ObjectiveTo analyze the epidemiological characteristics of a local dengue fever cluster outbreak in Qingpu District of Shanghai in 2024, and to provide a reference for subsequent dengue fever prevention and control. MethodsSeven confirmed local dengue fever cases reported through the National Notifiable Infectious Diseases Surveillance System in Qingpu District of Shanghai in 2024 were selected as the research subjects. Descriptive epidemiological methods were used to conduct investigation and analysis from the aspects of onset, medical treatment and reporting, clinical symptoms, travel and contact history within 15 days before onset, and activity trajectories. ResultsA total of 7 cases were identified in this outbreak. None of the cases had a travel history to dengue-endemic areas within 15 days prior to onset, while all had shared exposure environments and mosquito bite histories, indicating a local clustered transmission pattern. The main clinical manifestations included fever (100.00%) and myalgia (42.86%). All 7 cases were positive for dengue virus serotype 2 (DENV-2) by nucleic acid testing. Genetic sequencing showed that the virus strains belonged to the Cosmopolitan genotype and were most closely related to the epidemic DENV strains circulating in southern China in recent years. ConclusionThis outbreak might be a local secondary infection caused by the short-term stay of dengue fever-infected individuals, and the possible source of importation was dengue fever endemic areas in southern China.

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*

Depression (Dep) is one of the most common concomitant symptoms of Parkinson's disease (PD), but there is a lack of detailed pathologic evidence for the occurrence of PD-Dep. Currently, the management of symptoms from both conditions using conventional pharmacological interventions remains a formidable task. In this study, we found impaired activation of extracellular signal-related kinase (ERK), reduced levels of transcription and translation, and decreased expression of brain-derived neurotrophic factor (BDNF) in the medial prefrontal cortex (mPFC) of PD-Dep rats. We demonstrated that the abnormal phosphorylation of α-synuclein (pS129) induced tropomyosin-related kinase receptor type B (TrkB) retention at the neuronal cell membrane, leading to BDNF/TrkB signaling dysfunction. We chose SEW2871 as an ameliorator to upregulate ERK phosphorylation. The results showed that PD-Dep rats exhibited improvement in behavioral manifestations of PD and depression. In addition, a reduction in pS129 was accompanied by a restoration of the function of the BDNF/ERK signaling loop in the mPFC of PD-Dep rats.
Animals ; Brain-Derived Neurotrophic Factor/metabolism* ; alpha-Synuclein/metabolism* ; Male ; Prefrontal Cortex/drug effects* ; Rats, Sprague-Dawley ; Depression/metabolism* ; MAP Kinase Signaling System/drug effects* ; Rats ; Parkinson Disease/metabolism* ; Receptor, trkB/metabolism* ; Phosphorylation ; Disease Models, Animal ; Signal Transduction

Objective:To explore the regulatory mechanisms underlying the weak expression of ABO blood group antigens due to variants in the non-coding regions of the ABO gene. Methods:From June 2014 to October 2023, a total of 29 samples from the Taiyuan Blood Center and local hospitals, which were serologically identified as having weak ABO antigen expression without detectable coding region mutations, were selected for this study. Full-length ABO gene sequencing was performed using third-generation long-read sequencing technology (Pacific Biosciences) to obtain complete haplotype sequences of the ABO gene. Variants in the non-coding regions were compared and identified to infer their regulatory effects on weak antigen expression. The procedures followed in this study were in accordance with the ethical standards of the World Medical Association′s Declaration of Helsinki (2013 revision). The Medical Ethics Committee of Taiyuan Blood Center has granted an exemption from ethical review. Results:18 bp deletions in the -35 to -18 region of the promoter were identified in 7 samples. Variants in intron 1 (+ 5.8 kb) were detected in 7 samples, including ABO* A (28+ 5792_5793delCT (1 case) and ABO* B (28+ 5793T>C) located in the GATA binding region; ABO* B (28+ 5808C>T) (1 case) in the E-box region; and ABO* B (28+ 5875C>T) (4 cases) in the RUNX1 binding region. Nucleotide variants at splice sites were detected in 2 samples, namely ABO* B (C.98+ 1G>A) and ABO* B (C.204-2A>C). Conclusion:Variants in the non-coding regulatory sequences of the ABO gene are a significant factor contributing to weak ABO antigen expression. In clinical ABO sequencing, it is essential to screen not only the conventional coding regions but also the flanking sequences, introns, and splice sites of the ABO gene to facilitate precise blood transfusion.

