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Article(id=1322590033580773942, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202406334, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1718640000000, receivedDateStr=2024-06-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259636539, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259636539, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259636539, creator=13701087609, updateTime=1793259636539, updator=13701087609, issue=Issue{id=1322589954702688372, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793259617734, creator='13701087609', updateTime=1793260023378, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322591656147276425, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322591656147276426, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=144, endPage=148, ext={EN=ArticleExt(id=1322590035317215799, articleId=1322590033580773942, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Network analysis of symptoms of depression, anxiety, sleep disorders, and feelings of loneliness among the elderly and gender differences, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the relationships among symptoms of depression, anxiety, sleep disorders, and feelings of loneliness in a community-dwelling elderly population through network analysis, and to investigate the differences in network structure and core symptoms between genders.

Methods

A convenience sampling method was employed to survey 1 325 elderly individuals in Chengdu, Sichuan Province, regarding their symptoms of depression, anxiety, sleep disorders, and loneliness.Network analysis, centrality analysis, and robustness analysis were conducted to examine the associations among the symptoms.Additionally, differences in the network among subgroups by gender were analyzed.

Results

The most central symptoms in the network of community elderly were identified as“fatigue”, “sleep problems”,“low mood”, and “feeling neglected” (Z≥1). No statistically significant differences were found in the network structure and connection strength between different gender subgroups (P>0.05). However, the mediating centrality of“low mood” was higher in males than in females (P<0.05), while the strength centrality of “tension” was lower in males than in females (P<0.001).

Conclusion

The network structure of disease symptoms aids in understanding the mechanisms of mental health issues among the elderly and the relationships between different conditions. It is recommended that community health service centers focus on preventing and alleviating feelings of fatigue among the elderly, while also addressing their sleep issues, low mood, and feelings of neglect to enhance overall mental health.

, authors=Jing HUANG, Yu-xing TIAN, Wei-hong ZHENG, Yi-qi WANG, Pei-yuan QIU, authorsList=Jing HUANG, Yu-xing TIAN, Wei-hong ZHENG, Yi-qi WANG, Pei-yuan QIU, authorCompany=null, correspAuthors=Pei-yuan QIU, 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=1322590036554535492, articleId=1322590033580773942, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=抑郁、焦虑、睡眠障碍和孤独感症状间的网络分析和性别差异研究, columnId=1228016572640461823, journalTitle=现代预防医学, columnName=健康与社会行为, runingTitle=null, highlight=null, articleAbstract=
目的

本研究旨在通过网络分析探讨社区老年人群抑郁、焦虑、睡眠障碍和孤独感症状之间的关系，并探索网络结构与核心症状在不同性别间的差异。

方法

采用方便抽样对四川省成都市社区老年人的抑郁、焦虑、睡眠障碍和孤独感的症状进行调查，有效样本共1 325例。采用网络分析、中心性分析及其稳健性分析研究疾病症状之间的关联，此外，对不同性别亚组的老年人的网络差异进行分析。

结果

社区老年人网络最核心症状依次为“疲惫”“睡眠问题”“情绪低落”“被冷落”（Z≥1）。不同性别亚组的网络结构和网络连接强度的差异均无统计学意义（P>0.05），但男性“情绪低落”的中介中心性高于女性（P<0.05），“紧张”的强度中心性低于女性（P<0.001）。

结论

疾病症状的网络结构有助于深入理解老年人心理健康问题的机制及疾病之间的关系，建议社区卫生服务中心重点预防和缓解老年人的疲惫感，并关注睡眠问题、情绪低落和被冷落感，以更好地提升整体心理健康。

, authors=黄婧, 田宇星, 郑伟鸿, 王一淇, 邱培媛, authorsList=黄婧, 田宇星, 郑伟鸿, 王一淇, 邱培媛, authorCompany=null, correspAuthors=邱培媛, authorNote=

黄婧（2000—），女，硕士在读，研究方向：老年心理健康

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邱培媛，E-mail:
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Sociodemographic characteristics of the sample［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人数
年龄（岁）71.90±6.59
性别
男528（39.84）
女727（60.16）
教育水平
文盲341（25.74）
小学576（43.47）
初中及以上408（30.79）
居住地
农村795（60.00）
城镇530（40.00）
婚姻状况
已婚或同居1 028（77.58）
其他（丧偶、离异、分居或从未结婚）297（22.41）
是否有固定收入
是774（58.42）
否551（41.58）
量表评分
抑郁水平12.42±3.30
焦虑水平11.01±3.13
睡眠障碍水平8.28±2.64
孤独感水平3.42±1.07
), ArticleFig(id=1322590041826775668, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590033580773942, language=CN, label=表1, caption=

