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 safety of early antiplatelet therapy for non-cardioembolic mild stroke patients with thrombocytopenia.Methods:Data of acute ischemic stroke patients with baseline National Institutes of Health Stroke Scale(NIHSS)score≤3 and a platelet count<100×109/L were obtained from a multicenter register.Those who required anticoagulation or had other contraindications to antiplatelet therapy were excluded.Short-term safety outcomes were in-hospital bleeding events,while the long-term safety outcome was a 1-year all-cause death.The short-term neurological outcomes were evaluated by modified Rankin scale(mRS)score at discharge.Results:A total of 1868 non-cardioembolic mild stroke patients with thrombocytopenia were enrolled.Multivariate regression analyses showed that mono-antiplatelet therapy significantly increased the proportion of mRS score of 0-1 at discharge(OR=1.657,95%CI:1.253-2.192,P<0.01)and did not increase the risk of intracranial hemorrhage(OR=2.359,95%CI:0.301-18.503,P>0.05),compared with those without antiplatelet therapy.However,dual-antiplatelet therapy did not bring more neurological benefits(OR=0.923,95%CI:0.690-1.234,P>0.05),but increased the risk of gastrointestinal bleeding(OR= 2.837,95%CI:1.311-6.136,P<0.01)compared with those with mono-antiplatelet therapy.For patients with platelet counts≤75×109/L and>90×109/L,antiplatelet therapy significantly improved neurological functional outcomes(both P<0.05).For those with platelet counts(>75-90)×109/L,antiplatelet therapy resulted in a significant improvement of 1-year survival(P<0.05).For patients even with concurrent coagulation abnormalities,mono-antiplatelet therapy did not increase the risk of various types of bleeding(all P>0.05)but improved neurological functional outcomes(all P<0.01).There was no significant difference in the occurrence of bleeding events,1-year all-cause mortality risk,and neurological functional outcomes between aspirin and clopidogrel(all P>0.05).Conclusions:For non-cardioembolic mild stroke patients with thrombocytopenia,antiplatelet therapy remains a reasonable choice.Mono-antiplatelet therapy has the same efficiency as dual-antiplatelet therapy in neurological outcome improvement with lower risk of gastrointestinal bleeding.

Objective To observe the expression of Galectin-3 in peritoneal dialysis (PD) fluid in patients with different dialysis ages, and to conduct correlation analysis with vascular endothelial growth factor (VEGF) , fi-bronectin (FN) and related clinical indicators.Methods A total of 109 PD patients who were regularly followed up in the department of nephrology were divided into four groups according to different peritoneal dialysis ages.The concentrations of Galectin-3, VEGF and FN were determined by enzyme-linked immunosorbent assay.The expres-sion of Galectin-3 in peritoneal dialysate of the 4 groups was compared, the correlation with VEGF, FN and clinical related indexes was analyzed, and the correlation was analyzed by Spearman test.Results The concentration of VEGF in peritoneal dialysis patients in group D significantly increased (P<0.05) .Galectin-3 expression levels were positively correlated with VEGF (r =0.358 , P =0.022) , but not significantly correlated with FN (r =0.121, P=0.452).Galectin-3 was positively correlated with clinical indicators parathyroid hormone (PTH) (r=0.201, P=0.037), C-reactive protein (CRP) (r=0.357, P<0.001), left ventricular posterior wall dimensions (LVPWD) (r=0.213, P=0.026), and negatively correlated with clinical indicators total cholesterol (TC) (r=-0.316, P=0.001).Conclusion The concentration of Galectin-3 in the dialysate of long-term peritoneal dialy-sis patients is significantly elevated, indicating that the expression of galectin-3 increases with the extension of peri-toneal dialysis time, suggesting that the detection of galectin-3 levels may be helpful for the evaluation of early peri-toneal fibrosis.The positive correlation with VEGF may suggest its role in promoting peritoneal angiogenesis and fi-brosis.Moreover, it is positively correlated with clinical indicators PTH, CRP and LVPWD, suggesting that it has certain clinical guiding significance on microinflammatory state and myocardial remodeling.

Polycystic ovary syndrome(PCOS)is characterized by high heterogeneity and heredity,and its exact pathogenesis is still not clear.Some studies have shown that epigenetic disorders,such as hyperandrogen-induced methyla-tion or acetylation of lysine at different sites(K4,K9,and K27)in histone H3,methylation and demethylation modifica-tion of genes related to steroids,hormone receptors and follicular development,and transcriptional control of microRNA or long noncoding RNA,play a central role in the occurrence and development of PCOS.This article reviews the research ad-vances in epigenetic mechanisms(histone modifications,DNA methylation,and noncoding RNA)of PCOS,in order to provide a reference for the prediction and early prevention of PCOS.

