Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings

Increased Life Span due to Calorie Restriction in Respiratory-Deficient Yeast

A model for replicative life span extension by calorie restriction (CR) in yeast has been proposed whereby reduced glucose in the growth medium leads to activation of the NAD(+)–dependent histone deacetylase Sir2. One mechanism proposed for this putative activation of Sir2 is that CR enhances the rate of respiration, in turn leading to altered levels of NAD(+) or NADH, and ultimately resulting in enhanced Sir2 activity. An alternative mechanism has been proposed in which CR decreases levels of the Sir2 inhibitor nicotinamide through increased expression of the gene coding for nicotinamidase, PNC1. We have previously reported that life span extension by CR is not dependent on Sir2 in the long-lived BY4742 strain background. Here we have determined the requirement for respiration and the effect of nicotinamide levels on life span extension by CR. We find that CR confers robust life span extension in respiratory-deficient cells independent of strain background, and moreover, suppresses the premature mortality associated with loss of mitochondrial DNA in the short-lived PSY316 strain. Addition of nicotinamide to the medium dramatically shortens the life span of wild type cells, due to inhibition of Sir2. However, even in cells lacking both Sir2 and the replication fork block protein Fob1, nicotinamide partially prevents life span extension by CR. These findings (1) demonstrate that respiration is not required for the longevity benefits of CR in yeast, (2) show that nicotinamide inhibits life span extension by CR through a Sir2-independent mechanism, and (3) suggest that CR acts through a conserved, Sir2-independent mechanism in both PSY316 and BY4742

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning–based model that produced accurate and robust cranial CT tissue classification. // MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. // RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. // DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation

American Society of Clinical Oncology Summit on Addressing Obesity Through Multidisciplinary Provider Collaboration: Key Findings and Recommendations for Action

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139117/1/oby21987.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139117/2/oby21987_am.pd

A Very Low-Carbohydrate Diet Improves Symptoms and Quality of Life in Diarrhea-Predominant Irritable Bowel Syndrome

Patients with diarrhea-predominant IBS (IBS-D) anecdotally report symptom improvement after initiating a very low-carbohydrate diet (VLCD). This is the first study to prospectively evaluate a VLCD in IBS-D

Comparison of a low carbohydrate and low fat diet for weight maintenance in overweight or obese adults enrolled in a clinical weight management program

<p>Abstract</p> <p>Background</p> <p>Recent evidence suggests that a low carbohydrate (LC) diet may be equally or more effective for short-term weight loss than a traditional low fat (LF) diet; however, less is known about how they compare for weight maintenance. The purpose of this study was to compare body weight (BW) for participants in a clinical weight management program, consuming a LC or LF weight maintenance diet for 6 months following weight loss.</p> <p>Methods</p> <p>Fifty-five (29 low carbohydrate diet; 26 low fat diet) overweight/obese middle-aged adults completed a 9 month weight management program that included instruction for behavior, physical activity (PA), and nutrition. For 3 months all participants consumed an identical liquid diet (2177 kJ/day) followed by 1 month of re-feeding with solid foods either low in carbohydrate or low in fat. For the remaining 5 months, participants were prescribed a meal plan low in dietary carbohydrate (~20%) or fat (~30%). BW and carbohydrate or fat grams were collected at each group meeting. Energy and macronutrient intake were assessed at baseline, 3, 6, and 9 months.</p> <p>Results</p> <p>The LC group increased BW from 89.2 ± 14.4 kg at 3 months to 89.3 ± 16.1 kg at 9 months (<it>P </it>= 0.84). The LF group decreased BW from 86.3 ± 12.0 kg at 3 months to 86.0 ± 14.0 kg at 9 months (<it>P </it>= 0.96). BW was not different between groups during weight maintenance (<it>P </it>= 0.87). Fifty-five percent (16/29) and 50% (13/26) of participants for the LC and LF groups, respectively, continued to decrease their body weight during weight maintenance.</p> <p>Conclusion</p> <p>Following a 3 month liquid diet, the LC and LF diet groups were equally effective for BW maintenance over 6 months; however, there was significant variation in weight change within each group.</p

Dietary carbohydrate restriction as the first approach in diabetes management:Critical review and evidence base

AbstractThe inability of current recommendations to control the epidemic of diabetes, the specific failure of the prevailing low-fat diets to improve obesity, cardiovascular risk, or general health and the persistent reports of some serious side effects of commonly prescribed diabetic medications, in combination with the continued success of low-carbohydrate diets in the treatment of diabetes and metabolic syndrome without significant side effects, point to the need for a reappraisal of dietary guidelines. The benefits of carbohydrate restriction in diabetes are immediate and well documented. Concerns about the efficacy and safety are long term and conjectural rather than data driven. Dietary carbohydrate restriction reliably reduces high blood glucose, does not require weight loss (although is still best for weight loss), and leads to the reduction or elimination of medication. It has never shown side effects comparable with those seen in many drugs. Here we present 12 points of evidence supporting the use of low-carbohydrate diets as the first approach to treating type 2 diabetes and as the most effective adjunct to pharmacology in type 1. They represent the best-documented, least controversial results. The insistence on long-term randomized controlled trials as the only kind of data that will be accepted is without precedent in science. The seriousness of diabetes requires that we evaluate all of the evidence that is available. The 12 points are sufficiently compelling that we feel that the burden of proof rests with those who are opposed

Plasma Biomarkers of Brain Atrophy in Alzheimer's Disease

Peripheral biomarkers of Alzheimer's disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods—quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis