Hypoglycaemia in type 2 diabetes exacerbates amyloid-related proteins associated with dementia

ObjectiveHypoglycemia in diabetes (T2D) may increase the risk for Alzheimer's disease (AD). We hypothesized that hypoglycemia‐induced amyloid‐related protein changes would be exacerbated in T2D.MethodsA prospective, parallel study in T2D (n=23) and controls (n=23). Subjects underwent insulin‐induced hypoglycemia with blood sampling at baseline, hypoglycemia and post‐hypoglycemia; proteomic analysis of amyloid‐related proteins was undertaken.ResultsAt baseline: Amyloid‐precursor protein (APP) (

Machine Learning and Big Data for Neuro-Diagnostics: Opportunities and Challenges for Clinical Translation

In this report, we examine some developments in neurodiagnostics that make use of machine learning and other algorithms, with a particular focus on the potentials and challenges for clinical translation. As the ultimate aim of development of diagnostic algorithms is for their use in the diagnosis and treatment of patients, we focus particularly on the possibilities and challenges of clinical translation. We draw attention to the challenges faced in relating probabilistic predictions derived from such algorithms to individualised clinical interventions, and we highlight the importance of trust in the relationships that enable clinical translation of technologies – trust between researchers, clinicians, patients, and regulators

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients

Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly

Here be dragons: exploring the hinterland of science

This dissertation is an exploration of the hinterland of science and the strange ‘creatures’ dwelling there. In philosophical circles, the subject of pseudoscience has stirred relatively little philosophical excitement. The demarcation project has fallen on hard times, and many philosophers have grown suspicious of the very term ‘pseudoscience’, as it is believed to suggest a naïve conception of science and its borderlines. In this dissertation, I argue that, instead of abandoning the demarcation project altogether, we should search for more sophisticated tools to distinguish pseudoscience from bona fide science. The ‘silver bullet’ approach to pseudoscience is criticized, particularly with regard to the principle of methodological naturalism in science and the controversy about supernaturalism and intelligent design. I develop a theoretical framework for analyzing the structure of pseudosciences, based on the concepts of immunizing strategies and epistemic defense mechanisms. The recurrence of these theoretical features, which is illustrated with a number of case studies, demonstrates the surprising resilience of pseudoscience and other ‘irrational’ belief systems. These epistemological considerations are then integrated with cognitive and psychological findings on irrationality, in order to construct a broader framework for the generation and dissemination of belief systems (epidemiology of representations). I argue that the self-validating nature and internal epistemic rationale of certain ‘weird’ belief systems go some way to explaining their wide appeal and pervasiveness. We conclude that pseudosciences are worthy of philosophical investigation, and that the rumors of the death of demarcationism have been greatly exaggerated