Conventional chemotherapy based on cytotoxic drugs is facing tough challenges recently following the advances of monoclonal antibodies and molecularly targeted drugs. It is critical to inspire new potential to remodel the value of this classical therapeutic strategy. Here, we fabricate bisphosphonate coordination lipid nanogranules (BC-LNPs) and load paclitaxel (PTX) to boost the chemo- and immuno-therapeutic synergism of cytotoxic drugs. Alendronate in BC-LNPs@PTX, a bisphosphonate to block mevalonate metabolism, works as both the structure and drug constituent in nanogranules, where alendronate coordinated with calcium ions to form the particle core. The synergy of alendronate enhances the efficacy of paclitaxel, suppresses tumor metastasis, and alters the cytotoxic mechanism. Differing from the paclitaxel-induced apoptosis, the involvement of alendronate inhibits the mevalonate metabolism, changes the mitochondrial morphology, disturbs the redox homeostasis, and causes the accumulation of mitochondrial ROS and lethal lipid peroxides (LPO). These factors finally trigger the ferroptosis of tumor cells, an immunogenic cell death mode, which remodels the suppressive tumor immune microenvironment and synergizes with immunotherapy. Therefore, by switching paclitaxel-induced apoptosis to mevalonate metabolism-triggered ferroptosis, BC-LNPs@PTX provides new insight into the development of cytotoxic drugs and highlights the potential of metabolism regulation in cancer therapy.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

Objective:To analyze the MRI findings of solid pseudopapilloma of the pancreas (SPTs) and nonfunctional pancreatic neuroendocrine tumors (PNETs), and to establish and verify the prediction model of SPTs and PNETs.Methods:The clinical and MRI data of 142 patients with SPTs and 137 patients with PNETs who underwent surgical resection and were confirmed by pathology in the First Affiliated Hospital of Naval Medical University from January 2013 to December 2020 were collected continuously. Age, gender, body mass index (BMI), lesion size, location, shape, boundary, cystic change, T 1WI signal, T 2WI signal, enhancement peak phase, whether the enhancement degree was higher than that of pancreatic parenchyma in the enhancement peak phase, enhancement pattern, whether pancreatic duct and common bile duct were dilated, whether the pancreas shrank, and whether it invaded adjacent organs and vessels were recorded. According to the international consensus on prediction model modeling, patients were divided into training set (106 SPTs and 100 PNETs between January 2013 and December 2018), and validation set (36 SPTs and 37 PNETs between January 2019 and December 2020). The above characteristics of patients in training and validation set were analyzed by univariate and multivariate logistic regression, and a prediction model was established to distinguish SPTs and PNETs, and then visualized as a nomogram. The receiver operating characteristic curve (ROC) of the nomogram of training set and verification set was drawn, and the area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the prediction efficiency of the model, and the clinical application value of the prediction model was evaluated by decision curve analysis (DCA). Results:Univariate regression analysis showed that there were significant differences on age, gender, lesion size, shape, cystic change, T 1WI signal, peak phase of enhancement, degree of enhancement in peak phase, pattern of enhancement and invasion of adjacent organs between SPTs group and PNETs group (all P value <0.05). Multivariate regression analysis showed that the older age, male patients, the smaller lesion, no high signal on T 1WI, the enhancement peak phase located in arterial phase or venous phase, and the enhancement degree in peak phase higher than that of pancreatic parenchyma were the six independent predictors of PNETs. The prediction model was established by using these six factors and visualized as a nomogram. The formula for predicting PNETs probability was 4.31+ 1.13×age+ 1.31×tumor size-1.29×female-4.18×high T 1WI signal+ 1.28×the enhancement degree higher than that of pancreatic parenchyma -4.69 ×enhancement peak in delay phase. The prediction model was visualized as a nomogram. The AUC values in the training set and validation set were 0.99(95% CI0.977-1.000) and 0.97 (95% CI 0.926-1.000), respectively. The sensitivity, specificity and accuracy in the training set are 98.00%, 94.34% and 96.12% and in the validation set were 86.49%, 97.22% and 91.78% respectively. The results of decision curve analysis show that the prediction model can accurately diagnose SPTs and PNETs. Conclusions:The prediction model established in this study can accurately differentiate SPTs from PNETs, and can provide important information for clinical decision and prognosis.