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 report the blood group antigen and antibody specificity identification methods for a patient with high-frequency antibodies, and the process of finding and providing compatible blood for the patient. METHODS: A patient sent from the Blood Transfusion Department of Shanxi Provincial People's Hospital to Blood Transfusion Technology Research Laboratory of Taiyuan Blood Center in November 2022 was selected for the study. Classical serological methods were used to determine the patient's blood type, screen for unexpected antibodies, identify antibodies, and perform crossmatching. High-frequency antibody identification was carried out using red blood cells treated with various enzymes. Blood group genotyping was conducted using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF) and Sanger sequencing. Multiple strategies were employed to address the patient's blood source problem. The study was approved by the Medical Ethics Committee of Taiyuan Blood Center [Ethics No. 2024 Ethics Review No.(2)]. RESULTS: The patient's blood type was B, RhD positive. Initial screening of the patient's serum with multiple screening cells and antibody identification cells in saline medium was negative, but positive in antiglobulin medium. The patient's serum showed varying reaction intensities with red blood cells treated with different enzymes. MALDI-TOF mass spectrometry and Sanger sequencing revealed a homozygous nonsense variant c.376C>T (p.Gln126Ter) in the ABCG2 gene, resulting in the Jr(a-) phenotype. During family donor selection, the patient's son was found to have a heterozygous variant c.376C>T (p.Gln126Ter), and another heterozygous variant c.421C>A (p.Gln141Lys), which predicted a Jr(a+w) phenotype. Crossmatch tests confirmed the compatibility of blood from the patient's son, which was used to address the urgent blood requirement. Later, rare blood from a Jr(a-) donor from the Guangzhou Blood Center was used for the patient's ongoing treatment, saving the patient's life. CONCLUSION Combining classic serological testing with blood group gene typing techniques successfully identified the rare Jr(a-) blood type and high-frequency anti-Jra antibodies. Enzyme-treated red blood cell identification methods confirmed the presence of anti-Jra antibodies. By searching within the family and seeking help from other blood centers, compatible blood was found. This approach may provide insights for resolving similar complex blood matching problems in the future.
Humans ; Blood Grouping and Crossmatching/methods* ; Blood Group Antigens/immunology* ; Blood Transfusion ; Male ; Isoantibodies/blood* ; Female ; Genotype

OBJECTIVE: To explore the regulatory mechanisms underlying the weak expression of ABO blood group antigens due to variants in the non-coding regions of the ABO gene. METHODS: From June 2014 to October 2023, a total of 29 samples from the Taiyuan Blood Center and local hospitals, which were serologically identified as having weak ABO antigen expression without detectable coding region mutations, were selected for this study. Full-length ABO gene sequencing was performed using third-generation long-read sequencing technology (Pacific Biosciences) to obtain complete haplotype sequences of the ABO gene. Variants in the non-coding regions were compared and identified to infer their regulatory effects on weak antigen expression. The procedures followed in this study were in accordance with the ethical standards of the World Medical Association's Declaration of Helsinki (2013 revision). The Medical Ethics Committee of Taiyuan Blood Center has granted an exemption from ethical review. RESULTS: 18 bp deletions in the -35 to -18 region of the promoter were identified in 7 samples. Variants in intron 1 (+5.8 kb) were detected in 7 samples, including ABO*A (28+5792_5793delCT (1 case) and ABO*B (28+5793T>C) located in the GATA binding region; ABO*B (28+5808C>T) (1 case) in the E-box region; and ABO*B (28+5875C>T) (4 cases) in the RUNX1 binding region. Nucleotide variants at splice sites were detected in 2 samples, namely ABO*B (C.98+1G>A) and ABO*B (C.204-2A>C). CONCLUSION Variants in the non-coding regulatory sequences of the ABO gene are a significant factor contributing to weak ABO antigen expression. In clinical ABO sequencing, it is essential to screen not only the conventional coding regions but also the flanking sequences, introns, and splice sites of the ABO gene to facilitate precise blood transfusion.
ABO Blood-Group System/genetics* ; Humans ; Alleles ; Promoter Regions, Genetic ; Haplotypes ; Introns

