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:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

Objective:The study aimed to validate the Neuroception of Psychological Safety Scale (NPSS) in terms of reliability and validity among individuals with mental disorders in China.Methods:The Study followed Brislin′s translation principles to adapt the scale into Chinese. From February to June 2023, a total of 638 hospitalized patients with mental disorders (477 with schizophrenia and 161 with mood disorders) were selected through gender-stratified simple random sampling from the Shanghai Pudong New Area Mental Health Center and the Shanghai Baoshan Mental Health Center. The Chinese version of the NPSS and the Security Questionnaire (SQ) were administered. The reliability of the scale was measured using split-half reliability and test-retest reliability. Validity was assessed through content validity, structural validity, convergent validity, and discriminative validity analyses. In addition, SQ was used as a criterion tool to test the validity of the criterion through Pearson correlation analysis.Results:The Chinese version of the NPSS contained 29 items, with total scores ranging from 29 to 145. Higher total scores indicated greater psychological safety. Item analysis showed a decider value of 10.58 to 20.80 (>3), and the correlation between items and total scores ranged from 0.579 to 0.749 (all P<0.05). The item-level content validity index (I-CVI) for the items ranged from 0.86 to 1.00, while the average scale-level content validity index (S-CVI/Ave) was 0.99. Exploratory factor analysis extracted three common factors: social participation, empathy, and bodily sensations, which is consistent with the structure of the original scale, explaining a cumulative variance contribution rate of 62.551%. Confirmatory analysis revealed a satisfactory model fit, with average variance extracted (AVE) values for the three dimensions ranging from 0.523 to 0.645, and composite reliability(CR) ranging from 0.905 to 0.938. The standard loading coefficients for the items ranged from 0.608 to 0.859, and inter-factor correlation coefficients were all smaller than the square roots of their respective AVE values. Pearson correlation analysis indicated significant positive relationships between the Chinese NPSS and SQ ( r=0.822-0.846, P<0.01). Reliability analysis showed Cronbach′s alpha coefficients of 0.903-0.959 for the total scale and subscales. After a 3-week interval, test-retest reliability (70 patients) ranged from 0.874 to 0.983, and split-half reliability was 0.869-0.969. All model fit indices met established criteria. Conclusions:The Chinese version of the NPSS demonstrates good reliability and validity, making it suitable for both research and clinical applications in assessing psychological security among individuals with schizophrenia and mood disorders.

Group painting art therapy is a kind of therapy using painting art as the medium,which helps the individuals to improve their mental health and achieve recovery through group participation.In this kind of therapy,the participants express their emotions,thoughts and experiences through painting,share them with others,and obtain support and feedback,thereby this process promotes self-awareness,emotional expression and social interaction.The empirical research results indicate that when the group painting art therapy is ap-plied to the patients with schizophrenia during the rehabilitation phase,it has multifaceted effects,including al-leviating psychotic symptoms,improving medication adherence,relieving emotional distress,enhancing sleep quality,increasing self-awareness,improving cognitive function,and increasing social functioning and overall quality of life.In terms of intervention methods,the researchers have assessed the application efficacy of sole group painting art therapy in the rehabilitation of schizophrenia patients and have also explored its combined application with other treatments.For the future,more long-term follow-up studies can be conducted,and in-terdisciplinary cooperation can be strengthened,while focusing on community resource construction.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

