Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome

Humans ; Acute Disease ; Pancreatitis/diagnosis* ; Follow-Up Studies ; Patient Discharge ; Disease Progression

Endodontic diseases are a kind of chronic infectious oral disease.Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha.However,it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy(RCT).Recent research,encompassing bacterial etiology and advanced imaging techniques,contributes to our understanding of the root canal system's anatomy intricacies and the technique sensitivity of RCT.Success in RCT hinges on factors like patients,infection severity,root canal anatomy,and treatment techniques.Therefore,improving disease management is a key issue to combat endodontic diseases and cure periapical lesions.The clinical difficulty assessment system of RCT is established based on patient conditions,tooth conditions,root canal configuration,and root canal needing retreatment,and emphasizes pre-treatment risk assessment for optimal outcomes.The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT.These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics.

Objective:To explore the association of different obesity measurement indexes on serum C-reactive protein (CRP) in Chinese adult women.Methods:The data were obtained from baseline and follow-up surveys of the urban Breast Cancer Screening Program in Shuangliu District, Chengdu. A total of 441 adult women were included in the study. A questionnaire survey, physical examination, and laboratory testing were conducted on the subjects. Multivariate logistic regression model, two-level mixed effects logistic regression model, and restricted cubic spline method were used to investigate the linear and nonlinear correlation between different obesity measurement indexes and serum CRP in adult women.Results:For every 1 unit increase in BMI, waist circumference (WC), and adiposity, the risk of elevated serum CRP or exacerbation of chronic low-grade inflammation in adult women increased by 16.5%, 5.0%, and 11.1% ( P<0.05), respectively. Both BMI and adiposity were nonlinear correlated with serum CRP. Using BMI=24.0 kg/m 2 as the reference point, serum CRP level increased with the increase of BMI when BMI >24.0 kg/m 2. Using adiposity=30% as the reference point, serum CRP level increased with the increase of adiposity when adiposity >30%. Conclusions:Overall, obesity reflected by BMI had the strongest association with serum CRP in adult women, followed by body fat content reflected by adiposity, and central obesity reflected by WC had the weakest association with CRP. Adult women with BMI >24.0 kg/m 2 or adiposity >30% are at high risk for obesity-related inflammatory manifestations.

Objective:To identify the molecular pathogenic characteristics and genetic evolution of an outbreak of acute gastroenteritis in Shenyang, China.Methods:A total of 57 anal swabs and environmental swab samples from student cases in a university outbreak in Shenyang in November 2021 were collected. Fluorescence quantitative RT-PCR was used for routine detection of diarrheal virus genes, and traditional RT-PCR was used to amplify and sequence the positive specimens of Norovirus. Phylogenetic analysis was performed.Results:Of the 57 specimens, 26 were norovirus positive by fluorescence quantitative real-time PCR, and 15 specimens were successfully amplified for the capsid protein gene segment. Phylogenetic analysis showed that all 15 positive specimens were of the GI.6 genotype of Norovirus, with a homology of 99.99% among the specimens and a genetic similarity of 98.84% to the LC380411 strain in Japan in 2005.Conclusions:The pathogen of this outbreak, GI.6 Norovirus, is rarely detected in the northeast of China, and its genotype is a subtype that has rarely been reported worldwide.