研究对象基本情况［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人数
年龄（岁）71.90±6.59
性别
男528（39.84）
女727（60.16）
教育水平
文盲341（25.74）
小学576（43.47）
初中及以上408（30.79）
居住地
农村795（60.00）
城镇530（40.00）
婚姻状况
已婚或同居1 028（77.58）
其他（丧偶、离异、分居或从未结婚）297（22.41）
是否有固定收入
是774（58.42）
否551（41.58）
量表评分
抑郁水平12.42±3.30
焦虑水平11.01±3.13
睡眠障碍水平8.28±2.64
孤独感水平3.42±1.07
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抑郁、焦虑、睡眠障碍和孤独感症状间的网络分析和性别差异研究
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黄婧 , 田宇星 , 郑伟鸿 , 王一淇 , 邱培媛
现代预防医学 | 健康与社会行为 2025,52(1): 144-148
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现代预防医学 |健康与社会行为 2025 , 52 (1) : 144 -148
抑郁、焦虑、睡眠障碍和孤独感症状间的网络分析和性别差异研究
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黄婧, 田宇星, 郑伟鸿, 王一淇, 邱培媛
作者信息
  • 四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041
通讯作者:
邱培媛，E-mail:
作者简介:

黄婧（2000—），女，硕士在读，研究方向：老年心理健康

Network analysis of symptoms of depression, anxiety, sleep disorders, and feelings of loneliness among the elderly and gender differences
Jing HUANG, Yu-xing TIAN, Wei-hong ZHENG, Yi-qi WANG, Pei-yuan QIU
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202406334
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摘要
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目的

本研究旨在通过网络分析探讨社区老年人群抑郁、焦虑、睡眠障碍和孤独感症状之间的关系，并探索网络结构与核心症状在不同性别间的差异。

方法

采用方便抽样对四川省成都市社区老年人的抑郁、焦虑、睡眠障碍和孤独感的症状进行调查，有效样本共1 325例。采用网络分析、中心性分析及其稳健性分析研究疾病症状之间的关联，此外，对不同性别亚组的老年人的网络差异进行分析。

结果

社区老年人网络最核心症状依次为“疲惫”“睡眠问题”“情绪低落”“被冷落”（Z≥1）。不同性别亚组的网络结构和网络连接强度的差异均无统计学意义（P>0.05），但男性“情绪低落”的中介中心性高于女性（P<0.05），“紧张”的强度中心性低于女性（P<0.001）。

结论

疾病症状的网络结构有助于深入理解老年人心理健康问题的机制及疾病之间的关系，建议社区卫生服务中心重点预防和缓解老年人的疲惫感，并关注睡眠问题、情绪低落和被冷落感，以更好地提升整体心理健康。

网络分析  /  抑郁  /  焦虑  /  睡眠障碍  /  孤独感
Objective

To explore the relationships among symptoms of depression, anxiety, sleep disorders, and feelings of loneliness in a community-dwelling elderly population through network analysis, and to investigate the differences in network structure and core symptoms between genders.

Methods

A convenience sampling method was employed to survey 1 325 elderly individuals in Chengdu, Sichuan Province, regarding their symptoms of depression, anxiety, sleep disorders, and loneliness.Network analysis, centrality analysis, and robustness analysis were conducted to examine the associations among the symptoms.Additionally, differences in the network among subgroups by gender were analyzed.

Results

The most central symptoms in the network of community elderly were identified as“fatigue”, “sleep problems”,“low mood”, and “feeling neglected” (Z≥1). No statistically significant differences were found in the network structure and connection strength between different gender subgroups (P>0.05). However, the mediating centrality of“low mood” was higher in males than in females (P<0.05), while the strength centrality of “tension” was lower in males than in females (P<0.001).

Conclusion

The network structure of disease symptoms aids in understanding the mechanisms of mental health issues among the elderly and the relationships between different conditions. It is recommended that community health service centers focus on preventing and alleviating feelings of fatigue among the elderly, while also addressing their sleep issues, low mood, and feelings of neglect to enhance overall mental health.

Network analysis  /  Depression  /  Anxiety  /  Sleep disorders  /  Loneliness
黄婧, 田宇星, 郑伟鸿, 王一淇, 邱培媛. 抑郁、焦虑、睡眠障碍和孤独感症状间的网络分析和性别差异研究. 现代预防医学, 2025 , 52 (1) : 144 -148 . DOI: 10.20043/j.cnki.MPM.202406334
Jing HUANG, Yu-xing TIAN, Wei-hong ZHENG, Yi-qi WANG, Pei-yuan QIU. Network analysis of symptoms of depression, anxiety, sleep disorders, and feelings of loneliness among the elderly and gender differences[J]. Modern Preventive Medicine, 2025 , 52 (1) : 144 -148 . DOI: 10.20043/j.cnki.MPM.202406334
正文
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随着人口老龄化的加重，老年人群的健康问题引起普遍关注，其中心理卫生问题不容忽视。睡眠障碍[1]、抑郁[2]、焦虑[3]和孤独[4]在老年人中普遍存在，对老年人的生活质量造成负面影响的同时，也给家庭和社会造成了严重负担。研究表明，睡眠障碍、抑郁、焦虑和孤独感之间存在紧密联系，其症状之间亦可相互影响[5-6]，但目前很少有研究同时探索这些疾病症状之间的关系。从精神障碍的传统分类和维度视角来看，可观察的症状产生于潜在变量，症状之间的关联也可以用潜在变量来解释。然而，该传统视角忽略了症状本身之间相互促进的可能性[7]。为更加全面的研究精神病理学，Borsboom等[8]建议使用网络分析。
从网络视角来看，精神障碍起源于内部症状和外部事件的直接相互作用[8]。网络分析能有效识别各症状之间、症状群之间的相互作用关系，并确定网络结构中的核心症状。不仅有助于探索精神疾病内部以及疾病之间相互影响的机制，亦可提示疾病重点预防或治疗的干预方向，具有较大的实践价值。
因此，本研究使用网络分析对社区老年人的睡眠障碍、抑郁、焦虑和孤独感的网络结构进行研究，并探索网络结构与核心症状在不同性别间的差异，以期为深入理解老年人心理健康问题之间的关系并进行干预提供依据。
1　对象与方法
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1.1　研究对象
本研究采用方便抽样的方法，于2023年3—9月对四川省成都市龙泉驿区和温江区的社区老年人进行了调查。研究对象的纳入标准为：（1）居住于四川省成都市龙泉驿区和温江区；（2）年满60岁及以上；（3）完成以下四个精神健康评估量表：患者健康调查问卷抑郁量表（patient health questionnaire-9, PHQ-9）、广泛性焦虑障碍7项问卷（general anxiety disorder-7, GAD-7）、匹兹堡睡眠质量指数（Pittsburgh sleep quality index, PSQI）以及简明加州大学洛杉矶分校孤独感量表-3（loneliness scale, Univesity of California at Los Angels-3, UCLA-3）。研究共调查1 333名老年人，排除四个量表均未完成者5例，PSQI、PHQ-9和GAD-7三个量表均未完成者2例，PSQI量表未完成者1例，最终获得有效样本为1 325例。本研究由四川大学华西第四医院/华西公共卫生学院伦理委员会（GWll2022004）审查和批准，每位调查对象均签署了知情同意书。
1.2　测量量表
1.2.1　抑郁
采用PHQ-9评估被调查对象最近2周的抑郁症状[9]。该量表共包含了9个条目，包括兴趣减退、情绪低落、睡眠问题、疲惫、食欲改变、无价值感、注意力集中困难、精神/动作迟缓或急躁以及自杀倾向。量表采用0～3分李克特量表评估抑郁症状发生频率，总分0～27分，得分越高表示抑郁越严重。本研究抑郁量表具有较好的内部一致性，Cronbachα =0.793。
1.2.2　焦虑
采用GAD-7对被调查对象最近两周焦虑症状进行评估[10]。量表包括了7个条目：紧张、难以控制担忧、过多担忧、难以放松、静坐不能、烦恼急躁以及无故害怕，采用0～3分李克特量表评估焦虑症状发生频率。量表总分在0～21分之间，得分越高表示焦虑越严重。本研究焦虑量表具有较好的内部一致性，Cronbach α=0.886。
1.2.3　睡眠障碍
采用PSQI评估被调查对象最近一个月内的睡眠情况[11]。量表包括7个维度：睡眠效率、入睡时间、睡眠时长、睡眠中的困扰、睡眠质量、催眠药物使用和日间功能障碍。每个维度得分0～3分，得分越高表示睡眠障碍越严重。本研究睡眠障碍量表具有内部一致性尚可，Cronbach α=0.658。
1.2.4　孤独感
本研究采用UCLA-3[12]来评估社区老年人的孤独感。条目包括“缺乏陪伴”“被冷落”“与他人隔绝”。条目采用1～3的三级评分，分别代表“几乎从来没有”“有时”“经常”，得分越高代表孤独感越强。本研究孤独感量表具有较好的内部一致性，Cronbach α=0.839。
1.3　统计学方法
使用R软件4.2.2版本进行统计学分析。通过高斯图形模型（Gaussian graphical model, GGM）拟合和构建网络[13]。使用bootnet包中的扩展贝叶斯信息准则和图形最小绝对收缩和选择算子（the Bayes information criterion and the graphical least absolute shrinkage and selection operator，EBICgLASSO）进行偏相关矩阵的最优稀疏估计[14]。网络结构中，节点代表条目症状，节点之间的连线表示其偏相关关系，线条越粗表示关联越强。使用qgraph包中的centralityPlot函数估计并可视化症状的三个中心性指标，包括了强度中心性、紧密中心性和中介中心性[15]，其中，强度中心性为所有与该节点连接的节点边缘权重绝对值之和，其值越大，表示该节点越重要。桥梁中心性指标反映了节点在连接不同疾病症状中的作用，包括强度桥接中心性、紧密桥接中心性和中介桥接中心性。采用bootnet包评价网络模型结果的稳健性[16]，其中使用案例下降自举法计算相关稳定性系数（correlation stability coefficient, CS）来评估中心性指标的稳定性，CS大于0.50代表具有较好的稳定性。使用NetworkComparisionTest包对不同性别亚组的网络结构不变性、网络整体连接强度（global strength, GS）不变性和中心性不变性进行假设检验[17]，检验水准α=0.05。
2　结果
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2.1　研究对象基本情况
本研究共纳入1 325名老年人，包括男性528例（39.84%），女性727例（60.16%）。被调查对象平均年龄为（71.90±6.59）岁，341例（25.74%）未接受过小学及以上教育，居住地为农村的老年人共794例（59.92%）。抑郁量表总分为（12.42±3.30）分，焦虑量表总分为（11.01±3.13）分，睡眠障碍总得分为（8.28±2.64）分，孤独感总得分为（3.42±1.07）分。见表1。
2.2　老年人抑郁-焦虑-睡眠障碍-孤独感网络分析
社区老年人抑郁、焦虑、睡眠障碍和孤独感的网络结构如图1所示。抑郁、焦虑、睡眠障碍和孤独感的症状各自构成症状集群。在抑郁症状条目中，“情绪低落”和“兴趣减退”存在最紧密的联系（权重=0.42）；在焦虑症状中，“难以控制担忧”和“过多担忧”联系最为紧密（权重=0.48）；睡眠障碍症状中，“睡眠效率”和“睡眠时长”有最强联系（权重=0.52）；在孤独感症状条目中，“被冷落”与“与他人隔绝”（权重=0.48）、“缺乏陪伴”（权重=0.37）都有较强关联。同时，抑郁和睡眠障碍的症状节点之间出现了融合和交叉，其中，抑郁症状“睡眠问题”与睡眠障碍症状中的“入睡时间”（权重=0.33）和“睡眠质量”（权重=0.27）都具有紧密关联。
社区老年人抑郁、焦虑、睡眠障碍和孤独感网络的中心化结果如图2所示。网络的最核心症状包括了抑郁症状中的“疲惫”“睡眠问题”“情绪低落”，以及孤独感症状“被冷落”（Z≥1）。其中，“疲惫”具有最高的强度中心性、中介中心性和中介桥接中心性，其强度桥接中心性、紧密中心性和紧密桥接中心性也较高。案例下降自举法显示中心性指标估计值具有较强的稳定性，见图3。样本量下降到总体样本的30%时估计的中心性与总样本估计的中心性的相关系数仍保持在0.75及以上。
2.3　分性别亚组的抑郁-焦虑-睡眠障碍-孤独感网络分析
不同性别的抑郁-焦虑-睡眠障碍-孤独感网络结构如图4所示。两亚组在网络结构（M=0.249，P>0.05）和整体连接强度［GS（男）= 10.486，GS（女）=11.123，S=0.636，P>0.05］上的差异均无统计学意义。对两亚组的核心症状进行比较发现，男性网络中“情绪低落”症状的中介中心性高于女性（49 vs. 3，P<0.05），“紧张”症状在女性网络中的强度中心性高于男性（1.036 vs. 0.806，P<0.001)，结果具有统计学意义。其他症状的强度中心性、紧密中心性和中介中心性在两组间的差异均无统计学意义。
