compression-induced stenosis in 2 cases, long-segment stenosis and/or vascular occlusion in 3 cases. Three-dimensional reconstruction images possessed advantages in accurate identification of stenosis, identification of stenosis types and measurement of stenosis length. All patients were successfully implanted with portal vein stents. After stent implantation, the diameter of the minimum diameter of portal vein was increased [(6.2±0.9) mm vs. (2.6±1.7) mm, P<0.05], the flow velocity at anastomotic site was decreased [(57±19) cm/s vs. (128±27) cm/s, P<0.05], and the flow velocity at the portal vein adjacent to the liver was increased [(41±6) cm/s vs. (18±6) cm/s, P<0.05]. One patient suffered from intrahepatic hematoma caused by interventional puncture, which was mitigated after conservative observation and treatment. The remaining patients did not experience relevant complications. Conclusions Three-dimensional visualization technique may visually display the location, characteristics and severity of stenosis, which is beneficial for clinicians to make treatment decisions and assist interventional procedures. Timely implantation of portal vein stent may effectively reverse pathological process and improve portal vein blood flow.

Objective:To explore the risk factors for malnutrition after a tracheotomy and to construct a predictive model useful for its prevention through early intervention.Methods:Clinical data describing 440 tracheotomy patients were subjected to a retrospective analysis. The variables examined were age, sex, etiology, Glasgow Coma Score (GCS), activities of daily living (ADL) score, age-corrected Charlson comorbidity index (aCCI), food intake, swallowing function, incidence of infections, as well as any history of diabetes mellitus, hypertension, smoking or alcohol consumption. Patients identified as being at risk of malnutrition (NRS-2002≥3) were screened using the Nutritional Risk Screening tool (NRS-2002) and the European Society of Clinical Nutrition and Metabolism′s ESPEN2015 criteria. The subjects were thus categorized into a malnutrition group of 343 and a control group of 97. Unifactorial and multifactorial logistic regression analyses were performed, and stepwise regression was applied to include the factors found significant in the unifactorial analysis into the multifactorial logistic regression analysis, and to construct a column-line graph prediction model. The clinical utility of the model was assessed by applying the receiver operator characteristics (ROC) curves, calibration plots and decision curve analysis (DCA).Results:Of the 440 persons studied, 343 (78%) were malnourished. The multivariate logistic regression analysis showed that pulmonary infection, dysphagia, low GCS score and high aCCI score were significant risk factors for malnutrition after a tracheotomy. A prediction nomograph was constructed. After fitting and correcting, the area under the curve (AUC) of the prediction model′s ROC curve was 0.911, the specificity was 80.4%, and the sensitivity was 91.3%. That was significantly higher than the AUCs for pulmonary infection (0.809), dysphagia (0.697), aCCI (0.721) and GCS (0.802). Bootstrap self-sampling was used to verify the model internally. After 1000 samples the average absolute error between the predicted risk and the actual risk was 0.013, indicating good prediction ability. The DCA results demonstrated that the model has substantial clinical applicability across a range of nutritional interventions, particularly for threshold probability values ranging from 0 to 0.96.Conclusion:Pulmonary infection, dysphagia, low GCS score, and high aCCI score are risk factors for malnutrition among tracheotomy patients. The nomogram model constructed in this study has good predictive value for the occurrence of malnutrition among such patients.