Inhibition of pluripotential embryonic stem cell differentiation by purified polypeptides

Murine embryonic stem (ES) cells are pluripotent cell lines established directly from the early embryo which can contribute differentiated progeny to all adult tissues, including the germ-cell lineage, after re-incorporation into the normal embryo. They provide both a cellular vector for the generation of transgenic animals and a useful system for the identification of polypeptide factors controlling differentiation processes in early development. In particular, medium conditioned by Buffalo rat liver cells contains a polypeptide factor, ES cell differentiation inhibitory activity (DIA), which specifically suppresses the spontaneous differentiation of ES cells in vitro, thereby permitting their growth as homogeneous stem cell populations in the absence of heterologous feeder cells. ES cell pluripotentiality, including the ability to give rise to functional gametes, is preserved after prolonged culture in Buffalo rat liver media as a source of DIA. Here, we report that purified DIA is related in structure and function to the recently identified hematopoietic regulatory factors human interleukin for DA cells and leukaemia inhibitory factor. DIA and human interleukin DA/leukaemia inhibitory factor have thus been identified as related multifunctional regulatory factors with distinct biological activities in both early embryonic and hematopoietic stem cell systems.     

巨淋巴结增生症的认识

目的提高对巨淋巴结增生症（GLNH）的认识。方法通过1例经手术及病理证实的GLNH的病例分析，结合复习文献，对本病的影像诊断与鉴别诊断进行讨论。结果GLNH是一种异源性的罕见的淋巴组织增生性良性疾病，其组织形态上可分为透明血管型和浆细胞型两类。前者多见，常表现为纵隔病变，也有报告表现为体表的淋巴结无疼性肿大；后者好发于肠系膜和腹膜后淋巴结，增强后有不同程度的强化。结论GLNH具有特征性的CT表现，CT具有较高的诊断价值，在动脉期明显强化，延迟期持续强化。但确诊还得靠病理证实。     

巨淋巴结增生症的认识

目的 提高对巨淋巴结增生症(GLNH)的认识.方法 通过1例经手术及病理证实的GLNH的病例分析.结合复习文献,对本病的影像诊断与鉴别诊断进行讨论.结果 GLNH是一种异源性的罕见的淋巴组织增生性良性疾病,其组织形态上可分为透明血管型和浆细胞型两类.前者多见,常表现为纵隔病变,也有报告表现为体表的淋巴结无疼性肿大;后者好发于肠系膜和腹膜后淋巴结,增强后有不同程度的强化.结论 CLNH具有特征性的CT表现,CT具有较高的诊断价值,在动脉期明显强化,延迟期持续强化.但确诊还得靠病理证实.     

基于云模型聚类的淋巴结图像增强

淋巴结的病理变化对检测恶性肿瘤和判断肿瘤转移是一个重要依据,因此淋巴结图像的质量对医生进行病理分析非常重要.文章将云模型与峰值阈值法相结合,自动获得模糊C均值(FCM)聚类的初始聚类中心,大大提升FCM的聚类效果,也较好地保留淋巴结图像的边缘细节,减轻离散频率分布图的离散度的影响.实验结果表明,文章的方法使图像中模糊目标区域的边缘更清晰.