Sulfiredoxin-1 blood mRNA expression levels negatively correlate with hippocampal atrophy and cognitive decline.

Introduction: Cognitive decline, correlating with hippocampal atrophy, characterizes several neurodegenerative disorders having a background of low-level chronic inflammation and oxidative stress. Methods: In this cross-sectional study, we examined how cognitive decline and hippocampal subfields volume are associated with the expression of redox and inflammatory genes in peripheral blood. We analyzed 34 individuals with different cognitive scores according to Mini-Mental State Examination, corrected by age and education (adjMMSE). We identified a group presenting cognitive decline (CD) with adjMMSE<27 (n=14) and a normal cognition (NC) group with adjMMSE≥27 (n=20). A multiparametric approach, comprising structural magnetic resonance imaging measurement of different hippocampal segments and blood mRNA expression of redox and inflammatory genes was applied. Results: Our findings indicate that hippocampal segment volumes correlate positively with adjMMSE and negatively with the blood transcript levels of 19 genes, mostly redox genes correlating especially with the left subiculum and presubiculum. A strong negative correlation between hippocampal subfields atrophy and Sulfiredoxin-1 ( SRXN1) redox gene was emphasized. Conclusions: Concluding, these results suggest that SRXN1 might be a valuable candidate blood biomarker for non-invasively monitoring the evolution of hippocampal atrophy in CD patients.

A deformable model for tracking tumors across consecutive imaging studies.

OBJECTIVE: A deformable registration technique was developed and evaluated to track and quantify tumor response to radiofrequency ablation for patients with liver malignancies. MATERIALS AND METHODS: The method uses the combined power of global and local alignment of pre- and post-treatment computed tomography image data sets. The strategy of the algorithm is to infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). Using this framework, the major challenge for tracking tumor location is not the tissue mechanical properties for FEM modeling but rather the evaluation of boundary conditions. Three different methods were systematically investigated to automatically determine the boundary conditions defined by the correspondences on liver surfaces. RESULTS: Using both 2D synthetic phantoms and imaged 3D beef liver data we performed gold standard registration while measuring the accuracy of non-rigid deformation. The fact that the algorithms could support mean displacement error of tumor deformation up to 2 mm indicates that this technique may serve as a useful tool for surgical interventions. The method was further demonstrated and evaluated using consecutive imaging studies for three liver cancer patients. CONCLUSION: The FEM-based surface registration technique provides accurate tracking and monitoring of tumor and surrounding tissue during the course of treatment and follow-up.

Non-rigid registration of the liver in consecutive CT studies for assessment of tumor response to radiofrequency ablation.

This paper introduces a non-rigid registration approach for tracking tumor response to radiofrequency ablation (RFA) across consecutive imaging studies. The method described here exploits the combined power of global and local alignment of pre- and post-treatment CT liver images for a given patient. The distinguishing characteristics of the system is that it can infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). This technique may provide valuable information that could be beneficial in a range of surgical interventions as well as for the purposes of monitoring tissue response and therapy planning.