CT联合肿瘤标志物与MRI联合肿瘤标志物对于直肠癌患者术前诊断 的准确率与特异性的研究

目的:探讨CT联合肿瘤标志物与MRI联合肿瘤标志物对于直肠癌患者术前诊断的准确率与特异性,为大肠癌的术前诊断提供一定的理论依据。方法:选取我院在2015年01月至2016年01月间收治的86例直肠癌患者以及64例肠道良性病变患者分别作为观察组I组和观察II组的研究对象,另外选取来我院进行健康体检的80例人员作为对照组研究,分别分析CT联合肿瘤标志物与MRI联合肿瘤标志物(CEA、CA125、CA199、CA242、CA724)对大肠癌患者诊断的准确率与特异性之间的差别。结果:观察I组肿瘤标志物水平要明显高于观察II组和对照组,差异显著,具有统计学意义;肿瘤标志物CEA、CA125、CA199、CA242、CA724对大肠癌患者检测的阳性率分别为67.44%(58/86)、26.74%(23/86)、84.88%(73/86)、72.09%(62/86)、33.72%(29/86),肿瘤标志物并联检测对大肠癌的阳性检测率为94.19%(81/86)。CT联合肿瘤标志物对大肠癌的准确率为97.67%(84/86),特异性为94.44%(136/144);MRI联合肿瘤标志物对大肠癌的阳性检测率为100.00%(86/86),特异性为98.61%(142/144)。结论:CT联合肿瘤标志物对大肠癌诊断的准确率与特异性均不如MRI联合肿瘤标志物,因此MRI联合肿瘤标志物可作为大肠癌除病理学鉴定外最佳的诊断方式。     

HRR、PLR与中重度颅脑损伤患者短期死亡的关系研究

摘要 目的：探讨血红蛋白/红细胞分布宽度比值（HRR）、血小板/淋巴细胞比值（PLR）与中重度颅脑损伤（TBI）患者短期死亡的关系。方法：回顾性收集2019年9月～2021年9月徐州医科大学附属医院收治的162例中重度TBI患者的病历资料，根据患者入院30d内生存状态分为死亡组和存活组。计算HRR和PLR，采用多因素Logistic回归分析中重度TBI患者短期死亡的影响因素，受试者工作特征（ROC）曲线分析格拉斯哥昏迷量表（GCS）评分联合HRR、PLR对中重度TBI患者短期死亡的预测价值。结果：162例中重度TBI患者入院30 d内死亡率为35.80%（58/162）。与存活组比较，死亡组HRR降低，PLR升高（P＜0.05）。多因素Logistic回归分析显示，GCS评分＜9分、瞳孔散大、脑疝和HRR降低、PLR升高为中重度TBI患者短期死亡的独立危险因素（P＜0.05）。ROC曲线分析显示，HRR、PLR联合GCS评分预测中重度TBI患者短期死亡的曲线下面积最大，为0.924。结论：HRR降低和PLR升高与中重度TBI患者短期死亡相关，可能成为中重度TBI患者短期死亡的辅助预测指标，在GCS评分基础上联合HRR、PLR能提升对中重度TBI患者短期死亡的预测价值。     

