目的 构建并验证机械通气患儿肠内营养支持发生误吸的风险预测模型。方法 回顾性分析中山市博爱医院2021年3月—2023年3月儿童重症监护病房330例行机械通气并进行肠内营养的患儿临床资料,通过二元Logistic回归,获取机械通气患儿肠内营养支持发生误吸的预测因素,绘制列线图模型,并进行模型评价及验证。结果 330例机械通气患儿中,104例患儿发生误吸、226例未发生误吸。两组患儿在意识状态、机械通气方式、管饲量、胃残留量、胃管置入深度、促胃动力药、镇静剂等方面对比差异具有统计学意义（P<0.05）。二元Logistic结果显示,胃残留量、机械通气方式、管饲量、意识状态、胃管置入深度、促胃动力药、镇静剂是机械通气患儿肠内营养支持发生误吸的影响因素（P<0.05）。建模组AUC为0.810（95%CI：0.760~0.860）,Hosmer-Lemesh结果显示,χ²=3.245,P=0.846;外部验证组AUC为0.873（95%CI：0.831~0.914）,Hosmer-Lemesh结果显示,χ²=3.567,P=0.875。建模组和训练组DCA曲线大部分落于Y=0上方。建模组与外部验证组校准曲线均与参考曲线高度贴合,预测概率与实际概率接近,校准度良好。结论 基于胃残留量、机械通气方式、管饲量、意识状态、胃管置入深度、促胃动力药、镇静剂等7项指标构建的风险预测模型具有一定的临床价值,可作为医护人员识别肠内营养机械通气误吸高危患儿的工具。

Objective To establish and verify the risk prediction model of enteral nutritional aspiration in children with mechanical ventilation．Methods The clinical data of 330 children who underwent mechanical ventilation and enteral nutrition in the PICU of Zhongshan Boai Hospital from March 2021 to March 2023 were retrospectively analyzed．The independent predictive factors of enteral nutrition support aspiration in children with mechanical ventilation were obtained by binary Logistic regression,and the nomographic model was drawn,and the model was evaluated and verified. Results Among 330 children with mechanical ventilation,104 had aspiration and 226 did not．There were statistically significant differences between the two groups in consciousness state,mechanical ventilation mode,tube feeding amount,gastric residual amount,gastric tube insertion depth,gastric motivity drugs,sedatives,etc.（P<0.05）．Binary Logistic results showed that gastric residual amount,mechanical ventilation mode,tube feeding amount,state of consciousness,depth of gastric tube insertion,gastric motonics and sedatives were the influential factors of enteral nutritional aspiration in children with mechanical ventilation（P<0.05）．The AUC of the modeling group was 0.810（95%CI：0.760-0.860）,and the Hosmer-Lemesh result showed that χ²=3.245,P=0.846．The AUC of the external verification group was 0.873（95%CI：0.831-0.914）,and the Hosmer-Lemesh result showed that χ²=3.567,P=0.875．The DCA curves of modeling group and training group mostly were above Y=0．The calibration curves of the modeling group and the external verification group are highly fit to the reference curves,and the prediction probability was close to the actual probability,and the calibration degree was good．Conclusion sThe risk prediction model based on 7 indexes,including stomach residual amount,mechanical ventilation mode,tube feeding amount,state of consciousness,depth of gastric tube insertion,gastric motivity drug and sedative,with certain clinical value,and can be used as a tool for medical staff to identify children at high risk of enteral nutritional mechanical aspiration．

