1.Association between mobile phone addiction and high myopia among college students
Jian YIN ; Zeshi LIU ; Yan LI ; Yangyang GONG ; Naichuan CHEN ; Yuqi ZHAO ; Jia SONG ; Yanping ZHANG
International Eye Science 2025;25(2):301-305
AIM:To analyze the association between mobile phone addiction and high myopia among college students.METHODS:We conducted a cross-sectional questionnaire survey in December 2022 on all students of a university in Shaanxi Province, and the questionnaire included socio-demographic characteristics, mobile phone addiction, high myopia, and lifestyle. Binary Logistic regression model was used to analyze the association between mobile phone addiction and high myopia among college students.RESULTS:A total of 19 952 college students were included. The prevalence of high myopia was 7.31%. The rate of mobile phone addiction was 25.68%, and the mobile phone addiction score was 37.59±13.38. The incidence of high myopia among college students with mobile phone addiction was higher than non-mobile phone addiction(P<0.001). After adjusting for socio-demographic characteristics and lifestyle, the risk of high myopia among college students with mobile phone addiction was 1.274 times(95%CI:1.131-1.434)higher than non-mobile phone addiction. For each point increase of total mobile phone addiction score, withdrawal symptoms score, salience score, social comfort score, and mood changes score, the risk of high myopia among college students increased by 0.9%(95%CI:1.005-1.013), 2.0%(95%CI:1.010-1.030), 2.6%(95%CI:1.010-1.043), 4.8%(95%CI:1.030-1.066), and 3.3%(95%CI:1.014-1.052), respectively.CONCLUSION:Mobile phone addiction is significantly associated with the increased risk of high myopia among college students, and early intervention of mobile phone use may reduce the risk of high myopia among college students.
2.Hearing loss prevalence and burden of disease in China: Findings from provincial-level analysis.
Yu WANG ; Yang XIE ; Minghao WANG ; Mengdan ZHAO ; Rui GONG ; Ying XIN ; Jia KE ; Ke ZHANG ; Shaoxing ZHANG ; Chen DU ; Qingchuan DUAN ; Fang WANG ; Tao PAN ; Furong MA ; Xiangyang HU
Chinese Medical Journal 2025;138(1):41-48
BACKGROUND:
Without timely and effective rehabilitation, hearing loss may profoundly affect human life quality. China has a large population of hearing-impaired individuals, which imposes a heavy health burden on society. Moreover, this population is projected to increase rapidly owing to China's aging society.
METHODS:
We used data from a population-representative epidemiological investigation of hearing loss and ear diseases in four Chinese provinces. We estimated the national prevalence using multiple linear regression of the age-group proportions and prevalence in 31 provinces with clustering analysis. We used years lived with disability (YLDs) to analyze the disease burden and forecasted the prevalence of hearing loss by 2060 in China.
RESULTS:
An estimated 115 million people had moderate-to-complete hearing loss in 2015 across the 31 provinces of China (8.4% of 1.37 billion people). Of these, 85.7% were older than age 50 years (99 million people) and 2.4% were younger than 20 years old (2.8 million people). Of all YLDs attributable to hearing loss, 68.9% were attributable to moderate-to-complete cases. By 2060, a projected 242 million people in China will have moderate-to-complete hearing loss, a 110.0% increase from 2015.
CONCLUSIONS
The hearing loss prevalence in China is high. Population aging and socioeconomic factors substantially affect the prevalence and severity of hearing loss and the disease burden. The prevalence and severity of hearing loss are unevenly distributed across different provinces. Future public health policies should take these trends and regional variations into account.
