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.

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 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.

Pancreatic fibrosis (PF) is primarily distinguished by the stimulation of pancreatic stellate cells (PSCs) and excessive extracellular matrix deposition, which is the main barrier impeding drug delivery and distribution. Recently, nanomedicine, with efficient, targeted, and controllable drug release characteristics, has demonstrated enormous advantages in the regression of pancreas fibrotic diseases. Notably, paracrine signals from parenchymal and immune cells such as pancreatic acinar cells, islet cells, pancreatic cancer cells, and immune cells can directly or indirectly modulate PSC differentiation and activation. The intercellular crosstalk between PSCs and these cells has been a critical event involved in fibrogenesis. However, the connections between PSCs and other pancreatic cells during the progression of diseases have yet to be discussed. Herein, we summarize intercellular crosstalk in the activation of PSCs and its contribution to the development of common pancreatic diseases, including pancreatitis, pancreatic cancer, and diabetes. Then, we also examine the latest treatment strategies of nanomedicine and potential targets for PSCs crosstalk in fibrosis, thereby offering innovative insights for the design of antifibrotic nanomedicine. Ultimately, the enhanced understanding of PF will facilitate the development of more precise intervention strategies and foster individually tailored therapeutic approaches for pancreatic diseases.

Objective:To investigate the expression levels of long non-coding RNA (lncRNA) TCF7 and LSINCT5 in non-small cell lung cancer (NSCLC) tissues and the relationship with clinical pathological characteristics and prognosis of patients.Methods:NSCLC tissues and para-carcinoma tissues specimens of 108 NSCLC patients who underwent single-port thoracoscopic radical resection at Xiaogan Hospital Affiliated to Wuhan University of Science and Technology from June 2020 to October 2021 were collected. Real-time fluorescence quantitative PCR was applied to detect the expression of lncRNA TCF7 and LSINCT5 in NSCLC tissues and para-carcinoma tissues. The relationship between the expression of both and the clinical and pathological characteristics of patients was analyzed. Pearson correlation analysis was used to explore the correlation between the expression of lncRNA TCF7 and LSINCT5 in NSCLC tissues. Kaplan-Meier method was used for survival analysis. Cox proportional hazard regression model was applied to analyze the influencing factors of prognosis of NSCLC patients.Results:The relative expression of lncRNA TCF7 in NSCLC tissues was 1.62±0.53, which was significantly higher than that in para-carcinoma tissues (1.08±0.34, t=8.91, P<0.001). The relative expression of lncRNA LSINCT5 in NSCLC tissues was 1.54±0.48, which was significantly higher than that in para-carcinoma tissues (1.07±0.33, t=8.39, P<0.001). Pearson correlation analysis showed that the expression of lncRNA TCF7 and LSINCT5 in NSCLC tissues was positively correlated ( r=0.41, P<0.001). There were statistically significant differences in the expression of lncRNA TCF7 and LSINCT5 in NSCLC tissues among patients with different TNM stages ( χ2=6.28, P=0.012; χ2=5.40, P=0.020) and histological grades ( χ2=6.31, P=0.012; χ2=7.23, P=0.007). The 2-year survival rate of NSCLC patients with high expression of lncRNA TCF7 ( n=54) was 48.15%, which was significantly lower than that of patients with low expression ( n=54) (72.22%, χ2=6.53, P=0.011). The 2-year survival rate of NSCLC patients with high expression of lncRNA LSINCT5 ( n=55) was 47.23%, which was significantly lower than that of patients with low expression ( n=53) (73.58%, χ2=7.80, P=0.005). Univariate analysis showed that lymph node metastasis ( HR=1.55, 95% CI: 1.11-2.17, P=0.011), TNM stage Ⅲ ( HR=2.15, 95% CI: 1.32-3.48, P=0.002), poorly differentiated histologically grade ( HR=1.39, 95% CI: 1.11-1.73, P=0.004), positive lymph node status ( HR=1.75, 95% CI: 1.37-2.23, P<0.001), maximum diameter of tumor >2 cm ( HR=1.93, 95% CI: 1.09-3.43, P=0.024), high expression of lncRNA TCF7 (≥1.62) ( HR=1.77, 95% CI: 1.41-2.21, P<0.001), high expression of lncRNA LSINCT5 (≥1.54) ( HR=1.54, 95% CI: 1.21-1.97, P<0.001) were associated with prognosis of NSCLC patients who underwent single-port thoracoscopic radical resection. Multivariate analysis showed that TNM stage Ⅲ ( HR=1.25, 95% CI: 1.03-1.53, P=0.026), poor differentiated histologically grade ( HR=1.63, 95% CI: 1.07-2.48, P=0.023), high expression of lncRNA TCF7 ( HR=1.29, 95% CI: 1.03-1.62, P=0.025), high expression of lncRNA LSINCT5 ( HR=1.48, 95% CI: 1.14-1.93, P=0.004) were independent risk factors for prognosis of NSCLC patients. Conclusion:The expressions of lncRNA TCF7 and LSINCT5 are up-regulated in NSCLC tissues, and their expressions are positively correlated. Patients with high expression of lncRNA TCF7 and LSINCT5, patients in TNM stage Ⅲ, and patients with poorly differentiated histologically grade have a high risk of poor prognosis.

