1.Expert consensus on prognostic evaluation of cochlear implantation in hereditary hearing loss.
Xinyu SHI ; Xianbao CAO ; Renjie CHAI ; Suijun CHEN ; Juan FENG ; Ningyu FENG ; Xia GAO ; Lulu GUO ; Yuhe LIU ; Ling LU ; Lingyun MEI ; Xiaoyun QIAN ; Dongdong REN ; Haibo SHI ; Duoduo TAO ; Qin WANG ; Zhaoyan WANG ; Shuo WANG ; Wei WANG ; Ming XIA ; Hao XIONG ; Baicheng XU ; Kai XU ; Lei XU ; Hua YANG ; Jun YANG ; Pingli YANG ; Wei YUAN ; Dingjun ZHA ; Chunming ZHANG ; Hongzheng ZHANG ; Juan ZHANG ; Tianhong ZHANG ; Wenqi ZUO ; Wenyan LI ; Yongyi YUAN ; Jie ZHANG ; Yu ZHAO ; Fang ZHENG ; Yu SUN
Journal of Clinical Otorhinolaryngology Head and Neck Surgery 2025;39(9):798-808
Hearing loss is the most prevalent disabling disease. Cochlear implantation(CI) serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system(Favorable, Marginal, Poor). The consensus focuses on common hereditary non-syndromic hearing loss(such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1) and syndromic hereditary hearing loss(such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome), which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans
;
Cochlear Implantation
;
Prognosis
;
Hearing Loss/surgery*
;
Consensus
;
Connexin 26
;
Mutation
;
Sulfate Transporters
;
Connexins/genetics*
2.TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children
Xi MING ; Liqun WU ; Ziwei WANG ; Bo WANG ; Jialin ZHENG ; Jingwei HUO ; Mei HAN ; Xiaochun FENG ; Baoqing ZHANG ; Xia ZHAO ; Mengqing WANG ; Zheng XUE ; Ke CHANG ; Youpeng WANG ; Yanhong QIN ; Bin YUAN ; Hua CHEN ; Lining WANG ; Xianqing REN ; Hua XU ; Liping SUN ; Zhenqi WU ; Yun ZHAO ; Xinmin LI ; Min LI ; Jian CHEN ; Junhong WANG ; Yonghong JIANG ; Yongbin YAN ; Hengmiao GAO ; Hongmin FU ; Yongkun HUANG ; Jinghui YANG ; Zhu CHEN ; Lei XIONG
Journal of Nanjing University of Traditional Chinese Medicine 2024;40(7):722-732
Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.
3.Pathological mechanism of hypoxia-inducible factor-1α in tumours and the current status of research on Chinese medicine intervention
Yu LIU ; Li-Ying ZHANG ; Guo-Xiong HAO ; Ya-Feng QI ; Qian XU ; Ye-Yuan LIU ; Chao YUAN ; Peng ZHU ; Yong-Qi LIU ; Zhi-Ming ZHANG
The Chinese Journal of Clinical Pharmacology 2024;40(11):1670-1674
Traditional Chinese medicine can regulate the hypoxia-inducible factor-1α(HIF-1α)signalling pathway and slow down tumour progression mainly by inhibiting tumour angiogenesis,glycolysis,epithelial mesenchymal transition and other pathological processes.This paper,starting from HIF-1α and related factors,reviews its pathological mechanism in tumours and the research of traditional Chinese medicine interventions with the aim of providing theoretical references for the treatment of tumours with traditional Chinese medicine.
