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:To investigate the association of serum γ-glutamyltransferase (GGT) with stroke severity and outcome in patients with acute ischemic stroke (AIS).Methods:Patients with AIS admitted to the Department of Neurology, Hefei Second People's Hospital (Guangde Road Branch) from January 2023 to December 2023 were included retrospectively. According to the baseline National Institutes of Health Stroke Scale score, the patients were divided into mild stroke group (≤8) and moderate to severe stroke group (>8); According to the modified Rankin Scale score at 3 months after onset, the patients were divided into a good outcome group (0-2) and a poor outcome group (>2). Multivariate logistic regression analysis was used to determine the independent influencing factors of stroke severity and outcome, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of serum GGT for poor outcome. Results:A total of 136 patients with AIS were included, with 42 patients (30.88%) in the mild stroke group and 94 (69.12%) in the moderate to severe stroke group; 80 patients (58.82%) in the good outcome group and 56 (41.18%) in the poor outcome group. Multivariate logistic regression analysis showed that higher serum GGT was an independent related factor for moderate to severe stroke (odds ratio [ OR] 1.075, 95% confidence interval [ CI] 1.017-1.135; P<0.05) and poor outcome ( OR 1.131, 95% CI 1.069-1.197; P<0.05). The ROC curve analysis showed that the area under the curve for predicting poor outcome by serum GGT was 0.820 (95% CI 0.747-0.892). Conclusion:Serum GGT is significantly correlated with the severity of stroke in patients with AIS, and has certain predictive value for poor short-term outcome.

Objectives:To evaluate the predictive value of Tabular Prior-data Fitted Network(TabPFN) for short-term outcome in patients with spontaneous intracerebral hemorrhage (sICH), and compared with the Extreme Gradient Boosting (XGboost) model and traditional logistic regression (LR) model. Methods:Patients with sICH admitted to the Department of Neurology, Hefei Second People's Hospital from January 2018 to March 2024 were included retrospectively. The demographic and baseline data were collected. At 3 months after onset, the modified Rankin Scale score was used to determine the outcome, 0-2 was defined as good outcome and >2 was defined as poor outcome. All enrolled patients were randomly divided into a training set and a testing set at a ratio of 7:3. Feature selection was performed using recursive feature elimination (RFE) method, and then the selected feature variables were included into TabPFN, XGboost, and LR models for training and testing. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the models. Shapley additive explanations (SHAP) method was used for model interpretation.Results:A total of 547 patients with sICH were enrolled, including 367 males (67.1%), with a median age of 65 (interquartile range, 54-76) years. Two hundred twenty-six patients (41.3%) had poor outcome. Age, baseline blood pressure (systolic blood pressure, diastolic blood pressure), baseline laboratory tests (white blood cell count, red blood cell count, platelet count, neutrophil count, hemoglobin, fasting blood glucose, creatinine, uric acid, urea nitrogen, alanine aminotransferase, aspartate aminotransferase), hematoma rupture into the ventricle, island sign, baseline hematoma volume, and baseline National Institutes of Health Stroke Scale (NIHSS) score were selected as characteristic variables using RFE method. ROC curve analysis showed that the ROC AUC for TabPFN, Xgboost, and LR models predicting poor short-term outcome in the testing set were 0.918 (95% confidence interval [ CI] 0.870-0.966], 0.883 (95% CI 0.826-0.940), and 0.905 (95% CI 0.854-0.957), respectively. SHAP analysis showed that the top four important variables in the TabPFN model were baseline NIHSS score, baseline hematoma volume, baseline aspartate aminotransferase, and age. Conclusions:The TabPFN model is superior to the LR model and the XGBoost model in predicting poor outcome in patients with sICH. In the TabPFN model, baseline NIHSS score, baseline hematoma volume, aspartate aminotransferase, and age are the most important predictors of poor outcome in patients with sICH.Objectives To evaluate the predictive value of Tabular Prior-data Fitted Network(TabPFN) for short-term outcome in patients with spontaneous intracerebral hemorrhage (sICH), and compared with the Extreme Gradient Boosting (XGboost) model and traditional logistic regression (LR) model. Methods Patients with sICH admitted to the Department of Neurology, Hefei Second People's Hospital from January 2018 to March 2024 were included retrospectively. The demographic and baseline data were collected. At 3 months after onset, the modified Rankin Scale score was used to determine the outcome, 0-2 was defined as good outcome and >2 was defined as poor outcome. All enrolled patients were randomly divided into a training set and a testing set at a ratio of 7:3. Feature selection was performed using recursive feature elimination (RFE) method, and then the selected feature variables were included into TabPFN, XGboost, and LR models for training and testing. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the models. Shapley additive explanations (SHAP) method was used for model interpretation. Results A total of 547 patients with sICH were enrolled, including 367 males (67.1%), with a median age of 65 (interquartile range, 54-76) years. Two hundred twenty-six patients (41.3%) had poor outcome. Age, baseline blood pressure (systolic blood pressure, diastolic blood pressure), baseline laboratory tests (white blood cell count, red blood cell count, platelet count, neutrophil count, hemoglobin, fasting blood glucose, creatinine, uric acid, urea nitrogen, alanine aminotransferase, aspartate aminotransferase), hematoma rupture into the ventricle, island sign, baseline hematoma volume, and baseline National Institutes of Health Stroke Scale (NIHSS) score were selected as characteristic variables using RFE method. ROC curve analysis showed that the ROC AUC for TabPFN, Xgboost, and LR models predicting poor short-term outcome in the testing set were 0.918 (95% confidence interval [ CI] 0.870-0.966], 0.883 (95% CI 0.826-0.940), and 0.905 (95% CI 0.854-0.957), respectively. SHAP analysis showed that the top four important variables in the TabPFN model were baseline NIHSS score, baseline hematoma volume, baseline aspartate aminotransferase, and age. Conclusions The TabPFN model is superior to the LR model and the XGBoost model in predicting poor outcome in patients with sICH. In the TabPFN model, baseline NIHSS score, baseline hematoma volume, aspartate aminotransferase, and age are the most important predictors of poor outcome in patients with sICH.

Promoting wound healing is a common clinical challenge faced by surgeons, with a variety of repair method and materials currently available for clinical use. In recent years, with the continuous development of disciplines such as tissue engineering, regenerative medicine, and materials science, research on wound repair materials has progressed rapidly. This article will organize the classification, advantages and disadvantages, and the latest advancements in the preparation processes of wound repair materials. It will also discuss the current challenges faced by wound repair materials and future research directions, with the aim of providing a reference for related studies in wound repair.

Promoting wound healing is a common clinical challenge faced by surgeons, with a variety of repair method and materials currently available for clinical use. In recent years, with the continuous development of disciplines such as tissue engineering, regenerative medicine, and materials science, research on wound repair materials has progressed rapidly. This article will organize the classification, advantages and disadvantages, and the latest advancements in the preparation processes of wound repair materials. It will also discuss the current challenges faced by wound repair materials and future research directions, with the aim of providing a reference for related studies in wound repair.

Objective To explore the predictive value of coagulation indexes on the risk of venous thrombosis in perioperative period of hip fracture.Methods Totally 160 patients with hip fracture admitted to our hospital from February 2020 to December 2022 were retrospectively included.They were randomly divided into training set(n=112 cases)and verification set(n=48 cases).Further,according to whether deep vein thrombosis(DVT)occurred in the perioperative period,the patients in the training group were further divided into the occurrence group and the non-occurrence group.Use the least absolute shrinkage and selection operator(LASSO)to select variables to form a LASSO regression model.Draw receiver operating characteristic(ROC)curve to analyze the prediction efficiency of the model;The prediction efficiency of the verification model is realized through the verification set data.Results There was no statistically significant difference in gender,age,and other general and clinical data between the training set and the validation set(P>0.05).There were significant differences in sex,age,time from fracture to admission,body mass index,D-dimer,fibrinogen,total protein,albumin,prealbumin,globulin,hemoglobin,serum calcium,red blood cell volume,white blood cell count,red blood cell volume distribution width,activated partial thromboplastin time,prothrombin time and lymphocyte ratio between the patients in the training set(P<0.05).Six optimal variables with non-zero coefficients were selected by intraclass correlation coefficient(ICC)and LASSO,namely age,body mass index,D-dimer,fibrinogen,prothrombin time and activated partial thromboplastin time.The non-zero coefficients were 9.104,1.792,1.270,2.447,3.037 and-1.561 respectively.Conclusion The LASSO regression model formed by the combination of age,body mass index,D-dimer,fibrinogen,prothrombin time and activated partial thromboplastin time variables can serve as an auxiliary tool for predicting the risk of perioperative DVT formation in patients with hip fractures.

