1.A novel homozygous mutation of CFAP300 identified in a Chinese patient with primary ciliary dyskinesia and infertility.
Zheng ZHOU ; Qi QI ; Wen-Hua WANG ; Jie DONG ; Juan-Juan XU ; Yu-Ming FENG ; Zhi-Chuan ZOU ; Li CHEN ; Jin-Zhao MA ; Bing YAO
Asian Journal of Andrology 2025;27(1):113-119
Primary ciliary dyskinesia (PCD) is a clinically rare, genetically and phenotypically heterogeneous condition characterized by chronic respiratory tract infections, male infertility, tympanitis, and laterality abnormalities. PCD is typically resulted from variants in genes encoding assembly or structural proteins that are indispensable for the movement of motile cilia. Here, we identified a novel nonsense mutation, c.466G>T, in cilia- and flagella-associated protein 300 ( CFAP300 ) resulting in a stop codon (p.Glu156*) through whole-exome sequencing (WES). The proband had a PCD phenotype with laterality defects and immotile sperm flagella displaying a combined loss of the inner dynein arm (IDA) and outer dynein arm (ODA). Bioinformatic programs predicted that the mutation is deleterious. Successful pregnancy was achieved through intracytoplasmic sperm injection (ICSI). Our results expand the spectrum of CFAP300 variants in PCD and provide reproductive guidance for infertile couples suffering from PCD caused by them.
Adult
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Female
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Humans
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Male
;
Pregnancy
;
China
;
Ciliary Motility Disorders/genetics*
;
Codon, Nonsense
;
East Asian People/genetics*
;
Exome Sequencing
;
Homozygote
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Infertility, Male/genetics*
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Kartagener Syndrome/genetics*
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Pedigree
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Sperm Injections, Intracytoplasmic
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Cytoskeletal Proteins/genetics*
2.Correlation between Serum FGF-23, HPSE Levels and Early Renal Impairment in Patients with Multiple Myeloma.
Li-Fang MA ; Yan YUN ; Yan-Qi LIU ; Xue-Qin BAI ; Wen-Juan NI ; Zhi-Qin LI ; Yan LU ; Zhe LI ; Jing LI ; Guo-Rong JIA
Journal of Experimental Hematology 2025;33(3):822-827
OBJECTIVE:
To investigate the relationship between serum levels of fibroblast growth factor-23 (FGF-23), heparanase (HPSE) and early renal impairment (RI) in patients with multiple myeloma (MM).
METHODS:
A retrospective analysis was conducted on the clinical data of 125 MM patients who were initially diagnosed in the Department of Hematology of the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology from June 2020 to June 2023. The patients were divided into RI group (>176.80 μmol/L) and non-RI group (≤176.80 μmol/L) based on their serum creatinine levels when diagnosed. The baseline data and laboratory indexes of the two groups were compared. The relationship between serum FGF-23, HPSE and early RI in MM patients was analyzed.
RESULTS:
Among 125 newly diagnosed MM patients, 33 cases developed early RI, accounting for 26.40%. The proportion of light chain type, blood urea nitrogen (BUN), blood uric acid, lactate dehydrogenase, FGF-23, and HPSE levels in RI group were higher than those in non-RI group (all P <0.05). There was no statistical significant difference in other data between the two groups (P >0.05). Multivariate logistic regression analysis showed that BUN, FGF-23 and HPSE were associated with early RI in MM patients (all P <0.05). The serum FGF-23 level was divided into Q1-Q4 groups by quartile, and the serum HPSE level was divided into q1-q4 groups. The correlation analysis showed that with the increase of serum FGF-23 and HPSE levels, the incidence of early RI increased (r =0.668, 0.592). Furthermore, logistic regression analysis showed that after controlling for confounding factors, elevated levels of serum FGF-23 and HPSE were still influencing factors for early RI in MM patients (OR>1, P <0.05). According to Pearson's linear correlation test, there was a positive correlation between serum FGF-23 level and HPSE level (r =0.373).
CONCLUSION
There is a certain correlation between serum levels of FGF-23, HPSE and early RI in MM patients, and the incidence of early RI is higher in patients with abnormally high levels of both.