4.Small molecule deoxynyboquinone triggers alkylation and ubiquitination of Keap1 at Cys489 on Kelch domain for Nrf2 activation and inflammatory therapy
Linghu KE-GANG ; Zhang TIAN ; Zhang GUANG-TAO ; Lv PENG ; Zhang WEN-JUN ; Zhao GUAN-DING ; Xiong SHI-HANG ; Ma QIU-SHUO ; Zhao MING-MING ; Chen MEIWAN ; Hu YUAN-JIA ; Zhang CHANG-SHENG ; Yu HUA
Journal of Pharmaceutical Analysis 2024;14(3):401-415
Activation of nuclear factor erythroid 2-related factor 2(Nrf2)by Kelch-like ECH-associated protein 1(Keap1)alkylation plays a central role in anti-inflammatory therapy.However,activators of Nrf2 through alkylation of Keap1-Kelch domain have not been identified.Deoxynyboquinone(DNQ)is a natural small molecule discovered from marine actinomycetes.The current study was designed to investigate the anti-inflammatory effects and molecular mechanisms of DNQ via alkylation of Keap1.DNQ exhibited signif-icant anti-inflammatory properties both in vitro and in vivo.The pharmacophore responsible for the anti-inflammatory properties of DNQ was determined to be the α,β-unsaturated amides moieties by a chemical reaction between DNQ and N-acetylcysteine.DNQ exerted anti-inflammatory effects through activation of Nrf2/ARE pathway.Keap1 was demonstrated to be the direct target of DNQ and bound with DNQ through conjugate addition reaction involving alkylation.The specific alkylation site of DNQ on Keap1 for Nrf2 activation was elucidated with a synthesized probe in conjunction with liquid chromatography-tandem mass spectrometry.DNQ triggered the ubiquitination and subsequent degra-dation of Keap1 by alkylation of the cysteine residue 489(Cys489)on Keap1-Kelch domain,ultimately enabling the activation of Nrf2.Our findings revealed that DNQ exhibited potent anti-inflammatory capacity through α,β-unsaturated amides moieties active group which specifically activated Nrf2 signal pathway via alkylation/ubiquitination of Keap1-Kelch domain,suggesting the potential values of targeting Cys489 on Keap1-Kelch domain by DNQ-like small molecules in inflammatory therapies.
5.Advances of ceftazidime/avibactam in the treatment of carbapenem-resis-tant Klebsiella pneumoniae infection
Yuan-Qi ZHAO ; Ming-Jing CHENG ; Miao-Miao XIONG ; Min XIAO ; Xiu-Yu CUI ; Zi-Jian ZHOU ; Yi-Wei YU ; Wei-Dong ZHAO
Chinese Journal of Infection Control 2024;23(8):1047-1052
In recent years,the prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKP)infection has become a global public health issue.Ceftazidime/avibactam(CAZ/AVI)has been approved as a novel antimicrobial agent for the treatment of healthcare-associated pneumonia/ventilator-associated pneumonia,bloodstream infection,infection after kidney transplantation,and severe infection combined with liver cirrhosis.However,the use of CAZ/AVI has also led to the emergence of drug-resistant strains.The major mechanisms of drug-resistance include over-expression of blaKPC gene,mutation of β-lactamase and amino acids at key sites,changes in cell permeability caused by loss of membrane porin,and over-expression of efflux pump.This article reviews the research progress of CAZ/AVI in the treatment of CRKP infection,providing reference for clinical diagnosis and treatment.
6.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
7.Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Ming-Ying LU ; Chung-Feng HUANG ; Chao-Hung HUNG ; Chi‐Ming TAI ; Lein-Ray MO ; Hsing-Tao KUO ; Kuo-Chih TSENG ; Ching-Chu LO ; Ming-Jong BAIR ; Szu-Jen WANG ; Jee-Fu HUANG ; Ming-Lun YEH ; Chun-Ting CHEN ; Ming-Chang TSAI ; Chien-Wei HUANG ; Pei-Lun LEE ; Tzeng-Hue YANG ; Yi-Hsiang HUANG ; Lee-Won CHONG ; Chien-Lin CHEN ; Chi-Chieh YANG ; Sheng‐Shun YANG ; Pin-Nan CHENG ; Tsai-Yuan HSIEH ; Jui-Ting HU ; Wen-Chih WU ; Chien-Yu CHENG ; Guei-Ying CHEN ; Guo-Xiong ZHOU ; Wei-Lun TSAI ; Chien-Neng KAO ; Chih-Lang LIN ; Chia-Chi WANG ; Ta-Ya LIN ; Chih‐Lin LIN ; Wei-Wen SU ; Tzong-Hsi LEE ; Te-Sheng CHANG ; Chun-Jen LIU ; Chia-Yen DAI ; Jia-Horng KAO ; Han-Chieh LIN ; Wan-Long CHUANG ; Cheng-Yuan PENG ; Chun-Wei- TSAI ; Chi-Yi CHEN ; Ming-Lung YU ;
Clinical and Molecular Hepatology 2024;30(1):64-79
Background/Aims:
Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.
Methods:
We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.
Results:
The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.
