OBJECTIVE To investigate the influence of CYP2C19 gene polymorphism on platelet function and inflammatory cytokines in elderly patients with ischemic stroke， and to analyze potential factors associated with poor prognosis. METHODS A retrospective study was conducted on elderly patients with ischemic stroke admitted to our hospital from June 2024 to June 2025， wh o underwent CYP2C19 genotype testing and received antiplatelet therapy with clopidogrel. The levels of platelet function indicators and inflammatory cytokines before and after treatment were compared among patients with different metabolic phenotypes. Based on the prognosis at 6 months post-treatment， patients were divided into poor prognosis group and good prognosis group. Univariate analysis was performed on general data， metabolic phenotype， the levels of platelet function indicators and inflammatory cytokines. Variables with P ＜0.05 and the levels of inflammatory cytokines before treatment were included in a multivariate Logistic regression analysis to identify independent risk factors for poor prognosis. Multiple linear regression was used to further analyze the relationship between metabolic phenotypes and inflammatory cytokines. RESULTS A total of 448 elderly patients with ischemic stroke were included； among them， 162 cases were normal metabolic phenotype， 218 were intermediate metabolic phenotype， and 68 were poor metabolic phenotype. No rapid or ultrarapid metabolic phenotypes were observed. After treatment， platelet aggregation rate， the levels of P-selectin and platelet activated complex-1 （PAC-1）， high-sensitivity C-reactive Protein （hs-CRP）， interleukin-1β （IL-1β）， IL-6 and tumor necrosis factor-α （TNF-α） in the normal metabolic phenotype group， intermediate metabolic phenotype group， and poor metabolic phenotype group （except for platelet aggregation rate， and the levels of P-selectin and PAC-1 in the poor metabolic phenotype group） were significantly lower than those before treatment in the same group. Moreover， the above indicators in the normal metabolic phenotype group were significantly lower than those in the intermediate and poor metabolic phenotype groups at the corresponding time， and the levels of platelet function indicators in the intermediate metabolic phenotype group were significantly lower than those in the poor metabol ic phenotype group at the corresponding time （ P ＜0.05）. Univariate and multivariate Logistic regression analyses showed that combined with hypertension， combined with diabetes mellitus， and intermediate or poor metabolic genotypes were independent risk factors for poor prognosis in elderly patients with ischemic stroke （ P ＜0.05）. Multiple linear regression analysis showed that serum levels of hs-CRP， IL-1β， IL-6 and TNF-α before treatment were significantly higher in patients with intermediate and poor metabolic genotypes compared to those with normal metabolic genotype （ P ＜0.05）， with a greater magnitude of increase in inflammatory cytokines observed in the patients with poor metabolic genotype. CONCLUSIONS The elderly ischemic stroke patients with CYP2C19 intermediate and poor metabolic genotypes have poor inhibition effect on platelet and higher levels of inflammatory cytokines than normal metabolic genotype； CYP2C19 gene polymorphism， and in combination with hypertension and diabetes， can be used as independent predictors of poor prognosis.

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

PURPOSE: This study aims to identify the prevalence and risk factors of military training-related abdominal injuries and help plan and conduct training properly. METHODS: This questionnaire survey study was conducted from October 2021 to May 2022 among military personnel from 6 military units and 8 military medical centers and participants' medical records were consulted to identify the training-related abdominal injuries. All the military personnel who ever participated in military training were included. Those who refused to participate in this study or provided an incomplete questionnaire were excluded. The questionnaire collected demographic information, type of abdominal injury, frequency, training subjects, triggers, treatment, and training disturbance. Chi-square test and t-test were used to compare baseline information. Univariate and multivariate regression analyses were used to explore the risk factors associated with military training-related abdominal injuries. RESULTS: A total of 3058 participants were involved in this study, among which 1797 (58.8%) had suffered training-related abdominal injuries (the mean age was 24.3 years and the service time was 5.6 years), while 1261 (41.2%) had no training-related abdominal injuries (the mean age was 23.1 years and the service time was 4.3 years). There were 546 injured patients (30.4%) suspended the training and 84 (4.6%) needed to be referred to higher-level hospitals. The most common triggers included inadequate warm-up, fatigue, and intense training. The training subjects with the most abdominal injuries were long-distance running (589, 32.8%). Civil servants had the highest rate of abdominal trauma (17.1%). Age ≥ 25 years, military service ≥ 3 years, poor sleep status, and previous abdominal history were independent risk factors for training-related abdominal injury. CONCLUSION More than half of the military personnel have suffered military training-related abdominal injuries. Inadequate warm-up, fatigue, and high training intensity are the most common inducing factors. Scientific and proper training should be conducted according to the factors causing abdominal injuries.
Humans ; Military Personnel ; Risk Factors ; Prevalence ; Male ; Abdominal Injuries/etiology* ; Female ; Adult ; Surveys and Questionnaires ; Young Adult

