目的分析角蛋白19、C反应蛋白、白蛋白检测对结直肠癌术后的临床疗效以及预测价值。方法以新疆维吾尔自治区人民医院2021年1月—2022年1月收治的98例患者为研究对象。对比临床治疗前后疗效及预后下的角蛋白19、C反应蛋白、白蛋白检测水...目的分析角蛋白19、C反应蛋白、白蛋白检测对结直肠癌术后的临床疗效以及预测价值。方法以新疆维吾尔自治区人民医院2021年1月—2022年1月收治的98例患者为研究对象。对比临床治疗前后疗效及预后下的角蛋白19、C反应蛋白、白蛋白检测水平,通过受试者工作特征(receiveroperating characteristic,ROC)曲线评估其临床预估价值。结果治疗后,患者的角蛋白19、C反应蛋白、白蛋白水平均较治疗前下降,差异有统计学意义(P<0.05)。疗效良好组在角蛋白19、C反应蛋白水平上低于疗效欠佳组,白蛋白高于疗效欠佳组,差异有统计学意义(P<0.05)。存活组在角蛋白19、C反应蛋白水平上低于死亡组,白蛋白高于死亡组,差异有统计学意义(P<0.05)。基于ROC曲线,角蛋白19、C反应蛋白、白蛋白在最佳截断曲线下面积(area under the curve,AUC)值分别为0.822、0.921、0.899,均超过0.7,对直肠癌不良预后有预测价值。3项指标联合检测的敏感度、特异度、AUC值与约登指数均高于3项指标单一检测,差异有统计学意义(P<0.05)。结论角蛋白19、C反应蛋白、白蛋白检测能够有效预测结直肠癌患者疗效变化及预后。展开更多
Corona Virus Disease 2019(COVID-19)has brought the new challenges to scientific research.Isodon suzhouensis has good anti-inflammatory and antioxidant stress effects,which is considered as a potential treatment for CO...Corona Virus Disease 2019(COVID-19)has brought the new challenges to scientific research.Isodon suzhouensis has good anti-inflammatory and antioxidant stress effects,which is considered as a potential treatment for COVID-19.The possibility for the treatment of COVID-19 with I.suzhouensis and its potential mechanism of action were explored by employing molecular docking and network pharmacology.Network pharmacology and molecular docking were used to screen drug targets,and lipopolysaccharide(LPS)induced RAW264.7 and NR8383 cells inflammation model was used for experimental verification.Collectively a total of 209 possible linkages against 18 chemical components from I.suzhouensis and 1194 COVID-19 related targets were selected.Among these,164 common targets were obtained from the intersection of I.suzhouensis and COVID-19.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enriched 582 function targets and 87 target proteins pathways,respectively.The results from molecular docking studies revealed that rutin,vitexin,isoquercitrin and quercetin had significant binding ability with 3 chymotrypsin like protease(3CLpro)and angiotensin converting enzyme 2(ACE2).In vitro studies showed that I.suzhouensis extract(ISE)may inhibit the activation of PI3K/Akt pathway and the expression level of downstream proinflammatory factors by inhibiting the activation of epidermal growth factor receptor(EGFR)in RAW264.7 cells induced by LPS.In addition,ISE was able to inhibit the activation of TLR4/NF-κB signaling pathway in NR8383 cells exposed to LPS.Overall,the network pharmacology and in vitro studies conclude that active components from I.suzhouensis have strong therapeutic potential against COVID-19 through multi-target,multi-pathway dimensions and can be a promising candidate against COVID-19.展开更多
The COVID-19 pandemic has caused an unprecedented spike in confirmed cases in 230 countries globally. In this work, a set of data from the COVID-19 coronavirus outbreak has been subjected to two well-known unsupervise...The COVID-19 pandemic has caused an unprecedented spike in confirmed cases in 230 countries globally. In this work, a set of data from the COVID-19 coronavirus outbreak has been subjected to two well-known unsupervised learning techniques: K-means clustering and correlation. The COVID-19 virus has infected several nations, and K-means automatically looks for undiscovered clusters of those infections. To examine the spread of COVID-19 before a vaccine becomes widely available, this work has used unsupervised approaches to identify the crucial county-level confirmed cases, death cases, recover cases, total_cases_per_million, and total_deaths_per_million aspects of county-level variables. We combined countries into significant clusters using this feature subspace to assist more in-depth disease analysis efforts. As a result, we used a clustering technique to examine various trends in COVID-19 incidence and mortality across nations. This technique took the key components of a trajectory and incorporates them into a K-means clustering process. We separated the trend lines into measures that characterize various features of a trend. The measurements were first reduced in dimension, then clustered using a K-means algorithm. This method was used to individually calculate the incidence and death rates and then compare them.展开更多
