双源CT多定量参数对胃癌的诊断价值及与HER2的相关性

目的:探讨门脉期双源CT多个定量参数与胃腺癌病理分化程度及HER2的相关性。方法: 回顾性分析2018年7月至2019年4月间于陕西省人民医院行双源CT双能量扫描的48例经胃镜活检(21例)或手术病理证实（27例）的胃腺癌及30例正常胃的影像学资料，其中27例HER2指标明确，通过西门子第二代双源CT扫描获得静脉期双能量图像，利用syngo.via软件获得曲线斜率、门脉期碘浓度、标准化碘浓度；将患者分为胃腺癌与正常胃壁组，高、中、低分化胃腺癌组，HER2阳性组（+，++，+++）与HER2阴性组（-）。统计学方法采用Kappa一致性检验、ROC曲线法、两独立样本t检验及方差分析。结果:活检与术后病理结果具有较强的一致性（Kappa系数为0.701），两者无明显差异；胃腺癌与正常胃壁两组间能谱曲线斜率（1.35±0.24、2.19±0.71）及标准化碘浓度（0.31±0.079、0.54±0.157）均具有统计学意义（P<0.05），曲线下面积分别为0.992、0.919；低分化、中分化及高分化胃腺癌能谱曲线斜率值（3.07±0.67，2.63±0.57，2.01±0.39）组间及组内差异均具有统计学意义（P<0.05），低分化、中分化及高分化胃腺癌门脉期标准化碘浓度(0.60±0.167，0.52±0.089，0.36±0.039)组间差异具有统计学意义（P<0.05），中分化组与低分化组差异无统计学意义（P>0.05），高分化组与中、低分化组均具有统计学差异（P<0.05）。HER2阳性组与阴性组的能谱曲线斜率及标准化碘浓度值无统计学差异（P>0.05）。结论:能谱曲线斜率及门脉期标准化碘浓度值有助于对胃腺癌进行诊断并推测病理分化程度；双源CT定量参数与免疫组化指标HER2无相关性。     

Machine learning sleep duration classification in Preschoolers using waist-worn ActiGraphs

ObjectiveTo create a sleep duration classification technique for waist-worn ActiGraph accelerometers in preschool-aged children.MethodsChildren wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days (24 h/day). Ground truth nap, sleep, and wake were estimated through visual inspection of accelerometer data, guided by sleep log-sheets and previously published visual inspection heuristics. Raw accelerometer data (30Hz) were used to generate 144 features aggregated to 1-min epochs. Machine learning classification (ie, Random Forest and Hidden Markov Modeling [HMM]) predicted nap, sleep, and wake. A simplified prediction formula was also created using features (n = 10) with the highest mean decrease in Gini index during training of Random Forests, and temporally smoothed with rolling median calculations.ResultsChildren (n = 89, mean age = 4.5 years, 67% boys) contributed >600,000 min of accelerometer data. Overall classification accuracy of the Random Forest and HMM classifier was 96.2% (95%CI: 96.1, 96.2%), with a Kappa score of 0.93. Additionally, overall classification accuracy for the temporally smoothed simplified formula was 93.7% (95%CI: 93.6, 93.7%) with Kappa = 0.87. Nap prediction accuracy was 99.8% for the final machine learning model, and 86.1% for the simplified formula. For participant-level daily summaries, generally small but statistically significant differences were found between machine learning and ground truth behaviour predictions, whereas non-significant differences were found between the simplified formulas and ground truth predictions.ConclusionsPredictions for both machine learning and the simplified formula had almost perfect agreement with visual inspection ground truth measurements. Future research is needed to confirm these findings using polysomnography ground truth sleep measurements.     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.     

Toll-like receptor 2 expression on monocytes and microvascular complications in type 2 diabetic patients

Diabetes mellitus (DM) is a chronic debilitating illness, and atherosclerotic changes are inevitable and usually neglected during the follow-up of diabetic patients. Toll-like receptor 2 (TLR2) is under trial in many studies to hold responsibility for atherosclerosis process progression as they suggest a malfunction of these receptors expressed on monocytes in diabetic patients. This study aimed to assess the association between the TLR2 and type 2 diabetes mellitus (T2DM) in Egyptian diabetic patients and to investigate its relationship with some diabetic complications.MethodsThis study included a 60 diabetic patients group 1 (diabetic complicated), group 2 (diabetic non-complicated) and 30 age-matched normal healthy blood donors.ResultsToll-like receptors (TLRs) expression was significantly associated with T2DM. In this study, the mean fluorescent intensity (MFI) of TLR2 was 596.9 ± 84.78 in group 1, 326.23 ± 62.98 in group 2 while in group 3 it was 208.47 ± 156.73. There was a significant correlation between MFI of TLR2 and random blood sugar (RBS) and glycated haemoglobin (HbA1c) (p < 0.05).ConclusionTLR2 was overexpressed in diabetic patients with microvascular complications compared to diabetic non-complicated patients and normal healthy controls.     

