埃索美拉唑三联疗法治疗幽门螺杆菌相关性胃溃疡疗效的回顾性研究

摘 要 目的:探讨埃索美拉唑三联疗法治疗幽门螺杆菌（Hp）相关性胃溃疡的疗效。方法：采用回顾性研究方法,72例Hp相关性胃溃疡患者根据治疗方案的不同分为研究组和对照组两组,对照组患者给予奥美拉唑、莫西沙星、阿莫西林/克拉维酸钾治疗,研究组患者给予埃索美拉唑、莫西沙星、阿莫西林/克拉维酸钾治疗。两组疗程均为3周。观察两组患者治疗前后血清炎症因子水平变化;比较两组患者治疗前后临床症状评分平均值、Hp根除率、不良反应发生率、溃疡临床症状改善情况及临床疗效。结果：治疗后,两组患者的血清C反应蛋白、白细胞介素6水平以及临床症状评分平均值均较治疗前显著降低（P<0.05）,且研究组各项结果均低于对照组（P<0.05）。研究组患者的Hp根除率、临床症状改善的总有效率、临床疗效总有效率均显著高于对照组（P<0.05）,两组药品不良反应发生率比较,差异无统计学意义（P>0.05）。结论：埃索美拉唑三联疗法治疗Hp相关性胃溃疡疗效显著,Hp根除率高,能有效改善患者临床症状,降低血清炎症因子水平,且安全性高,适合临床推广应用。     

RETRACTED ARTICLE: Relationship between inflammatory cytokines and risk of depression,and effect of depression on the prognosis of high grade glioma patients

Journal of Neuro-Oncology - Depression is found to be associated with up-regulation of inflammatory cytokines. However, the relationship in high grade glioma (HGG) patients is still unclear. In...     

Uncovering neural distinctions and commodities between two creativity subsets: A meta‐analysis of fMRI studies in divergent thinking and insight using activation likelihood estimation

The dual‐process theory that two different systems of thought coexist in creative thinking has attracted considerable attention. In the field of creative thinking, divergent thinking (DT) is the ability to produce multiple solutions to open‐ended problems in a short time. It is mainly considered an associative and fast process. Meanwhile, insight, the new and unexpected comprehension of close‐ended problems, is frequently marked as a deliberate and time‐consuming thinking process requiring concentrated effort. Previous research has been dedicated to revealing their separate neural mechanisms, while few studies have compared their differences and similarities at the brain level. Therefore, the current study applied Activation Likelihood Estimation to decipher common and distinctive neural pathways that potentially underlie DT and insight. We selected 27 DT studies and 30 insight studies for retrospective meta‐analyses. Initially, two single analyses with follow‐up contrast and conjunction analyses were performed. The single analyses showed that DT mainly involved the inferior parietal lobe (IPL), cuneus, and middle frontal gyrus (MFG), while the precentral gyrus, inferior frontal gyrus (IFG), parahippocampal gyrus (PG), amygdala (AMG), and superior parietal lobe were engaged in insight. Compared to insight, DT mainly led to greater activation in the IPL, the crucial part of the default mode network. However, insight caused more significant activation in regions related to executive control functions and emotional responses, such as the IFG, MFG, PG, and AMG. Notably, the conjunction analysis detected no overlapped areas between DT and insight. These neural findings implicate that various neurocognitive circuits may support DT and insight.     

Using nomogram,decision tree,and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study

The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then used the ESD set as an external cohort to evaluate the models’ performance. In the gastrectomy set, multivariate analysis showed that gender (P=0.008), year when diagnosed (2006-2010 year, P=0.265; 2011-2015 year, P=0.001; and 2016-2020 year, P<0.001, respectively), tumor size (2-4 cm, P=0.001; and ≥4 cm, P<0.001, respectively), tumor grade (poorly-moderately, P=0.016; moderately, P<0.001; well-moderately, P<0.001; and well, P<0.001, respectively), vascular invasion (P<0.001), and pT stage (P<0.001) were independent risk factors for LNM in early gastric cancer. The area under the curve (AUC) for the validation set using the nomogram, Decision Tree, Naive Bayes, and FCNN models were 0.78, 0.76, 0.77, and 0.79, respectively. In conclusion, our multi-cohort study systematically investigated different LNM prediction methods for patients with early gastric cancer. These models were validated and shown to be reliable with AUC>0.76 for all. Specifically, the FCNN model showed the most accurate prediction of LNM risks in early gastric cancer patients with AUC=0.79. Based on the FCNN model, patients with LNM rates of >4.77% are strong candidates for gastrectomy rather than ESD surgery.