Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical...Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.展开更多
目的:近年来,肿瘤微环境在恶性肿瘤中的作用受到广泛关注,但胃癌与肿瘤微环境的关系尚不完全清楚。本研究旨在探讨核纤层蛋白B2(nuclear lamina protein B2,LMNB2)在胃癌中的表达及其与肿瘤微环境的关系。方法:通过癌症基因组图谱(The C...目的:近年来,肿瘤微环境在恶性肿瘤中的作用受到广泛关注,但胃癌与肿瘤微环境的关系尚不完全清楚。本研究旨在探讨核纤层蛋白B2(nuclear lamina protein B2,LMNB2)在胃癌中的表达及其与肿瘤微环境的关系。方法:通过癌症基因组图谱(The Cancer Genome Atlas,TCGA)分析LMNB2基因在胃癌组织及正常胃黏膜组织中的表达差异。收集92例手术切除的胃癌及相应癌旁5 cm以上的胃黏膜组织标本,采用免疫组织化学方法检测其中LMNB2的表达情况,并分析LMNB2的表达水平与胃癌患者临床病理特征之间的关系。运用Kaplan-Meier生存曲线分析LMNB2表达水平与胃癌生存预后的关系。通过R软件和TISIDB(an integrated repository portal for tumor-immune system interactions)数据库对目标基因进行基因本体(Gene Ontology,GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)富集分析,并分析其与肿瘤免疫特征之间的相关性。基于CIBERSORT(Cell-type Identification by Estimating Relative Subsets of RNA Transcripts)算法进行LMNB2表达与免疫细胞浸润水平相关性预测,评价LMNB2表达水平对肿瘤微环境的影响。结果:TCGA数据库分析显示:胃癌组织中的LMNB2基因表达水平较正常胃黏膜组织明显增高(P<0.001)。LMNB2的表达水平与胃癌患者的淋巴结转移、肿瘤浸润深度、TNM分期及远处转移均有关(均P<0.05),与患者性别、年龄、肿瘤大小等无关(均P>0.05)。Kaplan-Meier生存曲线显示:LMNB2表达阳性患者总体生存时间较LMNB2阴性患者显著缩短(P<0.001)。LMNB2差异基因主要集中富集在体液免疫反应的调节等过程。此外LMNB2差异基因的表达与M0型巨噬细胞、CD4^(+)记忆T细胞等免疫细胞浸润水平均呈正相关(均P<0.05)。结论:LMNB2在胃癌组织中呈高表达,与患者预后生存不良相关,LMNB2与胃癌预后和免疫微环境高度相关,有望成为胃癌免疫治疗的潜在生物标志物。展开更多
基金National Key Research and Development Program of China(2022YFC3502302)National Natural Science Foundation of China(82074580)Graduate Research Innovation Program of Jiangsu Province(KYCX23_2078).
文摘Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.
文摘目的:近年来,肿瘤微环境在恶性肿瘤中的作用受到广泛关注,但胃癌与肿瘤微环境的关系尚不完全清楚。本研究旨在探讨核纤层蛋白B2(nuclear lamina protein B2,LMNB2)在胃癌中的表达及其与肿瘤微环境的关系。方法:通过癌症基因组图谱(The Cancer Genome Atlas,TCGA)分析LMNB2基因在胃癌组织及正常胃黏膜组织中的表达差异。收集92例手术切除的胃癌及相应癌旁5 cm以上的胃黏膜组织标本,采用免疫组织化学方法检测其中LMNB2的表达情况,并分析LMNB2的表达水平与胃癌患者临床病理特征之间的关系。运用Kaplan-Meier生存曲线分析LMNB2表达水平与胃癌生存预后的关系。通过R软件和TISIDB(an integrated repository portal for tumor-immune system interactions)数据库对目标基因进行基因本体(Gene Ontology,GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)富集分析,并分析其与肿瘤免疫特征之间的相关性。基于CIBERSORT(Cell-type Identification by Estimating Relative Subsets of RNA Transcripts)算法进行LMNB2表达与免疫细胞浸润水平相关性预测,评价LMNB2表达水平对肿瘤微环境的影响。结果:TCGA数据库分析显示:胃癌组织中的LMNB2基因表达水平较正常胃黏膜组织明显增高(P<0.001)。LMNB2的表达水平与胃癌患者的淋巴结转移、肿瘤浸润深度、TNM分期及远处转移均有关(均P<0.05),与患者性别、年龄、肿瘤大小等无关(均P>0.05)。Kaplan-Meier生存曲线显示:LMNB2表达阳性患者总体生存时间较LMNB2阴性患者显著缩短(P<0.001)。LMNB2差异基因主要集中富集在体液免疫反应的调节等过程。此外LMNB2差异基因的表达与M0型巨噬细胞、CD4^(+)记忆T细胞等免疫细胞浸润水平均呈正相关(均P<0.05)。结论:LMNB2在胃癌组织中呈高表达,与患者预后生存不良相关,LMNB2与胃癌预后和免疫微环境高度相关,有望成为胃癌免疫治疗的潜在生物标志物。