目的 探讨动态对比增强磁共振成像（DCE-MRI）多参数定量特征对乳腺癌腋窝淋巴结转移（ALNM）风险的预测价值。方法 回顾性收集2020年3月—2022年11月在佛山市高明区人民医院经手术病理确诊的155例乳腺癌患者临床资料,根据患者是否发生ALNM分为ALNM 组（n=39）和无ALNM 组（n=116）。采用单因素分析和多因素Logistic回归分析乳腺癌发生ALNM的影响因素。结果 ALNM组和无ALNM 组患者的肿块质地、肿块直径、肿块部位、肿块形状、肿块内部强化特征等指标比较差异无统计学意义（t/χ²=2.249、0.977、1.369、0.524、2.158,P>0.05）。两组患者肿块表观扩散系数（ADC）值、腋窝淋巴结（ALN）短径、肿块边缘、动态增强时间-信号强度曲线（TIC）曲线等指标比较,差异有统计学意义（t/χ²=6.573、9.873、29.441、2.031,P<0.05）。二元Logistic回归模型结果显示,肿块ADC值、ALN 短径（≥5 mm）、TIC曲线（流出型）为乳腺癌ALNM发生的危险因素（OR=0.251、0.106、0.002,P<0.05）。结论 DCE-MRI多参数定量特征中,乳腺癌患者的肿块ADC值低、ALN 短径（≥5 mm）、TIC曲线（流出型）为乳腺癌ALNM发生的危险因素。

Objective To investigate the predictive value of multi-parameter quantitative features of dynamic contrast-enhanced magnetic resonance imaging（DCE-MRI）in the risk of axillary lymph node metastasis（ALNM）in breast cancer．Methods Clinical data of 155 patients with breast cancer diagnosed by surgery and pathology in Foshan Gaoming District People's hospital from March 2020 to November 2022 were retrospectively collected．According to whether the patients had ALNM,they were divided into ALNM group（n=39）and non-ALNM group（n=116）．Univariate analysis and multiple Logistic regression models were used to explore the influencing factors of ALNM in breast cancer．Results There was no significant difference in mass texture,mass diameter,mass location,mass shape and internal enhancement between the ALNM group and the non-ALNM group（t/χ²=2.249,0.977,1.369,0.524,2.158,P>0.05）．There were significant differences in ADC value,ALN short diameter,tumor margin and TIC curve between the two groups（t/χ²=6.573,9.873,29.441,2.031,P<0.05）．Binary Logistic regression model showed that ADC value,ALN short diameter（≥5 mm）and tumor margin（blur）were risk factors for the occurrence of breast cancer ALNM（OR=0.251,0.106,0.002,P<0.05）．Conclusions Among the multi-parameter quantitative features of DCE-MRI,the ADC value of breast cancer,the short diameter of ALN（≥5 mm）,and the edge of the tumor（blur）are the risk factors for the occurrence of ALNM in breast cancer．

目的 基于影像组学方法,探讨多层螺旋CT（MSCT）四期增强扫描单一/不同期相及不同容积感兴趣区（VOI）的选择,在术前预测原发性肝细胞癌（HCC）微血管侵犯（MVI）中的价值。方法 回顾性收集88例经手术病理证实为HCC并行术前MSCT四期增强扫描的患者,其中包括47例MVI阳性患者和41例MVI阴性患者。在MSCT增强扫描的动脉早期、动脉晚期、门静脉期及延迟期图像中手动逐层勾画肿瘤ROI,获得瘤体容积感兴趣区VOI（Vt）,然后基于计算机自动膨胀算法将Vt外扩10 mm获得瘤体及瘤周VOI（Vt+Vp）。使用Pyradiomics软件分别从Vt和Vt+Vp中提取影像组学特征,随后采用15种特征选择方法和10种分类器构建150个预测模型,并通过十折交叉检验以验证模型的效能。使用准确度、敏感度、特异度、受试者工作特性曲线下面积（AUC）评估模型的效能,并比较性能最优的前三个预测模型。结果 MSCT四期增强扫描图像中预测HCC MVI状态的影像组学模型在门静脉期的表现优于其它期相及各期相的不同组合,其中最大的AUC值在Vt和Vt+Vp两种ROI中分别为0.768和0.782。此外,基于Vt+Vp的影像组学模型对MVI的预测效能优于基于Vt的影像组学模型,基于Vt+Vp性能最优的预测模型的AUC值、准确度、敏感度和特异度分别0.782、0.728、0.745和0.705。结论 采用影像组学方法术前无创性预测HCC MVI状态首选增强扫描的门静脉期,ROI首选瘤体联合瘤周10 mm区域。

Objective To investigate the value of single or different phases of contrast-enhanced multi-slice spiral CT（MSCT）in different volumetric regions of interest（ROI）to preoperatively predict the state of microvascular invasion in primary hepatocellular carcinoma（HCC）based on radiomics methods．Methods A total of 88 patients with HCC confirmed by surgical pathology who underwent preoperative MSCT quadruple-enhanced scan were retrospectively recruited,including 47 MVI-positive patients and 41 MVI-negative patients．The ROI was manually delineated slice-by-slice in the early arterial phase,late arterial phase,portal venous phase,and equilibrium phase of enhanced MSCT images to obtain the volume of tumor VOI（Vt）,and then Vt was expanded by 10 mm through the computer expansion algorithm automatically to obtain the volume of tumor and peritumor（Vt+Vp）．Pyradiomics software was used to extract radiomic features from Vt and Vt+Vp,followed by 150 discriminant models constructed with 150 feature selection methods and 10 classifiers,and then 10-fold cross-validation was used to evaluate the performance of these models．Using accuracy,sensitivity,specificity,area under the receiver operating characteristic curve（AUC）to assess model performance．The top three predictive models with the best performance were also compared．Results The radiomics model for predicting HCC MVI status in portal venous phase among quadruple-enhanced MSCT images outperformed other phases and different combinations of phases,achieving the highest AUC values of 0.768 and 0.782 in Vt and Vt+Vp respectively．In addition,the prediction performance of the radiomics model based on Vt+Vp was superior to models based on Vt．AUC value,accuracy,sensitivity,and specificity of the model with the best performance based on Vt+Vp were 0.782,0.728,0.745 and 0.705 respectively．Conclusions Radiomics models based on the portal venous phase of contrast-enhanced MSCT and tumor combined with the 10mm peritumoral area were more recommended to be employed to preoperative non-invasively predict the state of HCC MVI．

目的 基于随机森林方法构建甲状腺功能减退（简称甲减）患病风险预测模型。方法 从MIMIC-IV数据库纳入5 735名甲减患者为病例组,4 803名非甲减患者为对照组,基于随机森林模型进行建模。同时利用逻辑回归、贝叶斯正则化神经网络、XGBoost作为比较模型。最后用准确率、F1分数、精确率、召回率、特异性以及AUC值评价四个机器学习模型性能。结果 随机森林模型准确率为0.85,F1分数为0.84,精确率为0.84,召回率为0.84,特异性为0.86,AUC值为0.91。在该模型中,促甲状腺激素、年龄、绝对淋巴细胞计数、血液中红细胞数、中性白细胞、性别、碱性磷酸酶、丙氨酸氨基转移酶、嗜酸性粒细胞绝对计数、尿素氮为甲减患者诊断重要性排前10的指标。结论 采用随机森林方法构建的甲减患病预测模型为甲减的早期诊断有潜在应用价值。

Objective To construct a risk prediction model for hypothyroidism based on the random forest model．Methods A total of 5 735 hypothyroidism patients were included from the MIMIC-IV database as the case group, and 4 803 non-hypothyroidism patients were included as the control group．Random forest models were constructed for both groups, and logistic regression, Bayesian regularized neural network, and XGBoost were used as comparative models．The performance of the four machine learning models was evaluated using accuracy, F1 score, precision, recall, specificity, and AUC value．Results The random forest model had an accuracy of 0.85, an F1 score of 0.84, a precision of 0.84, a recall of 0.84, a specificity of 0.86, and an AUC value of 0.91．In this model, thyroid-stimulating hormone, age, absolute lymphocyte count, red blood cell count in blood, neutrophil, gender, alkaline phosphatase, aspartate aminotransferase, absolute eosinophil count, and blood urea nitrogen were the top 10 indicators for diagnosing hypothyroidism patients．Conclusions The hypothyroidism disease prediction model constructed using the random forest method has potential application value for the early diagnosis of hypothyroidism．

