European Journal of Clinical Investigation最新文献

This review focuses on a special aspect of hepatic lipid storage and inflammation that occurs during nutritional excess in obesity. Mounting evidence supports that prolonged excess fatty acid (FA) uptake in the liver is strongly associated with hepatic lipid storage and inflammation and that the two processes are closely linked by a homeostatic mechanism. There is also strong evidence that bacterial lipids may enter the gut by a common mechanism with lipid absorption and that there is a set point to determine when their uptake triggers an inflammatory response in the liver. In fact, the progression from high uptake of FAs in the liver resulting in Metabolic dysfunction-associated steatotic liver disease (MASLD) to the development of the more serious Metabolic dysfunction-associated steatohepatitis (MASH) depends on the degree of inflammation and its progression from an acute to a chronic state. Thus, MASLD/MASH implicates both excess fatty acids and progressive inflammation in the aetiology of liver disease. We start the discussion by introduction of CD36, a major player in FA and lipopolysaccharide (LPS) uptake in the duodenum, liver and adipose tissue. We will then introduce CEACAM1, a major player in the regulation of hepatic de novo lipogenesis and the inflammatory response in the liver, and its dual association with CD36 in enterocytes and hepatocytes. We conclude that CEACAM1 and CD36 together regulate lipid droplet formation and inflammation in the liver.

The Fusobacterium genus comprises Gram-negative, obligate anaerobic bacteria that typically reside in the periodontium of the oral cavity, gastrointestinal tract, and female genital tract. The association of Fusobacterial spp. with colorectal tumours is widely accepted, with further evidence that this pathogen may also be implicated in the development of other malignancies. Fusobacterial spp. influence malignant cell behaviours and the tumour microenvironment in various ways, which can be related to the multiple surface adhesins expressed. These adhesins include Fap2 (fibroblast-activated protein 2), CpbF (CEACAM binding protein of Fusobacteria), FadA (Fusobacterium adhesin A) and FomA (Fusobacterial outer membrane protein A). This review outlines the influence of Fusobacteria in promoting cancer initiation and progression, impacts of therapeutic outcomes and discusses potential therapeutic interventions where appropriate.

Background: Rapid diagnosis of stroke and its subtypes is critical in early stages. We aimed to discover and validate blood-based protein biomarkers to differentiate ischemic stroke (IS) from intracerebral haemorrhage (ICH) using high-throughput proteomics.

Methods: We collected serum samples within 24 h from acute stroke (IS & ICH) and mimics patients. In the discovery phase, SWATH-MS proteomics identified differentially expressed proteins, which were validated using targeted proteomics in the validation phase. We conducted interaction network and pathway analyses using Cytoscape 3.10.0. We determined cut-off points using the Youden Index. We developed three prediction models using multivariable logistic regression analyses. We assessed the model performance using statistical tests.

Results: We included 20 IS and 20 ICH in the discovery phase and 150 IS, 150 ICH, and six stroke mimics in the validation phase. We quantified 375 proteins using SWATH-MS. Between IS and ICH, we discovered 20 differentially expressed proteins. In the validation phase, the combined prediction model including three biomarkers: GFAP (aOR 0.04; 95%CI .02-.11), MMP-9 (aOR .09; .03-.28), APO-C1 (aOR 5.76; 2.66-12.47) and clinical variables independently differentiated IS from ICH (accuracy: 92%, negative predictive value: 94%). Adding biomarkers to clinical variables improved discrimination by 26% (p < .001). Additionally, nine biomarkers differentiated IS from ICH within 6 h, while three biomarkers differentiated IS from mimics.

Conclusions: Our study demonstrated that GFAP, MMP-9 and APO-C1 biomarkers independently differentiated IS from ICH within 24 h and significantly improved the discrimination ability of prediction models. Temporal profiling of these biomarkers in the acute phase of stroke is warranted.