Conclusions
Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
8.Mechanism of Dengzhan Shengmai capsule in treating coronary heart disease based on network pharmacology and molecular docking technology
Hui YANG ; Yuan XIONG ; Long CHENG ; Ming QIAN ; Li JI
Journal of Clinical Medicine in Practice 2024;28(9):1-8
Objective To explore the potential target and mechanism of Dengzhan Shengmai capsule (DZSM) in the treatment of coronary heart disease (CHD) based on network pharmacology and molecular docking technology. Methods TCMSP and ETCM databases were employed to search the chemical components of DZSM. Swiss ADME database was used to screen active ingredients, and Swiss Target Prediction database was used to obtain potential targets of active ingredients. The CHD target was obtained by searching GeneCards and DisGeNET databases, and the DZSM-active ingredient-CHD target network was constructed. Molecular docking of key active ingredients and core targets was performed to verify binding properties. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed in the DAVID database. A mouse macrophage cell line (RAW264.7 cells) model induced by oxidized low density lipoprotein (ox-LDL) was used to test the therapeutic effect of scutellarin on CHD
9.Identification of key molecules in miRNA-mRNA regulatory network associated with high-grade serous ovarian cancer recurrence using bioinformatic analysis.
Pan Yang ZHANG ; Ming Mei HE ; Yuan Yuan ZENG ; Xiong Wei CAI
Journal of Southern Medical University 2023;43(1):8-16
OBJECTIVE:
To investigate the correlation of the potential functional microRNA (miRNA)-mRNA regulatory network with recurrence of high-grade serous ovarian carcinoma (HGSOC) and its biological significance.
METHODS:
This study was performed based on the data of 354 patients with HGSOC from the Cancer Genome Atlas database. In these patients, HGSOC was divided into different subtypes based on the pathways identified by GO analysis, and the correlations of the subtypes with HGSOC recurrence and differentially expressed miRNAs and mRNAs were assessed. Two relapse-related datasets were identified using the Gene Set Enrichment (GSE) database, from which the differentially expressed miRNAs were identified by intersection with the TCGA data. The target genes of these miRNAs were predicted using miRWalk 2.0 database, and these common differentially expressed miRNAs and mRNAs were used to construct the key miRNA-mRNA network associated with HGSOC recurrence. The expression of miR-506-3p and SNAI2 in two ovarian cancer cell lines was detected using RT-qPCR and Western blotting, and their targeted binding was verified using a double luciferase assay. The effect of miR-506-3p expression modulation on ovarian cancer cell migration was detected using scratch assay and Transwell assay.
RESULTS:
We screened 303 GO terms of HGSOC-related pathways and identified two HGSOC subtypes (C1 and C2). The subtype C1 was associated with a significantly higher recurrence rate than C2. The differentially expressed genes between C1 and C2 subtypes were mainly enriched in epithelial-mesenchymal transition (EMT). Five miRNAs were identified as potential regulators of EMT, and a total of 41 target genes were found to be involved in the differential expressions of EMT pathway between C1 and C2 subtypes. The key miRNA-mRNA network associated with HGSOC recurrence was constructed based on these 5 miRNAs and 41 mRNAs. MiR-506-3p was confirmed to bind to SNAI2, and up-regulation of miR-506-3p significantly inhibited SNAI2 expression and reduced migration and invasion of SKOV3 and CAOV3 cells (P < 0.05), while miR-506-3p knockdown produced the opposite effects (P < 0.05).
CONCLUSION
MiR-506-3p and SNAI2 are the key molecules associated with HGSOC recurrence. MiR-506-3p may affect EMT of ovarian cancer cells by regulating cell migration and invasion via SNAI2, and its expression level has predictive value for HGSOC recurrence.
Humans
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Female
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MicroRNAs/genetics*
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Neoplasm Recurrence, Local/genetics*
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Ovarian Neoplasms/genetics*
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Computational Biology
10.Platelet RNA signature independently predicts ovarian cancer prognosis by deep learning neural network model.
Chun-Jie LIU ; Hua-Yi LI ; Yue GAO ; Gui-Yan XIE ; Jian-Hua CHI ; Gui-Ling LI ; Shao-Qing ZENG ; Xiao-Ming XIONG ; Jia-Hao LIU ; Lin-Li SHI ; Xiong LI ; Xiao-Dong CHENG ; Kun SONG ; Ding MA ; An-Yuan GUO ; Qing-Lei GAO
Protein & Cell 2023;14(8):618-622


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