内毒素、PCT联合NLR对经皮肾镜碎石术后患者尿源性脓毒血症的预测价值

摘要 目的：探讨内毒素、降钙素原（PCT）联合中性粒细胞与淋巴细胞比值（NLR）对经皮肾镜碎石术（PCNL）术后患者发生尿源性脓毒血症的预测价值。方法：选取2020年5月-2023年5月于西安医学院第二附属医院和空军军医大学第一附属医院泌尿外科行PCNL的患者750例作为研究对象。根据尿源性脓毒症发生情况分为尿源性脓毒血症组（n=45）和非脓毒血症组（n=705）。检测PCNL术前血清内毒素、PCT、中性粒细胞与淋巴细胞水平，并计算NLR。对比两组血清内毒素、PCT水平及NLR。采用多因素Logistic回归模型分析PCNL术后患者发生尿源性脓毒血症的影响因素。绘制受试者工作特征（ROC）曲线分析血清内毒素、PCT联合NLR预测PCNL术后患者发生尿源性脓毒血症的临床效能。结果：与非脓毒血症组相比，尿源性脓毒血症组血清内毒素、PCT及NLR更高（P<0.05）。多因素Logistic回归模型分析结果显示，血清内毒素升高、PCT升高、NLR升高、尿白细胞阳性、术前发热及鹿角型结石是PCNL术后患者发生尿源性脓毒血症的独立危险因素（P<0.05）；ROC曲线分析结果显示，血清内毒素、PCT联合NLR检测预测PCNL术后患者发生尿源性脓毒血症的曲线下面积（AUC）为0.913，高于上述各指标单独检测。结论：PCNL术前血清内毒素、PCT和NLR升高可能与术后患者发生尿源性脓毒血症有关。血清内毒素、PCT水平升高、NLR升高、术前发热、尿白细胞阳性、鹿角型结石是PCNL术后患者发生尿源性脓毒血症的危险因素。血清内毒素、PCT联合NLR检测对PCNL术后患者发生尿源性脓毒血症具有较高预测价值。     

Ultrasound does not provide reliable results for the measurement of the patellar tendon cross sectional area

PurposeThis study examined the reliability of patellar tendon cross sectional area (CSA) measurement using brightness mode ultrasonography.MethodsThe patellar tendon CSA of fourteen participants was examined on two different days and at three different positions (proximal, median and distal). Five trials per day were conducted in each position, replacing the ultrasound probe on every trial. The images were examined by three different and equally experienced observers. We compared the mean of the five trials in each position to examine the day, observer and position effect. Further, Bland and Altman plots, root mean square (RMS) differences and intraclass correlation coefficients (ICC) were calculated.ResultsThere was a significant (p < 0.05) day, observer and position effect on the CSA, while the average ICC was 0.592. The Bland and Altman plots showed that differences between conditions or groups, should be in average lower than 37% or higher than 55% of the patellar tendon CSA to be important for clinical or intervention studies.ConclusionOur findings show low reliability of the method, which resulted from the low clarity and unclear visibility of tissue boundaries in the ultrasound images. Therefore, the measurement of the CSA of the patellar tendon using ultrasound does not provide accurate and reliable results.     

Are predators negative or positive predictors of farmland bird species community on a large geographical scale?

Species distribution models (SDMs) are numerical tools that combine species occurrence/density or species richness with environmental data in order to predict particular species’ distribution. In most cases only abiotic environmental parameters are used as predictors, while biotic interactions which control distribution of species and influence the goodness of fit of the SDM, such as predator–prey systems, have been broadly neglected. For this reason, we tested the usefulness of easy to detect predators, such as the Common Buzzard and the Common Raven, as positive and negative predictors, respectively, of farmland bird species richness. We analyzed factors affecting the density of both predators and farmland bird species using data from 958 1 × 1 km² study plots in Poland and a set of 22 environmental variables. Next, we also included these predators’ densities as additional predictors of farmland bird species. Habitat and climatological predictors were aggregated using the Principal Components Analysis and then related to the Common Raven's and the Common Buzzard's densities as well as farmland bird species richness using General Additive Models. Finally, completed models were assessed according to information – theoretic criteria. Our results showed that all the analyzed groups occurred in open areas; the Common Buzzard and passerine bird species preferred traditional farmland, while the Common Raven reached its highest density in modern intensive farmland. Importantly, we documented a significant increase in the goodness of fit of SDMs for farmland bird species, having added the density of predators as negative (Common Raven) and positive (Common Buzzard) predictors. Consequently, our findings suggest that species’ specific models can improve the predictive power of SDMs and can be used as an effective tool for predicting bird diversity with higher accuracy.     

Rates of evolution: Is there a conflict between neo-darwinian evolutionary theory and the fossil record?