Objective:The study aimed to validate the Neuroception of Psychological Safety Scale (NPSS) in terms of reliability and validity among individuals with mental disorders in China.Methods:The Study followed Brislin′s translation principles to adapt the scale into Chinese. From February to June 2023, a total of 638 hospitalized patients with mental disorders (477 with schizophrenia and 161 with mood disorders) were selected through gender-stratified simple random sampling from the Shanghai Pudong New Area Mental Health Center and the Shanghai Baoshan Mental Health Center. The Chinese version of the NPSS and the Security Questionnaire (SQ) were administered. The reliability of the scale was measured using split-half reliability and test-retest reliability. Validity was assessed through content validity, structural validity, convergent validity, and discriminative validity analyses. In addition, SQ was used as a criterion tool to test the validity of the criterion through Pearson correlation analysis.Results:The Chinese version of the NPSS contained 29 items, with total scores ranging from 29 to 145. Higher total scores indicated greater psychological safety. Item analysis showed a decider value of 10.58 to 20.80 (>3), and the correlation between items and total scores ranged from 0.579 to 0.749 (all P<0.05). The item-level content validity index (I-CVI) for the items ranged from 0.86 to 1.00, while the average scale-level content validity index (S-CVI/Ave) was 0.99. Exploratory factor analysis extracted three common factors: social participation, empathy, and bodily sensations, which is consistent with the structure of the original scale, explaining a cumulative variance contribution rate of 62.551%. Confirmatory analysis revealed a satisfactory model fit, with average variance extracted (AVE) values for the three dimensions ranging from 0.523 to 0.645, and composite reliability(CR) ranging from 0.905 to 0.938. The standard loading coefficients for the items ranged from 0.608 to 0.859, and inter-factor correlation coefficients were all smaller than the square roots of their respective AVE values. Pearson correlation analysis indicated significant positive relationships between the Chinese NPSS and SQ ( r=0.822-0.846, P<0.01). Reliability analysis showed Cronbach′s alpha coefficients of 0.903-0.959 for the total scale and subscales. After a 3-week interval, test-retest reliability (70 patients) ranged from 0.874 to 0.983, and split-half reliability was 0.869-0.969. All model fit indices met established criteria. Conclusions:The Chinese version of the NPSS demonstrates good reliability and validity, making it suitable for both research and clinical applications in assessing psychological security among individuals with schizophrenia and mood disorders.

BACKGROUND: Glutamine synthetase (GS) and arginase 1 (Arg1) are widely used pathological markers that discriminate hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma; however, their clinical significance in HCC remains unclear. METHODS: We retrospectively analyzed 431 HCC patients: 251 received hepatectomy alone, and the other 180 received sorafenib as adjuvant treatment after hepatectomy. Expression of GS and Arg1 in tumor specimens was evaluated using immunostaining. mRNA sequencing and immunostaining to detect progenitor markers (cytokeratin 19 [CK19] and epithelial cell adhesion molecule [EpCAM]) and mutant TP53 were also conducted. RESULTS: Up to 72.4% (312/431) of HCC tumors were GS positive (GS+). Of the patients receiving hepatectomy alone, GS negative (GS-) patients had significantly better overall survival (OS) and recurrence-free survival (RFS) than GS+ patients; negative expression of Arg1, which is exclusively expressed in GS- hepatocytes in the healthy liver, had a negative effect on prognosis. Of the patients with a high risk of recurrence who received additional sorafenib treatment, GS- patients tended to have better RFS than GS+ patients, regardless of the expression status of Arg1. GS+ HCC tumors exhibit many features of the established proliferation molecular stratification subtype, including poor differentiation, high alpha-fetoprotein levels, increased progenitor tumor cells, TP53 mutation, and upregulation of multiple tumor-related signaling pathways. CONCLUSIONS GS- HCC patients have a better prognosis and are more likely to benefit from sorafenib treatment after hepatectomy. Immunostaining of GS may provide a simple and applicable approach for HCC molecular stratification to predict prognosis and guide targeted therapy.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Sorafenib/therapeutic use* ; Liver Neoplasms/metabolism* ; Glutamate-Ammonia Ligase/metabolism* ; Hepatectomy ; Retrospective Studies ; Prognosis ; Neoplasm Recurrence, Local/surgery*

In early stage after liver transplantation(LT), coagulation function of recipients stays in a fragile balance. Affected by a variety of complex mechanisms, blood is usually hypercoagulable. An imbalance between coagulation factors and physiological anticoagulants, elevated level of vWF, an occurrence of fibrinolysis inhibition and dosing of immunosuppressive agents cause a hypercoagulable state in an early stage after LT. Blood hypercoagulability may lead to such thrombotic complications as hepatic artery, portal vein and deep vein thromboses. Some studies have demonstrated that postoperative prophylactic anticoagulation has some effect in reducing the risks of early postoperative thrombosis. However, there is still a great lack of high-quality evidence. This review summarized the latest researches on early coagulation dysfunction, thrombosis and preventive anticoagulation after LT.