Objective:To observe the efficacy and factors influencing sequential or combined tenofovir alafenamide fumarate (TAF) after treatment with entecavir (ETV) in patients with chronic hepatitis B (CHB) with low-level viremia (LLV).Methods:126 CHB cases treated with ETV antiviral therapy in the Department of Infectious Diseases of the First Affiliated Hospital of Nanchang University from January 2020-September 2022 were retrospectively collected. Patients were divided into a complete virologic response (CVR) group ( n = 84) and a low-level viremia (LLV) group ( n = 42) according to the HBV DNA level during treatment. Clinical characteristics and laboratory indicators of the two groups at baseline and 48 weeks were analyzed by univariate analysis. Patients in the LLV group were divided into three groups according to their continued antiviral treatment regimen until 96 weeks: continued use of ETV as a control group; replacement of TAF as a sequential group; and combination of ETV and TAF as a combined group. The data of the three groups of patients were analyzed by one-way analysis of variance for 48 weeks. HBV DNA negative conversion rate, HBeAg negative conversion rate, alanine aminotransferase (ALT), creatinine (Cr), and liver stiffness test (LSM) were compared among the three groups after 96 weeks of antiviral treatment. Multivariate logistic regression was used to analyze the independent factors influencing the occurrence of HBV DNA non-negative conversion in LLV patients at 96 weeks. Receiver operating characteristic curve (ROC) was used to evaluate the effectiveness of predicting the occurrence of HBV DNA non-negative conversion in LLV patients at 96 weeks. Kaplan-Meier was used to analyze the cumulative negative rate of DNA in LLV patients, and the Log-Rank test was used for comparison. HBV DNA and HBV DNA negative conversion rates during treatment were observed dynamically. Results:Univariate analysis showed statistically significant differences in age, BMI, HBeAg positivity rate, HBV DNA, HBsAg, ALT, AST, and LSM at baseline between the CVR group and the LLV group ( P < 0.05). Univariate analysis of variance revealed no statistically significant difference among the three groups of LLV patients at 48 weeks ( P > 0.05). HBV-DNA negative conversion rate in the sequential group and the combination group was significantly higher than that in the control group after 96 weeks of treatment (88.89% vs. 41.18%, 85.71% vs. 41.18%, χ2 = 10.404, P = 0.006). HBeAg negative conversion rate was higher than that of the control group, with no statistically significant difference ( P > 0.05).Compared with the control group, ALT, Cr, and LSM in the sequential group and the combined group were equally improved to varying degrees, with a statistically significant difference ( P < 0.05). Subsequent use of ETV and HBV DNA at 48 weeks were independent risk factors for HBV DNA positivity at 96 weeks in LLV patients ( P < 0.05). The AUC of HBV DNA at 48 weeks was 0.735 (95% CI: 0.578 ~ 0.891), the cut-off value was 2.63 log 10 IU/ml, and the sensitivity and specificity were 76.90% and 72.40%, respectively. DNA conversion rate was significantly lower in LLV patients receiving 48-week ETV and 48-week HBV DNA≥2.63 log10 IU/mL than in patients receiving sequential or combined TAF and 48-week HBV DNA < 2.63 log 10 IU/mL. HBV DNA negative conversion rates in the sequential group and combined group at 72 weeks, 84 weeks, and 96 weeks were higher than those in the control group during the period from 48 weeks to 96 weeks of continuous treatment, and the differences were statistically significant ( P < 0.05). Conclusion:Sequential or combined TAF antiviral therapy could more effectively improve the 96-week CVR rate, as well as hepatic and renal function, and alleviate the degree of hepatic fibrosis in CHB patients with LLV following ETV treatment. Subsequent use of ETV and HBV DNA load at 48 weeks were independent predictors of HBV DNA positivity at 96 weeks in LLV patients.

