检索结果-南通市图书馆

基于影像学指标构建永久性乙状结肠造口旁疝发病风险预测模型

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

中华疝和腹壁外科杂志（电子版） 2024年第1期18卷 75-82页

作者：刘化胜郑龙波秦琛王伟芹郑学风马金龙马正王洪霞刘磊胡继霖青岛大学附属医院胃肠外科山东266003 潍坊市益都中心医院乳腺甲状腺外科山东262550 青岛大学附属青岛市中心医院肿瘤科山东266042 青岛市市立医院呼吸与危重症医学科山东266300 青岛大学山东266071

目的对永久性乙状结肠造口旁疝的危险因素进行分析,并基于这些风险因素,构建并验证永久性乙状结肠造口旁疝发病风险的列线图预测模型,以期为造口患者提供精准的个体化治疗方案提供依据。方法回顾分析2010年1月至2018年1月在青岛大学附... 详细信息

目的对永久性乙状结肠造口旁疝的危险因素进行分析,并基于这些风险因素,构建并验证永久性乙状结肠造口旁疝发病风险的列线图预测模型,以期为造口患者提供精准的个体化治疗方案提供依据。方法回顾分析2010年1月至2018年1月在青岛大学附属医院接受永久性乙状结肠造口术的291例患者的病历资料,构建列线图预测模型;同时,回顾性收集2010年1月至2018年1月在青岛市立医院接受永久性乙状结肠造口患者63例用于预测模型的外部验证。采用单因素分析和多因素logistic回归分析筛选永久性乙状结肠造口术后发生造口旁疝的独立危险因素,基于独立危险因素建立列线图预测模型。使用Bootstrap方法进行内部验证并在验证组进行外部验证。结果2010年1月至2018年1月,291例患者接受了永久性乙状结肠造口术,2年内61例患者发生造口旁疝,230例患者未发生造口旁疝。经多因素Logistic回归分析发现,年龄(OR 1.063,95%CI 1.014~1.114,P=0.011)、性别(OR 3.927,95%CI 1.327~11.621,P=0.013)、体重指数(OR 1.553,95%CI 1.228~1.963,P=0.001)、白蛋白(OR 0.876,95%CI 0.783~0.981,P=0.022)、造口直径(OR 4.426,95%CI 1.976~9.915,P=0.001)、皮下脂肪指数(OR 1.028,95%CI 1.006~1.050,P=0.011)、腹直肌指数(OR 0.591,95%CI 0.352~0.993,P=0.047)是永久性乙状结肠造口患者发生造口旁疝的独立危险因素。基于独立危险因素建立的列线图预测模型,训练组和验证组的受试者工作特征曲线下面积分别为0.941和0.887,Hosmer-Lemeshow检验(P>0.05)和校准曲线显示实际概率与预测概率具有良好的一致性。结论根据永久性乙状结肠造口患者发生造口旁疝的独立危险因素建立的列线图预测模型,对预测永久性乙状结肠造口旁疝的发生具有较高的临床意义。为专业外科医生进行造口手术的患者提供了个性化和疾病相关评估。

关键词：造口旁疝列线图危险因素计算机断层扫描

Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Medicine 2022年第2期2卷 82-87页

作者： Jiaqi Lu Ruiqing Liu Yuejuan Zhang Xianxiang Zhang longbo zheng Chao Zhang Kaiming Zhang Shuai Li Yun Lu Department of Gastrointestinal Surgery Affiliated Hospital of Qingdao UniversityQingdaoShandong 266055China Department of Pathology Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China State Key Laboratory of Virtual Reality Technology and System Beihang UniversityBeijing 100191China Shandong Provincial Key Laboratory of Digital Medicine and Computer Assisted Surgery QingdaoShandong 266003China Research Institute of Digital Medicine and Computer Aided Surgery Qingdao UniversityQingdaoShandong 266000China

Objective Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical ***,there are significant differences and difficulties associated with manually identifying tumor *** study used the Faster region convolutional neural network(RCNN)model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal *** We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October *** pathologists used LabelImg software to identify tumor buds and to count their ***,1,000 images were screened,and the total number of tumor buds was approximately 3,226;the images were randomly divided into a training set and a test set at a ratio of 6:*** the images in the training set were manually identified,the identified buds in the 600 images were used to train the Faster RCNN identification *** the establishment of the artificial intelligence identification detection platform,400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor ***,by comparing the results of the Faster RCNN system and the identification information of pathologists,the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate *** selected performance indicators include accuracy,precision,specificity,***(receiver operator characteristic)and AUC(area under the curve)were used to quantify the performance of the system to automatically identify tumor budding areas and *** The AUC of the receiver operating charac