3　讨论
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本研究发现，在社区老年人的睡眠障碍-抑郁-焦虑-孤独感网络结构中，“疲惫”是最核心的症状，表明它可能是影响网络整体心理健康水平的关键节点。既往研究强调，干预核心症状可以改善网络中各疾病的整体情况，且这比干预其他症状更有效，因为核心症状能通过促进周边症状的缓解，达到更好效果[7-8]。Bai等[18]在一项基于疫情期间澳门居民［平均年龄（29.91±2.00）岁］的研究中发现，“疲惫”的强度中心性在失眠-抑郁-焦虑网络模型中排名第五，也证明了该症状的重要性。排名的差异可能是研究人群不同所导致，本研究对象是老年人，相较于年轻人更容易出现疲惫状态。在社区老年人的抑郁、睡眠障碍和焦虑的预防与治疗过程中，重点关注并优先干预“疲惫”这一症状，如合理安排运动和休息、补充营养，以及进行基于认知行为疗法的放松训练等[19-21]，可能更有助于改善总体的心理健康水平。
此外，网络结果显示孤独感症状“被冷落”也是核心症状之一，孤独感与抑郁、焦虑之间存在关联，提示可以通过缓解孤独感来减轻抑郁和焦虑的严重程度。孤独感是抑郁的独立危险因素，亦是预测自杀的敏感指标[22]。此外，当个体感到孤独时，社会参与减少、社交能力降低以及容易情绪低落等都可能导致焦虑产生[23]。伴随工作退休以及身体机能和认知功能的下降，老年人社会功能减少，此外，子女长期不在身边、老伴或朋友已故等原因都可能导致老年人社会交往活动减少，从而产生孤独感。因此，建议社区定期举办集体活动（如唱歌、健身操、当地风俗文化活动等），以及对出门不便的老年人进行志愿者家访等方式，给予社区老年人更多的陪伴与情感支持，以缓解“被冷落”等孤独感症状，从而更好地提高老年人的心理健康水平。
不同性别亚组网络分析发现，两亚组的网络结构和网络连接强度之间的差异无统计学意义，这与黄顺森等[24]和Claudia等[17]对抑郁网络的研究结果一致。这表明不同性别社区老年人的抑郁、焦虑、睡眠障碍和孤独感症状之间的相互作用机制整体是相似的。但是，不同性别亚组的网络核心症状中，男性“情绪低落”症状的中介中心性明显高于女性。高中介中心性预示着抑郁症状中的“情绪低落”在男性中可能发挥着桥梁的作用，重点关注并改善高中介中心性的症状，可能更有益于预防抑郁、焦虑、睡眠障碍和孤独感共病。此外，“紧张”症状在女性中的强度中心性高于男性，差异具有统计学意义，说明不同于男性，对女性进行干预时，“紧张”症状值得重点关注。可以通过音乐疗法、瑜伽、按摩和笑声疗法等方式来减缓“紧张”症状[25-27]，从而降低睡眠障碍-抑郁-焦虑-孤独感的整体水平。
尽管本研究具有一定的实际意义，但仍存在一些局限性。首先，研究为横断面设计，只能探索网络症状之间的相互关系，无法确定因果关联。其次，心理健康评估数据来自参与者的自我报告，可能存在一定的回忆偏倚。再次，本研究采用方便抽样，尽管是在多个社区卫生服务中心全体社区老年人体检期间进行，并对所有前来体检且愿意接受调查的老年人进行调查，但不可避免存在选择偏倚。未来的研究可以通过生化检验或影像学检查等客观指标以提高研究结果的准确性。此外，不同研究使用的疾病测量工具不同，其包含的症状也可能存在差异，未来应使用统一的工具量表来研究心理疾病以更好地对不同研究进行比较。最后，本研究考虑不同性别的疾病症状间相互作用机制可能存在差异，故对性别进行亚组网络分析，以后的研究可以考虑更多可能影响症状间相互作用机制的因素来进行相关亚组分析。
本研究使用网络分析对社区老年人的睡眠障碍、抑郁、焦虑和孤独感的网络结构进行研究，发现抑郁症状“疲惫”和“睡眠问题”，以及孤独感症状“被冷落”是网络的核心症状。不同性别亚组的网络结构差异无统计学意义，但网络核心症状有所差别。

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2025年第52卷第1期
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doi: 10.20043/j.cnki.MPM.202406334
  • 接收时间：2024-06-18
  • 首发时间：2026-10-29
  • 出版时间：2025-01-10
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  • 收稿日期：2024-06-18
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    四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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