康艾注射液联合OFL方案治疗胃癌随机对照多中心临床试验

 目的 探讨康艾注射液联合奥沙利铂（OXA）+氟尿嘧啶（5-Fu）+亚叶酸钙（LV）（OFL方案）治疗胃癌的临床疗效及安全性。方法 采用多中心、随机、对照的研究方式对全国五所医院在2012年3月至2015年6月期间收治的300例胃癌患者进行研究。随机将患者分成对照组和试验组，对照组予以OFL方案进行治疗，试验组在对照组基础上加用康艾注射液，治疗结束后比较两组患者近远期疗效以及治疗过程中不良反应发生情况。结果 （1）试验组的近期临床疗效（35.62%）明显高于对照组（22.86%）（P<0.05）；（2）治疗后试验组患者生活质量明显优于对照组（P<0.05）；（3）对照组和试验组PFS比较差异无统计学意义（25.89月vs. 26.35月）（P>0.05），两组患者3年生存率比较差异无统计学意义（P>0.05）；（4）试验组患者神经毒性和消化道不良反应发生率、血小板下降发生率均明显低于对照组（P<0.05）。结论 康艾注射液联合OFL方案可明显提高胃癌患者的近期临床疗效，减轻化疗不良反应。     

肾嫌色细胞癌与不典型透明细胞癌的CT纹理分析研究

目的研究影像组学方法在肾嫌色细胞癌和强化方式不典型的透明细胞癌二者中的应用。方法搜集行肾动脉CTA扫描的108例肾细胞癌患者的临床资料及影像学图像。应用影像组学中的Lasso回归统计方法和机器学习中的随机森林算法提取病例的CTA图像特征并使计算机学习,通过20次重复试验得到平均诊断准确率。患者的临床特征处理采用SPSS 20.0软件,计量资料用t检验,计数资料用χ^2检验,P<0.05为差异具有统计学意义。结果108例肾细胞癌中,透明细胞癌57例,嫌色细胞癌51例。两组病例临床特征中的性别和吸烟史差异具有统计学意义(P<0.05),透明细胞癌更多见于吸烟的男性患者。放射科医师对两组病例诊断的平均准确性为(45.42±3.32)%,低于Lasso回归(76.5±12.26)%和随机森林算法(78.5±6.3)%。在两组病例中,随机森林算法给出的总准确性、对嫌色细胞癌诊断的特异性要高于Lasso回归,Lasso回归对透明细胞癌诊断的敏感性高于随机森林算法。结论影像组学方法可以对肾嫌色细胞癌及透明细胞癌做出有效的鉴别诊断,且诊断能力高于放射科医师的能力。影像组学作为一种新兴的研究方法,有望为医学发展带来重要变革。     

高渗盐水和甘露醇在动脉瘤性蛛网膜下腔出血患者降低颅内压中的应用

目的比较3%高渗盐水和20%甘露醇治疗重症动脉瘤性蛛网膜下腔出血所致颅内压增高的疗效.方法25例动脉瘤性蛛网膜下腔出血患者出现颅内压增高事件时, 随机交替接受等渗透剂量的160 mL 3%高渗盐水与150 mL 20%甘露醇进行降低颅内压治疗, 连续监测患者颅内压、平均动脉压、脑灌注压及中心静脉压.记录有效降低颅内压持续时间、颅内压最大降幅及其时间, 用药前及用药后1 h、3 h血钠水平及血浆渗透压.结果3%高渗盐水和20%甘露醇均可降低颅内压(均 P < 0.01), 两者的降低颅内压作用持续时间及颅内压降幅差异均无统计学意义(均 P >0.05).患者脑灌注压较用药前均上升(均 P < 0.01), 平均动脉压先上升后下降, 但差异无统计学意义( P >0.05).患者中心静脉压稍有波动, 但差异均无统计学意义(均 P >0.05).20%甘露醇治疗后患者血钠下降, 3%高渗盐水治疗后患者血钠值上升, 变化均有统计学意义(均 P < 0.05).20%甘露醇及3%高渗盐水治疗后患者血浆渗透压均先上升后下降, 变化均有统计学意义(均 P < 0.01). 结论3%高渗盐水可作为治疗动脉瘤性蛛网膜下腔出血所致颅内压增高患者的一线治疗药物.     

Azoles versus conventional amphotericin B for the treatment of candidemia: A meta-analysis of randomized controlled trials

Because exclusive use of echinocandins can induce the drug-resistant strains, appropriate use of azoles and polyenes is still necessary in the treatment of candidemia. In this study, we conducted a meta-analysis of randomized controlled trials regarding the efficacy and safety of azole and polyene antifungals in the treatment of candidemia. MEDLINE and the Cochrane Register of Controlled Trials were used as reference databases, and papers published up to June 10, 2019 were searched. The search results were carefully scrutinized, duplicate references were removed, and the study was ultimately carried out using three reports. Among azole antifungals, fluconazole and voriconazole were extracted, however; only conventional amphotericin B (AMPH-B) was extracted among polyene antifungals. Treatment successes with the use of azoles and AMPH-B were compared, and findings showed that AMPH-B was significantly superior (RR = 0.90, 95% CI 0.82–1.00, p = 0.04). However, there was no significant difference in mortality (RR = 0.87, 95% CI 0.72–1.07, p = 0.19). Analysis of adverse events showed that renal disorders were significantly less common with azoles than with AMPH-B (RR = 0.26, 95% CI 0.10–0.68, p = 0.006). In conclusion, AMPH-B were superior to azoles in terms of efficacy, but had a risk of causing renal disorders.