目的 探讨子宫瘢痕的超声弹性成像结合厚度分析对剖宫产后再妊娠产妇子宫破裂的预测应用。方法 选择2020年1月—2021年12月在中山市中医院分娩的剖宫产术后再次妊娠经阴道分娩（VBAC）产妇作为研究对象。根据纳入和排除标准,共纳入子宫破裂的VBAC产妇32例、非子宫破裂的VBAC产妇90例。通过住院病历信息系统查询研究对象的基本信息及其在妊娠晚期（≥37周）用B超对研究对象行子宫瘢痕厚度和弹性的测量结果,采用受试者工作特征曲线（ROC）曲线分析子宫瘢痕厚度和弹性SI值对子宫破裂的预测作用。结果 子宫破裂组中年龄>35岁、妊娠>2次、与上次剖宫产间隔<2年、新生儿体质量≥3 kg、单层缝合者的比例高于非子宫破裂组（P<0.05）。122例产妇子宫瘢痕厚度的均值为（3.42±0.49）mm,SI的均值为（2.57±0.45）。ROC曲线分析结果显示：子宫瘢痕厚度单独预测子宫破裂的曲线下面积（AUC）为0.805（95%CI：0.730~0.880,P<0.05）,cut off值为3.05 mm,灵敏度为0.726,特异度为0.910,约登指数为0.636;子宫瘢痕SI单独预测子宫破裂的AUC为0.730（95%CI：0.635~0.824,P<0.05）,cut off值为2.11,灵敏度为0.767,特异度为0.781,约登指数为0.548;子宫瘢痕厚度联合预测子宫破裂的AUC为0.874（95%CI：0.812~0.937,P<0.01）,灵敏度为0.875,特异度为0.811,约登指数为0.686。子宫瘢痕厚度结合子宫瘢痕SI值预测子宫破裂的AUC高于单独使用子宫瘢痕厚度（Z=7.611,P=0.041）和子宫瘢痕SI值（Z=25.864,P=0.025）。结论 子宫瘢痕的超声弹性成像SI值联合子宫厚度可有效提高超声对于VBAC产妇子宫破裂的预测效能,具有一定的应用意义。

Objective To study the application of ultrasound elasticity imaging combined thickness analysis of uterine scar in predicting uterine rupture in women pregnant after cesarean section．Methods Pregnant women with vaginal birth after cesarean（VBAC）from January 2020 to December 2021 in Zhongshan Hospital of Traditional Chinese Medicine were selected as the research subjects．A total of 32 VBAC parturients with uterine rupture and 90 VBAC parturients without uterine rupture were included according to the inclusion and exclusion criteria．The basic information of the subjects was queried through the medical record information system of the hospital．In the third trimester（≥37 weeks）,the thickness and elasticity of uterine scar were measured by ultrasound,and the predictive effect of uterine scar thickness and elastic SI value on uterine rupture was analyzed by ROC curve．Results Chi-square test showed that the incidence of uterine rupture was higher in patients with age>35 years,pregnancy>2 times,interval from last cesarean section<2 years,newborn weight≥3kg,and the proportion of uterine rupture in single suture was higher than that in double suture（P<0.05）．The mean uterine scar thickness of 122 subjects was（3.42±0.49）mm,and the mean SI was（2.57±0.45）．The area under curve（AUC）of uterine scar thickness alone for predicting uterine rupture was 0.805（95%CI：0.730-0.880,P<0.05）,the cut off value was 3.05 mm,the sensitivity was 0.726,the specificity was 0.910,and the Youden coefficient was 0.636 by ROC curve analysis．The AUC of uterine scar SI alone for predicting uterine rupture was 0.730（95%CI：0.635-0.824,P<0.05）,the cut off value was 2.11,the sensitivity was 0.767,the specificity was 0.781,and the Youden coefficient was 0.548 by ROC curve analysis．The AUC of uterine scar thickness combination for predicting uterine rupture was 0.874（95%CI：0.812-0.937,P<0.01）,the sensitivity was 0.875,the specificity was 0.811,and the Youden coefficient was 0.686 by ROC curve analysis．The AUC predicted by uterine scar thickness combined with uterine scar SI value was higher than that predicted by uterine scar thickness alone（Z=7.611,P=0.041）and uterine scar SI value（Z=25.864,P=0.025）．Conclusions Elastic SI value of ultrasound imaging of uterine scar combined with uterine thickness can effectively improve the prediction efficiency of ultrasound for VBAC maternal uterine rupture,which has certain application significance,but further demonstration is still needed．