关键词： Artificial intelligence Tumor budding Colorectal cancer Pathological section

Artificial intelligence recognition of pathological T stage and tumor invasion in rectal cancer based on large panoramic pathological sections

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Medicine 2022年第3期2卷 141-151页

作者： Yiheng Ju longbo zheng Peng Zhao Fangjie Xin Fengjiao Wang Yuan Gao Xianxiang Zhang Dongsheng Wang Yun Lu Department of General Surgery Medical CenterAffiliated Hospital of Qingdao UniversityQingdaoShandong 266000China Department of Pathology Affiliated Hospital of Qingdao UniversityQingdaoShandong 266000China Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery QingdaoShandong 266071China Hainan Branch of Affiliated Hospital of Qingdao University Lingshui Li Autonomous CountyHainan 572400China

Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all ***,pathological diagnosis is becoming increasingly *** intelligence has demon-strated the ability to fully excavate image features and assist doctors in making *** panoramic patho-logical sections contain considerable amounts of pathological *** this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic *** We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September *** sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were *** patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:*** chose DeepLabV3+and ResNet50 as target models for our *** used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation ***,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging *** In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was *** the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the imag

关键词： Large panoramic pathological sections Rectal cancer Whole-slide imaging Deep learning Survival analysis

Uncovering key salt-tolerant regulators through a combined eQTL and GWAS analysis using the super pan-genome in rice

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

National Science Review 2024年第4期11卷 55-69页

作者： Hua Wei Xianmeng Wang Zhipeng Zhang longbo Yang Qianqian Zhang Yilin Li Huiying He Dandan Chen Bin Zhang Chongke zheng Yue Leng Xinglan Cao Yan Cui Chuanlin Shi Yifan Liu Yang Lv Jie Ma Wenchuang He Xiangpei Liu Qiang Xu Qiaoling Yuan Xiaoman Yu Tianyi Wang Hongge Qian Xiaoxia Li Bintao Zhang Hong Zhang Wu Chen Mingliang Guo Xiaofan Dai Yuexing Wang Xiaoming zheng Longbiao Guo Xianzhi Xie Qian Qian Lianguang Shang Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural Sciences Institute of Wetland Agriculture and Ecology Shandong Academy of Agricultural Sciences State Key Laboratory of Rice Biology China National Rice Research Institute National Key Facility for Crop Gene Resources and Genetic Improvement Institute of Crop Science Chinese Academy of Agricultural Sciences Yazhouwan National Laboratory

For sessile plants, gene expression plays a pivotal role in responding to salinity stress by activating or suppressing specific genes. However, our knowledge of genetic variations governing gene expression in response to salt stress remains limited in natural germplasm. Through transcriptome analysis of the Global Mini-Core Rice Collection consisting of a panel of 202 accessions, we identified 22 345 and 27 610expression quantitative trait loci associated with the expression of 7787 and 9361 e Genes under normal and salt-stress conditions, respectively, leveraging the super pan-genome map. Notably, combined with genome-wide association studies, we swiftly pinpointed the potential candidate gene STG5—a major salt-tolerant locus known as q STS5. Intriguingly, STG5 is required for maintaining Na+/K+homeostasis by directly regulating the transcription of multiple members of the Os HKT gene family. Our study sheds light on how genetic variants influence the dynamic changes in gene expression responding to salinity stress and provides a valuable resource for the mining of salt-tolerant genes in the future.

关键词： super pan-genome expression quantitative trait loci GWAS salt tolerance rice

A centromere map based on super pan-genome highlights the structure and function of rice centromeres

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Integrative Plant Biology 2024年第2期66卷 196-207页

作者： Yang Lv Congcong Liu Xiaoxia Li Yueying Wang Huiying He Wenchuang He Wu Chen longbo Yang Xiaofan Dai Xinglan Cao Xiaoman Yu Jiajia Liu Bin Zhang Hua Wei Hong Zhang Hongge Qian Chuanlin Shi Yue Leng Xiangpei Liu Mingliang Guo Xianmeng Wang Zhipeng Zhang Tianyi Wang Bintao Zhang Qiang Xu Yan Cui Qianqian Zhang Qiaoling Yuan Noushin Jahan Jie Ma Xiaoming zheng Yongfeng Zhou Qian Qian Longbiao Guo Lianguang Shang State Key Laboratory of Rice Biology China National Rice Research InstituteHangzhou 310006China Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhen 518120China Yazhouwan National Laboratory No.8 Huanjin RoadYazhou DistrictSanya City 572024China National Key Facility for Crop Gene Resources and Genetic Improvement Institute of Crop ScienceChinese Academy of Agricultural SciencesBeijing 100081China

Rice(Oryza sativa)is a significant crop worldwide with a genome shaped by various evolutionary *** centromeres are crucial for chromosome segregation,and contain some unreported *** to the diverse and complex centromere region,a comprehensive understanding of rice centromere structure and function at the population level is *** constructed a high-quality centromere map based on the rice super pangenome consisting of a 251-accession panel comprising both cultivated and wild species of Asian and African *** showed that rice centromeres have diverse satellite repeat CentO,which vary across chromosomes and subpopulations,reflecting their distinct evolutionary *** also revealed that long terminal repeats(LTRs),especially young Gypsy-type LTRs,are abundant in the peripheral CentO-enriched regions and drive rice centromere expansion and ***,high-quality genome assembly and complete telomere-to-telomere(T2T)reference genome enable us to obtain more centromeric genome information despite mapping and cloning of centromere genes being *** investigated the association between structural variations and gene expression in the rice centromere.A centromere gene,OsMAB,which positively regulates rice tiller number,was further confirmed by expression quantitative trait loci,haplotype analysis and clustered regularly interspaced palindromic repeats(CRISPR)/CRISPR-associated protein9 *** revealing the new insights into the evolutionary patterns and biological roles of rice centromeres,our finding will facilitate future research on centromere biology and crop improvement.

关键词： centromere super pan-genome CentoO satellite repeat rice

基于高分辨T2WI及弥散加权成像的放射组学模型预测Ⅱ~Ⅲ期直肠癌患者微卫星稳定性状态

在线全文

学校读者我要写书评

暂无评论

中华外科杂志 2023年第9期61卷 782-787页

作者：相帅郑龙波朱玲高源王东升刘尚龙张帅王童语卢云青岛大学附属医院胃肠外科青岛266000 山东省数字医学与计算机辅助手术重点实验室青岛266000 青岛大学附属医院放射科青岛266000

目的探讨基于高分辨T2WI及弥散加权成像(DWI)的放射组学模型在预测Ⅱ~Ⅲ期直肠癌患者微卫星稳定性状态中的价值。方法回顾性收集青岛大学附属医院2016年2月至2020年10月连续收治的175例Ⅱ~Ⅲ期直肠癌患者资料,男性119例,女性56例,年龄(6... 详细信息

目的探讨基于高分辨T2WI及弥散加权成像(DWI)的放射组学模型在预测Ⅱ~Ⅲ期直肠癌患者微卫星稳定性状态中的价值。方法回顾性收集青岛大学附属医院2016年2月至2020年10月连续收治的175例Ⅱ~Ⅲ期直肠癌患者资料,男性119例,女性56例,年龄(63.9±9.4)岁(范围:37~85岁)。微卫星稳定性患者152例,微卫星不稳定性患者23例。以7∶3的比例将患者随机分为训练组123例和验证组52例。利用ITK-SNAP软件在每位患者的T2WI及DWI图像中标记感兴趣区域,用PyRadiomics提取7类放射组学特征,在去除冗余特征及特征归一化后,使用最小绝对收缩和选择算子进行特征选择。基于支持向量机在训练组中构建1个临床模型、3个放射组学模型和1个临床-放射组学模型,并使用受试者工作特征曲线下面积、灵敏度、特异度、准确性在验证组中评估模型性能。结果筛选出与直肠癌患者微卫星状态最相关的3个临床特征(年龄、肿瘤分化程度、肿瘤下缘距肛缘距离)及6个放射组学特征(2个DWI相关特征和4个T2WI相关特征)。临床-放射组学模型在训练组中的曲线下面积为0.95,验证组中为0.81,优于临床模型(0.68,Z=0.71,P=0.04),与T2WI+DWI模型诊断效能相当(0.82,Z=-0.21,P=0.83)。结论术前T2WI与DWI的放射组学特征与Ⅱ~Ⅲ期直肠癌患者微卫星稳定性状态相关,基于此构建的模型表现出了较高的分类效能。

