The promise of portable remote auditory stimulation tools to enhance slow-wave sleep and prevent cognitive decline.

Dementia is the seventh leading cause of mortality, and a major source of disability and dependency in older individuals globally. Cognitive decline (and, to a lesser extent, normal ageing) are associated with sleep fragmentation and loss of slow-wave sleep. Evidence suggests a bidirectional causal link between these losses. Phase-locked auditory stimulation has emerged as a promising non-invasive tool to enhance slow-wave sleep, potentially ameliorating cognitive decline. In laboratory settings, auditory stimulation is usually supervised by trained experts. Different algorithms (simple amplitude thresholds, topographic correlation, sine-wave fitting, phase-locked loop, and phase vocoder) are used to precisely target auditory stimulation to a desired phase of the slow wave. While all algorithms work well in younger adults, the altered sleep physiology of older adults and particularly those with neurodegenerative disorders requires a tailored approach that can adapt to older adults' fragmented sleep and reduced amplitudes of slow waves. Moreover, older adults might require a continuous intervention that is not feasible in laboratory settings. Recently, several auditory stimulation-capable portable devices ('Dreem®', 'SmartSleep®' and 'SleepLoop®') have been developed. We discuss these three devices regarding their potential as tools for science, and as clinical remote-intervention tools to combat cognitive decline. Currently, SleepLoop® shows the most promise for scientific research in older adults due to high transparency and customizability but is not commercially available. Studies evaluating down-stream effects on cognitive abilities, especially in patient populations, are required before a portable auditory stimulation device can be recommended as a clinical preventative remote-intervention tool

Glucagon receptor gene mutations with hyperglucagonemia but without the glucagonoma syndrome

Pancreatic neoplasms producing exclusively glucagon associated with glucagon cell hyperplasia of the islets and not related to hereditary endocrine syndromes have been recently described. They represent a novel entity within the panel of non-syndromic disorders associated with hyperglucagonemia. This case report describes a 36-year-old female with a 10 years history of non-specific abdominal pain. No underlying cause was evident despite extensive diagnostic work-up. More recently she was diagnosed with gall bladder stones. Abdominal ultrasound, computerised tomography and magnetic resonance imaging revealed no pathologic findings apart from cholelithiasis. Endoscopic ultrasound revealed a 5.5 mm pancreatic lesion. Fine needle aspiration showed cells focally expressing chromogranin, suggestive but not diagnostic of a low grade neuroendocrine tumor. OctreoScan(®) was negative. Serum glucagon was elevated to 66 pmol/L (normal: 0-50 pmol/L). Other gut hormones, chromogranin A and chromogranin B were normal. Cholecystectomy and enucleation of the pancreatic lesion were undertaken. Postoperatively, abdominal symptoms resolved and serum glucagon dropped to 7 pmol/L. Although H and E staining confirmed normal pancreatic tissue, immunohistochemistry was initially thought to be suggestive of alpha cell hyperplasia. A count of glucagon positive cells from 5 islets, compared to 5 islets from 5 normal pancreata indicated that islet size and glucagon cell ratios were increased, however still within the wide range of normal physiological findings. Glucagon receptor gene (GCGR) sequencing revealed a heterozygous deletion, K349_G359del and 4 missense mutations. This case may potentially represent a progenitor stage of glucagon cell adenomatosis with hyperglucagonemia in the absence of glucagonoma syndrome. The identification of novel GCGR mutations suggests that these may represent the underlying cause of this condition

A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment.

OBJECTIVES: The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI). METHODS: Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects. RESULTS: Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics. CONCLUSION: Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features

A case of distal extrahepatic cholangiocarcinoma with two positive resection margins

Cholangiocarcinoma is an uncommon primary malignancy of the biliary tract that is challenging to diagnose and treat effectively due to its relatively silent and late clinical presentation. The present study reports a case of a 60-year-old male with distal extrahepatic cholangiocarcinoma with a 3-week history of painless obstructive jaundice symptoms and subjective weight loss. Imaging revealed an obstructing lesion in the common bile duct, just distal to the entrance of the cystic duct. Pathology revealed moderately differentiated cholangiocarcinoma with two positive proximal resection margins. The two positive resection margins presented a challenge during surgery and points to an urgent need for further studies to better illuminate diagnostic and therapeutic options for patients with similar clinicopathological presentation

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer's disease prediction and for aiding patient stratification in clinical trials.Comment: 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text overlap with arXiv:1805.0390

Modelling of Two-Phase Water Ejector in Rankine Cycle High Temperature Heat Pumps

Industrial high temperature heat pumps (HTHPs) can provide carbon-free process heat when operated with renewable energy sources. Using water as the working medium greatly increases the possible range of operation without the detrimental effects of traditional working fluids. One main challenge with this type of heat pump is the high compression ratio required to achieve a given temperature lift. As a result, water based heat pumps need several compression stages. Furthermore, the steam leaving the compressor is highly superheated. Ejectors driven by high pressure condensate allow to de-superheat the steam from the compressor outlet while simultaneously increasing its pressure. Thereby, the required power for compression as well as the number of compression stages can be reduced. This paper studies how the implementation of the two-phase water ejector influences the thermodynamic performance of Rankine cycle HTHP using a thermodynamic model of the ejector. Several cycle architectures are developed to study the ejector integration in the heat pump cycle, including traditional single-stage and multi-stage cycles. The cycles studies are conducted in the Modelica language, in the Modelon Impact environment. The study aims at informing about new developments in two-phase water ejectors and their application potential in Rankine cycle HTHPs. First simulations suggest an efficiency improvement of about 10% through the use of an ejector in the heat pump cycle

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample