Article(id=1309635226758906164, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309635220643607372, articleNumber=null, orderNo=null, doi=10.12048/j.issn.1674-229X.2026.02.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1764172800000, receivedDateStr=2025-11-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790170969905, onlineDateStr=2026-09-23, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790170969905, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790170969905, creator=13701087609, updateTime=1790170969905, updator=13701087609, issue=Issue{id=1309635220643607372, tenantId=1146029695717560320, journalId=1227999776411738115, year='2026', volume='36', issue='2', pageStart='81', pageEnd='160', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790170968446, creator='13701087609', updateTime=1790219084169, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309837032671772868, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309635220643607372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309837032671772869, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309635220643607372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=113, endPage=117, ext={EN=ArticleExt(id=1309635226960232757, articleId=1309635226758906164, tenantId=1146029695717560320, journalId=1227999776411738115, language=EN, title=Mining and Analysis of Adverse Reaction Signals of Zuranolone Based on the FAERS Database, columnId=null, journalTitle=Pharmacy Today, columnName=null, runingTitle=null, highlight=null, articleAbstract=
OBJECTIVE This study aims to utilize the FAERS database to identify and evaluate the adverse event signals of zuranolone after its market launch in the real world, so as to provide real-world evidence for its safe clinical application.
METHODS Collect the reports from the FAERS database for the fourth quarter of 2023 to the third quarter of 2025. Data is stored in MySQL, Medical Dictionary for Regulatory Activities (MedDRA) version 28.0 standardizes and systematically classifies adverse event terms, and Python is used for data cleaning and analysis. The Proportional Reporting Ratio (PRR) method and Bayesian Confidence Propagation Neural Network (BCPNN) method are used simultaneously to detect signals, thereby improving the accuracy of positive signals.
RESULTS There were a total of 883 reports where zuranolone was identified as the primary suspected drug. 70 positive signals were identified, involving 7 system organ classes (SOCs). The SOC with the highest number of positive signals was psychiatric disorders, followed by neurological disorders. The number of positive signals for these two categories accounted for 80% of the total positive signals. The top three adverse events in terms of signal intensity were neonatal drowsiness, perinatal depression, and intoxication.
CONCLUSION The adverse drug events (ADEs) of zuranolone mainly include mental disorders and neurological diseases. It may aggravate depressive symptoms and even induce suicidal behavior; during medication, it may impair the ability to operate precision instruments; it is necessary to strengthen the warning about infant exposure to zuranolone.
, authors=Bo HE
1, 2, Yan ZHENG
2, Xia LI
2, Fenfei GAO
1, *, authorsList=Bo HE, Yan ZHENG, Xia LI, Fenfei GAO, authorCompany=null, correspAuthors=Fenfei GAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309635227325137206, articleId=1309635226758906164, tenantId=1146029695717560320, journalId=1227999776411738115, language=CN, title=基于FAERS数据库对祖拉诺酮不良反应信号的挖掘与分析, columnId=1228268589086606089, journalTitle=今日药学, columnName=论著, runingTitle=null, highlight=null, articleAbstract=
目的 本研究旨在利用FAERS数据库，挖掘和评价祖拉诺酮上市后在真实世界下的不良事件信号，为其临床安全应用提供真实世界证据。
方法 收集FAERS数据库中2023年第4季度~2025年第3季度的报告。MySQL存储数据，MedDRA 28.0对不良事件术语进行标准化和系统器官分类，Python完成数据清洗及分析。采用比例报告比法和贝叶斯置信度传播神经网络法同时检测信号，提高阳性信号的准确性。
结果 以祖拉诺酮为首要怀疑药品的报告共883份，挖掘出阳性信号70个，累及7个系统-器官（SOC），阳性信号最多的SOC为精神病类，其次为各类神经系统疾病，两者的阳性信号数占总阳性信号数的80%。信号强度排名前三的分别为新生儿嗜睡、围产期抑郁症和醉酒感。
结论 祖拉诺酮的药品不良事件主要为精神病类和神经系统疾病，可能会导致抑郁症状加重，甚至出现自杀行为；服药期间对于精密仪器的操作会产生影响；需加强祖拉诺酮致婴幼儿暴露情况的警示。
, authors=何勃
1, 2, 郑岩
2, 李侠
2, 高分飞
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