Neo-darwinian and population genetics theory assumes that the necessary and sufficient set of conditions for all genetic, therefore evolutionary, change has been identified. Punctuationalists have assumed the opposite and cite the fossil record as evidence for change too rapid to be explained in neo-darwinian theory. Data is given here to provide estimates of the rate of evolution in hominid fossils, in living populations, and of that rate which would qualify as punctuational in the hominid fossil record. Evolution in living populations is orders of magnitude greater than that found in the fossil record and far greater than necessary to create apparently instantaneous saltations in the fossil record. It is suggested that such saltations may not represent more rapid rates of evolution but, rather, the persistence of evolutionary change in a given direction for a longer than normal period.     

Development of predictive models to identify advanced-stage cancer patients in a US healthcare claims database

BackgroundAlthough healthcare databases are a valuable source for real-world oncology data, cancer stage is often lacking. We developed predictive models using claims data to identify metastatic/advanced-stage patients with ovarian cancer, urothelial carcinoma, gastric adenocarcinoma, Merkel cell carcinoma (MCC), and non-small cell lung cancer (NSCLC).MethodsPatients with ≥1 diagnosis of a cancer of interest were identified in the HealthCore Integrated Research Database (HIRD), a United States (US) healthcare database (2010–2016). Data were linked to three US state cancer registries and the HealthCore Integrated Research Environment Oncology database to identify cancer stage. Predictive models were constructed to estimate the probability of metastatic/advanced stage. Predictors available in the HIRD were identified and coefficients estimated by Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation to control overfitting. Classification error rates and receiver operating characteristic curves were used to select probability thresholds for classifying patients as cases of metastatic/advanced cancer.ResultsWe used 2723 ovarian cancer, 6522 urothelial carcinoma, 1441 gastric adenocarcinoma, 109 MCC, and 12,373 NSCLC cases of early and metastatic/advanced cancer to develop predictive models. All models had high discrimination (C > 0.85). At thresholds selected for each model, PPVs were all >0.75: ovarian cancer = 0.95 (95% confidence interval [95% CI]: 0.94–0.96), urothelial carcinoma = 0.78 (95% CI: 0.70–0.86), gastric adenocarcinoma = 0.86 (95% CI: 0.83–0.88), MCC = 0.77 (95% CI 0.68–0.89), and NSCLC = 0.91 (95% CI 0.90 – 0.92).ConclusionPredictive modeling was used to identify five types of metastatic/advanced cancer in a healthcare claims database with greater accuracy than previous methods.     

川崎病患儿并发冠状动脉损伤的危险因素及预测模型的构建与评价

摘要 目的：分析川崎病患儿并发冠状动脉损伤（CAL）的危险因素，并构建和评价川崎病患儿并发CAL的预测模型。方法：选取2019年1月～2022年5月我院收治的342例川崎病患儿，根据是否并发CAL分为CAL组和非CAL组。收集所有患儿临床资料，采用单因素和多因素Logistic回归分析川崎病患儿并发CAL的影响因素，并构建预测模型，H-L检验和受试者工作特征（ROC）曲线检验预测模型拟合优度和对川崎病患儿并发CAL的预测价值。结果：342例川崎病患儿CAL发生率为16.67％（57/342）。单因素分析显示，CAL组发热持续时间≥10 d、静脉注射免疫球蛋白（IVIG）治疗延迟、IVIG无反应比例和单核细胞比例（MO％）、嗜酸性粒细胞比例（EO％）、C反应蛋白（CRP）、红细胞沉降率（ESR）、降钙素原（PCT）、心肌肌钙蛋白I（cTnI）、肌酸激酶同工酶（CK-MB）水平高于非CAL组（P均＜0.05）。多因素Logistic回归分析显示，发热持续时间≥10 d、IVIG治疗延迟、IVIG无反应和MO％、CRP、ESR、PCT、cTnI升高为川崎病患儿并发CAL的独立危险因素（P均＜0.05）。H-L检验川崎病患儿并发CAL的预测模型拟合效果良好。ROC曲线分析显示，该模型预测川崎病患儿并发CAL的曲线下面积（AUC）为0.911（95％CI：0.876～0.939）。结论：发热持续时间≥10 d、IVIG治疗延迟、IVIG无反应和MO％、CRP、ESR、PCT、cTnI升高为川崎病患儿并发CAL的危险因素，根据危险因素构建的川崎病患儿并发CAL预测模型价值较高。     

