Computation of an MRI brain atlas from a population of Parkinson’s disease patients

Abstract Parkinson’s Disease (PD) is a degenerative disorder of the brain. This study presents an MRI-based brain atlas of PD to characterize associated alterations for diagnostic and interventional purposes. The atlas standardizes primarily the implicated subcortical regions such as the globus pallidus (GP), substantia nigra (SN), subthalamic nucleus (STN), caudate nucleus (CN), thalamus (TH), putamen (PUT), and red nucleus (RN). The data were 3.0 T MRI brain images from 16 PD patients and 10 matched controls. The images used were T1-weighted ( T 1 w ), T2-weighted ( T 2 w ) images, and Susceptibility Weighted Images (SWI). The T1w images were the reference for the inter-subject non-rigid registration available from 3DSlicer. Anatomic labeling was achieved with BrainSuite and regions were refined with the level sets segmentation of ITK-Snap. The subcortical centers were analyzed for their volume and signal intensity. Comparison with an age-matched control group unravels a significant PD-related T1w signal loss in the striatum (CN and PUT) centers, but approximately a constant volume. The results in this study improve MRI based PD localization and can lead to the development of novel biomarkers

Entrepreneurial sons, patriarchy and the Colonels' experiment in Thessaly, rural Greece

Existing studies within the field of institutional entrepreneurship explore how entrepreneurs influence change in economic institutions. This paper turns the attention of scholarly inquiry on the antecedents of deinstitutionalization and more specifically, the influence of entrepreneurship in shaping social institutions such as patriarchy. The paper draws from the findings of ethnographic work in two Greek lowland village communities during the military Dictatorship (1967–1974). Paradoxically this era associated with the spread of mechanization, cheap credit, revaluation of labour and clear means-ends relations, signalled entrepreneurial sons’ individuated dissent and activism who were now able to question the Patriarch’s authority, recognize opportunities and act as unintentional agents of deinstitutionalization. A ‘different’ model of institutional change is presented here, where politics intersects with entrepreneurs, in changing social institutions. This model discusses the external drivers of institutional atrophy and how handling dissensus (and its varieties over historical time) is instrumental in enabling institutional entrepreneurship

Metabolic Signatures of Lung Cancer in Biofluids: NMR-Based Metabonomics of Blood Plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.ERDF - Competitive Factors Thematic Operational ProgrammeFCT/PTDC/ QUI/68017/2006FCOMP-01-0124-FEDER-007439SFRH/BD/ 63430/2009National UNESCO Committee - L'Oréal Medals of Honor for Women in Science 200Portuguese National NMR Network - RNRM

Outcome Prediction in Cerebral Venous Thrombosis: The IN-REvASC Score.

BACKGROUND We identified risk factors, derived and validated a prognostic score for poor neurological outcome and death for use in cerebral venous thrombosis (CVT). METHODS We performed an international multicenter retrospective study including consecutive patients with CVT from January 2015 to December 2020. Demographic, clinical, and radiographic characteristics were collected. Univariable and multivariable logistic regressions were conducted to determine risk factors for poor outcome, mRS 3-6. A prognostic score was derived and validated. RESULTS A total of 1,025 patients were analyzed with median 375 days (interquartile range [IQR], 180 to 747) of follow-up. The median age was 44 (IQR, 32 to 58) and 62.7% were female. Multivariable analysis revealed the following factors were associated with poor outcome at 90- day follow-up: active cancer (odds ratio [OR], 11.20; 95% confidence interval [CI], 4.62 to 27.14; P<0.001), age (OR, 1.02 per year; 95% CI, 1.00 to 1.04; P=0.039), Black race (OR, 2.17; 95% CI, 1.10 to 4.27; P=0.025), encephalopathy or coma on presentation (OR, 2.71; 95% CI, 1.39 to 5.30; P=0.004), decreased hemoglobin (OR, 1.16 per g/dL; 95% CI, 1.03 to 1.31; P=0.014), higher NIHSS on presentation (OR, 1.07 per point; 95% CI, 1.02 to 1.11; P=0.002), and substance use (OR, 2.34; 95% CI, 1.16 to 4.71; P=0.017). The derived IN-REvASC score outperformed ISCVT-RS for the prediction of poor outcome at 90-day follow-up (area under the curve [AUC], 0.84 [95% CI, 0.79 to 0.87] vs. AUC, 0.71 [95% CI, 0.66 to 0.76], χ2 P<0.001) and mortality (AUC, 0.84 [95% CI, 0.78 to 0.90] vs. AUC, 0.72 [95% CI, 0.66 to 0.79], χ2 P=0.03). CONCLUSIONS Seven factors were associated with poor neurological outcome following CVT. The INREvASC score increased prognostic accuracy compared to ISCVT-RS. Determining patients at highest risk of poor outcome in CVT could help in clinical decision making and identify patients for targeted therapy in future clinical trials

Body Fat Free Mass Is Associated with the Serum Metabolite Profile in a Population-Based Study

To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study. Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs). We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism. A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network