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 explore the efficacy and complication rates of the transcranial approach(TCA)and extended endoscopic endonasal approach(EEEA)for the treatment of craniopharyngiomas based on the QST classification,providing a scientific reference for clinical decision-making on surgical approach.Methods A total of 151 patients who underwent craniopharyngioma surgery at our center from January 2018 to December 2023 were enrolled.The patients were categorized into Q-CP(suprasellar type),S-CP(infundibular type),and T-CP(tuberal type)according to the QST classification.Systematic collection and analysis were performed on the outcomes of TCA and EEEA treatments,respectively.The differences in effectiveness between the two approaches were evaluated based on the QST classification.Results The improvement rate of visual symptoms was overall higher in the EEEA group than in the TCA group(59.1%vs.36.5%,P=0.006),and the visual deterioration rate was lower(3.0%vs.14.1%,P=0.006).However,the incidence of cerebrospinal fluid leakage was higher in the EEEA group(15.2%vs.3.5%,χ2=4.986,P=0.026).The incidence of postoperative seizures(8.2%vs.0,P=0.019),brain contusions(10.6%vs.0,P=0.005),and subdural hematoma(9.4%vs.0%,P=0.01)was higher in the TCA group.For patients with Q-CP type,the EEEA group had a higher rate of total tumor resection(92.9%vs.65.2%,P=0.025)and a lower recurrence rate(3.6%vs.21.7%,P=0.047),with shorter hospital stays and lower postoperative costs.The TCA group had higher intraoperative blood loss in this type(300 mL vs.200 mL,Z=-2.261,P=0.024).For S-CP type patients,the EEEA group showed a higher rate of total tumor resection(91.3%vs.74.2%)and a lower recurrence rate(0 vs.12.9%,P=0.031),with lower postoperative hospital costs.In T-CP type,due to the deeper location,EEEA showed limitations in protecting hypothalamic function and the TCA group had a better postoperative hypothalamic function score(P=0.035).Conclusion Based on QST classification,EEEA has advantages in Q-CP and S-CP types and is recommended as the preferred surgical procedure;In the T-CP type,TCA surgery is more helpful in protecting hypothalamic function.

BACKGROUND: Pulmonary arterial hypertension (PAH) presents a significant health burden in Asia and remains a critical challenge. This study aims to delineate the PAH burden in Asia from 1990 to 2021. METHODS: Using the latest data from the Global Burden of Disease 2021, we evaluated and analyzed the distributions and patterns of PAH disease burden among various age groups, sexes, regions, and countries in Asia. Additionally, we examined the associations between PAH disease burden and key health system indicators, including the socio-demographic index (SDI) and the universal health coverage (UHC) index. RESULTS: In 2021, there were 25,989 new PAH cases, 103,382 existing cases, 13,909 PAH-associated deaths, and 385,755 DALYs attributed to PAH in Asia, which accounted for approximately 60% of global PAH cases. The age-standardized rates (ASRs) for prevalence and deaths were 2.05 (95% uncertainty interval [UI]: 1.66-2.52) per 100,000 population and 0.31 (95% UI: 0.23-0.38) per 100,000 population, respectively. From 1990 to 2021, Asia reported the lowest ASRs for PAH prevalence but the highest ASRs for deaths compared to other continents. While the ASRs for prevalence increased slightly, ASRs for mortality and DALYs decreased over time. This increasing burden of PAH was primarily driven by population growth and aging. The burden was especially pronounced among individuals aged ≥60 years and <9 years, who collectively accounted for the majority of deaths and DALYs. Moreover, higher SDI and UHC levels were linked to reduced incidence, but higher prevalence rates. CONCLUSIONS Although progress has been made in reducing PAH-related mortality and DALYs, the disease continues to impose a substantial burden in Asia, particularly among older adults and young children. Region-specific health policies should focus on improving early diagnosis, expanding access to treatment, and effectively addressing the growing PAH burden in the region.
Humans ; Global Burden of Disease ; Male ; Female ; Middle Aged ; Adult ; Asia/epidemiology* ; Prevalence ; Aged ; Pulmonary Arterial Hypertension/mortality* ; Adolescent ; Young Adult ; Child ; Child, Preschool ; Infant ; Hypertension, Pulmonary/epidemiology*

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The European Society for Medical Oncology Asia Congress 2024 was held in Singapore from December 6 to 8,2024.The conference unveiled several groundbreaking studies in the field of hepatobiliary and pancreatic tumors,covering clinical applications related to neoadjuvant and adjuvant therapies,translational treatments,later-line therapies,and tumor biomarkers.These studies provide new insights into the clinical diagnosis and treatment of hepatobiliary and pancreatic malignancies and drive the development of related fields.This article focuses on the key topics in hepatobiliary and pancreatic malignancies presented at the conference,aiming to interpret the latest advances in the field and explore the hot issues and future directions for development in this area.