目的 探讨老年吸入性肺炎的危险因素,建立风险预测模型,以期降低老年吸入性肺炎的发病率。方法 选取2017年8月28日—2020年 10月30日广州市第一人民医院老年病科住院治疗的老年肺炎患者205例,按照是否发生吸入性肺炎分为吸入性肺炎组和非吸入性肺炎组,对比2组患者的各项指标,分析老年吸入性肺炎的危险因素,建立风险预测模型,采用ROC曲线对模型进行预测效果检验。结果 多因素Logistic回归分析结果显示,脑梗塞、帕金森、留置胃管、长期卧床为老年吸入性肺炎的危险因素(P<0.05)。模型公式为Logit(P)=-2.952+1.221X₂+2.417X₃+2.388X₈+1.683X₁₀。该模型ROC曲线下面积为0.894。结论 本研究中的模型预测效果良好,可为医护人员预测老年患者发生吸入性肺炎的概率,及时采取相应的预见性护理及干预性治疗。

Objective To explore the risk factors of aspiration pneumonia in the elderly and establish the risk prediction model, in order to reduce the incidence of aspiration pneumonia in the elderly. Methods A total of 205 elderly patients with pneumonia who were hospitalized in the department of geriatrics, Guangzhou First People's Hospital from August 28, 2017 to October 30, 2020, were divided into aspiration pneumonia group and non-aspiration pneumonia group according to whether aspiration pneumonia occurred. The indicators of the two groups of patients were compared, the risk factors of aspiration pneumonia in the elderly were analyzed, the risk prediction model was established, and the prediction effect of the model was tested by receiver operating characteristic curve. Results Multivariate Logistic regression analysis showed that cerebral infarction, Parkinson's disease, indwelling nasogastric tube, and being bedridden were risk factors for aspiration pneumonia in elderly patients (P<0.05). The model formula was Logit (P)=-2.952+1.221X₂+2.417X₃+2.388X₈+1.683X₁₀. The area under receiver operating characteristic curve of this model was 0.894. Conclusion The prediction effect of the model in this study was good, which could predict the probability of aspiration pneumonia in elderly patients for medical staff, and to timely take the corresponding predictive care and interventional treatment.

