BackgroundApart from endoscopic interventions, readily attainable cost-effective biomarkers for ulcerative colitis (UC) assessment are required. For this purpose, we evaluated differential leucocytic ratio, mainly neutrophil–lymphocyte ratio (NLR) and lymphocyte-monocyte ratio (LMR) as simple available indicators of disease activity in patients with ulcerative colitis.MethodsStudy conducted on 80 UC patients who were classified into two groups of 40 each according to Mayo score and colonoscopic findings. Group 1 (active UC) and group 2 (inactive UC). Another 40 group-matched healthy participants were enrolled. White blood cell count, NLR, LMR, C-reactive protein, and Erythrocyte sedimentation rate were measured and recorded.ResultsSignificant elevation of NLR was observed in active UC group compared to inactive UC and controls (2.63 ± 0.43, 1.64 ± 0.25, 1.44 ± 0.19 respectively; p < 0.0001). The optimal NLR cut-off value for active UC was > 1.91, with a sensitivity and a specificity of 90% and 90% respectively. The mean LMRs of active UC was significantly lower compared with inactive UC patients and controls (2.25 ± 0.51, 3.58 ± 0.76, 3.64 ± 0.49 respectively; p < 0.0001). The cut-off value of LMR for determining the disease activity was ≤ 2.88 with a sensitivity of 90% and a specificity of 90%. NLR, LMR, and CRP were found to be significant independent markers for discriminating disease activity (p = 0.000). Besides, NLR was significantly higher in patients with pancolitis and positively correlated with endoscopically severe disease.ConclusionNLRs and LMRs are simple non-invasive affordable independent markers of disease activity in UC.

A single baseline measurement of uNGAL level has the ability to determine type of kidney dysfunction in cirrhotic patients, perhaps accelerating management decisions and improving outcomes.

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning‐based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac‐induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30–45 years) and 22 patients (15 males; age range 25–50 years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3‐F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross‐validation of the method showed an accuracy of 83.7% (specificity = 86.6%, sensitivity = 81.8%).

Isolated HVs occlusion was the most common pattern of vascular involvement in Egyptian patients with primary BCS. Vascular pattern of involvement affected the clinical presentation and was related to the underlying thrombophilia in those patients.

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