Humans
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China/epidemiology*
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Hearing Loss/epidemiology*
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Prevalence
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Middle Aged
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Male
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Female
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Adult
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Aged
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Adolescent
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Young Adult
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Child
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Child, Preschool
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Infant
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Aged, 80 and over
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Cost of Illness
3.Therapeutic Study on The Inhibition of Neuroinflammation in Ischemic Stroke by Induced Regulatory T Cells
Tian-Fang KANG ; Ai-Qing MA ; Li-Qi CHEN ; Han GONG ; Jia-Cheng OUYANG ; Fan PAN ; Hong PAN ; Lin-Tao CAI
Progress in Biochemistry and Biophysics 2025;52(4):946-956
ObjectiveNeuroinflammation plays a crucial role in both the onset and progression of ischemic stroke, exerting a significant impact on the recovery of the central nervous system. Excessive neuroinflammation can lead to secondary neuronal damage, further exacerbating brain injury and impairing functional recovery. As a result, effectively modulating and reducing neuroinflammation in the brain has become a key therapeutic strategy for improving outcomes in ischemic stroke patients. Among various approaches, targeting immune regulation to control inflammation has gained increasing attention. This study aims to investigate the role of in vitro induced regulatory T cells (Treg cells) in suppressing neuroinflammation after ischemic stroke, as well as their potential therapeutic effects. By exploring the mechanisms through which Tregs exert their immunomodulatory functions, this research is expected to provide new insights into stroke treatment strategies. MethodsNaive CD4+ T cells were isolated from mouse spleens using a negative selection method to ensure high purity, and then they were induced in vitro to differentiate into Treg cells by adding specific cytokines. The anti-inflammatory effects and therapeutic potential of Treg cells transplantation in a mouse model of ischemic stroke was evaluated. In the middle cerebral artery occlusion (MCAO) model, after Treg cells transplantation, their ability to successfully migrate to the infarcted brain region and their impact on neuroinflammation levels were examined. To further investigate the role of Treg cells in stroke recovery, the changes in cytokine expression and their effects on immune cell interactions was analyzed. Additionally, infarct size and behavioral scores were measured to assess the neuroprotective effects of Treg cells. By integrating multiple indicators, the comprehensive evaluation of potential benefits of Treg cells in the treatment of ischemic stroke was performed. ResultsTreg cells significantly regulated the expression levels of both pro-inflammatory and anti-inflammatory cytokines in vitro and in vivo, effectively balancing the immune response and suppressing excessive inflammation. Additionally, Treg cells inhibited the activation and activity of inflammatory cells, thereby reducing neuroinflammation. In the MCAO mouse model, Treg cells were observed to accumulate in the infarcted brain region, where they significantly reduced the infarct size, demonstrating their neuroprotective effects. Furthermore, Treg cell therapy notably improved behavioral scores, suggesting its role in promoting functional recovery, and increased the survival rate of ischemic stroke mice, highlighting its potential as a promising therapeutic strategy for stroke treatment. ConclusionIn vitro induced Treg cells can effectively suppress neuroinflammation caused by ischemic stroke, demonstrating promising clinical application potential. By regulating the balance between pro-inflammatory and anti-inflammatory cytokines, Treg cells can inhibit immune responses in the nervous system, thereby reducing neuronal damage. Additionally, they can modulate the immune microenvironment, suppress the activation of inflammatory cells, and promote tissue repair. The therapeutic effects of Treg cells also include enhancing post-stroke recovery, improving behavioral outcomes, and increasing the survival rate of ischemic stroke mice. With their ability to suppress neuroinflammation, Treg cell therapy provides a novel and effective strategy for the treatment of ischemic stroke, offering broad application prospects in clinical immunotherapy and regenerative medicine.
4.Changing resistance profiles of Haemophilus influenzae and Moraxella catarrhalis isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Hui FAN ; Chunhong SHAO ; Jia WANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Yunsheng CHEN ; Qing MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Wenqi SONG ; Kaizhen WEN ; Yirong ZHANG ; Chuanqing WANG ; Pan FU ; Chao ZHUO ; Danhong SU ; Jiangwei KE ; Shuping ZHOU ; Hua ZHANG ; Fangfang HU ; Mei KANG ; Chao HE ; Hua YU ; Xiangning HUANG ; Yingchun XU ; Xiaojiang ZHANG ; Wenen LIU ; Yanming LI ; Lei ZHU ; Jinhua MENG ; Shifu WANG ; Bin SHAN ; Yan DU ; Wei JIA ; Gang LI ; Jiao FENG ; Ping GONG ; Miao SONG ; Lianhua WEI ; Xin WANG ; Ruizhong WANG ; Hua FANG ; Sufang GUO ; Yanyan WANG ; Dawen GUO ; Jinying ZHAO ; Lixia ZHANG ; Juan MA ; Han SHEN ; Wanqing ZHOU ; Ruyi GUO ; Yan ZHU ; Jinsong WU ; Yuemei LU ; Yuxing NI ; Jingrong SUN ; Xiaobo MA ; Yanqing ZHENG ; Yunsong YU ; Jie LIN ; Ziyong SUN ; Zhongju CHEN ; Zhidong HU ; Jin LI ; Fengbo ZHANG ; Ping JI ; Yunjian HU ; Xiaoman AI ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Xuesong XU ; Chao YAN ; Yi LI ; Shanmei WANG ; Hongqin GU ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Jihong LI ; Bixia YU ; Cunshan KOU ; Jilu SHEN ; Wenhui HUANG ; Xiuli YANG ; Likang ZHU ; Lin JIANG ; Wen HE ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(1):30-38
Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91%of the total clinical isolates and 4.07%of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89%were isolated from children and 33.11%were isolated from adults.More than 90%of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79%in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06%were isolated from children and 19.94%isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0%.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.