关键词：直肠肿瘤微卫星不稳定性诊断,计算机辅助机器学习放射组学

卫矛醇通过调节Toll样受体4/髓样分化因子88/核因子-κB信号通路缓解脂多糖诱导的猪肠道上皮细胞损伤

在线全文

学校读者我要写书评

暂无评论

动物营养学报 2024年第6期36卷 3952-3962页

作者：王晗刘正群朱龙博李宁郑梓闫峻穆淑琴孙超西北农林科技大学动物科技学院杨凌712100 天津市农业科学院畜牧兽医研究所天津市畜禽分子育种与生物技术重点实验室天津市畜禽健康养殖工程技术中心天津300381

本试验旨在通过构建猪肠道上皮细胞(IPEC-J2细胞)经脂多糖(LPS)刺激后细胞损伤模型,揭示卫矛醇(DUL)对LPS处理后的IPEC-J2细胞屏障受损和炎症损伤的缓解作用及其可能的分子机制。采用细胞计数试剂盒-8(CCK-8)检测IPEC-J2细胞活力,以确定... 详细信息

本试验旨在通过构建猪肠道上皮细胞(IPEC-J2细胞)经脂多糖(LPS)刺激后细胞损伤模型,揭示卫矛醇(DUL)对LPS处理后的IPEC-J2细胞屏障受损和炎症损伤的缓解作用及其可能的分子机制。采用细胞计数试剂盒-8(CCK-8)检测IPEC-J2细胞活力,以确定LPS造模浓度(150μg/mL)和最佳DUL处理浓度(200μmol/L)。试验设3个组:对照(CON)组、LPS组和DUL组。IPEC-J2细胞贴壁后,CON组先用完全培养基处理24 h,然后用DMEM培养基处理24 h;LPS组先用完全培养基处理24 h,然后用含有150μg/mL LPS的DMEM培养基处理24 h;DUL组先用含有200μmol/L DUL的完全培养基处理24 h,然后用含有150μg/mL LPS的DMEM培养基处理24 h。观察各组IPEC-J2细胞生长状态,划痕试验评价细胞的迁移和增殖能力,实时荧光定量PCR(RT-qPCR)法和蛋白质免疫印迹法(Western blot)检测屏障和炎症相关基因的mRNA和蛋白相对表达水平。结果表明:1)在IPEC-J2细胞上,确定200μmol/L的DUL为最佳处理浓度,150μg/mL的LPS为有效致炎浓度。2)相较于LPS组,DUL预处理缓解了细胞密度降低、细胞空泡增多、细胞边界不清晰的情况。3)相较于LPS组,DUL预处理显著增加了细胞迁移距离和细胞迁移面积(P<0.05)。4)相较于LPS组,DUL预处理显著升高了屏障相关基因闭锁小带蛋白-1(ZO-1)、闭锁蛋白(OCLN)、封闭蛋白1(CLDN1)的mRNA和蛋白相对表达水平及黏蛋白2(MUC2)、大肿瘤抑制激酶1(LATS1)、YES相关蛋白1(YAP1)的mRNA相对表达水平(P<0.05)。5)相较于LPS组,DUL预处理显著降低了通路相关基因髓样分化因子88(Myd88)、核因子-κB抑制蛋白-α(IκB-α)、核因子-κB(NF-κB)、白细胞介素-1β(IL-1β)的mRNA和蛋白相对表达水平和Toll样受体4(TLR4)的蛋白相对表达水平以及核苷酸结合寡聚化结构域样受体蛋白3(NLRP3)、白细胞介素-18(IL-18)的mRNA表达相对表达水平(P<0.05)。由此可见,DUL通过下调TLR4/Myd88/NF-κB信号通路相关蛋白表达,调节炎症因子分泌,提高紧密连接相关基因表达,缓解LPS引起的IPEC-J2细胞屏障损伤和炎症反应。

关键词：卫矛醇 IPEC-J2细胞 TLR4/Myd88/NF-κB信号通路炎性损伤肠道屏障

基于病理大切片的深度卷积神经网络对于直肠癌脉管癌栓的预测价值

在线全文

学校读者我要写书评

暂无评论

精准医学杂志 2022年第2期37卷 131-136页

作者：鞠宜衡张宪祥郑龙波赵鹏卢云青岛大学附属医院胃肠外二科山东青岛266003 青岛大学附属医院病理科山东省数字医学与计算机辅助手术重点实验室青岛大学附属医院海南分院