OBJECTIVE: To analyze the gene mutation characteristics and survival time of patients with newly diagnosed acute myeloid leukemia (AML) based on next-generation sequencing(NGS) gene detection. METHODS: A retrospective analysis was conducted on the clinical data of 92 patients with AML (non APL) admitted to our hospital from January 2018 to May 2022. AML related genes tested were using NGS, the mutation characteristics and survival time of AML patients were analyzed. RESULTS: Among the 92 patients, 41 were males and 51 were females. A total of 38 types of gene mutations were detected. Six-two patients carried at least one gere mutation, while no gene mutations were detected in 30 patients. In the group with favourable prognosis (n =14), the frequencies of higher gene mutations were NRAS, KIT (21.43%, n =3), KRAS (14.29%, n =2). In the group with intermediate prognosis (n =64), the gene mutation frequencies from high to low were DNMT3A (18.75%, n =12), NPM1 (17.19%, n =11), IDH2, FLT3-ITD, CEBPA (12.50%, n =8), TET2 (10.94%, n =7). In the poor prognosis group (n =14), ASXL1, TP53, EZH2, NRAS had higher gene mutation frequency than others(14.29 %, n =2 ). Statistical analysis revealed that KIT had a relative hotspot of mutations in the intermediate-risk group, and DNMT3A had a relative hotspot of mutations in the high-risk group (P < 0.05). The correlation analysis of genes with high mutation rates in different prognostic groups, such as NRAS, KIT, IDH2, DNMT3A, NPM1, and FLT3-ITD, with prognosis found that KIT was a factor affecting OS (P < 0.05), while no significant differences were observed for the others(P >0.05). CONCLUSION The frequency of gene mutations is high in AML patients, 67.4% of the patients carried at least one gene mutation. The mutation frequency varies among different genes in patients with different karyotypes, and there are obvious dominant mutations. KIT and DNMT3A can be used as factors for evaluating the prognosis of AML.
Humans ; Leukemia, Myeloid, Acute/genetics* ; Nucleophosmin ; Mutation ; Prognosis ; Retrospective Studies ; Male ; Female ; High-Throughput Nucleotide Sequencing ; Middle Aged ; DNA Methyltransferase 3A ; Adult ; Aged ; Survival Analysis ; Proto-Oncogene Proteins c-kit/genetics*

Objective To investigate the alterations of cerebral blood flow(CBF)in type 2 diabetic mellitus(T2DM) patients without cognitive impairment by using arterial spin labeling(ASL)technique.Methods A total of 23 T2DM patients without cognitive impairment and 23 healthy controls(HC)matched by age,sex,and education attainment were recruited.Their clinical data were collected,and neuropsychological tests and cerebral magnetic resonance imaging were performed.Then,the outcomes of clinical features,neuropsychological tests,and global and regional CBF were compared between the two groups.The significant regional zCBF(z-transformed relative CBF)values were extracted and correlated with clinical data and neuropsychological scores in T2DM patients,controlling age,sex,and education.Results No significant difference was found in whole brain CBF between the two groups(P=0.155),while significantly higher CBF was identified in the left superior temporal gyrus and left insula in the T2DM group(Gaussian random field correction,initial threshold P < 0.001,cluster level P < 0.05).No correlation was observed between the significant regional zCBF values and the clinical data or the neuropsychological scores in T2DM patients(all P>0.05).Conclusion Alterations in cerebral hemodynamics may precede cognitive function changes in T2DM,suggesting that the ASL technique is promising for early monitoring of cerebral hemodynamic changes associated with cognitive impairment in patients with T2DM.
Humans ; Diabetes Mellitus, Type 2/physiopathology* ; Cerebrovascular Circulation ; Middle Aged ; Male ; Female ; Magnetic Resonance Imaging ; Case-Control Studies ; Cognitive Dysfunction ; Neuropsychological Tests ; Aged

The inference of tissue origin of body fluid stains is crucial for case investigation and court proceedings.However,traditional methods for identification of body fluid stains,such as morpho-logical,chemical,and immunoassay identifications have certain limitations,and there is an urgent need for more efficient methods for confirmatory experiments.In recent years,the rapid development of tran-scriptomics technology has provided new means for the identification of tissue origin of body fluid stains.Different types of RNA in the transcriptome have their own advantages.This paper elaborates in detail on the application of different types of RNA,such as mRNA,miRNA,circRNA,lncRNA,piRNA and microbial transcriptomics in body fluid identification,and summarizes their respective ad-vantages and limitations,in order to provide a reference for related research.