Objective:To develop a visualized nomogram with a predictive value to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) patients with chronic pancreatitis (CP) history.Methods:The clinical and radiological data of 5 433 CP patients acoording to the Asia-Pacific Diagnostic Criteria between February 2011 and February 2021 in the First Affiliated Hospital of Naval Medical University were retrospectively analyzed, and 71 PDAC patients with CP history and 67 MFCP who underwent surgery or biopsy and pathologically confirmed were eventually enrolled. The training set included 44 patients with MFCP and 59 patients with PDAC who were diagnosed between February 2011 and April 2018. The validation set consisted of 23 patients with MFCP and 12 patients with PDAC who were diagnosed between May 2018 and February 2021. Univariate and multivariate logistic regression analyses were performed to develop a prediction model for PDAC and MFCP, and the model was visualized as a nomogram. ROC was used to evaluate the predictive efficacy of the nomogram, and the clinical usefulness was judged by decision curve analysis.Results:The univariate analysis showed that a significant association with pancreatic cancer were observed for the duct-to-parenchyma ratio ≥0.34, pancreatic duct cut-off, pancreatic portal hypertension, arterial CT attenuation, portal venous CT attenuation, delayed CT attenuation, and vascular invasion in both the training and validation cohorts, but the duct-penetrating sign in the training cohort only. The multivariable logistic regression analysis showed that statistically significant differences (all P value <0.05) existed in cystic degeneration, a duct-to-parenchyma ratio ≥0.34, the duct-penetrating sign, pancreatic portal hypertension and arterial CT attenuation between the two cohorts. The above parameters were selected for the logistic regression model. The predicted model=3.65-2.59×cystic degeneration+ 1.26×duct-to-parenchyma ratio≥0.34-1.40×duct-penetrating sign+ 1.36×pancreatic portal hypertension-0.05×arterial CT attenuation. Area under the curve, sensitivity, specificity and accuracy of the model-based nomogram were 0.87 (95 CI 0.80-0.94), 89.0%, 75.0% and 83.5% in the training cohort, and 0.94 (95 CI 0.82-0.99), 91.7%, 100% and 97.1% in the validation cohort, respectively. Decision curve analysis showed that when the nomogram differentiated MFCP from PDAC patients with CP history at a rate of 0.05-0.85, the application of the nomogram could benefit the patients. Conclusions:The nomogram based on CT radiological features accurately differentiated MFCP from PDAC patients with CP history and provide reference for guiding the treatment and judging the prognosis.

Objective:To develop and validate a visualized computed tomography nomogram for differentiating focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC).Methods:This retrospective review included 42 consecutive patients with fAIP diagnosed according to the International Consensus Diagnostic Criteria and 242 consecutive patients with PDAC confirmed by pathology between January 2011 and December 2018 in the First Affiliated Hospital of Naval Medical University. Among them, 209 consecutive patients (25 fAIP and 184 PDAC) were enrolled in the development cohort; Seventy-five consecutive patients (17 fAIP and 58 PDAC) were enrolled in the validation cohort. CT image characteristics, including lesion location, size, enhancement mode and degree of mass enhancement in portal vein phase, pancreatic parenchymal atrophy, main pancreatic duct dilation, common bile duct dilation, cyst, acute obstructive pancreatitis, and vascular invasion were compared. Univariate and multivariate analysis were used to screen the independent predictive factors for fAIP and PDAC, based on which the nomogram was constructed and visualized. The receiver operating characteristic curve (ROC) was drawn and area under the curve (AUC) was calculated to evaluate the differential efficacy of the nomogram. The clinical usefulness of the nomogram was evaluated by decision curve analysis.Results:There were statistically significant differences on common bile duct dilation and the mode and degree of enhancement in portal phase between fAIP group and PDAC group in training set and validation set ( P<0.05). Univariate regression analysis showed that common bile duct dilation and degree of mass enhancement in portal vein were closely correlated with fAIP and PDAC phase between the two groups in training set and validation set; mass enhancement mode in portal vein phase and main pancreatic duct dilation were closely correlated with fAIP and PDAC in training set. Multivariate logistic regression analysis showed that common biliary duct dilatation ( OR=0.26, 95% CI 0.06-1.10, P=0.07), main pancreatic duct dilation ( OR=9.46, 95% CI 1.60-56.04, P<0.01) and mass mild hyper-enhancing in portal vein phase ( OR=0.003, 95% CI 0.0003-0.0278, P<0.0001) were the three independent predictors for fAIP and PDAC. Thus, the equation for predicting the probability of PDAC was 4.51-1.33× no dilatation of the common bile duct+ 2.25× the main pancreatic duct dilated-5.84× mass mild hyper-enhancing during the portal phase. The individualized prediction nomogram using these predictors of the fAIP achieved an AUC of 0.97 (95% CI 0.95-0.99) in the development set and 0.97(95% CI0.94-1.00) in the validation set. The sensitivity, specificity and accuracy of the model were 87.5%, 100% and 89% in the training set; and 94.83%, 94.12% and 94.67% in the validation set, respectively. The decision curve analysis demonstrated that the nomogram was clinically useful when the nomogram differentiated fAIP and PDAC at a rate of >0.2. Conclusions:The nomogram based on common bile duct dilation, main pancreatic duct dilation and mass enhancement in portal vein phase can be used as a useful tool for predicting fAIP and PDAC and provide valuable evidence for clinical decision.

Objective To make good use of the incentive function of the reward policy of scientific research achievements in hospitals ,shorten the reward cycle of scientific and technological achievements ,reduce the management cost ,achieve the ul-timate goal of details management of scientific and technological achievements .Methods Through retrospective analysis of the incentive process to identify possible barriers ,reconstruct the rewarding procedures of scientific and technological achievements by bringing in informatization and publicity strategy ,compare the bonus distribution cycle with the traditional model .Results The results show that the reward model based on the scientific research management system reduces the bonus arrival waiting period by about 62％ ,saves management costs and improves staff compliance .Conclusions According to the 4-year practice experiences ,the new management model has further released the positive impact of the hospital award policy ,solved the key problem of restricting the rewarding effect by technological means in the process of rewarding scientific research achievements .