目的基于病理大切片数字图像构建预测直肠癌脉管癌栓的深度卷积神经网络(DCNN)模型,以提高临床医生对该病的预测能力。方法收集2019年1月—2019年10月青岛大学附属医院西海岸院区120例患者(内部数据集)、市南院区+崂山院区40例患者(外... 详细信息

目的基于病理大切片数字图像构建预测直肠癌脉管癌栓的深度卷积神经网络(DCNN)模型,以提高临床医生对该病的预测能力。方法收集2019年1月—2019年10月青岛大学附属医院西海岸院区120例患者(内部数据集)、市南院区+崂山院区40例患者(外部数据集)的直肠癌术后病理标本,经石蜡包埋、切片、染色、扫描后共获得2400张病理大切片HE染色数字图像。将内部数据集患者按7∶3比例随机分为训练组(84例)和测试组(36例),用以构建和测试预测脉管癌栓的DCNN模型,以外部数据集患者的数字图像验证该模型。通过准确率、精准率、灵敏度、特异度、受试者工作特征(ROC)曲线及曲线下面积(AUC)、精度-召回率曲线(PR)及曲线下面积(AP)等指标来测试和验证模型的性能。结果本研究成功建立了脉管癌栓预测的DCNN模型。该模型的测试结果显示,准确率为85.2%,精准率为83.8%,灵敏度为84.6%,特异度为84.8%,AUC值为0.83,AP值为0.80;验证结果显示,准确率为84.7%,精准率为83.1%,灵敏度为84.3%,特异度为84.1%,AUC值为0.82,AP值为0.78,其中在验证结果中,模型的自动预测时间、准确率、灵敏度及特异度与病理医师人工HE染色阅片结果相比,均有显著差异(χ^(2)=5.028~6.842,t=-73.84,P<0.05)。结论基于病理大切片数字图像构建的预测直肠癌脉管癌栓的DCNN模型与人工HE染色阅片相比对直肠癌患者脉管癌栓具有较高的预测能力,对临床医生诊断直肠癌脉管癌栓具有一定的辅助作用。

关键词：直肠癌脉管癌栓病理大切片人工智能深度卷积神经网络神经网络,计算机模型构建大数据诊断

胃上部癌全胃切除术后肺部并发症预测模型的构建

在线全文

学校读者我要写书评

暂无评论

精准医学杂志 2023年第3期38卷 232-236页

作者：杨永康刘尚龙高源刘睿清郑龙波解文韬卢云青岛大学附属医院胃肠外科山东青岛266555

目的通过分析胃上部癌全胃切除术后肺部并发症(PPCs)相关危险因素,建立列线图风险预测模型。方法回顾性分析我院2020年6月1日—2021年9月30日因胃上部癌行全胃切除手术197例患者的临床资料,采用单因素及多因素Logistic回归分析PPCs相... 详细信息

目的通过分析胃上部癌全胃切除术后肺部并发症(PPCs)相关危险因素,建立列线图风险预测模型。方法回顾性分析我院2020年6月1日—2021年9月30日因胃上部癌行全胃切除手术197例患者的临床资料,采用单因素及多因素Logistic回归分析PPCs相关危险因素,并构建PPCs列线图预测模型,采用一致性指数、受试者工作特征曲线、校准曲线评估预测模型效能,采用决策曲线分析(DCA)曲线评估模型的临床获益度。结果 197例患者术后发生肺部并发症71例,发生率为36%;多因素Logistic回归分析显示,年龄、糖尿病、吸烟史、术前血清白蛋白26 kg/m2为胃上部癌全胃切除术PPCs发生的独立危险因素。构建的列线图预测模型验证结果,受试者工作特征曲线下面积为0.806,显示模型预测效能良好;校准曲线示预测结果与实际并发症发生率具有良好的一致性;构建的DCA曲线显示明显正向净收益,该预测模型临床获益度良好。结论通过独立危险因素构建的列线图风险预测模型预测效能良好,为胃上部癌全胃切除患者PPCs的预测提供了依据。

关键词：胃肿瘤胃切除术手术后并发症肺疾病危险因素列线图