Thermal management of an interventional medical device with double layer encapsulation

This paper presents a thermal study of a double-layer encapsulation for an interventional medical device, which operates temporarily inside the human esophagus for cardiac imaging. The surface temperature of test samples, representing the device, was assessed by experiments and numerical simulations. The test samples consisted of a heat source, a heat sink and a double-layer encapsulation consisting of a 3D printed biocompatible polymer (thickness 0.9 mm), with an electroplated Cu inner layer (0, 10, 80 or 150 µm thick). The surface temperature of test samples was measured in a tissue-mimicking thermal phantom at 37°C, with different heat source power levels. Experimental results showed that the maximum steady-state surface temperature could be reduced significantly by a 10 µm thick Cu layer (compared to no Cu layer). Increasing the Cu layer thickness further had a rather small effect, at least for low power levels. The maximum steady-state surface temperature was an exponential function of the Cu layer thickness. Test samples with a Cu electroplated polymer encapsulation and a heat source power of 0.5 W satisfied the maximum temperature limit for thermal safety (43°C) when the Cu layer was thicker than about 80 µm. Simulated surface temperatures were in good agreement with experimental values, for a model using two different thermal contact conductance coefficients (for different materials) for the sample-phantom boundary condition. The simulation model was also used to suggest alternative materials for the outer layer of an encapsulation with a metal inner layer, for reducing the surface temperature.publishedVersio

Tick-transmitted co-infections among erythema migrans patients in a general practice setting in Norway:a clinical and laboratory follow-up study

Background Erythema migrans (EM) is the most common manifestation of Lyme borreliosis. Here, we examined EM patients in Norwegian general practice to find the proportion exposed to tick-transmitted microorganisms other than Borrelia, and the impact of co-infection on the clinical manifestations and disease duration. Methods Skin biopsies from 139/188 EM patients were analyzed using PCR for Neoehrlichia mikurensis, Rickettsia spp., Anaplasma phagocytophilum and Babesia spp. Follow-up sera from 135/188 patients were analyzed for spotted fever group (SFG) Rickettsia, A. phagocytophilum and Babesia microti antibodies, and tested with PCR if positive. Day 0 sera from patients with fever (8/188) or EM duration of ≥ 21 days (69/188) were analyzed, using PCR, for A. phagocytophilum, Rickettsia spp., Babesia spp. and N. mikurensis. Day 14 sera were tested for TBEV IgG. Results We detected no microorganisms in the skin biopsies nor in the sera of patients with fever or prolonged EM duration. Serological signs of exposure against SFG Rickettsia and A. phagocytophilum were detected in 11/135 and 8/135, respectively. Three patients exhibited both SFG Rickettsia and A. phagocytophilum antibodies, albeit negative PCR. No antibodies were detected against B. microti. 2/187 had TBEV antibodies without prior immunization. There was no significant increase in clinical symptoms or disease duration in patients with possible co-infection. Conclusions Co-infection with N. mikurensis, A. phagocytophilum, SFG Rickettsia, Babesia spp. and TBEV is uncommon in Norwegian EM patients. Despite detecting antibodies against SFG Rickettsia and A. phagocytophilum in some patients, no clinical implications could be demonstrated

Visual processing deficits in patients with schizophrenia spectrum and bipolar disorders and associations with psychotic symptoms, and intellectual abilities

Abstract Objective Low-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ. Methods Patients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI). Results Robust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, β = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, β = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (β = −0.14, t = −1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (β = −0.3, t = −3.43, p = .0007). This association was found within healthy controls (t = −3.72, p = .0003) and patients with bipolar disorder (t = −2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold. Conclusion Patients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.publishedVersio

Assessment of cognitive function, structural brain changes and fatigue 6 months after treatment of neuroborreliosis

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Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21–49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10−6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10−15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention

Comparison of organic and conventional food and food Production. Part IV: Human health – hygiene and pathogens

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Comparison of organic and conventional food and food Production. Part V: Human health – pesticide residues

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Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18–87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20–88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78–0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74–0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders

Comparison of organic and conventional food and food production. Part II: Animal health and welfare in Norway

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Meta-analysis of Alzheimer’s disease on 9,751 samples from Norway and IGAP study identifies four risk loci

Source at https://doi.org/10.1038/s41598-018-36429-6.A large fraction of genetic risk factors for Alzheimer’s Disease (AD) is still not identified, limiting the understanding of AD pathology and study of therapeutic targets. We conducted a genome-wide association study (GWAS) of AD cases and controls of European descent from the multi-center DemGene network across Norway and two independent European cohorts. In a two-stage process, we first performed a meta-analysis using GWAS results from 2,893 AD cases and 6,858 cognitively normal controls from Norway and 25,580 cases and 48,466 controls from the International Genomics of Alzheimer’s Project (IGAP), denoted the discovery sample. Second, we selected the top hits (p −6) from the discovery analysis for replication in an Icelandic cohort consisting of 5,341 cases and 110,008 controls. We identified a novel genomic region with genome-wide significant association with AD on chromosome 4 (combined analysis OR = 1.07, p = 2.48 x 10-8). This finding implicated HS3ST1, a gene expressed throughout the brain particularly in the cerebellar cortex. In addition, we identified IGHV1-68 in the discovery sample, previously not associated with AD. We also associated >i>USP6NL/ECHDC3 and BZRAP1-AS1 to AD, confirming findings from a follow-up transethnic study. These new gene loci provide further evidence for AD as a polygenic disorder, and suggest new mechanistic pathways that warrant further investigation