Burkitt lymphoma is a highly aggressive B-cell lymphoma and the fastest proliferating human malignant tumor.If the disease is found in the early stage,the patient could have a high possibility to be cured successfully,whereas the prognosis is poor in the late stage.Burkitt lymphoma can occur in children and adults,and it is categorized as local(Africa),sporadic,and immunodeficiency associated type.Sporadic Burkitt lymphoma mainly affects children and ado-lescents,and the most common initial sites are abdominal organs and lymph nodes.Sporadic Burkitt lymphoma manifested by initial oral and maxillofacial lesions is rela-tively rare.Here,a case of pediatric sporadic Burkitt lym-phoma,with oral and maxillofacial lesions as the first symptoms,was reported.The patient was treated in the Department of Periodontology,Shandong University School and Hospital of Stomatology.After timely checkup was pro-vided,the patient was transferred to another hospital and had good results.In this article,an incidence of Burkitt lympho-ma,with oral and maxillofacial lesions as the first symptom,was reviewed to provide reference for oral clinicians to achieve early diagnosis and treatment of patients with Burkitt lymphoma with oral diseases and improve the success rate of treatment.

BackgroundSibling relationships play a critical role in shaping anxiety symptoms in firstborn children. Anxiety symptoms often originate in early childhood and can persist into adolescence and adulthood. However， there is insufficient research on anxiety symptoms in preschool children， especially firstborn preschool children. ObjectiveTo explore the influencing factors of anxiety symptoms among firstborn preschool children， so as to provide references for the intervention of anxiety symptom for children in families with multiple children. MethodsFrom October to December 2021， a total of 8 449 children from 234 kindergartens in Longhua District of Shenzhen were included using a cluster sampling method. Sibling Inventory of Behavior （SIB） and Spence Preschool Anxiety Scale （SPAS） were used to investigate. Logistic regression analysis was used to identify influencing factors of anxiety symptoms in firstborn preschool children. ResultsA total of 8 419 （99.64%） valid questionnaires were collected. Anxiety symptoms were detected in 344（4.09%） firstborn preschool children. Statistically significant differences were observed between anxiety group and non-anxiety group in terms of household registration， monthly family income， maternal age， maternal education level， paternal education level， family living conditions and whether they are left-behind children （χ²/t=9.906， 33.490， 5.136， 13.485， 9.690， 17.332， 21.975， P<0.05 or 0.01）. Compared with non-anxiety group， children in the anxiety group scored higher on the SIB dimensions of rivalry， aggression and avoidance （t=165.322， 74.471， 286.419， P<0.01）， and lower on companionship， empathy and teaching （t=59.133， 42.417， 39.112， P<0.01）. Risk factors for anxiety symptoms in firstborn preschool children included left-behind children， as well as negative sibling relationships characterized by rivalry and avoidance （OR=1.195， 1.143， 1.260， P<0.05 or 0.01）. ConclusionFirstborn preschool children who are left-behind are more susceptible to anxiety symptoms. Negative sibling relationships， characterized by competition and avoidance， may also contribute to the emergence of anxiety symptoms in firstborn preschool children.

