OBJECTIVE: To investigate the clinical efficacy and advantages of robot-assisted total knee arthroplasty (TKA) in patients with varus knee osteoarthritis. METHODS: Between October 2022 and June 2023, a total of 59 patients with severe knee osteoarthritis resulting in varus were treated with total knee arthroplasty, aged from 59 to 81 years with an average (70.90±4.63) years, including 19 mals and 40 females. The patients were divided into two groups based on the surgical method used:28 patients in the robot group and 31 patients in the traditional group. The robot group consisted of 8 males and 20 femalse patients, with an average age of (70.54±4.80) years and an average disease duration of (14.89±8.72) months. The traditional group consisted of 11 males and 20 females patients, with an average age of (71.39±4.5) years and an average disease duration of (12.32±6.73) months. The operative duration, amount of bleeding during the operation, postoperative activity time after the operation, hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), and complications were compared between the two groups before and after the operation. Lateral tibia component (LTC), frontal tibia component (FTC), frontal femoral component (FFC) and lateral femoral component (LFC) were measured 6 months after operation Additionally, the degree of knee joint motility, American Knee Society score (KSS), and visual analogue scale(VAS) were compared before and after the operation. RESULTS: All patients had gradeⅠwound healing without any complications, and all patients were followed up for 6 to 8 months, with an average of (6.5±1.5) months. There were no significant differences preoperative imaging evaluation indexes (including HKA, LDFA, and MPTA), preoperative knee mobility, preoperative VAS, and preoperative KSS between the two groups (P>0.05). Comparing the operation time (109.11±7.16) min vs. (83.90±7.85) min, length of the incision (16.60±2.33) cm vs. (14.47±1.41) cm, intraoperative bleeding (106.93±6.15) ml vs. (147.97±7.62) ml, postoperative activity time (17.86±1.84) h vs. (21.77±2.68) h, between the two groups showed statistically significant differences (P<0.05). There were significant differences in FFC (88.96±0.84)° vs. (87.93±1.09)° and LFC (88.57±1.10)° vs. (87.16±1.2)° between the two groups at 6 months after operation (P<0.05). The robotic group 1, 3, 6 months after KSS (75.96±3.96), (81.53±3.78), (84.50±3.29) scores, VAS (3.68±0.67), (2.43±0.79), (0.54±0.64), knee joint mobility (113.32±4.72) °, (123.93±3.99) °, (135.36±2.34) °;Traditional group KSS (73.77±4.18), (76.48±3.60), (80.19±3.28) scores, VAS (4.16±1.04), (3.03±0.75), (1.42±0.76) scores, knee joint mobility (109.19±6.95) °, (119.94±6.08) °, (134.48±2.14) °. Compared to before surgery, both groups showed significant improvement in KSS, VAS and knee mobility during the three follow-up visits (P<0.001). Additionally, postoperative HKA (180.39±1.95)° vs. (178.52±2.23)°, LDFA (89.67±0.63) ° vs. (89.63±0.63)°, and MPTA (89.44±0.55)° vs. (89.29±0.60)° were significantly improved in both groups compared to before surgery (P<0.001). The robotic group had higher KSS than the traditional group at 1, 3, and 6 months after surgery (P<0.05). The robotic group also had lower VAS than the traditional group at 1, 3, and 6 months after surgery (P<0.05). Furthermore, knee mobility was higher in the robotic group than those in the traditional group at 1 and 6 months after surgery (P<0.05), but there was no significant difference between the two groups at 6 months after surgery. CONCLUSION Robot-assisted total knee arthroplasty is a safe and effective method for total knee replacement. The use of robotics can improve the limb axis and prosthesis alignment for patients with preoperative varus deformity, resulting in better clinical and imaging outcomes compared to the conventional group.
Humans ; Female ; Male ; Arthroplasty, Replacement, Knee/methods* ; Aged ; Middle Aged ; Osteoarthritis, Knee/physiopathology* ; Aged, 80 and over ; Robotic Surgical Procedures/methods*

