BackgroundArrhythmia and obsessive-compulsive disorder （OCD） frequently co-occur in clinical and epidemiological settings， yet their shared genetic basis and potential heart-brain axis mechanisms remain unclear. ObjectiveTo systematically evaluate the genetic correlation between arrhythmia and OCD， and to elucidate their underlying molecular genetic mechanisms， so as to provide molecular evidence for the "heart-brain axis" to support risk assessment and integrated clinical strategies for these comorbidities. MethodsThe aggregated data from the genome-wide association study （GWAS） of arrhythmia in the UK Biobank （7 207 cases and 477 391 controls） and the GWAS data of OCD released by the Psychiatric Genomics Consortium （2 688 cases and 7 037 controls） were integrated， all of which were limited to individuals of European ancestry. The genome-wide genetic correlations were estimated using the linkage disequilibrium score regression （LDSC） and the high-definition likelihood （HDL）. Local genetic correlation analysis was conducted using the local analysis of variance annotation （LAVA）. Multi-trait analysis of GWAS （MTAG） was employed to identify pleiotropic loci. Shared risk genes were identified by combining summary-data based Mendelian randomization （SMR） and transcriptome-wide association study （TWAS）. Functional enrichment analysis was performed based on the functional mapping and annotation （FUMA） platform. ResultsBoth LDSC （r_g=0.248， 95% CI： 0.159–0.336， P=4.82×10^-3） and HDL （r_g=0.294， 95% CI： 0.237–0.351， P=5.87×10^-4） revealed significant positive genetic correlation between arrhythmia and OCD. LAVA identified 23 significantly local correlated regions in the genome （P<2.0×10^-5）. MTAG discovered 11 genome-wide significant pleiotropic SNPs， among which rs12754189 （intron of KCNN3） had potential functional harmfulness （CADD>12.37）. SMR and TWAS jointly identified 20 shared genes， enriched in neural-cardiovascular tissues such as the cerebral cortex， amygdala， and left ventricle， and involved in DNA damage response， RNA metabolism， transcriptional regulation， and FAS signaling pathway （FDR<0.05）. ConclusionArrhythmia and OCD share a common genetic basis. The co-morbidity mechanism may involve the common vulnerability of neurons and cardiac muscle cells in terms of gene expression regulation and stress response， supporting the role of the brain-heart axis in the pathophysiology of both conditions.

Objective:To investigate the clinical characteristics and prognostic significance of germline mutations in patients with myelodysplastic neoplasms (MDS) .Methods:Clinical data from 407 patients with MDS [male, 252; female, 155; median age, 64 (range, 19-85) years] diagnosed at the First Affiliated Hospital of Zhejiang University School of Medicine were retrospectively analyzed. The clinical features and prognostic effects of germline mutations were evaluated.Results:The prevalence of germline mutations in patients with MDS was 5.9% (24/407), peaking at 20.0% in the group aged 21-30 years. The spectrum of germline mutations comprised DDX41 (9 cases, 2.2%), TP53 (3 cases, 0.7%), and single cases of RUNX1, TET2, MPL, CBL, ATRX, CEBPA, ETV6, IDH1, KDM5C, SBDS, GNAS, and CTC1. Patients with germline mutations exhibited significantly lower peripheral WBC counts than those without (1.87×10 9/L vs 2.50×10 9/L, P=0.018), but showed comparable median overall survival (21.3 months vs 21.1 months, P=0.97). Patients with DDX41 germline mutations, compared with those with other germline mutations, had a significantly older median age (65 vs 54 years, P=0.010), lower WBC counts (1.51×10 9/L vs 2.31×10 9/L, P=0.040), increased mean corpuscular volume (111.80 fl vs 97.25 fl, P=0.003), and a higher prevalence of normal karyotypes (100.0% vs 53.3%, P=0.022). The most frequently co-occurring somatic mutations in DDX41 germline mutation carriers were ASXL1, TET2, and RUNX1. Conclusion:In this study, the detection rate of germline mutations in MDS patients was 5.9% (24/407), peaking at 20% in the group aged 21-30 years. DDX41 and TP53 were the most prevalent germline mutations. DDX41 mutation carriers displayed distinct clinical characteristics; however, germline mutations overall showed no significant prognostic effect.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

