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*

Objective Most patients with chronic insomnia depend on long-term medication， which may easily lead to a poor treatment outcome and adverse drug reactions， and transcranial alternating current stimulation （tACS）， as a noninvasive neuromodulation technique， can improve chronic insomnia. This study aims to investigate the clinical efficacy of tACS combined with pharmacotherapy in the treatment of chronic insomnia. Methods A total of 46 patients with chronic insomnia were enrolled and randomly divided into pharmacotherapy group with 20 patients and pharmacotherapy+tACS treatment group with 26 patients. The tACS electrodes were attached to the frontal region and the bilateral mastoids， with a frequency of 77.5 Hz and a current intensity of 15 mA， for 40 minutes each time， once a day for 10 consecutive days. The primary outcome measures were Pittsburgh Sleep Quality Index （PSQI） score and its improvement rate after 4 weeks， and the secondary outcome measures included the scores of Hamilton Depression Rating Scale （HAMD）， Hamilton Anxiety Rating Scale （HAMA）， Mini-Mental State Examination （MMSE）， and Montreal Cognitive Assessment （MoCA） and their improvement rates. Results Compared with the pharmacotherapy group， the pharmacotherapy+tACS treatment group had a significant reduction in PSQI score （P<0.05）， with an improvement rate of 37% and 21%， respectively， suggesting that the combined therapy had a better effect in improving sleep quality. The pharmacotherapy+tACS treatment group also had reductions in HAMA and HAMD scores， suggesting improvements in anxiety and depression symptoms， and there were no significant differences in MMSE and MoCA scores between the two groups. Conclusion Pharmacotherapy combined with tACS has a better effect than pharmacotherapy alone in improving sleep quality and anxiety and depression symptoms in patients with chronic insomnia， and therefore， it has good application prospects in clinical practice.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is the predominant form of chronic liver disease worldwide. This study was designed to investigate the proportion and characteristics of MAFLD within the general Chinese population and to identify the contributory risk factors for liver fibrosis among MAFLD individuals. METHODS: The participants were recruited from a cohort undergoing routine health evaluations at the Third Hospital of Hebei Medical University between May 2019 and March 2023. The diagnosis of MAFLD was based on the established clinical practice guidelines. The fibrosis-4 index score (FIB-4) was employed to evaluate hepatic fibrosis, with a FIB-4 score of ≥1.3 indicating significant fibrosis. Binary logistic regression analyses were used to determine risk factors associated with significant hepatic fibrosis in MAFLD. RESULTS: A total of 22,970 participants who underwent comprehensive medical examinations were included in the analysis. The overall proportion of MAFLD was 28.77% (6608/22,970), with 16.87% (1115/6608) of these patients showing significant fibrosis as assessed using FIB-4. Independent risk factors for significant liver fibrosis in MAFLD patients were male (odds ratio [OR] = 0.676, 95% confidence interval [CI]: 0.558-0.821), hepatitis B surface antigen (HBsAg) positivity (OR = 2.611, 95% CI: 1.557-4.379), body mass index ≥23.00 kg/m 2 (OR = 0.632, 95% CI: 0.470-0.851), blood pressure ≥130/85 mmHg (OR = 1.885, 95% CI: 1.564-2.272), and plasma glucose ≥5.6 mmol/L (OR = 1.815, 95% CI: 1.507-2.186) (all P <0.001). CONCLUSIONS The proportion of MAFLD in an urban Chinese population is 28.77%. About 16.87% of MAFLD patients presented with significant liver fibrosis. Independent risk factors for significant liver fibrosis in MAFLD patients should be noticed.
Humans ; Male ; Female ; Liver Cirrhosis/pathology* ; Middle Aged ; Risk Factors ; Adult ; Fatty Liver/pathology* ; Aged ; China/epidemiology* ; Logistic Models ; Urban Population ; East Asian People

