Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.

Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.

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.

Hip fracture in the elderly is characterized by high incidence, high disability rate, and high mortality and has been recognized as a public health issue threatening their health. Surgery is the preferred choice for the treatment of elderly patients with hip fracture. However, lower extremity deep venous thrombosis (DVT) has an extremely high incidence rate during the perioperative period, and may significantly increase the risk of patients′ death once it progresses to pulmonary embolism. In response to this issue, the clinical guidelines and expert consensuses all emphasize active application of comprehensive preventive measures, including basic prevention, physical prevention, and pharmacological prevention. In this prevention system, basic prevention is the basis of physical and pharmacological prevention. However,there is a lack of unified and definite recommendations for basic preventive measures in clinical practice. To this end, the Orthopedic Nursing Professional Committee of the Chinese Nursing Association and Nursing Department of the Orthopedic Branch of the China International Exchange and Promotive Association for Medical and Health Care organized relevant nursing experts to formulate Expert consensus on perioperative basic prevention for lower extremity deep venous thrombosis in elderly patients with hip fracture ( version 2024) . A total of 10 recommendations were proposed, aiming to standardize the basic preventive measures for lower extremity DVT in elderly patients with hip fractures during the perioperative period and promote their subsequent rehabilitation.

Abstract: Objective To conduct genotyping and molecular tracing analysis on Plasmodium vivax samples from a cluster of P. vivax malaria outbreak in order to provide a reference for case geographical origin determination. Methods Blood samples from 4 patients in a vivax malaria cluster in Longhui County, Hunan Province from June to July 2018 were collected for species identification by qPCR, and 9 microsatellite molecular markers were used to genotype the parasite strains from four samples. The population genetic STRUCTURE analysis was performed based on the VivaxGEN-MS microsatellite genotype database of P. vivax in the Asia Pacific Malaria Elimination Network, to determine the genetic subgroups and geographical origin of the strains. Results By qPCR, all 4 cases were identified as Plasmodium vivax infection, and 9 microsatellite loci of the 4 cases were successfully typed, and the four samples had different genetic haplotypes, among which case 1, case 3, and case 4 were infected by a single clonal strain, and case 2 was infected by a polyclonal strain. When all P. vivax samples were divided into 2 subpopulations (K=2) by STURCTURE analysis, 4 Hunan samples were classified into tropical genetic subpopulations (comprising strains from Ethiopia, Iran, Bhutan, Malaysia, Indonesia, and southern China). When the samples were divided into 4 subgroups by STURCTURE analysis (K=4), the 4 Hunan samples were classified as South Asian/Southeast Asian genetic subgroups (originating from Bhutan, Malaysia, Indonesia, and southern China). Conclusions The results of molecular tracing do not support that the 4 P. vivax strains in this outbreak originated from the population of central China. The technology of molecular tracing of P. vivax can provide objective evidence for determining the source of infection in malaria cases during the stage of malaria elimination and post-elimination.

Objective:To explore the clinical features of immunocompetent primary central nervous system lymphoma (PCNSL) and influencing factors for prognosis of immunocompetent patients with PCNSL.Methods:A retrospective analysis was performed; 69 immunocompetent patients with PCNSL confirmed by pathology in First Medical Center of PLA General Hospital from January 2016 to January 2024 were enrolled; initial symptoms, Eastern Cooperative Oncology Group (ECOG) score, and results of laboratory and pathological examinations in these patients were collected. Patients were divided into biopsy confirmed group ( n=43) and lesion resection confirmed group ( n=26) according to different diagnostic methods; patients were also divided into chemotherapy group ( n=48), chemotherapy+radiotherapy group ( n=9) and surgical resection group ( n=12) according to different treatment methods. Clinical outcomes of these patients in different groups at the end of follow-up were compared, and the influencing factors for short-term prognosis (6 months after treatment) were identified. All patients were followed up for 12.80 (6.00, 36.40) months. The short-term prognosis was evaluated by modified Rankin scale (mRS) 6 months after treatment (mRS scores of 0-2: good prognosis; mRS scores of 3-6: poor prognosis). Overall survival (OS) was recorded at the end of follow-up. Results:Among the 69 immunocompetent patients with PCNSL, 37 were males and 32 were females; median onset age was 59 years, ranged 24-83 years. Focal neurologic deficits of different degrees (34/69, limb weakness, sensory disturbances, ataxia, or eye involvement) were the most common initial symptoms, followed by headache (14/69), dizziness (10/69), cognitive dysfunction (9/69), epilepsy (1/69) and psychiatric disorders (1/69). Forty-five patients underwent cerebrospinal fluid examination: 17 had cerebrospinal fluid pressure≥200 mmH 2O (1 mmH 2O=9.8 Pa); 10 had increased white blood cell count (>10×10 6/L), reaching to (16.5[11.0, 20.0])×10 6/L; 32 had increased protein level, reaching to 758.10 (547.83, 948.13) mg/L. Cerebrospinal fluid cytology was performed in 15 patients, and tumor cells were found in only 1 patient. Cranial MRI showed that intracranial solitary lesions were more common (60.87%, 42/69), and most lesions were at the basal ganglia region (40.58%, 28/69). PET/CT showed a obviously higher metabolism of the lesions (97.06, 33/34), with maximum standardized uptake of 22.9 (13.9, 30.55) g/mL. All patients had diffuse large B-cell lymphoma (DLBCL). By the end of follow-up, 28 patients died. Logistic regression analysis showed that ECOG score≥2 ( OR=9.210, 95% CI: 2.558-32.896, P=0.001) and positive MYC ( OR=0.088, 95% CI: 0.008-0.973, P=0.047) were independent risk factors for poor short-term prognosis. Cox proportional hazard regression model analysis showed that ECOG score≥2 ( HR=5.135, 95% CI: 2.230-11.827, P<0.001), positive B-cell lymphoma 6 (BCL-6, HR=0.226, 95% CI: 0.079-0.649, P=0.006) and chemotherapy or chemotherapy+radiotherapy ( HR=0.392, 95% CI: 0.157-0.980, P=0.045) were independent prognostic factors for OS. Conclusions:In immunocompetent patients with PCNSL, focal neurological deficits are more common at the onset, and fever is rare. Patients with ECOG score≥2 are more likely to have poor short-term prognosis and short OS. MYC-positive patients will have a better short-term prognosis; BCL-6 positive patients and patients treated with chemotherapy or chemotherapy+radiotherapy will have longer OS.