5.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.
6.Surveillance of antimicrobial resistance in clinical isolates of Escherichia coli:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shanmei WANG ; Bing MA ; Yi LI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; 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 ; 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 ; 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 WEN ; 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(1):39-47
Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)%.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0%vs 29.5%).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7%vs 7.8%,0.8%vs 0.1%,both P<0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)%with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9%and 1.8%,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1%).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10%.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P<0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.
7.2024 Update of Chinese Guidelines for the Management of Hyperuricemia and Gout Part Ⅱ: Recommendations for Patients with Common Comorbidities
Changgui LI ; Mingshu SUN ; Zhen LIU ; Detian LI ; Changqian WANG ; Zibin TIAN ; Yuxiang DAI ; Zhe FENG ; Chengfu XU ; Dongbao ZHAO ; Feng WEI ; Bo BAN ; Chao XIE ; Zhenmei AN ; Jia LIU ; Zhuo LI ; Yuwei HE ; Xinde LI ; Fei YAN ; Lin HAN ; Lidan MA ; Xiaoyu CHENG ; Tian LIU ; Xufei LUO ; Lingling CUI ; Ying GONG ; Can WANG ; Yaolong CHEN ; Zhaohui LYU ; Yip Ronald ML ; Jiajun ZHAO
Chinese Journal of Endocrinology and Metabolism 2025;41(11):918-929
The aim of this updated guideline is to provide comprehensive recommendations for the management of gout in patients with common comorbidities, such as chronic kidney disease(CKD), cardiovascular disease(CVD), diabetes, osteoarthritis(OA), and gastrointestinal disorders. This guideline was developed by a multidisciplinary expert panel consisting of specialists in endocrinology, rheumatology, nephrology, cardiology, gastroenterology, and methodology. The development process adhered to standard methodologies, including PICO(population, intervention, comparator, and outcomes) question deconstruction, systematic literature review, the Grading of Recommendations Assessment, Development and Evaluation(GRADE) for evidence and recommendation evaluation, Delphi voting, and expert consensus. The guideline presents 26 evidence-based recommendations addressing 7 clinical questions for patients with hyperuricemia and gout in the context of comorbidities. Key recommendations include the maintenance of strict serum urate targets, particularly for patients with CKD stage≥3, chronic gouty arthritis, and OA, in order to prevent disease progression. In patients with CVD or diabetes, intra-articular triamcinolone is preferred over systemic glucocorticoids. Prioritized anti-inflammatory treatments for patients with CKD, gastrointestinal diseases and OA are recommended. The guideline also introduces emerging therapies, such as interleukin-1 inhibitors and selective urate transport inhibitors, as potential treatment options for refractory cases. The update offers a comprehensive, patient-centered approach to managing gout, particularly in individuals with associated comorbidities. Multidisciplinary collaboration and emerging new treatments and evidence ensure the optimization of the recommendations.
