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RISS 인기검색어
- 검색키워드
- 저자 : Andrew Nicolaides (검색결과 2 건)
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( Danny Con ),( Bridgette Andrew ),( Steven Nicolaides ),( Daniel R Van Langenberg ),( Abhinav Vasudevan ) 대한장연구학회 2022 Intestinal Research Vol.20 No.1
Background/Aims: The residual risk of colectomy after infliximab salvage in steroid-refractory acute severe ulcerative colitis (ASUC) is required to inform the need for subsequent maintenance biologic therapy. The aim of this study was to determine the dynamic response of common serum biomarkers to infliximab salvage and assess their utility in predicting subsequent colectomy. Methods: A retrospective single-center cohort study was conducted on all patients who received infliximab salvage for steroid-refractory ASUC between January 1, 2010, and July 31, 2019. Biomarkers were assessed on admission and days 1 and 3 post infliximab, and included C-reactive protein (CRP)-albumin-ratio (CAR), CRP-lymphocyte-ratio (CLR), platelet-lym phocyte-ratio (PLR) and neutrophil-lymphocyte-ratio (NLR). Results: Of 94 patients (median age, 35 years; 67% of male), 20% required colectomy at 12 months. Biomarkers on day 3 post-infliximab best differentiated nonresponders, who had higher CRP, lower albumin and lower lymphocyte count (each P< 0.05). Day 3 predictive performance (area under the curve) for 12-month colectomy was best for CAR (0.871) and CLR (0.874), which were similar to Lindgren (0.829; P >0.05) but superior to Mayo (0.726), partial Mayo (0.719), PLR (0.719), Ho index (0.714), NLR (0.675), Travis score (0.657) and endoscopic Mayo (0.609) (each P<0.05). A day 3 CAR cutoff of 0.47 mg/g had 79% sensitivity, 80% specificity, 94% negative predictive value (NPV) to predict colectomy; while a day 3 CLR cutoff of 6.0 mg/10<sup>9</sup> had 84% sensitivity, 84% specificity, 96% NPV. Conclusions: CAR and CLR measured on day 3 post infliximab salvage for steroid-refractory ASUC represent simple and routinely performed bio markers that appear to be strong predictors of colectomy. Prospective studies are required to confirm the utility of these predic tive scores. (Intest Res 2022;20:101-113)
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Elisa Cuadrado-Godia,Pratistha Dwivedi,Sanjiv Sharma,Angel Ois Santiago,Jaume Roquer Gonzalez,Mercedes Balcells,John Laird,Monika Turk,Harman S. Suri,Andrew Nicolaides,Luca Saba,Narendra N. Khanna,Jas 대한뇌졸중학회 2018 Journal of stroke Vol.20 No.3
Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer’s and Parkinson’s disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.
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