基于仝小林院士脏腑风湿理论探讨新型冠状病毒肺炎康复期的中药治疗

仝小林院士将新型冠状病毒肺炎定名为"寒湿疫"，并以此理论为基础制定了初期、中期、重症期及恢复期的中医治疗方案，同时基于仝院士学术理论体系中的"脏腑风湿"理论，根据恢复期 "余毒未清，正虚邪恋"的病机特点，探讨其符合具备脏腑风湿行成3个基本要素：即外受寒湿裹挟戾气为必要外因；脏腑内虚为重要基础；邪疫伏留胶着，正邪交争为致病关键。故在辨证施治中可应用脏腑风湿理论以调理脾胃，化湿透邪，补益肺脾，顾护阳气，养阴生津。     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

妊娠期甲状腺癌多学科综合治疗

妊娠期甲状腺癌的诊治是目前甲状腺疾病诊治的热点和难点问题之一。妊娠期发现甲状腺癌对临床医生和孕妇都是一个极大的挑战，应通过多学科协作，尽快明确诊断，全面、合理的评估孕妇和胎儿的情况，选择最合适的治疗和随诊方式，避免对孕妇和胎儿产生副反应，维持正常孕期，获得最佳的治疗效果。     

Preoperative Risk Factors Associated With PACU Pediatric Respiratory Complications: An Integrative Review

PurposeThis article reviews state of the science of preoperative risk factors associated with postanesthesia care unit (PACU) pediatric respiratory complications.DesignAn integrative review.MethodsA search of PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE, Scopus, Cochrane, and Joanna Briggs Institute databases was performed. Thirty-one articles, published between 2006 and 2018, were appraised for quality and the level of evidence using the Johns Hopkins Nursing Evidence-Based Practice Model.FindingsThese articles were grouped into the following categories: age, American Society of Anesthesiologists status, gender, airway comorbidities, syndromes, anomalies, pulmonary comorbidities, ethnicity, obesity, neurologic comorbidities, and cardiac comorbidities.ConclusionsEvidence identified significant preoperative and anesthesia risk factors that are associated with PACU pediatric respiratory complications. This article reveals the importance for the perioperative team to identify, assess for, communicate, and develop a management plan for pediatric respiratory complications.