腹壁子宫内膜异位症恶变的病例罕见,其发病机制尚未明确,利用基因检测可以为研究提供新方向,进一步完善诊断标准及治疗方案。本文报道1例腹壁子宫内膜透明细胞癌,并复习相关文献来介绍该疾病,以及了解其临床特征、诊断及鉴别诊断、治疗、预后因素,以提高该疾病的诊疗水平。The malignant transformation of abdominal endometriosis is rare, and its pathogenesis is not yet clear. The use of genetic detection can provide a new direction for research, and further improve the diagnostic criteria and treatment plan. This paper reports a case of clear cell carcinoma of abdominal wall endometrial, reviews relevant literature to introduce the disease, and understands its clinical features, diagnosis, differential diagnosis, treatment and prognostic factors, in order to improve the level of diagnosis and treatment of this disease.
类器官(organoids)是通过细胞分选和三维培养建立的创新体外模型,在妇科疾病建模、药物筛选及肿瘤个体化治疗中展现出重要价值。作为全球高发的妇科恶性肿瘤,子宫内膜癌(endometrial cancer)的发病率逐年上升且呈年轻化趋势。尽管类器官技术尚未进入临床转化阶段,但其为探索该疾病的精准治疗提供了全新平台。本文综述类器官在子宫内膜癌研究中的最新进展。Organoids are innovative in vitro models established through cell sorting and three-dimensional culture, demonstrating significant value in gynecological disease modeling, drug screening, and personalized cancer treatment. As a highly prevalent gynecological malignancy worldwide, the incidence of endometrial cancer is increasing year by year and showing a trend of younger age of onset. Although the organoid technology has not yet reached the stage of clinical translation, it provides a new platform for exploring the precise treatment of this disease. This paper reviews the latest progress of organoids in the research of endometrial cancer.
急性缺血性脑卒中(AIS)是一种严重影响人类健康相关的疾病,其拥有高发病率和高死亡率,并与吸烟、高脂饮食等不良生活习惯相关,人工智能(AI)比如机器学习(ML)和深度学习(DL),可以实现从临床及辅助检查尤其是成像学检查中提取特征数据,经过算法处理,得出可信结果。近几年AI更多地应用于医院系统的工作中,并成为临床工作及科研项目有力的帮手。本文全面综述了AI预测急性缺血性脑卒中(AIS)患者在经过血管内治疗,尤其是经过血栓切除术治疗后的预后情况,从而实现精准有效的临床管理和护理决策。此外,本文还批判性地评估了现有研究的局限性,并且指出了新的研究方向,最终目标是提高AIS患者的生存率。Acute ischemic stroke (AIS) is a serious human health-related disease that is characterized by elevated morbidity and mortality rates. It is often linked to detrimental lifestyle behaviors, including smoking and high-fat dietary intake. The advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) methodologies, facilitates the extraction and analysis of feature data derived from clinical and ancillary assessments, particularly imaging studies. These data are processed through sophisticated algorithms to yield reliable outcomes. In recent years, AI has been increasingly integrated into hospital systems, emerging as a formidable tool in both clinical practice and research initiatives. This paper presents a comprehensive analysis of AI applications in predicting the prognosis of acute ischemic stroke (AIS) patients following endovascular interventions, with a particular focus on thrombectomy procedures. The objective is to enhance the accuracy and efficacy of clinical management and care decision-making processes. Furthermore, the study critically examines the limitations inherent in current research and identifies prospective avenues for future investigation, ultimately aiming to improve the survival outcomes of AIS patients.
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