Germin and Germin-like protein （GLP） have various proposed roles in plant developmental stages and stress- related processes. A novel GLP cDNA clone was isolated from a cDNA library of Tamarix hispida. ThGLP, coded ...Germin and Germin-like protein （GLP） have various proposed roles in plant developmental stages and stress- related processes. A novel GLP cDNA clone was isolated from a cDNA library of Tamarix hispida. ThGLP, coded 225aa, possesses conserved motif of plant germin and Germin-like protein. ThGLP belongs to true germin subfamily through phylogenetic analyses. Gene expression profiles in roots and leaves were evaluated using real-time quantitative RT-PCR. The results show that the gene was highly induced by drought, salt, low temperature, CdCl2 and abscisic acid treatments. Our results demonstrate that the ThGLP gene is expressed in leaves and roots, is involved in different abiotic stress re-sponses and controlled by an ABA-dependent signaling pathway.展开更多
背景与目的:弥漫大B细胞淋巴瘤(diffuse large B-cell lymphoma,DLBCL)的生发中心B细胞样(germinal center B-cell-like,GCB)亚型和非GCB(non-GCB)亚型在患者预后和治疗上存在差异,但目前依赖有创病理学检查。本研究基于多参数MRI构建...背景与目的:弥漫大B细胞淋巴瘤(diffuse large B-cell lymphoma,DLBCL)的生发中心B细胞样(germinal center B-cell-like,GCB)亚型和非GCB(non-GCB)亚型在患者预后和治疗上存在差异,但目前依赖有创病理学检查。本研究基于多参数MRI构建影像组学和深度学习模型,旨在于术前无创性区分这两种亚型。方法:本研究回顾性分析2013年3月—2024年12月在复旦大学附属华山医院及外院经病理学检查确诊的DLBCL患者。使用多参数MRI扫描数据,结合4种影像组学机器学习[支持向量机(support vector machine,SVM)、逻辑回归(logistic regression,LR)、高斯过程(Gaussian process,GP)和朴素贝叶斯(Naive Bayes,NB)]和3种深度学习[密集连接卷积网络121(densely-connected convolutional networks 121,DenseNet121)、残差网络101(residual network 101,ResNet101)和高效网络B5(Efficient Net-b5)]建立DLBCL亚型分类模型。此外,两名经验不同的放射科医师在盲法下基于MRI图像独立分类DLBCL。模型和医师的诊断性能均通过接收者操作特征曲线下面积(area under the curve,AUC)、准确度(accuracy,ACC)和F1分数(F1-score,F1)等指标进行量化评估,以衡量其区分GCB和non-GCB亚型的能力。本研究经复旦大学附属华山医院伦理委员会批准(KY2024-663),所有患者均知情同意。结果:本研究共纳入173例患者(GCB型55例,non-GCB型118例)。影像组学和深度学习方法能有效地区分DLBCL亚型。其中,GP影像组学模型(基于T1-CE+T2-FLAIR+ADC序列)和DenseNet121深度学习模型(基于T1-CE+T2-FLAIR+ADC序列)表现最佳,在内部验证集上分别取得优异性能(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774),并在外部验证集上保持稳健。并且,最优AI模型的分类效能优于经验丰富的放射科医师(医师最高AUC=0.678)。结论:基于多参数MRI特征的影像组学与深度学习模型可有效地鉴别DLBCL的GCB与non-GCB亚型。其中,GP与DenseNet121模型在处理复杂图像数据、特别是融合多序列特征组进行亚型分类时,呈现出优异的性能。展开更多
基金This study was supported by national natural science foundation (Grant No. 30972386)Central university basic scientific business specific foundation (Grant No. DL09BA22)Genetically modified organisms breeding major projects (Grant No.2009ZX08009-098B)
文摘Germin and Germin-like protein （GLP） have various proposed roles in plant developmental stages and stress- related processes. A novel GLP cDNA clone was isolated from a cDNA library of Tamarix hispida. ThGLP, coded 225aa, possesses conserved motif of plant germin and Germin-like protein. ThGLP belongs to true germin subfamily through phylogenetic analyses. Gene expression profiles in roots and leaves were evaluated using real-time quantitative RT-PCR. The results show that the gene was highly induced by drought, salt, low temperature, CdCl2 and abscisic acid treatments. Our results demonstrate that the ThGLP gene is expressed in leaves and roots, is involved in different abiotic stress re-sponses and controlled by an ABA-dependent signaling pathway.
文摘背景与目的:弥漫大B细胞淋巴瘤(diffuse large B-cell lymphoma,DLBCL)的生发中心B细胞样(germinal center B-cell-like,GCB)亚型和非GCB(non-GCB)亚型在患者预后和治疗上存在差异,但目前依赖有创病理学检查。本研究基于多参数MRI构建影像组学和深度学习模型,旨在于术前无创性区分这两种亚型。方法:本研究回顾性分析2013年3月—2024年12月在复旦大学附属华山医院及外院经病理学检查确诊的DLBCL患者。使用多参数MRI扫描数据,结合4种影像组学机器学习[支持向量机(support vector machine,SVM)、逻辑回归(logistic regression,LR)、高斯过程(Gaussian process,GP)和朴素贝叶斯(Naive Bayes,NB)]和3种深度学习[密集连接卷积网络121(densely-connected convolutional networks 121,DenseNet121)、残差网络101(residual network 101,ResNet101)和高效网络B5(Efficient Net-b5)]建立DLBCL亚型分类模型。此外,两名经验不同的放射科医师在盲法下基于MRI图像独立分类DLBCL。模型和医师的诊断性能均通过接收者操作特征曲线下面积(area under the curve,AUC)、准确度(accuracy,ACC)和F1分数(F1-score,F1)等指标进行量化评估,以衡量其区分GCB和non-GCB亚型的能力。本研究经复旦大学附属华山医院伦理委员会批准(KY2024-663),所有患者均知情同意。结果:本研究共纳入173例患者(GCB型55例,non-GCB型118例)。影像组学和深度学习方法能有效地区分DLBCL亚型。其中,GP影像组学模型(基于T1-CE+T2-FLAIR+ADC序列)和DenseNet121深度学习模型(基于T1-CE+T2-FLAIR+ADC序列)表现最佳,在内部验证集上分别取得优异性能(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774),并在外部验证集上保持稳健。并且,最优AI模型的分类效能优于经验丰富的放射科医师(医师最高AUC=0.678)。结论:基于多参数MRI特征的影像组学与深度学习模型可有效地鉴别DLBCL的GCB与non-GCB亚型。其中,GP与DenseNet121模型在处理复杂图像数据、特别是融合多序列特征组进行亚型分类时,呈现出优异的性能。