Humans
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Multiple Myeloma/complications*
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Fibroblast Growth Factor-23
;
Retrospective Studies
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Fibroblast Growth Factors/blood*
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Glucuronidase/blood*
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Male
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Female
;
Middle Aged
;
Renal Insufficiency/blood*
;
Aged
3.Distribution and resistance profiles of bacterial strains isolated from cerebrospinal fluid in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Juan MA ; Lixia ZHANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Han SHEN ; Wanqing ZHOU ; Wenen LIU ; Yanming LI ; Yi XIE ; Mei KANG ; Dawen GUO ; Jinying ZHAO ; Zhidong HU ; Jin LI ; Shanmei WANG ; Yafei CHU ; Yunsong YU ; Jie LIN ; Yingchun XU ; Xiaojiang ZHANG ; Jihong LI ; Bin SHAN ; Yan DU ; Ping JI ; Fengbo ZHANG ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Xiaobo MA ; Yanping ZHENG ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Hua YU ; Xiangning HUANG ; Sufang GUO ; Xuesong XU ; Chao YAN ; Fangfang HU ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Fang DONG ; Zhiyong LÜ ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Chuanqing WANG ; Pan FU ; Yunjian HU ; Xiaoman AI ; Ziyong SUN ; Zhongju CHEN ; Hong ZHANG ; Chun WANG ; Yuxing NI ; Jingyong SUN ; Kaizhen WEN ; Yirong ZHANG ; Ruyi GUO ; Yan ZHU ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Shifu WANG ; Yunsheng CHEN ; Qing MENG ; Yong ZHAO ; Ping GONG ; Ruizhong WANG ; Hua FANG ; Jilu SHEN ; Jiangshan LIU ; Hongqin GU ; Jiao FENG ; Shunhong XUE ; Bixia YU ; Wen HE ; Lin JIANG ; Longfeng LIAO ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(3):279-289
Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3%)and from outpatient and emergency care patients(4.7%).Overall,19.6%of the isolates were from children and 80.4%were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0%and 32.0%,respectively.Coagulase negative Staphylococcus accounted for 73.0%of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2%in children and 45.6%in adults.The prevalence of MRCNS was 67.6%in adults and 69.5%in children.A small number of vancomycin-resistant Enterococcus faecium(2.2%)and linezolid-resistant Enterococcus faecalis(3.1%)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2%and 76.4%in children,70.5%and 63.5%in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3%and 47.7%in children,6.4%and 47.9%in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0%and 37.1%in children,81.7%and 39.9%in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.
4.Changing antibiotic resistance profiles of the bacterial strains isolated from geriatric patients in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Xiaoman AI ; Yunjian HU ; Chunyue GE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Wenhui HUANG ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(3):290-302
Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3%of the total number of clinical isolates from all patients.Overall,21.8%of the 514 715 strains were gram-positive bacteria,and 78.2%were gram-negative bacteria.Majority(90.9%)of the strains were isolated from inpatients.About 42.9%of the strains were isolated from respiratory specimens,and 22.9%were isolated from urine.More than half(60.7%)of the strains were isolated from male patients,and 39.3%isolated from females.About 51.1%of the strains were isolated from patients aged 65-<75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8%in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2%,1.5%)and linezolid-resistant strains(3.4%,0.3%)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3%,4.0%,and 1.7%in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2%)to imipenem and meropenem were 20.9%and 22.3%,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7%-7.8%of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6%)to imipenem and meropenem were 68.4%and 70.6%respectively,while 28.5%and 24.3%of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-<75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.