目的 利用网络药理学技术,分析黄甲软肝颗粒治疗肝纤维化的作用网络,以及黄甲软肝颗粒治疗肝纤维化的潜在作用机制,并在体内动物实验进行初步验证。方法 采用中药系统药理学分析平台中寻找黄甲软肝颗粒中10味中药相关的化学成分和作用靶点,通过GeneCards等数据库筛选肝纤维化疾病相关的靶标;对药物与疾病靶点相映射得到黄甲软肝颗粒治疗肝纤维化的作用靶点,运用cytoscape将疾病靶点与复方活性成分靶点的交集-交集部分对应的活性成分”构建“C(成分)-T(靶点)”作用网络。将交集靶点利用 DAVID数据库进行GO富集分析和KEGG富集分析,以获得其潜在作用机制。最后,通过黄甲软肝颗粒防治CCl₄导致SD大鼠肝纤维化的体内实验进行初步验证,考察末次给药后大鼠体质量和肝脏指数,采用微板法检测SD大鼠血清中天冬氨酸氨基转移酶(AST)、丙氨酸氨基转移酶(ALT)水平,苏木精-伊红染色观察肝脏病理学变化。结果 预测筛选得到黄甲软肝颗粒共有117个潜在活性成分,266个活性成分对应靶点,161个交集靶点,关键成分有槲皮素、山奈酚、丹参酮IIA、芒柄花黄素等,关键靶点有PTGS2、PTGS1、NCOA1、ACHE、HTR、RXRA、ADRB2、IL1B等。GO 分析共包含 960条富集结果,其中生物过程845 条,分子功能 63条,细胞组成 52 条;KEGG 分析共得出68条通路,与本次研究较相关的通路主要包括TNF信号通路、Toll样受体信号通路、Rap1信号通路、胞质DNA传感途径、ErbB信号通路、VEGF信号通路等。体内动物实验研究表明,黄甲软肝颗粒能显著降低大鼠的肝脏指数和血清ALT、AST,改善肝组织病理学指标。结论 黄甲软肝颗粒可通过多成分、多途径、多靶点协同发挥治疗肝纤维化的作用,本研究为黄甲软肝颗粒治疗肝纤维化疾病的物质基础、作用机制及临床应用的进一步研究奠定基础。

Objective To analyze the effective network of Huangjia Ruangan Granules in treating liver fibrosis and its potential mechanism by using network pharmacology, and preliminary verify by animal in vivo experiments. Methods From the Chinese Medicine System Pharmacology Analysis Platform, we searched for the chemical constituents and targets of 10 Chinese herbs in Huangjia Ruangan Granules, and screened the targets related to liver fibrosis diseases through GeneCards and other databases. The drug and disease target were mapped to the target of Huangjia Ruangan Granules for the treatment of liver fibrosis, and the active component corresponding to the intersection of the disease target and the compound active component target was constructed using cytoscape “C (component)-T (target)” action network. The intersection target was used for GO enrichment analysis and KEGG enrichment analysis with DAVID database to obtain its potential mechanism of action. Finally, through the in vivo experiment of using Huangjia Ruangan Granules to prevent and treat CCl₄ leaded liver fibrosis in SD rats, the rats' body weight and liver index after the last dose were recorded, and the levels of aminotransferase (AST) and alanine aminotransferase (ALT) in the serum of SD rats were detected by the microplate method, hematoxylin-eosin staining were used to observe liver pathological changes. Results Predictive screening showed that Huangjia Ruangan Granules had 117 potential active ingredients, 266 active ingredients corresponded to targets, and 161 intersection targets. The key ingredients was quercetin, kaempferol, tanshinone IIA, formononetin, etc. The key targets were PTGS2, PTGS1 NCOA1, ACHE, HTR, RXRA, ADRB2, IL1B, etc. GO analysis showed a total of 960 enrichment results, including 845 biological processes, 63 molecular functions, and 52 cell compositions; KEGG analysis revealed a total of 68 pathways, the related pathways included TNF signaling pathway, Toll-like receptor signaling pathway, Rap1 signaling pathway, cytoplasmic DNA sensing pathway, ErbB signaling pathway and VEGF signaling pathway, etc. In vivo animal experiments had shown that Huangjia Ruangan Granules could significantly reduce the liver index and serum ALT and AST levels of rats, and improve liver histopathological indicators. Conclusions Huangjia Ruangan Granules treated liver fibrosis through multi-component, multi-pathway and multi-target synergy. This research laid the groundwork for the material basis, mechanism and clinical application of Huangjia Ruangan Granules in treating liver fibrosis diseases.