Objective:To explore the molecular mechanism of dexmedetomidine(Dex)protecting ischemia/reperfusion(I/R)myocardium.Methods:Wild type mice were grouped into control(Control)group,sham operation(Sham)group,WT I/R group,WT Dex group,and Dectin-1 knock out mice were grouped into KO I/R group and KO Dex group in the in vivo study(n=6).TTC stain-ing was used to determine the myocardial infarction area(%)of the above six groups of mice.HE staining and pathological analyze was used to determine the myocardial injury.Serum TNF-α,IL-6 and IL-10 levels in mice were detected by ELISA.Flow cytometry(FCM)was used to count and sort of infiltrating M2 macrophages and neutrophils in myocardium.qPCR assay was used to determine the Dectin-1 mRNA expression in the above sorted cells.Results:TTC results showed that there was no myocardial infarction in the mice of Control group and Sham group.Compared with the WT I/R group,the infarct volume was significantly lower in WT Dex group,KO I/R group and KO Dex group(P<0.05).Compared with the KO I/R group,the infarct volume was reduced in KO Dex group(P<0.05).The results of HE staining showed that the myocardial fibers of the WT I/R group of mice were disorderly arranged,with a large number of broken myocardial fibers,while the myocardial fibers of the WT Dex group,KO I/R group and KO Dex group of mice had a little breakage,the structural damage was not significant,and the myocardial arrangement was relatively neat.The degree of myocardi-al injury of mice in KO Dex group were less than that in KO I/R group mice.ELISA results showed that compared with Sham group,the serum TNF-α and IL-6 levels of the mice in WT I/R group were significantly increased,and the IL-10 level was significantly de-creased.Compared with WT I/R group,serum TNF-α and IL-6 levels of the mice in WT Dex group and KO I/R group were significant-ly decreased,and IL-10 level was significantly increased.Compared with KO I/R group,the serum TNF-α and IL-6 levels of the mice in KO Dex group were significantly decreased,and the IL-10 level was significantly increased(P<0.05).FCM cell counting results showed that compared with Sham group,a large number of M2 macrophages and neutrophils were infiltrated in the myocardium of WT I/R group of mice(P<0.05).Compared with WT I/R group,the M2 macrophages and neutrophils infiltrated in the myocardium were significantly decreased in WT Dex group,KO I/R group and KO Dex group of mice(P<0.05).While there was no significant differ-ence between the KO I/R group and the KO Dex group mice(P>0.05).qPCR results showed that compared with Sham group,the ex-pression level of Dectin-1 mRNA in the myocardial infiltrated M2 macrophages and neutrophils were significantly up-regulated in WT I/R group of mice(P<0.05).While compared with WT I/R group,the expression level of Dectin-1 mRNA in Dex group of mice was sig-nificantly lower(P<0.05).Mice in KO I/R group and KO Dex group did not express Dectin-1.Conclusion:The protective mecha-nisms of Dex preconditioning on I/R injured myocardium involves reducing the infiltrating number of M2 macrophages and neutrophils in myocardium after I/R injury,which may be achieved by inhibiting the expression of Dectin-1.

Objective:To develop a short-term prognostic model of spontaneous cerebral hemorrhage based on eXtreme Gradient Boosting (XGBoost) machine learning, and to compare its predictive performance with a Logistic regression model.Methods:Patients with sICH admitted to Department of Neurology, Second People's Hospital of Hefei from January 2018 to March 2022 were chosen; their general demographic characteristics, medical history, laboratory indices and cranial imaging data were retrospectively collected. The prognoses of patients 90 d after discharge were evaluated according to modified Rankin Scale (mRS) scores (good prognosis: mRS scores<3; poor prognosis: mRS scores≥3). XGboost and multiple Logistic regression models were used to screen out the factors for prognoses of patients 90 d after discharge, and area under receiver operating characteristic (ROC) curves, sensitivity, specificity and prediction accuracy of the 2 models were analyzed and compared.Results:A total of 413 patients with sICH were included; 180 patients(43.6 %) had poor prognosis and 233 (56.4%) had good prognosis 90 d after discharge. Multivariate Logistic regression results showed that age≥65 years, hemorrhage into the ventricle, hematoma volume of 20-40 mL, hematoma volume>40 mL and National Institutes of Health Stroke Scale (NIHSS) scores were independent influencing factors for short-term prognoses of sICH ( P<0.05). The variables in the XGBoost model were ranked in order of importance: NIHSS scores, systolic blood pressure at admission, Glasgow coma scale (GCS) scores, age≥65 years, hemorrhage into the ventricle, hematoma volume of 20-40 mL, and hematoma volume>40 mL. AUC of XGBoost model in predicting the prognosis was 0.895 (95% CI: 0.820-0.947), enjoying sensitivity of 68.89%, specificity of 94.83%, and prediction accuracy of 83.5%. AUC of Logistic regression model in predicting the prognosis was 0.894 (95% CI: 0.818-0.946), enjoying sensitivity of 93.33%, specificity of 70.69%, and prediction accuracy of 80.58%. Conclusion:The short-term prognostic model based on XGboost for sICH patients has high predictive efficacy, whose predictive accuracy is better than traditional Logistic regression.