文摘目的分析角蛋白19、C反应蛋白、白蛋白检测对结直肠癌术后的临床疗效以及预测价值。方法以新疆维吾尔自治区人民医院2021年1月—2022年1月收治的98例患者为研究对象。对比临床治疗前后疗效及预后下的角蛋白19、C反应蛋白、白蛋白检测水平,通过受试者工作特征(receiveroperating characteristic,ROC)曲线评估其临床预估价值。结果治疗后,患者的角蛋白19、C反应蛋白、白蛋白水平均较治疗前下降,差异有统计学意义(P<0.05)。疗效良好组在角蛋白19、C反应蛋白水平上低于疗效欠佳组,白蛋白高于疗效欠佳组,差异有统计学意义(P<0.05)。存活组在角蛋白19、C反应蛋白水平上低于死亡组,白蛋白高于死亡组,差异有统计学意义(P<0.05)。基于ROC曲线,角蛋白19、C反应蛋白、白蛋白在最佳截断曲线下面积(area under the curve,AUC)值分别为0.822、0.921、0.899,均超过0.7,对直肠癌不良预后有预测价值。3项指标联合检测的敏感度、特异度、AUC值与约登指数均高于3项指标单一检测,差异有统计学意义(P<0.05)。结论角蛋白19、C反应蛋白、白蛋白检测能够有效预测结直肠癌患者疗效变化及预后。
基金supported by the National Natural Science Foundation of China(82170481)Anhui Natural Science Foundation(2008085J39 and 2108085MH314)+2 种基金Excellent Top-notch Talents Training Program of Anhui Universities(gxbjZD2022073)Anhui Province Innovation Team of Authentic Medicinal Materials Development and High Value Utilization(2022AH010080)Suzhou University Joint Cultivation Postgraduate Research Innovation Fund Project(2023KYCX04).
文摘Corona Virus Disease 2019(COVID-19)has brought the new challenges to scientific research.Isodon suzhouensis has good anti-inflammatory and antioxidant stress effects,which is considered as a potential treatment for COVID-19.The possibility for the treatment of COVID-19 with I.suzhouensis and its potential mechanism of action were explored by employing molecular docking and network pharmacology.Network pharmacology and molecular docking were used to screen drug targets,and lipopolysaccharide(LPS)induced RAW264.7 and NR8383 cells inflammation model was used for experimental verification.Collectively a total of 209 possible linkages against 18 chemical components from I.suzhouensis and 1194 COVID-19 related targets were selected.Among these,164 common targets were obtained from the intersection of I.suzhouensis and COVID-19.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enriched 582 function targets and 87 target proteins pathways,respectively.The results from molecular docking studies revealed that rutin,vitexin,isoquercitrin and quercetin had significant binding ability with 3 chymotrypsin like protease(3CLpro)and angiotensin converting enzyme 2(ACE2).In vitro studies showed that I.suzhouensis extract(ISE)may inhibit the activation of PI3K/Akt pathway and the expression level of downstream proinflammatory factors by inhibiting the activation of epidermal growth factor receptor(EGFR)in RAW264.7 cells induced by LPS.In addition,ISE was able to inhibit the activation of TLR4/NF-κB signaling pathway in NR8383 cells exposed to LPS.Overall,the network pharmacology and in vitro studies conclude that active components from I.suzhouensis have strong therapeutic potential against COVID-19 through multi-target,multi-pathway dimensions and can be a promising candidate against COVID-19.
文摘The COVID-19 pandemic has caused an unprecedented spike in confirmed cases in 230 countries globally. In this work, a set of data from the COVID-19 coronavirus outbreak has been subjected to two well-known unsupervised learning techniques: K-means clustering and correlation. The COVID-19 virus has infected several nations, and K-means automatically looks for undiscovered clusters of those infections. To examine the spread of COVID-19 before a vaccine becomes widely available, this work has used unsupervised approaches to identify the crucial county-level confirmed cases, death cases, recover cases, total_cases_per_million, and total_deaths_per_million aspects of county-level variables. We combined countries into significant clusters using this feature subspace to assist more in-depth disease analysis efforts. As a result, we used a clustering technique to examine various trends in COVID-19 incidence and mortality across nations. This technique took the key components of a trajectory and incorporates them into a K-means clustering process. We separated the trend lines into measures that characterize various features of a trend. The measurements were first reduced in dimension, then clustered using a K-means algorithm. This method was used to individually calculate the incidence and death rates and then compare them.