Objective To investigate the influencing factors for low-level viremia (LLV) and their dynamic changes in chronic hepatitis B (CHB) patients treated with nucleos(t)ide analogues (NAs) for the first time. Methods A retrospective analysis was performed for 78 CHB patients who attended Department of Infectious Diseases, The First Affiliated Hospital of Nanchang University, from November 2020 to March 2022 and received antiviral therapy with NAs for at least 12 months, and according to HBV DNA level during treatment, they were divided into sustained virologic response (SVR) group with 58 patients and LLV group with 20 patients. The independent samples t -test or the Mann-Whitney U test was used for comparison of continuous data between two groups, and the chi-square test or the Fisher's exact test was used for comparison of categorical data between two groups. The multivariate Logistic regression analysis was used to investigate the independent influencing factors for LLV and establish a predictive model, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of this model. The Kaplan-Meier method was used to analyze cumulative HBV DNA negative conversion rate, and the Log-rank test was used for comparison. The analysis of variance with repeated measures was used to analyze the differences in HBV DNA and HBsAg between the two groups or within each group at weeks 0, 12, 24, 36, and 48. Results Compare with the SVR group, the LLV group had significantly higher HBeAg positive rate (90.0% vs 48.3%, χ 2 =10.701, P =0.001), log(HBV DNA) value (7.26±1.46 vs 5.65±1.70, t =-4.178, P < 0.001), and log(HBsAg) value (4.53±0.86 vs 3.44±0.93, t =-4.813, P < 0.001) and significantly lower age [29 (26-34) vs 33 (30-43), Z =-2.751, P =0.009], alanine aminotransferase (ALT) [67.0 (54.0-122.0)U/L vs 111.0 (47.0-406.0)U/L, Z =-2.203, P =0.028], aspartate aminotransferase [43.5 (32.8-62.8) U/L vs 77.5 (35.0-213.0)U/L, Z =-2.466, P =0.014], and liver stiffness measurement [7.7 (6.3-8.5)kPa vs 8.9 (7.2-11.4)kPa, Z =-2.022, P =0.043]. The multivariate logistic regression analysis showed that baseline HBV DNA (odds ratio [ OR ]=2.365, 95% confidence interval [ CI ]: 1.220-4.587, P =0.011), HBsAg ( OR =4.229, 95% CI : 1.098-16.287, P =0.036), and ALT ( OR =0.965, 95% CI : 0.937-0.994, P =0.018) were independent influencing factors for LLV in CHB patients, and the predictive model of Logit(MLLV)=-8.668+1.441×lgHBsAg+0.598×lgHBV DNA-0.016×ALT was established based on these factors, which had a larger area under the ROC curve than HBV DNA, HBsAg, and ALT (0.931 vs 0.774/0.856/0.666), with a sensitivity of 85.00% and a specificity of 93.10% at the optimal cut-off value of 0.44. The CHB patients with baseline HBV DNA > 7.29 lgIU/mL or HBsAg > 4.38 lgIU/mL had a significantly lower DNA negative conversion rate than those with DNA ≤7.29 lgIU/mL or HBsAg ≤4.38 lgIU/mL ( χ 2 =22.52 and 26.35, both P < 0.001). In the CHB patients, the highest reduction rates of HBV DNA and HBsAg were observed at weeks 12 and 24, respectively, and the LLV group had significantly higher levels of HBV DNA and HBsAg than the SVR group at weeks 0, 12, 24, 36, and 48 (HBV DNA: t =-4.084, -4.526, -5.688, -7.123, and -6.266, all P < 0.001; HBsAg: t =-4.652, -4.691, -4.952, -4.804, and -4.407, all P < 0.001). Conclusion For the CHB patients treated with NAs for the first time, those with high HBV DNA load, high HBsAg quantification, and low ALT level at baseline are more likely to develop LLV, and dynamic monitoring of these indices is of great significance to observe the onset of LLV.