Objective To study the risk factors of preoperative deep vein thrombosis(DVT)in middle-aged and elderly patients with lower limb fractures,and establish and evaluate the constructed nomogram model.Methods A retrospective analysis was conducted on the clinical data of totally 240 middle-aged and elderly patients with lower limb fractures at Quanzhou First Hospital Affiliated to Fujian Medical University from July 2023 to June 2024.According to whether the patient had DVT before surgery,they were divided into a DVT group of 75 cases and a non DVT group of 165 cases.The differences in clinical data and some labora-tory indicators levels at the time of initial admission,including platelets(PLT),prothrombin time(PT),ac-tive partial thromboplastin time(APTT),fibrinogen(FIB),thrombin time(TT),D-dimer(D-D),total pro-tein(TP),albumin(ALB),triglycerides(TG),cholesterol(CHOL),high-density lipoprotein cholesterol(HDL-C),low density lipoprotein cholesterol(LDL-C)between two groups were compared.Logistic binary regression was used to analyze the risk factors of DVT and a nomogram model was constructed.Receiver oper-ating characteristic(ROC)curve was used to evaluate the diagnostic efficacy of various risk factors and nomo-gram model in screening DVT.Results Compared age and D-D levels of the DVT group were significantly higher(P＜0.001),while the levels of APTT,TP and ALB were significantly lower(P＜0.05),and the pro-portion of DVT combined with internal diseases and the proportion of fractures in the femur were significantly higher(P＜0.001).The above-mentioned differential indicators were all single risk factors for DVT.ROC curve analysis showed that except for comorbidities of internal medicine,all other risk factors could effectively screen for DVT(P＜0.05),the area under the curve(AUC)was 0.573-0.706,and the constructed nomo-gram model could effectively improve AUC to 0.797.Conclusion The nomogram model constructed based on single risk factors for DVT in this study is helpful for clinical assessment of the risk of DVT occurrence in pa-tients at the early stage of admission,intuitively and effectively.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To understand the molecular evolutionary characteristics of the recombinant strain GII.3[P12] of norovirus acute gastroenteritis outbreaks and aggregated outbreaks in China from 2022 to 2023.Methods:Epidemiological information, case information, clinical samples, as well as detection and genotyping information of norovirus outbreaks and aggregated outbreaks from 2022 to 2023 were collected; positive samples of the GII.3[P12] recombinant strain were subjected to nucleic acid extraction, whole-genome amplification, and sequence analysis; and homology simulation method were used to construct a three-dimensional structure and predict antigenic epitopes.Results:From January 2022 to December 2023, a total of 1 136 norovirus outbreaks and aggregated outbreaks were reported in China′s norovirus outbreak surveillance network, and genotyping result were successfully obtained for 942 outbreaks, with GII dominating, accounting for 76.0% (716/942), and the proportion of GI and mixed genotypes being 15.8% (149/942) and 8.2% (77/942). Norovirus outbreaks caused by GII were dominated by GII.3[P12] (22.5%, 161/716), while other major genotypes included GII.17[P17] (18.7%, 134/716), GII.4_Sydney 2012[P16] (11.6%, 83/716) and GII.6[P7] ( 11.3%, 81/716). 2022-2023 Outbreaks caused by GII.3[P12] were concentrated in February-March (54.0%, 87/161), with the main outbreaks occurring in nursery and primary schools (87.5%), the mode of transmission was mainly human-to-human (68.9%), and the main susceptible population was children aged 3-7 years (93.3%). In this study, the genome sequences of 25 GII.3[P12] recombinant strains were obtained, and according to the phylogenetic analysis, it was shown that the GII.3[P12] recombinant strains in China in 2022-2023 belonged to the Cluster IV cluster of sublineage b (7 strains) and sublineage c (18 strains). A total of 11 linear and 8 conformational epitopes were predicted by epitope prediction analysis, and the predicted linear and conformational epitopes had overlapping positions, and each conformational epitope was part of the predicted linear epitope with conserved potential antigen-binding and receptor-binding sites.Conclusions:The recombinant strain GII.3[P12] is one of the epidemic strains that will cause outbreaks and clusters of norovirus in China in 2022-2023, and its genome did not undergo significant mutation.