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
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。
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.