5.Factors influencing the physical activity of patients receiving a percutaneous coronary intervention soon after discharge
Qing WEN ; Xiaorong MAO ; Xiaoli TANG ; Haiyan WU ; Xiaojuan YANG ; Juan CHENG ; Qunhua MA
Chinese Journal of Physical Medicine and Rehabilitation 2025;47(9):792-798
Objective:To analyze the physical activity level of patients treated with a percutaneous coronary intervention (PCI) for coronary artery disease in the early out-of-hospital recovery phase, and the factors influencing it.Methods:Patients who had been discharged within the previous 6 months after their first PCI treatment were surveyed using a general information questionnaire, the long form of the International Physical Activity Questionnaire (IPAQ), the Chinese version of the Tilburg Frailty Scale, the Social Support Rating Scale, and for their ability in the activities of daily living. Epidemiological descriptive methods were used to analyze the reported physical activity levels, and multifactoral logistic regression was applied to explore the influencing factors. The receiver operating characteristics (ROC) curve was drawn to evaluate the predictive value of the risk factors.Results:A total of 394 former patients were surveyed, including 117 (30%) reporting a low level of physical activity, 202 (51%) describing a moderate level and 75 (19%) claiming a high level. The univariate analysis revealed significant differences in physical activity levels among those of different ages, with different chronic co-morbidities, and with different frailty and self-care ability. Multifactoral logistic regression analysis showed that advanced age, chronic co-morbidities, frailty and little self-care ability are significant predictors of a low level of physical activity. The area under the ROC curve for predicting the physical activity level by combining those four factors was 0.89 (95% CI 0.84-0.94), with a sensitivity of 0.89 and a specificity of 0.80. Conclusions:The physical activity level of patients treated with PCI for coronary disease is moderately low early after their release from the hospital. Targeted intervention to increase it is called for.
6.Changing prevalence and antibiotic resistance profiles of carbapenem-resistant Enterobacterales in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Wenxiang JI ; Tong JIANG ; Jilu SHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yuanhong XU ; Ying HUANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yingchun XU ; Xiaojiang ZHANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hong ZHANG ; Chun WANG ; Wenhui HUANG ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(4):445-454
Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43%(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38%,9.73%,and 8.47%,respectively.The prevalence of CRE strains in Escherichia coli was 1.99%.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)%,followed by blood(20.88±3.40)%and urine(18.40±3.45)%.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)%.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.
7.Changing distribution and antibiotic resistance profiles of the respiratory bacterial isolates in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Ying FU ; Yunsong YU ; Jie LIN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(4):431-444
Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6%of the isolates were Gram-negative bacteria and 17.4%were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)%and(94.0±0.1)%,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P<0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9%.The prevalence of penicillin-resistant S.pneumoniae was 1.4%.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8%and 41.3%,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7%,20.8%,9.4%,29.8%,and 73.3%,respectively.The prevalence of β-lactamase was 96.1%in Moraxella catarrhalis and 60.0%in Haemophilus influenzae.The H.influenzae isolates from children(<18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P<0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.
8.Adolescent Smoking Addiction Diagnosis Based on TI-GNN
Xu-Wen WANG ; Da-Hua YU ; Ting XUE ; Xiao-Jiao LI ; Zhen-Zhen MAI ; Fang DONG ; Yu-Xin MA ; Juan WANG ; Kai YUAN
Progress in Biochemistry and Biophysics 2025;52(9):2393-2405
ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.
9.Study of Reference Materials for Quantitative Analysis of Gene Copy Numbers of Lentiviral Vectors
Yin-Bo HUO ; Jia-Qi YANG ; Qing TAO ; Wen LIANG ; Li XU ; Lan-Ying LI ; Xiao-Lei ZUO ; Juan YAN ; Min DING ; Ai-Wen MA ; Gang LIU
Chinese Journal of Analytical Chemistry 2025;53(9):1555-1565
Lentiviral vectors(LVs)are key gene delivery tools for integrating target genes into the host genome,but they may also pose risks of insertional mutagenesis.The vector copy number(VCN)in cells is critical for determining the safety of gene modification.However,the reliability and accuracy of its quantification process are influenced by multiple factors.Developing cell reference materials with specific vector copy numbers represents a viable approach to enhance the reliability and consistency of measurement results,enabling quality control of the quantification process and traceability of outcomes.However,the preparation of such reference materials faces challenges in cell sample design,preparation protocols,and advanced quantification techniques.In this study,T lymphocyte cell line Jurkat-based reference materials with LV gene copy numbers of 1 and 2 copy/cell were developed.A high-precision duplex digital polymerase chain reaction(dPCR)method was established to quantify the LV gene and endogenous genes simultaneously.Additionally,the results of dPCR were cross-validated through next-generation sequencing and flow cytometric analysis.Ultimately,confocal microscopy characterization results showed that the developed cell reference materials had intact morphology.The quantification result of VCN-1 was(1.07±0.11)copy/cell,and that of VCN-2 was(2.09±0.21)copy/cell.These cell reference materials demonstrated compliance with stability and homogeneity requirements,and could be applied for quality control throughout the VCN measurement workflow and metrological traceability,improving the accuracy,comparability,and validity of copy number measurements.