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To investigate associations between cardiac biomarkers with stroke severity and short-term outcome in patients with acute ischemic stroke (AIS).Methods:Patients with AIS admitted to the Affiliated Hospital of Qingdao University from June 2018 to February 2024 whose etiological classification was large artery atherosclerosis (LAA), small vessel occlusion (SVO) or cardioembolism (CE) were included retrospectively. According to the National Institutes of Health Stroke Scale score at admission, patients were divided into mild stroke group (≤8) and moderate to severe stroke group (>8). According to the modified Rankin Scale score at discharge, patients were divided into good outcome group (≤2) and poor outcome group (>2). Multivariate logistic regression analysis was used to determine the independent correlation between cardiac biomarkers and short-term outcome. The predictive value of cardiac biomarkers for poor outcome in patients with AIS and different stroke etiology subtypes were evaluated using receiver operating characteristic (ROC) curves. Results:A total of 2 151 patients with AIS were enrolled, including 1 256 males (58.4%), aged 67.40±11.34 years. 1 079 patents were LAA type (50.2%), 679 were SVO type (31.6%), and 393 were CE type (18.3%); 1 223 were mild stroke (56.86%) and 928 (43.14%) were moderate to severe stroke; 1 357 patients (63.09%) had good short-term outcome, and 794 (36.91%) had poor short-term outcome. Multivariate logistic regression analysis showed that N-terminal pro-B type natriuretic peptide (NT-proBNP), NT-proBNP/creatine kinase (CK) isoenzyme MB (CK-MB) ratio, and CK-MB/CK ratio were independent risk factors for poor short-term outcome. ROC curve analysis shows that the CK-MB/CK ratio had a higher predictive value for short-term poor outcome in patients with AIS (the area under the curve, 0.859, 95% confidence interval 0.839-0.879). Various cardiac biomarkers had a higher predictive value for short-term outcome of CE type and LAA type, but the predictive value for short-term outcome of SVO type was lower. Conclusions:Cardiac biomarkers are associated with the severity and poor outcome of AIS. NT-proBNP/CK-MB and CK-MB/CK ratios have higher predictive value for short-term poor outcome of AIS, especially in patients with CE type.

Post traumatic depression (PTD) is a serious complication after traumatic brain injury, with high incidence rate; PTD seriously affects the rehabilitation, outcome and quality of life of patients. Due to unclear pathogenesis of PTD, effective treatments have not yet been found in clinical practice. Repetitive transcranial magnetic stimulation (rTMS), as a new non-invasive neuroregulatory technique, has been used in major depression disorder (MDD). Few clinical evidence on PTD treated by rTMS is noted and optimal rTMS treatment regimen has not yet been defined.This article reviews the clinical studies of rTMS in PTD in recent years, with a view to provide references for clinical application.

Objective To study the expression levels of serum complement C1q/tumor necrosis factor-related protein 6(CTRP6)in women in early pregnancy and to explore its relationship with gestational diabetes mellitus(GDM).Methods Women at the Second Affiliated Hospital of Zhengzhou University from March 2021 to March 2022 were prospectively and consecutively selected from 10 to 13 weeks gestation for outpatient obstetric check-ups.The age,height,weight,and time of last menstruation of pregnant women were collected,and the levels of total cholesterol(TC),triglyceride(TG),high density lipoprotein(HDL),low density lipoprotein(LDL),fasting plasma glucose(FPG),glycosylated hemoglobin(HbA1c),fasting insulin(FINS)and CTRP6 were measured in early pregnancy,and the pre-pregnancy body mass index(BMI),baseline BMI,prenatal BMI,and homeostatic model assessment of insulin resistance(HOMA-IR)were calculated.All pregnant women underwent a 75g oral glucose tolerance test at 24-28 weeks of gestation and were divided into GDM group and normal glucose tolerance(NGT)group according to the test results.The clinical data and laboratory indexes of the two groups in early pregnancy were compared,and the correlation between serum CTRP6 and various indexes in early pregnancy and its relationship with GDM were analyzed.Results A total of 213 maternal cases were enrolled,and 203 cases were completed for follow-up.Among them,52 mothers were diagnosed with GDM,with a GDM prevalence rate of 25.62%.Serum CTRP6,age,pre-pregnancy BMI,baseline BMI,antenatal BMI,TC,LDL,FPG,HbA1c,FINS,and HOMA-IR were higher in GDM group compared to NGT group,with a statistically significant difference(P<0.05).CTRP6 in early pregnancy was positively correlated with age,pre-pregnancy BMI,baseline BMI,prenatal BMI,TG,LDL,FPG,HbA1c,FINS,HOMA-IR,and negatively correlated with HDL(P<0.05).After correcting for age,BMI,glycolipid metabolism index and HOMA-IR,CTRP6 in early pregnancy remained an independent factor in the development of GDM.Conclusion Elevated serum CTRP6 in early pregnancy is associated with GDM and is an independent risk factor for GDM.

Abstract Tobacco use is not only harmful to adolescents physical and mental health, but is also closely related to future adults smoking prevalence, and interventions can delay the age at which adolescents take their first puff as much as possible. The article summarizes and analyzes the effectiveness of tobacco control interventions based on schools, communities or families, and a combination of multiple venues. It compares the characteristics of offline, online, and a combination of offline and online tobacco control interventions for adolescents,so as to provide references for the design and implementation of adolescent tobacco control interventions, and the effective, sustainable, and replicable adolescent tobacco control intervention models.