Objective To explore the relationship between C1q tumor necrosis factor related protein 13(CTRP13)and gestational diabetes mellitus(GDM),and the predictive value of CTRP13 for adverse pregnancy outcomes in GDM patients.Methods A total of 163 pregnant women who underwent prenatal examination in the Second Affiliated Hospital of Zhengzhou University from January to May 2023 were enrolled in GDM group(n=83)and normal glucose tolerance(NGT)group(n=80)according to the results of oral glucose tolerance test at 24-28 weeks of gestation.Collect general information,biochemical data,and adverse pregnancy outcomes from two groups of subjects.Analyze the correlation between serum CTRP13 and glucose and lipid metabolism indicators in GDM pregnant women.The receiver operating characteristic(ROC)curve was drawn to analyze the predictive value of CTRP13 for adverse pregnancy outcomes in GDM pregnant women.Results The pre pregnancy body mass index,prenatal body mass index,triglycerides,total cholesterol,low density lipoprotein,fasting blood glucose,2-hours plasma glucose,glycosylated hemoglobin,fasting insulin,and homeostasis model assessment of insulin resistanc index in GDM group was significantly higher than those in NGT group,while CTRP13 was significantly lower than that in NGT group(P＜0.05).Serum CTRP13 was negatively correlated with total cholesterol,triglycerides,low density lipoprotein,2-hours plasma glucose,and glycosylated hemoglobin in GDM patients(P＜0.05).The incidence of adverse pregnancy outcomes such as gestational hypertension and premature rupture of membranes in GDM group were significantly higher than those in NGT group(P＜0.05).The area under the curve of serum CTRP13 for predicting adverse pregnancy outcomes in GDM pregnant women was 0.805,with sensitivity and specificity of 75.80％and 76.00％,respectively.Conclusion Serum CTRP13 is associated with glucose and lipid metabolism in GDM pregnant women,and is a potential biomarker for predicting adverse pregnancy outcomes in GDM pregnant women.

Hepatic stellate cell(HSC)activation is a key link in the development of liver fibrosis.The metabolic reprogramming of activated HSC has become a hot topic in current research,especially the change of glycolysis is an important factor in regulating HSC activation.Based on the metabolic reprogramming in the process of HSC activation,this paper expounds the mechanism of regulating HSC activation and liver fibrosis through glycolysis,and reviews the research progress of traditional Chinese medicine and its active ingredients in regulating HSC glycolysis to prevent and treat liver fibrosis.Liver fibrosis is a complex pathological process involving multiple factors and pathways.From the perspective of regulating the glycolysis of activated HSC,it can provide a new idea for the development of anti-liver fibrosis drugs.