目的 探讨两种不同机器学习算法在妊娠期糖尿病(gestational diabetes mellitus,GDM)风险预测中的应用。方法 选取2019年7月—2020年8月在广州市妇女儿童医疗中心及广东省计划生育专科医院进行产前检查的孕早期妇女520例,其中妊娠期糖尿病孕妇200例,随机抽取同期正常孕妇320例,收集孕妇的一般资料和孕早期(8～12周)的生化指标、血常规和凝血功能等检测资料。利用这些分析变量建立支持向量机(SVM)和Logistic回归(LR)预测模型。根据模型预测能力和模型实用性,如准确率、精确率、真阳性(TP)率、假阳性(FP)率、召回率、F测度、受试者工作特征曲线(ROC)进行效果评价。结果 两种预测模型的分类准确率总体为86%。SVM模型在真阳性(TP)率、假阳性(FP)率、召回率、F测度、受试者工作特征曲线(ROC)方面优于LR模型。结论 在分类与预测方面,支持向量机算法比Logistic回归模型更具有实用价值。

Objective To explore the application of two different machine learning algorithms in the risk prediction of gestational diabetes mellitus (GDM). Methods A total of 520 pregnant women with gestational diabetes mellitus were selected from Women and Children's Medical Center and Guangdong Family Planning Hospital from July 2019 to August 2020, including 200 cases of gestational diabetes mellitus, and 320 normal pregnant women in the same period. The general information of pregnant women and the detection data of biochemical indexes, blood routine test and coagulation function in early pregnancy (8～12 weeks) were collected. Support vector machine (SVM) and logistic regression (LR) prediction models were established by using these analysis variables. According to the predictive ability and practicability of the model, something like accuracy rate, precision ratio, true positive (TP) rate, false positive (FP) rate, recall rate, F-measure and receiver operating characteristic curve (ROC) were evaluated. Results The classification accuracy of the two models was 86%. SVM model is better than LR model in TPrate, FPrate, recall rate, F measure and ROC. Conclusion Support vector machine is more practical than logistic regression model in classification and prediction.

目的 基于网络药理学方法预测银杏叶治疗心肌缺血的潜在靶点及信号通路。方法 利用 TCMSP 平台筛选生物利用度(OB)≥ 30% 和类药性(DL)≥ 0.18 的活性成分及作用靶点。利用GeneCards和OMIM数据库检索心肌缺血疾病相关靶点,并提取药物成分和心肌缺血疾病的共有靶点作为关键靶点。通过在线TRING平台构建PPI网络,并采用Cytoscape 软件构建可视化的“化合物-靶点-通路”网络,进一步进行GO 功能富集分析和KEGG通路富集分析。结果 筛选得到 27种潜在的药效成分,2 164个化合物靶点,531个心肌缺血相关靶基因。两者取交集后获得疾病-类药活性成分40个共同靶点,PPI 蛋白互作网络自由度较高的节点依次为:IL6、VEGFA、CASP3、MAPK8、MYC、NOS3。GO 功能富集分析得到42个 GO 条目,KEGG 通路富集分析得到42条信号通路。结论 银杏叶治疗心肌缺血主要GO 能力富集在半胱氨酸肽链内切酶活性,内肽酶活力,激活转录因子结合,DNA结合转录激活剂活性,RNA聚合酶II特异性等功能,调控TNF信号通路,糖尿病并发症的年龄愤怒信号, 细胞凋亡,PI3K-Akt信号通路等信号,进一步达到对心肌缺血疾病的治疗。

Objective To predict the potential targets and signal pathways of ginkgo leaf in the treatment of myocardial ischemia based on network pharmacology. Methods The active components and targets of bioavailability (OB) ≥ 30% and drug-like (DL) ≥ 0.18 were screened by TCMSP platform.The related targets of myocardial ischemic diseases were searched by GeneCards and OMIM database, the components and the common targets of myocardial ischemic diseases were extracted as the key targets. To build the PPI network through the online STRING platform, a visual “compound-target-pathway” network was constructed to further analyze the functional enrichment of GO and the enrichment of KEGG pathway. Results 27 potential active components, 2 164 compound targets and 531 myocardial ischemia related target genes were screened. After the intersection of the two, 40 common targets of disease-class active components were obtained. The nodes with higher degree of freedom of PPI protein interaction network were IL6、VEGFA、CASP3、MAPK8、MYC and NOS3.42 entries were obtained by GO functional enrichment analysis and 42 signal pathways were obtained by KEGG pathway enrichment analysis. Conclusion Ginkgo leaf may be a target of cysteine-type endopeptidase activity,endopeptidase activity,activating transcription factor binding,DNA-binding transcription activator activity, RNA polymerase II-specific function. TNF signaling pathway, AGE-RAGE signaling pathway in diabetic complications, apoptosis, PI3K-Akt signaling pathway were regualted to achieve the treatment of myocardial ischemia disease.

