自身免疫性胃炎(autoimmune gastritis)又称A型胃炎,是一种主要以胃体萎缩为主要特征的CD4+ T细胞介导的自身免疫性疾病。虽然其临床表现呈现多样化及非特异性,但近些年来我国AIG的发病率并不低。为增加临床医生对自身免疫性胃炎的认识及给临床医生提出诊疗疾病的目标和对策,本文将自身免疫性胃炎的流行病学、发生机理、临床表现、症状和治疗方式做了总结。 Autoimmune gastritis (AIG), also known as type A gastritis, is a CD4+ T-cell-mediated autoimmune disease mainly characterized by atrophy of the gastric body. Although its clinical manifestations are diverse and non-specific, the incidence of AIG in China has not been low in recent years. In order to raise the attention of clinicians to autoimmune gastritis and to provide clinicians with directions and strategies for diagnosis and treatment of this disease, this paper reviews the epidemiology, pathogenesis, clinical manifestations, diagnosis, and therapeutic progress of autoimmune gastritis.
原发性胆汁性肝硬化(Primary Biliary Cholangitis, PBC)和自身免疫性肝炎(Autoimmune Hepatitis, AIH)是不同的自身免疫性慢性肝病,这2种疾病在同一患者中共存称为重叠综合征。干燥综合征(Sjögren Syndrome, SS)是自身免疫性肝病(Autoimmune Liver Disease, AILD)最常并发的肝外自身免疫病。本文报告了一例AIH-PBC重叠综合征合并SS的病例,探讨其临床表现、诊断和治疗策略。Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are two common clinical autoimmune liver diseases, and some patients have both diseases;this feature is called AIH-PBC overlap syndrome. Sjögren syndrome is one of the most common extrahepatic autoimmune diseases among patients with autoimmune liver diseases. This article presents the case report of an elderly female patient who was diagnosed with AIH-PBC overlap syndrome combined with Sjögren syndrome, and discusses its clinical presentation, diagnosis and treatment strategies.
腹膜炎是全球重症监护病房患者败血症的第二大死亡原因,脓毒血症的早期预测对于及时干预并最终改善预后至关重要。本研究基于新型的机器学习算法,建立并验证腹膜炎患者发展为脓毒血症的预测模型,研究结果提示机器学习模型可以成为预测腹膜炎患者预测脓毒血症的可靠工具,并且,随机森林算法模型具有最佳的预测性能,这种机器学习方法可用于帮助临床医生对于高风险因素的认识并早期干预以降低死亡率。Peritonitis is the second leading cause of sepsis-related mortality in intensive care unit (ICU) patients worldwide. Early prediction of sepsis is critical for timely intervention and ultimately improving prognosis. This study established and validated a predictive model for the development of sepsis in peritonitis patients using novel machine learning algorithms. The findings suggest that machine learning models can be a reliable tool for predicting sepsis in peritonitis patients. Among them, the random forest algorithm model showed the best predictive performance. This machine learning approach can help clinicians recognize high-risk factors and intervene early to reduce mortality.
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