Standardisation of Parameters during Endovenous Laser Therapy of Truncal Varicose Veins - Experimental Ex-vivo Study

BackgroundVein shrinkage is a surrogate marker for successful laser treatment of varicose veins. However, many controversies still remain concerning the best laser parameters to use. The aim of this study was standardisation of intraoperative energy dosages and pull-back rates to achieve optimal clinical results.DesignEx-vivo study in surgically removed saphenous trunks.Material and methodsGreat saphenous veins were removed by Babcock stripping and irradiated with laser energy delivered by a laser diode emitting at 980nm. In total, 279 vein segments (5cm long) were treated using powers from 5–15W. Vein segments were opened longitudinally and the circumference measured in the treated and untreated regions to assess thermal shrinkage.ResultsThe greatest shrinkage and minimum number of perforations was achieved using lower or medium power (8 to 12W) with longer exposure to administer laser energy. The median percentage vein shrinkage was 50% (power 5W), 45% (8W), 40% (10W), 45% (12W) and 59% (15W). When a higher power was used (15W), the perforations were more frequent and carbonisation was marked.ConclusionsOur data suggests that similar efficacy with fewer vein perforations may be obtained with low or medium power settings and increased exposure when undertaking laser obliteration of saphenous trunks. This may result in fewer adverse events such as ecchymosis following treatment in patients

Molecular structure retrieval directly from laboratory-frame photoelectron spectra in laser-induced electron diffraction

Ubiquitous to most molecular scattering methods is the challenge to retrieve bond distance and angle from the scattering signals since this requires convergence of pattern matching algorithms or fitting methods. This problem is typically exacerbated when imaging larger molecules or for dynamic systems with little a priori knowledge. Here, we employ laser-induced electron diffraction (LIED) which is a powerful means to determine the precise atomic configuration of an isolated gas-phase molecule with picometre spatial and attosecond temporal precision. We introduce a simple molecular retrieval method, which is based only on the identification of critical points in the oscillating molecular interference scattering signal that is extracted directly from the laboratory-frame photoelectron spectrum. The method is compared with a Fourier-based retrieval method, and we show that both methods correctly retrieve the asymmetrically stretched and bent field-dressed configuration of the asymmetric top molecule carbonyl sulfide (OCS), which is confirmed by our quantum-classical calculations

Clinical and genetic analysis of 29 Brazilian patients with Huntington’s disease-like phenotype

Huntington’s disease (HD) is a neurodegenerative disorder characterized by chorea, behavioral disturbances and dementia, caused by a pathological expansion of the CAG trinucleotide in the HTT gene. Several patients have been recognized with the typical HD phenotype without the expected mutation. The objective of this study was to assess the occurrence of diseases such as Huntington’s disease-like 2 (HDL2), spinocerebellar ataxia (SCA) 1, SCA2, SCA3, SCA7, dentatorubral-pallidoluysian atrophy (DRPLA) and choreaacanthocytosis (ChAc) among 29 Brazilian patients with a HD-like phenotype. In the group analyzed, we found 3 patients with HDL2 and 2 patients with ChAc. The diagnosis was not reached in 79.3% of the patients. HDL2 was the main cause of the HD-like phenotype in the group analyzed, and is attributable to the African ancestry of this population. However, the etiology of the disease remains undetermined in the majority of the HD negative patients with HD-like phenotype. Key words: Huntington’s disease, Huntington’s disease-like, chorea-acanthocytosis, Huntington’s disease-like 2

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease

Seizures as an early symptom of autosomal dominant Alzheimer's disease

Our objective was to assess the reported history of seizures in cognitively asymptomatic mutation carriers for autosomal dominant Alzheimer's disease (ADAD) and the predictive value of seizures for mutation carrier status in cognitively asymptomatic first-degree relatives of ADAD patients. Seizure occurrence in the Dominantly Inherited Alzheimer Network observational study was correlated with mutation carrier status in cognitively asymptomatic subjects. Of 276 cognitively asymptomatic individuals, 11 (4%) had experienced seizures, and nine of these carried an ADAD mutation. Thus, in the Dominantly Inherited Alzheimer Network population, seizure frequency in mutation carriers was significantly higher than in noncarriers (p = 0.04), and the positive predictive value of seizures for the presence of a pathogenic mutation was 81.8%. Among cognitively asymptomatic ADAD family members, the occurrence of seizures increases the a priori risk of 50% mutation-positive status to about 80%. This finding suggests that ADAD mutations increase the risk of seizures

Relationship of serum beta-synuclein with blood biomarkers and brain atrophy

Background: Recent data support beta-synuclein as a blood biomarker to study synaptic degeneration in Alzheimer's disease (AD). Methods: We provide a detailed comparison of serum beta-synuclein immunoprecipitation - mass spectrometry (IP-MS) with the established blood markers phosphorylated tau 181 (p-tau181) (Simoa) and neurofilament light (NfL) (Ella) in the German FTLD consortium cohort (n = 374) and its relation to brain atrophy (magnetic resonance imaging) and cognitive scores. Results: Serum beta-synuclein was increased in AD but not in frontotemporal lobar degeneration (FTLD) syndromes. Beta-synuclein correlated with atrophy in temporal brain structures and was associated with cognitive impairment. Serum p-tau181 showed the most specific changes in AD but the lowest correlation with structural alterations. NfL was elevated in all diseases and correlated with frontal and temporal brain atrophy. Discussion: Serum beta-synuclein changes differ from those of NfL and p-tau181 and are strongly related to AD, most likely reflecting temporal synaptic degeneration. Beta-synuclein can complement the existing panel of blood markers, thereby providing information on synaptic alterations. Highlights: Blood beta-synuclein is increased in Alzheimer's disease (AD) but not in frontotemporal lobar degeneration (FTLD) syndromes. Blood beta-synuclein correlates with temporal brain atrophy in AD. Blood beta-synuclein correlates with cognitive impairment in AD. The pattern of blood beta-synuclein changes in the investigated diseases is different to phosphorylated tau 181 (p-tau181) and neurofilament light (NfL)

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

INTRODUCTION: The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD). METHODS: We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset. RESULTS: There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease. DISCUSSION: Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance

Computational Identification of Phospho-Tyrosine Sub-Networks Related to Acanthocyte Generation in Neuroacanthocytosis

Acanthocytes, abnormal thorny red blood cells (RBC), are one of the biological hallmarks of neuroacanthocytosis syndromes (NA), a group of rare hereditary neurodegenerative disorders. Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. Previous studies have shown that changes in RBC membrane protein phosphorylation state affect RBC membrane mechanical stability and morphology. Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). The experimentally determined phosphoproteomic data-sets allowed us to relate the subsequent network analysis to the pathogenetic background. To reduce the network complexity, we combined several algorithms of topological network analysis including cluster determination by shortest path analysis, protein categorization based on centrality indexes, along with annotation-based node filtering. We first identified XK- and VPS13A-related protein-protein interaction networks by identifying all the interactomic shortest paths linking Xk and chorein to the corresponding set of proteins whose tyrosine phosphorylation was altered in patients. These networks include the most likely paths of functional influence of Xk and chorein on phosphorylated proteins. We further refined the analysis by extracting restricted sets of highly interacting signaling proteins representing a common molecular background bridging the generation of acanthocytes in MLS and ChAc. The final analysis pointed to a novel, very restricted, signaling module of 14 highly interconnected kinases, whose alteration is possibly involved in generation of acanthocytes in MLS and ChAc