铁死亡是一种以铁依赖性脂质过氧化为特征的程序性细胞死亡形式。近年来研究表明，铁死亡与缺氧应答的关键调控因子——缺氧诱导因子（HIF）存在密切关联。HIF（包括HIF-1α、HIF-2α、HIF-3α三种亚型）调控的铁死亡在结直肠癌、胃癌、溃疡性结肠炎及其他胃肠黏膜损伤性疾病中发挥作用，影响疾病的发生发展。但目前关于HIF-铁死亡通路在不同胃肠道疾病中的差异化作用及调控机制尚未完全阐明。因此，本文对HIF各亚型调控铁死亡的分子机制及其在胃肠道疾病中的作用进行综述，以期为靶向HIF-铁死亡通路治疗相关疾病提供新的思路。

       Ferroptosis is a form of regulated cell death characterized by iron-dependent lipid peroxidation．Recent 

studies have demonstrated a close association between ferroptosis and hypoxia-inducible factor（HIF），the key  regulator of the hypoxic response．Ferroptosis regulated by HIF（comprising three isoforms：HIF-1α，HIF-2α，and HIF-3α）plays a  role in colorectal cancer，gastric cancer，ulcerative colitis，and other gastrointestinal mucosal injury diseases，impacting their initiation and progression．However，the differential roles and regulatory mechanisms of the HIF-ferroptosis pathway in various gastrointestinal diseases remain incompletely elucidated．Therefore，this  review  summarizes the molecular mechanism networks through which individual HIF isoforms regulate ferroptosis and their roles in gastrointestinal diseases，with the aim of providing new perspectives for targeting the HIF-ferroptosis pathway to treat relevant diseases．

       目的   通过机器学习方法构建脓毒症谵妄患者30 d死亡的预测模型，并识别关键预测因子。方法   采用基于医疗信息集成重症监护数据库（Medical Information Mart for Intensive Care IV）的回顾性队列研究方法，boruta筛选重要特征，并通过决策树，K近邻，LightGBM，随机森林，支持向量机，XGBoost构建模型进行分析，通过ROC曲线下面积进行评估，利用F1分数、召回率、精确率、特异度、灵敏度和阳性预测值比较模型表现。结果  XGBoost模型在训练集和验证集中的ROC曲线下面积分别为0.906和0.762，表明该模型具有良好的预测能力，入院年龄、红细胞分布宽度和白细胞计数是最重要的预测因子。结论   基于机器学习的脓毒症谵妄患者预后预测模型展现出良好的预测效能，为临床早期干预提供了重要参考依据。

       Objective  To construct a  30-day mortality  prediction model for  patients with  sepsis-associated  delirium using machine learning methods and identify key predictive factors．Methods  A  retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care IV database．Important features were selected using the Boruta algorithm，and models including Decision Tree，K-Nearest Neighbors，LightGBM，Random Forest，Support Vector Machine，and XGBoost were constructed and analyzed．Model performance was evaluated using the area under the reciver operater characteristic（ROC）curve（AUC），along with F1 score，recall，precision，specificity，sensitivity，and positive predictive value．Results  The XGBoost model demonstrated strong predictive performance，with AUC values of 0.906 in the training set and 0.762 in the test set．Key predictors identified included admission age，red blood cell distribution width，and white blood cell count．Conclusions  The machine learning-based prediction model for sepsis-associated delirium prognosis exhibits robust predictive efficacy，providing a valuable tool for early clinical intervention．

目的 探讨重楼皂苷Ⅰ（PPI）对慢性髓系白血病细胞（K562）细胞的抑制作用及可能的作用机制。方法 采用CCK-8法筛选药物最适浓度,将培养时间为24 h的药物最适浓度作为后续实验的干预浓度。分组如下：（1）空白组;（2）PPI组;（3）抑制剂组;（4）PPI+抑制剂组。采用CCK-8法检测细胞增殖率;AO/EB染色观察细胞形态;流式细胞术检测凋亡率;ROS检测试剂盒检测活性氧（ROS）含量、还原型谷胱甘肽含量检测试剂盒检测谷胱甘肽（GSH）含量、细胞亚铁比色法测试盒检测细胞亚铁（Fe²⁺）含量;qRT-PCR法和蛋白免疫印迹法检测各组肿瘤蛋白53（p53）、钠氯离子依赖性氨基酸转运蛋白11（SLC7A11）、谷胱甘肽过氧化物酶4（GPX4）mRNA及蛋白表达量。结果 PPI抑制K562细胞的生长,且呈一定的剂量及时间依赖性（与同时间段的对照组0μmol/L比较,均P<0.01）。与空白组相比,PPI抑制K562细胞的增殖,提高了凋亡率,而铁死亡抑制剂（Ferrostian-1）的使用逆转了PPI对K562凋亡的促进作用（P<0.01）。与空白组相比,PPI组ROS、Fe²⁺含量升高,GSH含量下降,而铁死亡抑制剂的使用可下调ROS、Fe²⁺,上调GSH的含量（P<0.01）。PPI组较空白组p53 mRNA和蛋白表达水平升高,而SLC7A11、GPX4 mRNA和蛋白表达水平下降（P<0.05）;PPI+抑制剂组细胞较重楼皂苷组p53 mRNA和蛋白表达水平下降,而SLC7A11、GPX4 mRNA和蛋白表达水平升高（P<0.05）。结论 PPI能够有效抑制K562细胞增殖,促进K562细胞铁死亡,其分子机制可能与p53信号通路的调控有关。

Objective To investigate the inhibitory effect of polyphyllin I（PPI）on chronic myeloid leukemia cells（K562）and its possible mechanism．Methods K562 cell line was cultured in suitable environment,and the optimal concentration of the drug was screened by CCK-8 method．The optimal concentration of the drug cultured for 24 hours was used as the intervention concentration of the follow-up experiment．Cells were divided into the following groups：（1）blank group,（2）saponins group,（3）inhibitor group and（4）saponins + inhibitor group．The cell proliferation rate was detected by CCK-8 method．The cell morphology was observed by AO/EB staining．The apoptosis rate was detected by flow cytometry．The contents of reactive oxygen species（ROS）,glutathione（GSH）and ferrous（Fe²⁺）in different groups were detected,and the expression of mRNA and protein in different groups were detected by qRT- PCR and Western blot respectively．Results PPI significantly inhibited the growth of K562 cells in a dose-and time-dependent manner．Compared with the blank group,PPI significantly inhibited the proliferation of K562 cells and increased the apoptosis rate of K562 cells,while the use of ferroptosis inhibitor（Ferrostian-1）reversed the promoting effect of PPI on apoptosis of K562 cells．Compared with the blank group,the contents of reactive oxygen species（ROS）and ferrous iron（Fe²⁺）increased and the content of glutathione（GSH）decreased in the saponins group．The use of Ferrostian-1 could down-regulate the contents of ROS and Fe²⁺ and increase the content of GSH in the cells treated with the drug．Compared with the blank group,the expression of p53 mRNA and protein in the saponins group increased,while the expression of SLC7A11,GPX4 mRNA and protein decreased．The expression of p53 mRNA and protein in the saponins + inhibitor group was lower than that in the saponins group,while the expression levels of SLC7A11,GPX4 mRNA and protein increased．Conclusions PPI can effectively inhibit the proliferation of K562 cells and promote ferroptosis in K562 cells．The molecular mechanism can be related to the regulation of p53 signal pathway．

目的 构建并验证主动脉夹层B型（TBAD）患者急性期预后的列线图预测模型,帮助临床医生在急性期内更准确地评估TBAD患者的死亡风险,并制定更合适的治疗策略。方法 回顾性分析从重症监护医学信息数据库v2.2 中提取的399例 TBAD患者的人口学资料和临床资料,结局为TBAD患者急性期（≤14 d）内死亡。先采用最小绝对收缩选择算法回归筛选特征变量,再采用多因素分析确定独立预后因素,并据此构建预测模型。通过受试者工作特征曲线、校准曲线、决策曲线分析（DCA）评价列线图预测模型的性能和临床适用性。结果 APS Ⅲ评分、二氧化碳总量、红细胞分布宽度为TBAD患者14 d内死亡的独立预测因素。列线图预测模型在内部验证中的受试者工作特征曲线下面积为0.776（95% CI：0.691 ~ 0.860）,Hosmer-Lemeshow 检验P=0.604,校准曲线和标准曲线高度重合,表明该模型具有良好的区分度和校准度。同时,DCA曲线显示,预测模型在大部分的阈值概率范围内提供了显著的净收益。结论 本研究基于APS Ⅲ评分、二氧化碳总量、红细胞分布宽度构建的列线图预测模型可以较准确地预测TBAD患者14 d内的死亡风险,有助于临床医生制定更合适的个体化治疗策略。

Objective To develop and verify a nomogram for predicting acute phase outcomes in patients with type B aortic dissection（TBAD）,enabling clinicians to more precisely evaluate mortality risk in TBAD patients during the acute stage and to devise better treatment plans．Methods This retrospective study analyzed demographic and clinical data of 399 TBAD patients from the Medical Information Mart for Intensive Care IV v2.2,focusing on mortality within 14 days of the acute phase in TBAD patients． Initially,the Least Absolute Shrinkage and Selection Operator regression was employed for feature variable selection,and then multivariate analysis was used to identify independent prognostic factors for constructing the predictive model．The nomogram predictive model's effectiveness and clinical applicability were assessed via the Receiver Operating Characteristic curve,calibration curve,and Decision Curve Analysis（DCA）．Results Acute Physidogy Score Ⅲ score,total carbon dioxide,and red blood cell distribution width emerged as independent predictors of 14-day mortality in TBAD patients．The internal validation of the nomogram predictive model showed an area under the curve of 0.776（95%CI：0.691-0.860）,with a Hosmer-Lemeshow test P-value of 0.604． The close alignment of the calibration and standard curves suggested the model's strong discriminative power and calibration． Furthermore,the DCA curve revealed that the predictive model offered substantial net benefits within a wide range of threshold probabilities．Conclusions This study's nomogram,developed using APS Ⅲ score,total carbon dioxide,and red blood cell distribution width,accurately predicts the 14-day mortality risk in TBAD patients,assisting clinicians in creating better personalized treatment plans．

目的 探讨养老机构老年人死亡态度及其影响因素。方法 选取2018年1月—2022年12月广州市养老机构的593名老年人进行问卷调查,统计养老机构老年群体对死亡的态度,并分析其死亡态度发生的影响因素。结果 经过研究发现,对死亡的态度呈自然接受的老年人数量最少,为42例,占比为7.1%,其次从高到低依次为逃离接受老年人308例,占比为51.9%;死亡恐惧老年人83例,占比为14.0%;死亡逃避老年人81例,占比为13.7%;趋近接受老年人79例,占比为13.3%。患者的死亡态度与患者所患的疾病病种有关,影响死亡态度的因素包括：生理心理因素、文化思想因素、社会环境因素,其中发生概率最高的是生理心理因素,占比为50.9,其次从高到低依次为文化思想因素,占比为26.5%;社会环境因素,占比为22.6%。死亡态度中趋近接受维度的分值相对更高,其次依次为死亡恐惧、自然接受、死亡逃避以及逃离接受。单因素分析显示差异有统计学意义的项目包含家庭内讨论死亡、性别、健康自评状况、年龄、患病种数、经济结构。结论 在养老机构中,大多数老年人对死亡是无法自然接受的,大部分老年群体对死亡的态度是逃离接受,部分群体对死亡的态度是恐惧、逃避;而影响老年群体死亡态度的因素主要是生理心理因素。

Objective To explore the death attitudes and its influencing factors among the elderly in nursing institutions. Methods A questionnaire survey on death attitudes was conducted among 593 elderly individuals in nursing institutions in Guangzhou from January 2018 to December 2022,and their possible influencing factors were analyzed. Results There were 51.9% of the elderly individuals whose death attitudes were characterized by escape acceptance,while 7.1% demonstrated natural acceptance．However,the numbers of the elderly individuals with approaching acceptance,fear of death and escape from death showed no significant statistical difference,all being lower than the number of the elderly individuals with escape acceptance．The influencing factors of death attitude included physiological and psychological factors,cultural and ideological factors,and social environment factors．The probability of physiological and psychological factors influencing death attitudes was higher than that of other factors（P<0.05）．The score for the dimension of approaching acceptance in death attitudes was higher than that for other dimensions（P<0.05）. Conclusions In nursing institutions,the majority of elderly individuals cannot naturally accept death．Most elderly individuals exhibit an attitude of escape acceptance towards death,while some exhibit an attitude of fear and escape．However,the factors influencing the elderly's attitudes towards death are mainly physiological and psychological factors．

目的 探究铁死亡相关的lncRNA在肺鳞状上皮细胞癌(简称肺鳞癌)患者中的预后意义。方法 从美国癌症和肿瘤基因图谱数据库(the Cancer Genome Atlas,TCGA)中下载肺鳞癌数据551例,包括49例正常对照样本和502例肺鳞癌患者样本。筛选出与铁死亡相关基因的共表达的lncRNA,使用单变量Cox回归进一步筛选lncRNA,然后,使用Lasso回归和多元Cox回归分构建铁死亡相关的lncRNA模型。建立基于模型的风险评分,并使用Cox回归测试其是否为独立的预后因素。铁死亡相关lncRNAs的功能富集使用基因本体(Gene Ontology)和京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Genomes)可视化。结果 4个预后铁死亡相关的lncRNA(AC253536.6,FLJ46906LUCAT,AC022150.2)显著不同,这构建了铁死亡相关的lncRNA模型。此模型将肺鳞癌患者分为低风险组和高风险组。基于模型的风险评分是肺鳞癌患者的显著独立因素(HR =2.116,95％CI=1.513~2.961;P<0.001)。此外,4个lncRNA在铁死亡过程,代谢和肿瘤经典途径中均显著富集。结论 4个铁死亡相关的lncRNAs可能是肺鳞癌患者的分子生物标志物和治疗靶标。

Objective To explore the prognostic significance of ferroptosis related lncRNAs in patients with lung squamous cell carcinoma. Methods Data of 551 lung squamous cell carcinoma cases was downloaded from the Cancer Genome Atlas (TCGA) of the United States, including 49 normal control samples and 502 lung squamous cell carcinoma samples. The lncRNAs co-expressed with genes related to ferroptosis was screened out. Univariate Cox regression was used to further screen out the lncRNAs. Then, Lasso regression and multiple Cox regression were used to construct lncRNA models related to ferroptosis. A model-based risk score system was established and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of ferroptosis related lncRNAs were visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Results The four prognostic ferroptosis related lncRNAs (AC253536.6, FLJ46906 LUCAT, AC022150.2) were significantly different, and the ferroptosis lncRNAs model was constrncted with them. This model divided lung squamous cell carcinoma patients into low-risk group and high-risk group. The model-based risk score was a significant independent factor for patients with lung squamous cell carcinoma (HR=2.116, 95% CI=1.513-2.961; P<0.001). In addition, the four lncRNAs were significantly enriched in metabolism and tumor classical pathways during the ferroptosis process. Conclusions The four ferroptosis lncRNAs could be molecular biomarkers and therapeutic targets for patients with lung squamous cell carcinoma.

目的 探究序贯器官衰竭评分(sequential organ failure assessment,SOFA)联合可溶性程序性死亡因子-1(soluble programmed death-1,sPD-1)水平对脓毒症患者的预后影响。方法 选我院2019年3月—2021年3月期间86例脓毒症患者为研究对象,依据其预后情况(28 d转归)分为生存组(59例)、死亡组(27例),记录两组患者sPD-1、炎症细胞因子水平、SOFA评分及急性生理学与慢性健康状况评价系统Ⅱ(APACHEⅡ)评分情况,分析28 d死亡危险因素,并以ROC曲线分析SOFA+sPD-1对脓毒症患者预后评估价值。结果 死亡组年龄、C反应蛋白、降钙素原、sPD-1水平及SOFA、APACHEⅡ评分均较生存组高(P＜0.05);经Logistic回归分析,sPD-1、SOFA、APACHEⅡ为28 d死亡脓毒症患者独立预测因素(P＜0.05);联合预测后,SOFA+sPD-1的ROC曲线下面积最大,为0.862,敏感度、特异度分别为88.89%、88.14%。结论 在对脓毒症患者预后评估中,sPD-1、SOFA评分均为28 d病死独立预测因素,且SOFA+sPD-1对脓毒症患者转归预测能力更为理想。

Objective To explore the effect of sequential organ failure assessment (SOFA) combined with soluble programmed death factor-1 (sPD-1) level on the prognosis of patients with sepsis. Methods A total of 86 patients with sepsis in our hospital from March 2019 to March 2021 were selected and divided into survival group (59 cases) and death group (27 cases) according to their prognosis (28-day outcome). The levels of sPD-1, inflammatory cytokines, SOFA and APACHEⅡ scores of two groups were recorded. The risk factors of 28-day mortality were analyzed. The prognostic values of SOFA+sPD-1 in patients with sepsis were analyzed by ROC curve. Results Age, C-reactive protein (CRP), procalcitonin (PCT) and sPD-1 levels and SOFA, APACHEⅡ scores of death group were higher than those of survival group (P＜0.05). By Logistic regression analysis, sPD-1 level, SOFA and APACHEⅡ scores were identified as independent predictors of 28-day death in patients with sepsis (P＜0.05). After combining prediction, the area under the ROC curve of SOFA+sPD-1 was the largest (0.862), and the sensitivity and specificity were 88.89% and 88.14% respectively. Conclusion In the prognosis evaluation of patients with sepsis, both sPD-1 level and SOFA score were independent predictors of 28-day mortality, and SOFA+sPD-1 was more effective in predicting the prognosis of patients with sepsis.

目的 探讨特发性肺纤维化(IPF)患者和结缔组织病相关性纤维化间质性肺疾病(CTD-fILD)患者急性加重(AE)的短期内死亡的危险因素。方法 回顾性分析2017年10月—2019年9月在深圳大学和广州医科大学附属第一医院住院的25例 AE-CTD-fILD和26例AE-IPF患者临床信息,Kaplan-Merier法对两组患者进行生存分析,Cox回顾分析年龄、性别、吸烟、白细胞总数、C反应蛋白、红细胞沉降率及肿瘤指标在急性加重患者死亡中的作用。结果 与AE-CTD-fILD比较,AE-IPF患者组男性比例、年龄、吸烟比例较高,红细胞沉降率较低(24/26 vs 10/25,P<0.001;63.77±9.97 vs 58.00±10.32,P=0.048;16/26 vs 9/25,P=0.02;28.07±29.45 vs 64.35±40.34,P=0.002 );90天内,26例AE-IPF患者11例死亡,25例AE-CTD-fILD患者5例死亡,死亡率无明显差异(42.3% vs 20%,P=0.073);Cox回归分析显示,白细胞计数是AE-IPF和AE-CTD-fILD患者的死亡危险因素(HR=1.305,P=0.001;HR=1.529,P=0.009);CA15-3是AE-IPF患者死亡危险因素(HR=1.015,P=0.005)。结论 急性加重IPF和CTD-fILD患者短期内死亡风险相似,白细胞计数及外周CA15-3水平可能是肺纤维化急性加重患者短期内死亡的危险因素。

Objective To explore the risk factors for acute exacerbation (AE) in patients with idiopathic pulmonary fibrosis (IPF) and connective tissue disease associated with fibrotic interstitial lung disease (CTD-fILD). Methods We retrospectively reviewed 25 patients with AE-CTD-fILD and 26 patients with AE-IPF, and Kaplan-Merier was used to analyze the survival of the two groups of patients. The impact of age,gender, smoking,WBC,CRP,ESR and tumor markers on acute exacerbation death were performed by Cox regression analysis. Results The AE-IPF patients had a higher proportion of men,age and smoking,and a lower ESR compared with AE-CTD-fILD patients(24/26 vs 10/25,P<0.001;63.77±9.97 vs 58.00±10.32,P=0.048;16/26 vs 9/25,P=0.02;28.07±29.45 vs 64.35±40.34,P=0.002 ). 11 cases of 26 patients with AE-IPF and 5 cases of 25 patients with AE-CTD-fILD died within 90 days, Log-rank tests showed patients with CTD-fILD had similar mortality rate compared with IPF patients after AE(42.3% vs 20%,P=0.073). The WBC count was negatively correlated with survival and the independent predictors for patients with AE-IPF and AE-CTD-fILD after adjusting for other clinical variates in Cox regression models(HR=1.305,P=0.001;HR=1.529,P=0.009). CA15-3 may be a risk factor for death of AE-IPF patients(HR=1.015,P=0.005). Conclusion AE-CTD-fILD and AE-IPF were associated with similar poor short-term survival, WBC count and plasma CA15-3 may be the independent survival predictors respectively for patients with acute exacerbation of pulmonary fibrosis in short term.

       目的   构建并验证主动脉夹层B型（TBAD）患者急性期预后的列线图预测模型，帮助临床医生在急性期内更准确地评估TBAD患者的死亡风险，并制定更合适的治疗策略。方法   回顾性分析从重症监护医学信息数据库v2.2 中提取的399例 TBAD患者的人口学资料和临床资料，结局为TBAD患者急性期（≤14 d）内死亡。先采用最小绝对收缩选择算法回归筛选特征变量，再采用多因素分析确定独立预后因素，并据此构建预测模型。通过受试者工作特征曲线、校准曲线、决策曲线分析（DCA）评价列线图预测模型的性能和临床适用性。结果  APS Ⅲ评分、二氧化碳总量、红细胞分布宽度为TBAD患者14 d内死亡的独立预测因素。列线图预测模型在内部验证中的受试者工作特征曲线下面积为0.776（95% CI：0.691 ~ 0.860），Hosmer-Lemeshow 检验P=0.604，校准曲线和标准曲线高度重合，表明该模型具有良好的区分度和校准度。同时，DCA曲线显示，预测模型在大部分的阈值概率范围内提供了显著的净收益。结论   本研究基于APS Ⅲ评分、二氧化碳总量、红细胞分布宽度构建的列线图预测模型可以较准确地预测TBAD患者14 d内的死亡风险，有助于临床医生制定更合适的个体化治疗策略。