Multimorbidity has become a widely recognized public health problem worldwide. Identifying multimorbidity patterns can improve not only the efficiency of healthcare resource utilization but also patients' prognosis. This article summarizes three common approaches for the identification of multimorbidity patterns: association analysis methods (including association rule mining and network analysis), classification methods (including cluster analysis, latent class analysis, and latent transition analysis), and dimensionality reduction and feature extraction methods (including principal component analysis, factor analysis, and multiple correspondence analysis), introduces the application of these methods using data from the UK Biobank to identify multimorbidity patterns and discusses and compares the results of case analysis to provide reference for the selection of appropriate methods for multimorbidity pattern research.

Objective:To investigate the clinical characteristics and prognostic significance of germline mutations in patients with myelodysplastic neoplasms (MDS) .Methods:Clinical data from 407 patients with MDS [male, 252; female, 155; median age, 64 (range, 19-85) years] diagnosed at the First Affiliated Hospital of Zhejiang University School of Medicine were retrospectively analyzed. The clinical features and prognostic effects of germline mutations were evaluated.Results:The prevalence of germline mutations in patients with MDS was 5.9% (24/407), peaking at 20.0% in the group aged 21-30 years. The spectrum of germline mutations comprised DDX41 (9 cases, 2.2%), TP53 (3 cases, 0.7%), and single cases of RUNX1, TET2, MPL, CBL, ATRX, CEBPA, ETV6, IDH1, KDM5C, SBDS, GNAS, and CTC1. Patients with germline mutations exhibited significantly lower peripheral WBC counts than those without (1.87×10 9/L vs 2.50×10 9/L, P=0.018), but showed comparable median overall survival (21.3 months vs 21.1 months, P=0.97). Patients with DDX41 germline mutations, compared with those with other germline mutations, had a significantly older median age (65 vs 54 years, P=0.010), lower WBC counts (1.51×10 9/L vs 2.31×10 9/L, P=0.040), increased mean corpuscular volume (111.80 fl vs 97.25 fl, P=0.003), and a higher prevalence of normal karyotypes (100.0% vs 53.3%, P=0.022). The most frequently co-occurring somatic mutations in DDX41 germline mutation carriers were ASXL1, TET2, and RUNX1. Conclusion:In this study, the detection rate of germline mutations in MDS patients was 5.9% (24/407), peaking at 20% in the group aged 21-30 years. DDX41 and TP53 were the most prevalent germline mutations. DDX41 mutation carriers displayed distinct clinical characteristics; however, germline mutations overall showed no significant prognostic effect.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

Multimorbidity has become a widely recognized public health problem worldwide. Identifying multimorbidity patterns can improve not only the efficiency of healthcare resource utilization but also patients' prognosis. This article summarizes three common approaches for the identification of multimorbidity patterns: association analysis methods (including association rule mining and network analysis), classification methods (including cluster analysis, latent class analysis, and latent transition analysis), and dimensionality reduction and feature extraction methods (including principal component analysis, factor analysis, and multiple correspondence analysis), introduces the application of these methods using data from the UK Biobank to identify multimorbidity patterns and discusses and compares the results of case analysis to provide reference for the selection of appropriate methods for multimorbidity pattern research.

Objective:To analyze the mutation characteristics of rpoB gene in rifampicin-resistant Brucella strains. Methods:DNA of 4 rifampicin-resistant Brucella strains (JSY-26, G-9, WSY-13 and AW-3) isolated from Xinjiang Uygur Autonomous Region was selected, rifampicin rpoB gene was amplified by PCR and its nucleotide sequence was sequenced. The rpoB gene sequences of rifampicin-resistant Brucella standard strain (RB51) and sensitive strain (ALT-8) were used as reference, the mutation sites and types of the rpoB gene inside and outside the rifampicin resistance determination region (RRDR) of the 4 rifampicin-resistant Brucella strains were analyzed by Mega 7.0 software. Results:Through sequence alignment, both JSY-26 and WSY-13 strains underwent a single base point mutation at the RRDR 1 576 bp of the rpoB gene, with the base changing from guanine (G) to adenine (A). The G-9 strain underwent a single base point mutation at the RRDR 1 606 bp of the rpoB gene, with the base changing from cytosine (C) to A. The AW-3 strain showed 5 mutations of 3 types outside rpoB gene RRDR at 2 536, 2 537, 2 626, 2 636 and 2 654 bp, namely 3 insertion mutations [thymine (T) insertion once and C insertion twice], 1 deletion mutation (C deletion), and 1 single base point mutation (from G to C mutation).Conclusion:The RRDR mutations in the rpoB gene of the rifampicin-resistant Brucella strains are mainly characterized by single base point mutations, while multiple insertion and deletion mutations occur outside the RRDR.