Objective:To explore the risk factors of cement displacement after percutaneous vertebral augmentation in patients with osteoporotic vertebral compression fracture (OVCF).Methods:A case-control analysis was made on clinical data of 1 538 patients with OVCF admitted to Honghui Hospital Affiliated to Xi′an Jiaotong University from January 2016 to June 2021. There were 377 males and 1 161 females, aged from 45-115 years [(71.7±10.8)years]. Percutaneous vertebroplasty (PVP) or percutaneous vertebroplasty (PKP) was performed. Patients were divided into cement displacement group ( n=78) and cement non-displacement group ( n=1 460) according to the radiographic outcomes. Factors related to cement displacement were analyzed by univariate analysis, including age, gender, body mass index (BMI), preoperative bone mineral density, underlying diseases, involved vertebral segments, surgical methods, surgical approaches, cement leakage (anterior edge), viscosity of cement, dispersion ratio of cement, degree of cement interweaving, sagittal position of cement, targeted location of cement, distance from cement to upper and lower endplates and duration of brace wearing. Independent risk factors for bone cement displacement were identified by multivariate Logistic regression analysis. Results:Univariate analysis showed that bone cement displacement was significantly correlated with BMI, preoperative bone mineral density, involved vertebral segments, operation methods, cement leakage (anterior edge), viscosity of cement, dispersion ratio of cement, degree of cement interweaving, sagittal position of cement, targeted location of cement, distance from cement to upper and lower endplates and duration of brace wearing (all P<0.05), but there was no correlation with gender, age, underlying diseases or surgicales approach (all P>0.05). Multivariate Logistic analysis showed that the preoperative bone mineral density ( OR=2.45, 95% CI 1.81-7.50, P<0.01), operation methods ( OR=4.56, 95% CI 1.86-8.44, P<0.01), cement leakage (anterior edge) ( OR=5.77, 95% CI 2.85-9.20, P<0.01), viscosity of cement ( OR=7.36, 95% CI 1.01-1.77, P<0.01), dispersion ratio of cement ( OR=6.84, 95% CI 1.69-13.39, P<0.01), degree of cement interweaving ( OR=8.97, 95% CI 2.29- 14.97, P<0.01), sagittal position of cement ( OR=6.39, 95% CI 1.06-9.47, P<0.01), targeted location of cement ( OR=7.93, 95% CI 1.64-11.84, P<0.01), distance from cement to upper and lower endplates ( OR=6.78, 95% CI 1.84-6.96, P<0.01) and duration of brace wearing ( OR=9.55, 95% CI 2.26- 9.38, P<0.01) were significantly correlated with bone cement displacement after percutaneous vertebral augmentation. Conclusion:Low bone mineral density preoperatively, PKP, cement leakage into the vertebral anterior edge, low viscosity of cement, small dispersion ratio of cement, small degree of cement interweaving, cement filling in the anterior 1/3 and anterior middle 2/3 of the vertebral body in sagittal plane, non-targeted injection of cement, long distance from cement to upper and lower endplates and short duration of brace wearing are independent risk factors of cement displacement after percutaneous vertebral augmentation for OVCF.

Objective:To identify the etiology and genetics of the human parainfluenza virus type 3 (HPIV3) virus which caused an acute respiratory tract infection outbreak in a primary school in Shenyang.Methods:Throat swab samples were collected from 17 students of the primary school where the epidemic of acute respiratory infection outbreak in December 2020 in Shenyang, Liaoning province. TaqMan low-density arrays (TLDA) real-time PCR was performed to simultaneously detect multiple respiratory pathogens. The HN gene was amplified using nested RT-PCR and sequenced, followed by phylogenetic analysis for those HPIV3 positive samples.Results:Of the 17 specimens, 10 were HPIV3 positive by TLDA Real-time PCR, and were accompanied by conditional pathogen infection, consequently, amplification result ed in 7 complete HN sequences. Phylogenetic analysis showed that the infected HPIV3 virus of the outbreak belonged to HPIV subtype C3a. All the 7 strains detected in this study belonged to subbranch C3a.1 evolutionary branch, with a nucleotide homology of 99.9%, a nucleotide homology of 94.56 with the prototype strain Wash/47885/57 and 99.5% with the most phylogenetically close strain of ZJ/11-s-165/KP690785/CHN/11.Conclusions:The HPIV3 virus caused the acute respiratory tract infection outbreak in Shenyang in 2020 and HPIV subtype C3a1 was detected firstly in Northeast China.

