非酒精性脂肪性肝病(NAFLD),或代谢功能障碍相关的脂肪变性肝病(MASLD),是一种与超重有关的肝脏疾病,肥胖,糖尿病,和代谢综合征。非酒精性脂肪性肝炎(NASH),或代谢功能障碍相关脂肪性肝炎(MASH),是NAFLD/MASLD的一种形式,随着时间的推移而发展。虽然脂肪变性是一个突出的组织学特征,并且在显微镜下可以大致识别,NASH/MASH患者的肝活检可能表现出其他几种异常,例如门静脉和小叶区域的单核炎症,以膨胀和程序性细胞死亡(凋亡)为特征的肝细胞损伤,错误折叠的肝细胞蛋白内含物(Mallory-Denk体,MDB),巨型线粒体作为透明内含物,和纤维化。球囊性肝细胞损伤仍然是NASH/MASH的定义特征。纤维化模式的特征是窦周纤维化(“鸡丝”)和中央静脉周围纤维化的初始表达。儿童可能患有以脂肪变性为特征的进行性NAFLD/MASLD的替代形式,炎症,和纤维化,主要在肝腺泡的Rappaport1区。为了识别,合成,并分析在综合叙述回顾中使用评分评估NAFLD/MASLD的含义所产生的科学知识。文章的搜索是在2000年1月1日至2023年12月31日期间在PubMed/MEDLINE上进行的,Scopus,WebofScience,和Cochrane数据库。这个搜索得到了灰色搜索的补充,包括互联网浏览器(例如,谷歌)和教科书。以下研究问题指导了该研究:“使用评分评估NAFLD/MASLD的基本数据是什么?”选择过程的所有阶段均由作者进行。在发现的1783篇文章中,75个样本被包括在分析中,这是通过参考文献和灰色文献中的另外25篇文章实施的。分析的研究表明对肝活检进行评分的有益效果。尽管酒精性脂肪性肝炎(ASH)和NASH/MASH之间存在相似性,在肝脏酒精性疾病中看到的某些肝细胞损伤模式在NASH/MASH中不会发生,包括以脂肪性肝炎为特征的胆汁淤积,酒精性泡沫变性,和硬化性以透明坏死为主.一般来说,富含中性粒细胞的细胞浸润,突出的透明夹杂物和MDB,胆汁淤积,与NASH/MASH相比,明显的细胞周围纤维化应更有利于酒精引起的肝细胞损伤的诊断。多种分级和分期方法可在调查和临床试验中实施,每个都有优点和缺点。主要使用的系统是Brunt,NASHCRN(NASH临床研究网络),和SAF(脂肪变性,活动,和纤维化)系统。临床研究已经利用几种方法将实验室和人口统计学观察与组织学发现与快速商业化药物临床试验的最佳平台联系起来。机器学习程序(人工智能)对于开发新平台以评估当前和未来药物配方的益处至关重要。
Nonalcoholic fatty liver disease (NAFLD), or metabolic dysfunction-associated steatotic liver disease (MASLD), is a liver condition that is linked to overweight, obesity, diabetes mellitus, and metabolic syndrome. Nonalcoholic steatohepatitis (NASH), or metabolic dysfunction-associated steatohepatitis (MASH), is a form of NAFLD/MASLD that progresses over time. While steatosis is a prominent histological characteristic and recognizable grossly and microscopically, liver biopsies of individuals with NASH/MASH may exhibit several other abnormalities, such as mononuclear inflammation in the portal and lobular regions, hepatocellular damage characterized by ballooning and programmed cell death (apoptosis), misfolded hepatocytic protein inclusions (Mallory-Denk bodies, MDBs), megamitochondria as hyaline inclusions, and fibrosis. Ballooning hepatocellular damage remains the defining feature of NASH/MASH. The fibrosis pattern is characterized by the initial expression of perisinusoidal fibrosis (\"chicken wire\") and fibrosis surrounding the central veins. Children may have an alternative form of progressive NAFLD/MASLD characterized by steatosis, inflammation, and fibrosis, mainly in Rappaport zone 1 of the liver acinus. To identify, synthesize, and analyze the scientific knowledge produced regarding the implications of using a score for evaluating NAFLD/MASLD in a comprehensive narrative review. The search for articles was conducted between 1 January 2000 and 31 December 2023, on the PubMed/MEDLINE, Scopus, Web of Science, and Cochrane databases. This search was complemented by a gray search, including internet browsers (e.g., Google) and textbooks. The following research question guided the study: \"What are the basic data on using a score for evaluating NAFLD/MASLD?\" All stages of the selection process were carried out by the single author. Of the 1783 articles found, 75 were included in the sample for analysis, which was implemented with an additional 25 articles from references and gray literature. The studies analyzed indicated the beneficial effects of scoring liver biopsies. Although similarity between alcoholic steatohepatitis (ASH) and NASH/MASH occurs, some patterns of hepatocellular damage seen in alcoholic disease of the liver do not happen in NASH/MASH, including cholestatic featuring steatohepatitis, alcoholic foamy degeneration, and sclerosing predominant hyaline necrosis. Generally, neutrophilic-rich cellular infiltrates, prominent hyaline inclusions and MDBs, cholestasis, and obvious pericellular sinusoidal fibrosis should favor the diagnosis of alcohol-induced hepatocellular injury over NASH/MASH. Multiple grading and staging methods are available for implementation in investigations and clinical trials, each possessing merits and drawbacks. The systems primarily used are the Brunt, the NASH CRN (NASH Clinical Research Network), and the SAF (steatosis, activity, and fibrosis) systems. Clinical investigations have utilized several approaches to link laboratory and demographic observations with histology findings with optimal platforms for clinical trials of rapidly commercialized drugs. It is promising that machine learning procedures (artificial intelligence) may be critical for developing new platforms to evaluate the benefits of current and future drug formulations.