       Objective  To develop and verify a nomogram for predicting acute phase outcomes in patients with type B aortic dissection（TBAD），enabling clinicians to more precisely evaluate mortality  risk in TBAD patients during the acute stage and to devise better treatment plans．Methods  This retrospective study analyzed demographic and clinical data of 399 TBAD patients from the Medical Information Mart for Intensive Care IV v2.2，focusing on mortality within 14 days of the acute phase in TBAD patients．Initially，the Least Absolute Shrinkage and Selection Operator regression was employed for feature variable selection，and then multivariate analysis was used to identify independent prognostic factors for constructing the predictive model．The nomogram predictive model’s effectiveness and clinical applicability were assessed via the Receiver Operating Characteristic curve，calibration curve，and Decision Curve Analysis（DCA）．Results  Acute Physidogy Score Ⅲ score，total carbon dioxide，and red blood cell distribution width emerged as independent predictors of 14-day mortality in TBAD patients．The internal validation of the nomogram predictive model showed an area under the curve of 0.776（95%CI：0.691-0.860），with a Hosmer-Lemeshow test P-value of 0.604．The close alignment of the calibration and standard curves suggested the model’s strong discriminative power and calibration．Furthermore，the DCA curve  revealed that the predictive model offered substantial net benefits within a wide  range of threshold probabilities．Conclusions  This study's nomogram，developed using APS Ⅲ score，total carbon dioxide，and  red blood cell distribution width，accurately predicts the 14-day mortality risk in TBAD patients，assisting clinicians in creating better personalized treatment plans．

        目的   分析护生死亡态度的潜在类别及其影响因素，为开设符合我国国情的死亡教育课程提供参考。方法   采用便利抽样法，选取唐山市高校护生为调查对象。采用一般资料调查表、死亡态度描绘量表进行调查，基于潜在剖面分析护生死亡态度的类别及不同类别的影响因素。结果   共发放问卷520份，回收问卷516份，回收率为99.23%，剔除无效问卷11份，有效回收率为97.87%。护生死亡态度描绘量表得分为（96.91±13.20）分，经过剖面分析可划分为死亡态度积极-自然接受型（63.60%）和死亡态度消极-恐惧死亡型（36.40%）2个潜在类别。Logistic回归分析结果显示，陪伴临终亲友、未接触过死亡相关场所、半年内有亲友离世的护生属于死亡态度消极-恐惧死亡型的概率较大，自身性格偏外向、研究生学历的护生属于死亡态度积极-自然接受型的概率较大（均P＜0.05）。结论   护生死亡态度存在明显的分类特征，可分为死亡态度积极-自然接受型和死亡态度消极-恐惧死亡型2个潜在类别；相关院校可针对不同类别特征的护生开设死亡教育相关课程，以期帮助护生更深刻地认识死亡，树立科学死亡观。

       Objective  To analyze the potential categories and influencing factors of nursing students’ death attitude，to provide reference for setting up death education courses in line with China’s situation．Methods  Convenient sampling method was used to select nursing students in Tangshan．General data questionnaire and death attitude description scale were used to investigate the categories of nursing students’ death attitude and their influencing factors based on latent profiles．Results  A total of 520 questionnaires were sent out and 516 were collected with a rate of 99.23%．Eleven invalid questionnaires were excluded with an effective rate of 97.87%．The score of the death attitude description scale of nursing students was（96.91±13.20），which could be divided into two potential categories：positive death attitude - natural acceptance type（51.49%）and negative death attitude - fear type（48.51%）after profile analysis．Logistic regression analysis showed that nursing students who accompanied their dying relatives and friends，had no visit to death-related places，and had relatives and friends who died within six months had a higher probability of negative death attitude - fear of death，while nursing students with extrovert personality and graduate degree had a higher probability of positive death attitude - natural acceptance（all P＜0.05）．Conclusions  There were obvious classification characteristics of nursing students’ attitude towards death，which can be divided into two latent categories：positive attitude towards death - natural acceptance type and negative attitude towards death - fear type．Relevant colleges and universities can set up death education courses for nursing students with different characteristics，in order to help nursing students have a deeper understanding of death and establish a scientific view of death．