  目的   基于SEER数据库分析三阴性乳腺癌（TNBC）的预后，并建立Cox回归临床预测模型且进行内部验证。方法   使用SEER*Stat软件（8.4.2版）筛选2010—2015年诊断为TNBC的病例，进行单因素和Cox多因素回归以及向后逐步回归分析，明确与生存相关的独立危险因素，构建预测TNBC患者3年和5年癌症特异生存（CSS）率的Nomogram图，并用受试者工作特征曲线，Harrell’s一致性指数，临床预测模型校准曲线以及决策曲线对该模型进行评估及内部验证，以评估该模型的临床预测效能。结果   共筛选出符合纳入标准的TNBC患者5 564例，按照7∶3的比例随机拆分为训练集（n=3 894）和验证集（n=1 670）。通过单因素，多因素分析显示TNM分期、放射治疗、化学治疗以及手术和其他治疗的先后顺序是与TNBC患者CSS显著相关的独立危险因素（P＜0.05）。利用上述预后相关因素建立Nomogram图模型。训练集的C-index为0.731（95%CI：0.712~0.749），验证集的C-index为0.719（95%CI：0.688~0.749），训练集和验证集3年和5年生存ROC曲线的曲线下面积均＞0.7，区分度较好，且校准曲线拟合良好。结论  TNM分期、放射治疗、化学治疗以及手术和其他治疗的先后顺序是TNBC的独立预后因素，基于此建立的Nomogram图临床预测模型区分度、准确度以及临床适用性较好，能较好地预测TNBC患者的生存预后。

    Objective  To analyze the prognosis of triple negative breast cancer（TNBC）based on the SEER database，and to establish a Cox regression clinical prediction model with internal validation．Methods  Cases diagnosed with TNBC from 2010 to 2015 were screened using SEER*Stat software（version 8.4.2），and univariate and Cox multifactorial  regression as well as backward stepwise regression analyses were performed to identify the independent risk factors associated with survival，and to construct a clinical prediction model for predicting the three- and five-year cancer specific survival（CSV）of TNBC patients．Survival（CSS）rates of TNBC patients at 3 and 5 years，and the model was evaluated and internally validated using the ROC curve，Harrell’s consistency index（C-index），clinical prediction model calibration curve，and decision-making curve（DCA curve）to assess the predictive efficacy of the model for clinical prediction．Results  A total of 5 564 TNBC patients meeting the inclusion criteria were screened and randomly split into a training set（n=3 894）and a validation set（n=1 670）according to a 7∶3 ratio．By univariate，multivariate analysis showed that T-stage，N-stage，M-stage，radiotherapy，chemotherapy，and the sequence of surgery and other treatments were independent risk factors significantly associated with CSS in TNBC patients．The above prognostic-related factors were utilized to build a Nomogram plot model．The C-index was 0.731（95%CI：0.712-0.749）for the training set and 0.719（95%CI：0.688-0.749）for the validation set，and the areas under the curves of the 3- and 5-year survival ROC curves of both the training and validation sets were ＞0.7，which was a good differentiation，and the calibration curves were well-fitted．Conclusions  T-stage，N-stage，M-stage，radiotherapy，chemotherapy，and the  sequence of  surgery and other treatments are independent prognostic factors for TNBC，and the Nomogram clinical prediction model based on this has good differentiation，accuracy，and clinical utility，and can better predict the survival prognosis of TNBC patients．