Objectives:To preliminarily investigate the safety and efficacy of same-day discharge(SDD)following radiofrequency catheter ablation for arrhythmia.Methods:A total of 50 consecutive patients who underwent radiofrequency catheter ablation for arrhythmia in the SDD strategy at Fuwai Hospital from 8 July 2024 to 18 September 2024 were included in this analysis.The study evaluated the immediate success rate of the ablation,the rate of all-cause and arrhythmia-related readmission,outpatient or emergency visits and incidence of complications within 30 days post ablation,and recurrence rate of arrhythmias over a 3-month follow-up period.Results:The average age of the 50 patients was(47.2±16.1)years old,32 patients(64.0%)were male.Radiofrequency catheter ablation was performed in 47 patients(94.0%),including 18(36.0%)atrial fibrillation(AF)ablation.Three patients(6.0%)underwent electrophysiological study only.The immediate success rate for ablation patients was 100%(47/47).None of the patients developed vascular puncture-related or ablation-related complications.The average hospital stay and postoperative observation time were(6.84±1.13)hours and(3.40±1.12)hours,respectively.The all-cause and arrhythmia-related readmission,outpatient or emergency visits rates within 30 days were 12.0%(6/50)and 2.0%(1/50),respectively.Two patients(4.0%)post ablation experienced AF recurrence during the 3-months follow-up period.Conclusions:Radiofrequency catheter ablation for arrhythmias in SDD strategy is safe,effective,and feasible.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective To analyze the acupoint selection law in acupuncture treatment for sinusitis using data mining techniques.Methods Relevant literature on acupuncture treatment for sinusitis was retrieved from CNKI,Wanfang Data,VIP and CBM from January 1,2000 to December 20,2024.Descriptive statistics were performed on the frequency of acupoint usage,meridian tropism,and regional distribution.SPSS Modeler 18.0,Cytoscape 3.10.3 and Origin Pro were employed to conduct association rule mining,co-occurrence network analysis,and systematic cluster analysis on the acupoints.Results A total of 69 articles were included,yielding 114 acupoint prescriptions involving 65 distinct acupoints.The most frequently used acupoints were Yingxiang,Yintang and Hegu,etc.;the most commonly used meridians were large intestine meridian,Governor Vessel,gallbladder meridian and bladder meridian.Acupoint selection was predominantly concentrated in the head/neck region,lower limbs and upper limbs.Among the specific acupoint categories,Jiaohui acupoint,Wushu acupoint and Yuan acupoint were used most frequently.The acupoint combinations with the strongest associations were Hegu-Yingxiang,Hegu-Yintang-Yingxiang and Fengchi-Yingxiang.The top 22 high-frequency acupoints could be grouped into 5 clusters.Conclusion Acupuncture treatment for sinusitis can exert the functions to disperse wind and unblock the orifices,drain heat and resolve turbidity,expel pathogens and alleviate pain,tonify deficiency and dissipate cold,as well as harmonize qi and blood.The characteristics of acupoint combination include a primary focus on local points supplemented by distal points,pattern differentiation-based selection,and a propensity for unblocking yang qi.The core acupuncture formula is Yingxiang-Yintang-Hegu-Fengchi.