Objective:To investigate the clinical value of radiomics based on computed tomography (CT) examination in preoperative differential diagnosis of pancreatic serous cystadenoma (SCA) and mucinous cystadenoma (MCA).Methods:The retrospective case-control study was conducted. The clinicopathological and imaging data of 154 patients with pancreatic cystic neoplasms who were admitted to the First Affiliated Hospital, Zhejiang University School of Medicine from January 2012 to December 2019 were collected. There were 24 males and 130 females, aged (50±13)years. Of the 154 patients, 99 cases were diagnosed as SCA and 55 cases were diagnosed as MCA. All the 154 patients underwent plain and enhanced CT scan of pancreas before operation. The clinical characteristics, radiology features and radiomics features of all patients were collected to construct the clinical characteristics model, radiology model, radiomics model and fused model. The receiver operating characteristic (ROC) curve of each model was drawn, and those constructed models were evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Based on the optimal model, the nomogram was constructed. Observation indicators: (1) establishment and validation of clinical characteristics model; (2) establishment and validation of radiology model; (3) establishment and validation of radiomics model; (4) establishment and validation of fused model; (5) nomogram of fused model. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Results:(1) Establishment and validation of clinical characteristics model: 3 clinical characteristics, including age, symptoms and preoperative serum CA19-9, were selected using multinomial logistic linear regression analysis to construct the clinical characteristics model. Result of the multinomial logistic linear regression analysis was expressed by formula ①: clinical characteristics model score=0.635-0.007×age+0.054×clinical symptoms+0.108×preoperative serum CA19-9. The ROC curve for the test dataset of clinical characteristics model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of clinical characteristics model were 0.611(95% confidence interval as 0.488?0.734, P<0.05), 56.6%, 66.7%, 56.3%, 41.5%, 78.4% for the training dataset and 0.771(95% confidence interval as 0.624?0.919, P<0.05), 77.8%, 63.1%, 88.5%, 80.1%, 76.7% for the test dataset, respectively. (2) Establishment and validation of radiology model: 5 radiology characteristics, including tumor location, the number of tumors, tumor diameter of cross section, lobulated tumor and polycystic tumor (more than 6), were selected using multinomial logistic linear regression analysis to construct the radiology model. Result of the multinomial logistic linear regression analysis was expressed by formula ②: radiology model score=?0.034+0.300×tumor location+0.202×the number of tumors+0.014×tumor diameter of cross section?0.251×lobulated tumor?0.170×polycystic tumor (more than 6). The ROC curve for the test dataset of radiology model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of radiology model were 0.862(95% confidence interval as 0.791?0.932, P<0.05), 78.8%, 81.8%, 77.5%, 62.8%, 90.2% for the training dataset and 0.853(95% confidence interval as 0.713?0.994), P<0.05), 88.9%, 89.4%, 88.5%, 85.0%, 92.0% for the test dataset, respectively. (3) Establishment and validation of radiomics model: 4 categories of a total 1 067 radiomics features were extracted from 154 patients with pancreatic cystic neoplasms, including 7 first-order histogram features, 53 texture features, 848 wavelet features and 159 local binary pattern features. A total of 896 stable radiomics features were retained to construct the model, based on the condition of intraclass correlation coefficient >0.9. After selected by variance threshold and correlation coefficient threshold, 350 radiomics features were retained. Fifty synthetic radiomics features were constructed based on the original features in order to obtain potential radiomics features, and the total number of radiomics features was 400. After analyzed by the five-fold recursive feature elimination, 22 radiomics features were screened out, including 13 wavelet features, 7 synthetic radiomics features and 2 local binary pattern features. The support vector machine algorithm was used to construct the radiomics model. The penalty coefficient 'C' and parameter 'γ' of the radiomics model were 35.938 and 0.077, respectively. The kernel function of the radiomics model was 'radial basis function kernel'. The ROC curve of radiomics model using 5-fold cross validation was drawn. The average AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the radiomics model were 0.870 ( P<0.05), 83.1%, 81.8%, 83.8%, 73.8% and 89.2%, respectively. (4) Establishment and validation of fused model: the fused model was constructed after selecting the tumor location and lobulated tumor of radiology characteristics and radiomics score. Result of the multinomial logistic linear regression analysis was expressed by formula ③: fused model socre=?0.154+0.218×tumor location?0.223×lobulated tumor+0.621×radiomics score. The ROC curve for the test dataset of fused model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of fused model were 0.893(95% confidence interval as 0.828?0.958, P<0.05), 83.7%, 81.8%, 84.5%, 71.1%, 90.9% for the training dataset and 0.966(95% confidence interval as 0.921?0.999, P<0.05), 91.1%, 84.2%, 96.2%, 94.1%, 89.3% for the test dataset, respectively. (5) Nomogram of fused model: the nomogram of fused model was illustrated with the Youden index of 0.416. Conclusion:The prediction model based on the radiomics signature and radiological features extracted from preoperative CT examination can make the differential diagnosis of pancreatic SCA from MCA.