10.Mechanism of senegenin in improving lipopolysacchride-induced inflammatory response of BV2 microglial cell
Bing-Tao MU ; Min-Fang GUO ; Jing-Wen YU ; Jia-Lei CAO ; Feng-Jun YANG ; Si-Wei JIA ; Qing SU ; Tao MENG ; Cun-Gen MA ; Jie-Zhong YU ; Li-Juan SONG
Medical Journal of Chinese People's Liberation Army 2025;50(2):188-196
Objective To investigate the mechanism by which Senegenin(SEN)alleviates microglial inflammatory response through the nuclear factor erythroid 2-related factor 2(Nrf2)/NOD-like receptor protein 3(NLRP3)pathway.Methods BV2 mouse microglia cells were randomly divided into control group,model group,SEN group and MCC950 group.Cells in control group were not treated,and cells in model group were added with 1 μg/ml lipopolysaccharide(LPS);Cells in SEN group were added with 1 μg/ml LPS+4 μmol/L SEN,and cells in MCC950 group were added with 1 μg/ml LPS+10 μmol/L MCC950 for 24 hours.CCK-8 method was used to detect the effect of different concentrations of SEN on the viability of BV2 cells.Griess method was used to determine the release amount of nitric oxide(NO)in the supernatant.Real-time fluorescent quantitative PCR was used to determine the mRNA expression levels of NLRP3,lymphocyte apoptosis-associated spect-like protein containing a CARD(ASC),caspase-1,interleukin(IL)-1β and IL-18 mRNA.Immunofluorescence staining was used to detect the expression levels of ASC,IL-1β,Nrf2 and heme oxygenase-1(HO-1).Western blotting was used to detect the expression levels of NLRP3,caspase-1,ASC,IL-1β,IL-18,Nrf2,HO-1,nuclear factor kappa B(NF-κB)and inducible nitric oxide synthase(iNOS).Results The results of CCK-8 method showed that there was no significant difference in the viability of BV2 cells treated with 2~20 μmol/L SEN compared with control group(P>0.05).Compared with control group,the viability of BV2 cells in model group decreased significantly(P<0.05).Compared with model group,the viability of BV2 cells in 4 μmol/L SEN group was significantly restored(P<0.05).Compared with control group,the results of Griess method showed that the release amount of NO in cells of model group increased significantly(P<0.05);the results of real-time PCR showed that the expression levels of NLRP3,ASC,caspase-1,IL-1β and IL-18 mRNA in cells of model group increased significantly(P<0.05);the results of Western blotting showed that the protein expression levels of NLRP3,ASC,caspase-1,IL-1β and IL-18 proteins in cells of model group increased significantly(P<0.05),and the immunofluorescence staining results showed that the expression levels of iNOS and NF-κB protein in cells of model group increased,and the expression levels of Nrf2 and HO-1 decreased,with statistically significant differences(P<0.05).Compared with model group,the release amount of NO in cells of SEN group and MCC950 group decreased,and the expression levels of NLRP3,ASC,caspase-1,IL-1β and IL-18 mRNA and proteins decreased,with statistically significant differences(P<0.05);in the SEN group,the expression levels of iNOS and NF-κB decreased,and immunofluorescence staining showed that Nrf2 was translocated into the nucleus,and the expression levels of Nrf2 and HO-1 proteins increased significantly,with statistically significant differences(P<0.05).Conclusions SEN could alleviate the inflammatory response of mouse microglia cells induced by LPS and inhibit the activation and expression of NLRP3 inflammasome,with an effect comparable to that of the inflammasome inhibitor MCC950.The mechanism may be related to the regulation of the expression of upstream factors Nrf2 and HO-1.

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