血脂、血小板参数、HOMA-IR与阻塞性睡眠呼吸暂停低通气综合征合并高血压患者PSG参数的相关性及其预测价值研究

摘要 目的：探讨血脂、血小板参数、稳态模型胰岛素抵抗指数（HOMA-IR）与阻塞性睡眠呼吸暂停低通气综合征（OSAHS）合并高血压患者多导睡眠图（PSG）参数的相关性及其预测价值。方法：选择2020年1月至2022年11月徐州医科大学附属沭阳医院收治的163例OSAHS患者，根据是否合并高血压将其分为单纯OSAHS组（78例）及高血压组（85例），检测两组血脂、血小板参数、HOMA-IR、PSG参数；Pearson相关性分析血脂、血小板参数、HOMA-IR与PSG参数的相关性；多因素Logistic回归分析OSAHS合并高血压的危险因素；受试者工作特征（ROC）曲线分析血脂、血小板参数、HOMA-IR预测OSAHS合并高血压的价值。结果：高血压组甘油三酯（TG）、总胆固醇（TC）、低密度脂蛋白胆固醇（LDL-C）、平均血小板体积（MPV）、HOMA-IR、微觉醒指数（MAI）、呼吸暂停低通气指数（AHI）、氧减指数（ODI）高于单纯OSAHS组（P＜0.05），高密度脂蛋白胆固醇（HDL-C）水平低于单纯OSAHS组（P＜0.05）。高血压组TG、TC、LDL-C、MPV、HOMA-IR与MAI、AHI、ODI呈正相关（P＜0.05），HDL-C与MAI、AHI、ODI呈负相关（P＜0.05）。高体质量指数、高HOMA-IR及TG、MPV水平升高是OSAHS患者合并高血压的危险因素（P＜0.05）。联合TG、MVP、HOMA-IR预测OSAHS患者合并高血压的曲线下面积高于以上三指标单独预测。结论：OSAHS合并高血压患者TG、MPV水平及HOMA-IR显著增高，且与MAI、AHI、ODI呈正相关，TG、MPV、HOMA-IR联合检测对OSAHS患者合并高血压的预测价值较高。     

自发性早产的影响因素及血清fFN、MCP-1、PAF的预测价值分析

摘要 目的：探讨自发性早产的影响因素,分析母体血清胎儿纤维连接蛋白（fFN）、单核细胞趋化蛋白1（MCP-1）、血小板激活因子（PAF）预测自发性早产的价值。方法：选取2019年6月至2020年6月于我院产科门诊产检的300例产妇为研究对象,其中45例发生自发性早产（早产组）,255例未发生自发性早产（对照组）。两组受试者均于妊娠30周采集静脉血,检测血清fFN、MCP-1、PAF水平。收集相关资料,采用多因素Logistic回归分析影响自发性早产发生的因素。受试者工作特征曲线（ROC）分析fFN、MCP-1、PAF预测自发性早产的价值。结果：单因素分析结果显示早产组体质量指数＜19 kg/m2、辅助生殖技术助孕、早产史、子宫颈手术史、妊娠间隔时间＜18个月、妊娠期并发胎膜早破、妊娠期并发宫颈缩短、妊娠期间吸烟比例、血清fFN、MCP-1、PAF水平高于对照组（P＜0.05）。多因素Logistic回归分析结果示妊娠期合并胎膜早破、宫颈缩短、早产史、fFN水平偏高、MCP-1水平偏高、PAF水平偏高等,是自发性早产的危险影响因素（OR＞1,P＜0.05）。fFN、MCP-1、PAF及其联合应用预测自发性早产的曲线下面积（AUC）(0.95CI）分别为0.736（0.480～0.990）、0.713（0.488～0.935）、0.735（0.495～0.967）、0.866（0.782～0.940）,联合应用的预测效能高于单独指标。结论：妊娠期合并胎膜早破、宫颈缩短、早产史、血清fFN、MCP-1、PAF水平升高是自发性早产的危险因素,联合fFN、MCP-1、PAF水平可预测自发性早产风险。