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 evaluate the safety and efficacy of accelerator-based boron neutron capture therapy (AB-BNCT) in the treatment of recurrent and refractory malignant tumors.Methods:The data of 14 patients admitted to Xiamen Humanity Hospital from September 2022 to April 2023 were prospectively collected, including 7 patients with primary brain malignancies and 7 patients with locally recurrent inoperable head and neck malignancies. All patients received intravenous infusion of boron drug (NBB-001, p-dihydroxyborylphe nylalanine, a patented freeze-dried formulation) at a total nominal dosage of 500 mg/kg (11 patients) or 750 mg/kg (3 patients), and were irradiated with neutrons (operating with NeuPex system). Adverse events after treatment were recorded and assessed. The primary efficacy endpoint was the 90 d objective response rate (ORR), while the secondary endpoints included progression-free survival (PFS) and complete response rate (CRR). Data were compiled and analyzed by SAS 9.4 software. The rate and 95% CI were calculated using Clopper-Pearson method. Results:The median dose delivered to 80% of the target volume (D 80%) was 16.80 GyE (range: 8.93-23.79 GyE). The most common adverse reactions were hyperamylasemia, alopecia, and hyperprolactinemia. Five patients experienced 8 cases of grade 3 or above adverse events, including 1 case of grade 4 acute kidney injury and 7 cases of grade 3 adverse events. All adverse events were recovered after observation or treatment. At 90 d after treatment, the ORR of all patients was 9/14 (64%, 95% CI: 35%-87%), disease control rate (DCR) was 10/14 (71%, 95% CI: 42%-92%), CRR was 2/14 (14%, 95% CI: 2%-42%); and the best overall response during the entire course included an ORR of 10/14 (71% ,95% CI: 42%-92%), DCR of 13/14 (93%, 95% CI: 66%-100%), and CRR of 3/14 (21% ,95% CI: 5%-51%). The 1-year survival rate for head and neck malignancies was 71.4%, and the 2-year survival rate was 42.8%. The 1-year survival rate for recurrent brain malignancies was 42.8%. Conclusion:AB-BNCT demonstrates favorable safety and promising efficacy in treating primary brain malignancies and recurrent/refractory head and neck malignancies, representing a potential therapeutic option.

Objective This study aims to explore the efficacy and complication rates of the transcranial approach(TCA)and extended endoscopic endonasal approach(EEEA)for the treatment of craniopharyngiomas based on the QST classification,providing a scientific reference for clinical decision-making on surgical approach.Methods A total of 151 patients who underwent craniopharyngioma surgery at our center from January 2018 to December 2023 were enrolled.The patients were categorized into Q-CP(suprasellar type),S-CP(infundibular type),and T-CP(tuberal type)according to the QST classification.Systematic collection and analysis were performed on the outcomes of TCA and EEEA treatments,respectively.The differences in effectiveness between the two approaches were evaluated based on the QST classification.Results The improvement rate of visual symptoms was overall higher in the EEEA group than in the TCA group(59.1%vs.36.5%,P=0.006),and the visual deterioration rate was lower(3.0%vs.14.1%,P=0.006).However,the incidence of cerebrospinal fluid leakage was higher in the EEEA group(15.2%vs.3.5%,χ2=4.986,P=0.026).The incidence of postoperative seizures(8.2%vs.0,P=0.019),brain contusions(10.6%vs.0,P=0.005),and subdural hematoma(9.4%vs.0%,P=0.01)was higher in the TCA group.For patients with Q-CP type,the EEEA group had a higher rate of total tumor resection(92.9%vs.65.2%,P=0.025)and a lower recurrence rate(3.6%vs.21.7%,P=0.047),with shorter hospital stays and lower postoperative costs.The TCA group had higher intraoperative blood loss in this type(300 mL vs.200 mL,Z=-2.261,P=0.024).For S-CP type patients,the EEEA group showed a higher rate of total tumor resection(91.3%vs.74.2%)and a lower recurrence rate(0 vs.12.9%,P=0.031),with lower postoperative hospital costs.In T-CP type,due to the deeper location,EEEA showed limitations in protecting hypothalamic function and the TCA group had a better postoperative hypothalamic function score(P=0.035).Conclusion Based on QST classification,EEEA has advantages in Q-CP and S-CP types and is recommended as the preferred surgical procedure;In the T-CP type,TCA surgery is more helpful in protecting hypothalamic function.

Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. In recent years, metabolomics has emerged as a key systems biology approach for analyzing small-molecule metabolites in cells, tissues and organisms. It provides new strategies for early diagnosis and metabolic profiling. Additionally, metabolomics plays a crucial role in studying resistance mechanisms in lung cancer. Tumor cell metabolic reprogramming is a key driving factor in the initiation and progression of lung cancer. Metabolomics studies have revealed how lung cancer cells regulate critical pathways such as energy metabolism, lipid metabolism, and amino acid metabolism to adapt to the demands of rapid proliferation and invasive metastasis. This review summarizes the latest advances in metabolomics research in lung cancer, focusing on the characteristics of metabolic reprogramming, the identification of potential metabolic biomarkers, and the prospects of metabolomics in early diagnosis and the elucidation of resistance mechanisms in lung cancer.
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Humans ; Metabolomics/methods* ; Lung Neoplasms/pathology* ; Animals ; Biomarkers, Tumor/metabolism*

Objective:To retrospectively analyze the results of auditory examination,vestibular function examination and laboratory examination of 63 patients diagnosed as vestibular neuritis.Methods:A total of 63 patients diagnosed with vestibular neuritis hospitalized in the Department of Otolaryngology, Head and Neck Surgery of the Third Affiliated Hospital of Sun Yat-sen University, from October 2012 to December 2022 were recruited. All patients met the diagnostic criteria for the 2022 Bárány association vestibular neuritis. Clinical data and the results of pure tone audiometry, electrocochleogram, video electronystagmogram, caloric test, cervical vestibuloevoked myogenic potential（cVEMP）, ocular vestibuloevoked myogenic potential（oVEMP）, video head impulse test（vHIT） was collected.A total of 63 age-and sex-matched healthy subjects in the physical examination center were randomly selected as the control group. The differences of blood indexs and lipid metabolism indexes between the two groups were compared. Results:In patients with vestibular neuritis, 50 out of 63 patients presented normal threshold in pure tone audiometry, 8 out of 63 patients had bilateral high-frequency sensorineural hearing loss and 5 out of 63 patients had unilateral mild high-frequency sensorineural hearing loss, 56 out of 63 cases completed the electrocochleogram, of which 3 cases had a binaural-SP/AP amplitude ratio≥0.4, 5 cases had monaural amplitude ratio ≥0.4. Fifty-five out of 63 patients completed the caloric test with CP values greater than 30% in all. The ratio of patients completed cVEMP, oVEMP and vHIT were 46 cases, 22 cases and 30 cases, respectively. 17 out of 63 cases completed all the four vestibular function tests. According to these tests, 49 patients could determine the extent of injury，including 27 cases with unilateral superior vestibular nerve injury, 21 cases with unilateral superior and inferior vestibular nerve injury and 1 case with unilateral inferior vestibular nerve injury. There were significant differences in neutrophil value（P<0.001）, lymphocyte value（P<0.005）, neutrophil/lymphocyte ratio（P<0.001） and apolipoprotein A1（P<0.001） between patient group and control group. Inflammatory markers were risk factors for patients with vestibular neuritis. The OR values of neutrophil value and blood neutrophil/lymphocyte ratio were 1.81（1.38-2.37, P<0.001） and 2.11（1.41-3.16, P<0.001）, respectively. Apolipoprotein A1 was a protective factor for patients with vestibular neuritis, and the OR value was 0.004（0.001-0.042, P<0.001）. Conclusion:Electrocochleogram could be used in vestibular neuritis patients with normal pure tone threshold to test whether there is hidden hearing loss in these patients. Neutrophil value, lymphocyte value, neutrophil/lymphocyte ratio and apolipoprotein A1 were correlated with vestibular neuritis. The Neutrophil value and neutrophil/lymphocyte ratio were risk factors for morbidity.
Humans ; Vestibular Neuronitis/physiopathology* ; Retrospective Studies ; Female ; Male ; Audiometry, Pure-Tone ; Hearing Loss, Sensorineural/physiopathology* ; Middle Aged ; Adult ; Vestibular Function Tests ; Vestibular Evoked Myogenic Potentials ; Aged