目的 探讨联合检测白细胞计数和C反应蛋白对早期诊断结肠癌术后吻合口漏的意义。方法 回顾性分析山东省菏泽市立医院胃肠外科2009～2012年期间收治的183例结肠癌患者的临床资料，其中术后未发生吻合口漏171例（无吻合口漏组），发生吻合口漏12例（有吻合口漏组），所有患者在术前和术后均无其他感染性并发症。对2组患者术前和术后白细胞计数及C反应蛋白浓度进行了观察与分析。结果 有吻合口漏组患者的平均住院时间为（35±5） d，术后死亡3例（25.0%），长于或高于无吻合口漏组的（12±2） d及5例（2.9%），P＜0.05。术后2组患者白细胞计数在发生漏早期无明显差异，有吻合口漏组患者白细胞计数在漏出现临床症状时显著升高（P＜0.05）。术后2组患者C反应蛋白浓度都较术前增高，无吻合口漏组患者在术后第3天开始逐渐降低；有吻合口漏组患者在术后第4天至第11天与无吻合口漏组患者相比明显增高（P＜0.05）。结论 C反应蛋白相对于白细胞计数在早期诊断吻合口漏方面具有更重要的意义，术后第4天以后出现的C反应蛋白下降后再次上升或持续性升高可能提示有吻合口漏发生。
ObjectiveTo explore the value of a radiomics model based on ultrasound imaging in predicting the HER-2 status of breast cancer prior to surgery.MethodsA total of 230 patients with invasive breast cancer were retrospectively analyzed, all the patients underwent preoperative breast ultrasound examination. According to the order of examination time, the patients were categorized into training group (n=115) and validation group (n=115). Image J software was used to manually delineate the lesion area in the ultrasound image along the tumor boundary. Pyradiomics was used to extract 1 820 features from each lesion area, and three statistical methods were used to screen features. A logistic regression model was used to construct ultrasound imaging radiomics model. The receive operating characteristic curve (ROC), calibration curve and decision curve were used to evaluate the performance and value of ultrasound imaging radiomics model in predicting HER-2 status.ResultsNine key image features were identified to construct ultrasound imaging radiomics model. The area of under the ROC curve of the model in the training group and the validation group were 0.82 (95%CI 0.74 to 0.90) and 0.81 (95%CI 0.72 to 0.89), respectively. The calibration curve showed that the model had a good calibration in both the training and validation groups.ConclusionsUltrasound-based imaging radiomics model is of significant value in predicting the HER-2 status of breast cancer prior to surgery.
Objective To systematically review injury, death, and their causes in elderly people in China from 2000 to 2020 and to prevent and reduce the occurrence of injuries and death. Methods The CNKI, VIP, WanFang Data, PubMed, SinoMed, and Web of Science databases were searched to collect studies on injury and death among elderly people over 60 years of age who resided in China from January 2000 to December 2020. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. A meta-analysis was then performed using R 4.1.2 software. Results A total of 41 studies with 187 488 subjects were included, including 125 million elderly individuals. The pooled injury mortality rate was 135.58/105 (95%CI 113.36/105 to 162.14/105, P<0.001). Subgroup analysis showed that male injury death (146.00/105, 95%CI 116.00 to 183.74, P=0.001) was significantly higher than that of females (127.90/105, 95%CI 102.31 to 159.88, P=0.001) and that overall injury mortality increased exponentially with age (R2=0.957), especially in those over 80 years old. The spatial distribution showed that the injury death rate in the central region was higher than that in the east and west and higher in the countryside than in the city. The time of death distribution showed that after China became an aging society (2000-2020), the time of death was significantly later than before (1990-2000). There were more than 12 types of injuries that caused death, the top three of which were falling, traffic accidents, and suicide. Conclusion From 2000 to 2020, the injury mortality rate of the elderly people in China initially increase and then slightly decrease. The phenomenon affects more men than women, especially those beyond the age of 80. Regional differences are identified, and the types of injuries that cause death are mainly falls, traffic accidents, and suicide. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
Wearable devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.