Goldenmckinnon8302

PURPOSE Epilepsy is a chronic neurological disorder that is often diagnosed in childhood and may negatively impact physical, social and psychological abilities. Most tools measuring quality of life (QoL) rely on parent/caregiver feedback rather than the child's perspective. MDL-800 molecular weight CHEQOL-25 is a QoL tool that documents both child and caregiver perspectives across five domains. The primary objective was to determine the QoL of children living with epilepsy (CWE) using the CHEQOL-25 tool in a Kenyan paediatric population. Other objectives were to describe the correlation between the caregivers' and children's' perspectives and describe factors affecting QoL. METHOD We conducted a cross-sectional study across four sites in Nairobi. Quantitative data was collected using a self-administered CHEQOL-25 questionnaire. Caregivers and their children aged 7-15 years attending neurology clinics participated in the study. We used Kappa statistics to compare child and caregiver responses. RESULTS A total of 354 participants were interviewed (177 children and 177 caregivers). A good QoL was reported by 60.5 % of children with a similar caregiver perception of 56.5 %. Caregivers with little education and male caregivers were associated with a poor QoL (p = 0.01); other socio-demographic factors had little impact on the measured QoL of CWE. Parent and child questionnaires correlated well in terms of response in terms of interpersonal (p = 0.001) and intrapersonal (p = 0.004) domains. CONCLUSION This study demonstrated that a good quality of life was reported by the majority of CWE and their caregivers, although some factors such as a male caregiver gender and lower level of education were associated with poor QoL. Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI. V.OBJECTIVE Regional cortical thinning in dementia with Lewy bodies (DLB) and Parkinson disease dementia (PDD) may underlie some aspect of their clinical impairments; cortical atrophy likely reflects extensive Lewy body pathology with alpha-synuclein deposits, as well as associated Alzheimer's disease co-pathologies, when present. Here we investigated the topographic distribution of cortical thinning in these Lewy body diseases compared to cognitively normal PD and healthy non-PD control subjects, explored the association of regional thinning with clinical features and evaluated the impact of amyloid deposition. METHODS Twenty-one participants with dementia with Lewy bodies (DLB), 16 with Parkinson disease (PD) - associated cognitive impairment (PD-MCI and PDD), and 24 cognitively normal participants with PD underwent MRI, PiB PET, and clinical evaluation. Cortical thickness across the brain and in regions of interest (ROIs) was compared across diagnostic groups and across subgroups stratified by amyloid statusher the distinct topography of cortical thinning in DLB and PD-associated cognitive impairment might have value as a diagnostic and/ or outcome biomarker in clinical trials. BACKGROUND We aimed to investigate the role of angiopoietin (Angpt) as a predictive biomarker for sepsis by evaluating associations between plasma Angpt and various inflammatory cytokines and mortality in critically ill patients with sepsis. METHODS This study was a retrospective cohort study of the prospectively collected samples and clinical data of 145 patients with sepsis who were admitted to the medical intensive care unit (ICU) of a 2000-bed university tertiary referral hospital in South Korea. We collected plasma within 24 h of medical ICU admission, and several biomarkers (Angpt-1 and -2, Tie2, vascular endothelial growth factor, interleukin (IL)-1β, IL-10, IL-18, IL-6, interferon gamma-induced protein-10, and tumor necrosis factor-α) were measured using a Human Magnetic Luminex Screening Assay kit. RESULTS Plasma Angpt-2 was correlated with IL-6 (rs = 0.555) and tumor necrosis factor-α (rs = 0.559). Plasma Angpt-2 (rs = 0.530) and Angpt-2/1 (rs = 0.562) were correlated with the Sequential Organ Failure Assessment (SOFA) score. The area under the curve (AUC) for the 28-day mortality prediction for the plasma Angpt-2/1 ratio was 0.736; AUCs for the Acute Physiology and Chronic Health Evaluation II (APACHE II) and SOFA scores were 0.659 and 0.745, respectively. Using multivariate Cox proportional hazard regression analysis for 28-day mortality, we found that acute respiratory distress syndrome (hazard ratio (HR) = 2.235, 95% CI = 1.163-4.296,p = 0.016), APACHE II score (HR = 1.127, 95% CI = 1.037-1.224,p = 0.005), and Angpt-2/1 > 3.2 (HR = 2.522, 95% CI = 1.205-5.278,p = 0.014) were risk factors for 28-day mortality. CONCLUSIONS Plasma Angpt-2 was related to cytokines, but Angpt-2/1 ratio was a good predictor of 28-day mortality in patients with sepsis.