BACKGROUND/OBJECTIVES: Activating brown adipose tissue (BAT) and browning of white adipose tissue (WAT) can protect against obesity and obesity-related metabolic conditions.Cryptotanshinone (CT) regulates lipid metabolism and significantly ameliorates insulin resistance. Adenosine-5'-monophosphate (AMP)-activated protein kinase (AMPK), a receptor for cellular energy metabolism, is believed to regulate brown fat activity in humans.MATERIALS/METHODS: The in vivo study included high-fat-fed obese mice administered orally 200/400 mg/kg/d CT. They were evaluated through weight measurement, the intraperitoneal glucose tolerance test (IPGTT), intraperitoneal insulin tolerance test (IPITT), cold stimulation test, serum lipid (total cholesterol, triglycerides, and low-density lipoprotein) measurement, hematoxylin and eosin staining, and immunohistochemistry.Furthermore, the in vitro study investigated primary adipose mesenchymal stem cells (MSCs) with incubation of CT and AMPK agonists (acadesine)/inhibitor (Compound C).Cells were evaluated using Oil Red O staining, Alizarin red staining, flow cytometry, and immunofluorescence staining to identify and observe the osteogenic versus adipogenic differentiation. Quantitative real-time polymerase chain reaction and the Western blot were used to observe related gene expression. RESULTS: In the diet-induced obesity mouse model mice CT suppressed body weight, food intake, glucose levels in the IPGTT and IPTT, serum lipids, the volume of adipose tissue, and increased thermogenesis, uncoupling protein 1, and the AMPK pathway expression. In the in vitro study, CT prevented the formation of lipid droplets from MSCs while activating brown genes and the AMPK pathway. AMPK activator enhanced CT’s effects, while the AMPK inhibitor reversed the effects of CT. CONCLUSION CT promotes adipose tissue browning to increase body thermogenesis and reduce obesity by activating the AMPK pathway. This study provides an experimental foundation for the use of CT in obesity treatment.

Objective:To investigate the epidemic status and molecular characteristics of Borrelia burgdorferi in ticks in Xinjiang Uygur Autonomous Region (referred to as Xinjiang). Methods:From April to June 2020, 312 samples of Ixodes were collected in 6 areas of Yili, Alashankou, Hutubi, Qinghe, Fuhai and Wujiaqu, Xinjiang. Nested PCR and fluorescence quantitative PCR were used to detect Borrelia burgdorferi in ticks. The positive samples by both methods were genotyped and identified by nested PCR products. Results:The positive rates of nested PCR and fluorescence quantitative PCR were 8.97% (28/312) and 11.86% (37/312), respectively. Among them, the fluorescence quantitative PCR positive rate of Qinghe was the highest of 35.29% (12/34), and the positive rate of Fuhai was the lowest of 2.00% (1/50). The positive samples by both methods was 26. Genotyping results showed that 12 samples were highly homologous to Borrelia garinii, 10 copies to Borrelia burgdorferi sensu stricto, and 4 copies to Borrelia afzelii. Conclusions:The positive rate of Borrelia burgdorferi in ticks in Xinjiang is higher, which has confirmed that there are 3 pathogenic Borrelia burgdorferi genotypes in Xinjiang. The dominant genotype is Borrelia garinii, followed by Borrelia burgdorgferi sensu stricto genotype and Borrelia afzelii genotype.