8.Investigation on the Oligomeric Status and Thermal Stability Properties of Pathological Mutations of KDSR in Progressive Symmetrical Erythematokeratosis
Jia-Cong SUN ; Li WANG ; Xue GONG ; Zhen-Lu LI ; Cheng CHEN
Chinese Journal of Biochemistry and Molecular Biology 2025;41(8):1169-1178
Progressive symmetric erythrokeratodermia(PSEK)is a rare hereditary skin disease charac-terized by symmetrical erythema,hyperkeratosis and multiorgan lesions.Its clinical phenotypes are highly heterogeneous and may be accompanied by symptoms such as thrombocytopenia,which can be fatal in se-vere cases.The genotype-phenotype association mechanism of PSEK is extremely complex.Currently,it is known that mutations in multiple genes such as GJB3,KDSR,and KRT83 can cause the disease.A-mong them,3-ketodihydrosphingosine reductase(KDSR)has been found to harbor nearly 20 clinical mu-tations.These mutations interfere with the de novo ceramide synthesis pathway,disrupt the homeostasis of the skin barrier,and cause platelet production disorders and multi-organ lesions,making it a current research hotspot in the molecular mechanism of PSEK.The pathogenic mutations of KDSR are widely and uniformly distributed throughout the entire protein,rather than being limited to the traditionally recog-nized active center,suggesting that the impairment of the KDSR enzymatic activity is not the only cause of PSEK.In view of this,this study selected four typical mutants of KDSR(KDSRQG55-56R,KDSRn38C,KDSRY186F,KDSRG182S),and first used recombinant expression technology to prepare pure and homoge-neous mutant proteins.Subsequently,thermal stability experiments as well as oligomerization analysis were conducted on these four mutant proteins.The results showed that the Tm values of the four mutants were significantly lower than that of the wild type.Particularly,KDSRF138C and KDSRQG55-56R were nearly completely denatured at physiological temperature.This result was perfectly consistent with the further Rosetta energy analysis.In conclusion,this study took several pathological mutations of the PSEK patho-genic factor KDSR as the research object and discovered that the conformational stability of KDSR might be closely related to the occurrence of PSEK pathogenicity,indicating that the imbalance of conformation-al homeostasis is very likely to be one of the common contributing factors of many genetic diseases,inclu-ding PSEK.This provides a new theoretical basis and reference for explaining the molecular mechanism of genotype-phenotype heterogeneity in many genetic diseases.
9.Bushen Zhuanggu Formula promotes bone repair in nontraumatic osteonecrosis of the femoral head via regulating PKC-RAS-ERK-ETS1-RANKL signaling axis
Zhang CHU ; Ma ZHAOCHEN ; Li TAO ; Liu YUDONG ; Jia YAN ; Li QUN ; Liu CHUNFANG ; Lin YA ; Gong CHUNZHU ; Lin NA ; Chen WEIHENG ; Zhang YANQIONG
Science of Traditional Chinese Medicine 2025;3(3):239-249
Background:Bushen Zhuanggu Formula(BZF),derived from the classic Yougui Pills,has shown favorable clinical efficacy in treating advanced nontraumatic osteonecrosis of the femoral head(NONFH),particularly by promoting bone repair.However,its underlying mechanisms remain unclear.Objective:This study aimed to explore the mechanisms by which BZF promotes bone repair in advanced NONFH.Materials and methods:A total of 518 potential BZF targets were identified from the ETCM v2.0 database.Transcriptomic profiling of clinical cohorts revealed 485 differentially expressed genes in advanced NONFH patients compared to healthy controls.A drug target-disease gene interaction network was constructed to identify candidate BZF targets involved in NONFH pathogenesis.In vivo experiments were conducted to validate the effects of BZF in a rat model of advanced NONFH.Results:Network analysis identified key pathways associated with blood circulation obstruction,immune-inflammatory imbalance,and abnormal bone metabolism.Protein kinase C alpha(PKCA),Ras proto-oncogene(RAS),mitogen-activated protein kinase 3(ERK),ETS proto-oncogene 1(ETS1),and receptor activator of nuclear factor-κB ligand(RANKL)formed a signaling axis implicated in NONFH pathogenesis.BZF treatment alleviated joint inflammation,preserved trabecular bone morphology,reduced bone loss,and promoted bone repair.Mechanistically,BZF significantly downregulated the expression of PKCA,RAS,ERK,ETS1,and RANKL,improved blood circulation,and inhibited osteoclast activation while promoting osteoblast activation.Conclusion:BZF may promote bone repair in advanced NONFH by enhancing blood circulation and modulating the PKC-RAS-ERK-ETS1-RANKL signaling axis,thereby reversing dysregulated bone metabolism.
10.Construction and performance evaluation of a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury
Tao MEI ; Zheyong JIA ; Lie CHEN ; Peng CAO ; Wei XIAO ; Weiqiang MAO ; Jianwu GONG ; Lixin XU
Chinese Journal of Trauma 2025;41(11):1048-1058
Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

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