OBJECTIVE To analyze the risk factors contributing to piracetam-associated thrombocytopenia and develop a predictive model for risk prediction. METHODS The electronic medical record information of inpatients treated with piracetam was collected retrospectively from the First Affiliated Hospital of Guangxi Medical University from January 2021 to December 2023， including gender， age， underlying diseases， combined medication， and laboratory data， etc. Patients were divided into the occurrence group and the non-occurrence group according to whether thrombocytopenia occurred， and the differences in clinical data between the two groups were compared. The independent risk factors were determined through univariate/multivariate Logistic regression analysis. A nomogram was drawn to visually present the independent risk factors， and a risk prediction model was constructed. The predictive efficacy of the model was evaluated using the receiver operating characteristic （ROC） curve， Bootstrap internal validation and calibration curve. RESULTS A total of 224 patients were included， among which 196 cases were in the non- occurrence group and 28 cases in the occurrence group. The incidence of thrombocytopenia was 12.50%. The results of the univariate Logistic regression analysis showed that the proportion of patients using three or more combined antibiotics and the level of serum creatinine in the occurrence group were significantly higher than those in the non-occurrence group， while the level of hemoglobin was significantly lower （P＜0.05）. The results of the multivariate Logistic regression analysis revealed that the use of three or more combined antibiotics， low hemoglobin level and high serum creatinine level were independent risk factors for piracetam-associated thrombocytopenia （P＜0.05）. The constructed risk prediction model was LogitP＝ －1.114+1.256×three or more combined antibiotics－0.017×hemoglobin level+0.009×serum creatinine level. The AUC of the ROC curve of this model was 0.757， and the optimal cut-off value was 0.474； the AUC of the ROC curve of the Bootstrap internal validation was 0.733； the apparent curve and the bias-corrected curve were close to the ideal curve. CONCLUSIONS The use of three or more antibiotics， along with low hemoglobin level and high serum creatinine level， are identified as independent risk factors for piracetam-associated thrombocytopenia. The developed risk prediction model demonstrates good predictive value.

Haloacetic acids(HAAs),as a class of disinfection byproducts in drinking water,pose potential threats to human health,so the rapid,accurate and simultaneous detection of HAAs is of great significance for ensuring drinking water safety.Aiming at the challenges in HAAs detection and risk analysis,a novel method for synchronous rapid detection of seven kinds of HAAs in drinking water based on in situ derivatization technology and headspace gas chromatography was developed in this study.Through single-factor optimization experiments,the optimal reaction parameters for in situ derivatization were determined,including the type and dosage of salting-out agent,the acidity of reaction system,the amount of phase transfer catalyst,the dosage of derivatization agent,and the extraction solvent volume.Methodologic validation showed that the seven kinds of HAAs exhibited excellent linear relationships within their respective detection concentration ranges(R2>0.998).The method detection limits(MDLs)ranged from 0.04 to 0.33 μg/L,and the limits of quantification(LOQs)were between 0.14 and 1.34 μg/L.For real water samples,the average spiked recoveries of the seven HAAs ranged from 90.9%to 107.7%,with relative standard deviation(RSDs)between 1.55%and 6.49%,and the HAAs contents in all tested samples were below the limits specified in the Standards for Drinking Water Quality(GB 5749-2022)of China.This method was featured with simple operation,fast analysis speed,high sensitivity,and good accuracy,providing an efficient and reliable technical support for routine monitoring of HAAs contaminants in drinking water and showing promising application value for widespread promotion.

Excessive fat accumulation,viral infections and sustained inflammatory responses caused by non-alcoholic and alcoholic factors can contribute to liver inflammation,fibrosis and carcinogenesis,promoting the development of chronic liver disease.Gaining an in-depth understanding of the etiologic factors and underlying mechanisms that lead to chronic liver disease can help identify potential therapeutic targets for targeted therapy.Lactate,as an important substance in hepatic metabolism,has been found to be involved in the process of chronic liver disease through various pathways,and this review will provide a useful reference for the prevention and treatment of chronic liver disease.

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.

Excessive fat accumulation,viral infections and sustained inflammatory responses caused by non-alcoholic and alcoholic factors can contribute to liver inflammation,fibrosis and carcinogenesis,promoting the development of chronic liver disease.Gaining an in-depth understanding of the etiologic factors and underlying mechanisms that lead to chronic liver disease can help identify potential therapeutic targets for targeted therapy.Lactate,as an important substance in hepatic metabolism,has been found to be involved in the process of chronic liver disease through various pathways,and this review will provide a useful reference for the prevention and treatment of chronic liver disease.