Objective:To explore the clinical features of immunocompetent primary central nervous system lymphoma (PCNSL) and influencing factors for prognosis of immunocompetent patients with PCNSL.Methods:A retrospective analysis was performed; 69 immunocompetent patients with PCNSL confirmed by pathology in First Medical Center of PLA General Hospital from January 2016 to January 2024 were enrolled; initial symptoms, Eastern Cooperative Oncology Group (ECOG) score, and results of laboratory and pathological examinations in these patients were collected. Patients were divided into biopsy confirmed group ( n=43) and lesion resection confirmed group ( n=26) according to different diagnostic methods; patients were also divided into chemotherapy group ( n=48), chemotherapy+radiotherapy group ( n=9) and surgical resection group ( n=12) according to different treatment methods. Clinical outcomes of these patients in different groups at the end of follow-up were compared, and the influencing factors for short-term prognosis (6 months after treatment) were identified. All patients were followed up for 12.80 (6.00, 36.40) months. The short-term prognosis was evaluated by modified Rankin scale (mRS) 6 months after treatment (mRS scores of 0-2: good prognosis; mRS scores of 3-6: poor prognosis). Overall survival (OS) was recorded at the end of follow-up. Results:Among the 69 immunocompetent patients with PCNSL, 37 were males and 32 were females; median onset age was 59 years, ranged 24-83 years. Focal neurologic deficits of different degrees (34/69, limb weakness, sensory disturbances, ataxia, or eye involvement) were the most common initial symptoms, followed by headache (14/69), dizziness (10/69), cognitive dysfunction (9/69), epilepsy (1/69) and psychiatric disorders (1/69). Forty-five patients underwent cerebrospinal fluid examination: 17 had cerebrospinal fluid pressure≥200 mmH 2O (1 mmH 2O=9.8 Pa); 10 had increased white blood cell count (>10×10 6/L), reaching to (16.5[11.0, 20.0])×10 6/L; 32 had increased protein level, reaching to 758.10 (547.83, 948.13) mg/L. Cerebrospinal fluid cytology was performed in 15 patients, and tumor cells were found in only 1 patient. Cranial MRI showed that intracranial solitary lesions were more common (60.87%, 42/69), and most lesions were at the basal ganglia region (40.58%, 28/69). PET/CT showed a obviously higher metabolism of the lesions (97.06, 33/34), with maximum standardized uptake of 22.9 (13.9, 30.55) g/mL. All patients had diffuse large B-cell lymphoma (DLBCL). By the end of follow-up, 28 patients died. Logistic regression analysis showed that ECOG score≥2 ( OR=9.210, 95% CI: 2.558-32.896, P=0.001) and positive MYC ( OR=0.088, 95% CI: 0.008-0.973, P=0.047) were independent risk factors for poor short-term prognosis. Cox proportional hazard regression model analysis showed that ECOG score≥2 ( HR=5.135, 95% CI: 2.230-11.827, P<0.001), positive B-cell lymphoma 6 (BCL-6, HR=0.226, 95% CI: 0.079-0.649, P=0.006) and chemotherapy or chemotherapy+radiotherapy ( HR=0.392, 95% CI: 0.157-0.980, P=0.045) were independent prognostic factors for OS. Conclusions:In immunocompetent patients with PCNSL, focal neurological deficits are more common at the onset, and fever is rare. Patients with ECOG score≥2 are more likely to have poor short-term prognosis and short OS. MYC-positive patients will have a better short-term prognosis; BCL-6 positive patients and patients treated with chemotherapy or chemotherapy+radiotherapy will have longer OS.

Objectives:To investigate the clinical and genetic features of young Chinese patients with myeloproliferative neoplasms (MPN) .Methods:In this cross-sectional study, anonymous questionnaires were distributed to patients with MPN patients nationwide. The respondents were divided into 3 groups based on their age at diagnosis: young (≤40 years) , middle-aged (41-60 years) , and elderly (>60 years) . We compared the clinical and genetic characteristics of three groups of MPN patients.Results:1727 assessable questionnaires were collected. There were 453 (26.2%) young respondents with MPNs, including 274 with essential thrombocythemia (ET) , 80 with polycythemia vera (PV) , and 99 with myelofibrosis. Among the young group, 178 (39.3%) were male, and the median age was 31 (18-40) years. In comparison to middle-aged and elderly respondents, young respondents with MPN were more likely to present with a higher proportion of unmarried status (all P<0.001) , a higher education level (all P<0.001) , less comorbidity (ies) , fewer medications (all P<0.001) , and low-risk stratification (all P<0.001) . Younger respondents experienced headache (ET, P<0.001; PV, P=0.007; MF, P=0.001) at diagnosis, had splenomegaly at diagnosis (PV, P<0.001) , and survey (ET, P=0.052; PV, P=0.063) . Younger respondents had fewer thrombotic events at diagnosis (ET, P<0.001; PV, P=0.011) and during the survey (ET, P<0.001; PV, P=0.003) . JAK2 mutations were found in fewer young people (ET, P<0.001; PV, P<0.001; MF, P=0.013) ; however, CALR mutations were found in more young people (ET, P<0.001; MF, P=0.015) . Furthermore, mutations in non-driver genes (ET, P=0.042; PV, P=0.043; MF, P=0.004) and high-molecular risk mutations (ET, P=0.024; PV, P=0.023; MF, P=0.001) were found in fewer young respondents. Conclusion:Compared with middle-aged and elderly patients, young patients with MPN had unique clinical and genetic characteristics.