Objective:To explore the role of nuclear receptor-binding SET-domain protein 1(NSD1)in the pathogenesis of nonobstructive azoospermia(NOA)by regulating the expressions of relevant genes.Methods:We detected the expression of NSD1 in the testis tissue of 7 male patients with obstructive azoospermia(OA)and 18 with NOA by qPCR and immunofluorescence assay,and determined the modification level of H3K36me2 in the testes of two groups of patients by immunofluorescence staining,Western blot and immunoprecipitation(IP).We examined the difference in the enrichment of H3K36me2 in the testis tissue by chromatin IP-based sequencing(ChIP-Seq),analyzed the genomic distribution and target genes using bioinformatics,and verified the expression levels of the target genes in the testes of the two groups of patients by qPCR.Results:Compared with the patients with OA,those with NOA showed dramatically decreased mRNA and protein expressions of NSD1(P=0.000 8).The binding of NSD1 to H3K36me2 was observed in the testis tissue of both the two groups of patients,while the modification level of H3K36me2 was evidently reduced in the NOA males.H3K36me2 was distributed mainly in the intergenic region in the testes of the two groups of patients,but the enrich-ment of H3K36me2 was obviously decreased in the NOA group.The differentially H3K36me2-enriched genes were involved in various biological processes,including tissue development,and cell morphogenesis.Results of ChIP-Seq and qPCR showed significantly down-regulated expressions of the target genes KIT,SPO11 and ACRV1 in the testis tissue of the NOA males compared with those in the OA patients(P＜0.01).Conclusion:The levels of NSD1 and H3K36me2 are decreased in testis tissue of the NOA patient,H3K36me2 is highly enriched in the spermatogenesis-related key genes KIT,SPO11 and ACRV1,and the down-regulated expression of NSD1 impairs spermatogenesis.

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 explore the efficacy and safety of retrograde intrarenal surgery (RIRS) using a flexible negative pressure suction sheath in the treatment of upper urinary tract stones >2 cm in diameter, to provide reference for the diagnosis and treatment of such disease. [Methods] Clinical data of 155 patients who underwent surgery for upper urinary tract stones during Nov.2022 and Nov.2023 at the Second Affiliated Hospital of Shaanxi University of Chinese Medicine were retrospectively analyzed.The patients were divided into 3 groups: percutaneous nephrolithotripsy (PCNL) group (n=54), conventional sheath RIRS group (n=41), and flexible sheath RIRS group (n=60). The general and clinical data of the 3 groups were compared. [Results] The PCNL group had more patients with severe hydronephrosis (22.22% vs. 4.88%, 5.00%, P=0.027) and smaller IPA involving the lower calyx [(36.17±17.6)° vs. (48.57±17.56)°, (47.41±10.82)°, P=0.014] than the conventional sheath RIRS group and flexible sheath RIRS group.Three days after operation, the stone-free rate (SFR) was 90.74%, 53.66% and 78.33% in the PCNL, conventional sheath RIRS, and flexible sheath RIRS groups, respectively (P<0.05). At 1 month postoperatively, the SFR was 92.59%, 73.17%, and 81.67%, with no statistically significant difference between the PCNL and flexible sheath RIRS groups (P>0.05), but was higher than that in the conventional sheath RIRS group (P<0.05). The PCNL group had shorter operation time than the two RIRS groups [(65.22±17.67) min vs. (91.73±20.57) min, (94.38±24.75) min, P<0.001], longer postoperative hospital stay [(5.0(4.0, 7.0) d vs.3.0(2.0, 4.0) d, 3.0(2.0, 4.0) d, P<0.001], greater decrease in hemoglobin level [(18.00±5.78) g/L vs. (5.57±5.16) g/L, (7.42±5.09) g/L, P<0.001], and higher visual analogue scale (VAS) score [(4.83±1.48) min vs. (2.95±1.07) min, (3.05±1.21) min, P<0.001], while there was no difference between the two RIRS groups (P>0.05). The costs were lower in the flexible sheath RIRS group than in the conventional sheath RIRS group but higher than in the PCNL group [(23 311.19±1 341.20)yuan vs.(24 550.49±1 172.51)yuan, (15 351.97±1 101.4)yuan, P<0.001]. The overall incidence of complications was similar among the three groups, but stone street occurred only in the conventional sheath RIRS group. [Conclusion] For the treatment of patients with upper urinary tract stones >2 cm, RIRS has shorter postoperative hospital stay, lower hemoglobin decrease, and lower VAS score compared to PCNL; the early postoperative SFR of flexible sheath RIRS is superior to that of conventional sheath RIRS, and the 1-month SFR is comparable to that of PCNL, with a low incidence of stone street.

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.