A 13- to 17-year Retrospective Evaluation of the Clinical Performances of Anterior and Posterior Lithium Disilicate Restorations onto Teeth and Implants.

This retrospective study aimed at evaluating the clinical outcomes of lithium disilicate prostheses onto teeth and implants. A total of 860 restorations were delivered to 312 patients, including crowns, veneers and onlays. Patients with uncontrolled gingival inflammation and/or periodontitis were excluded, whilst subjects with occlusal parafunctions were included.

Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to low-voltage-fast onset seizures in Dravet syndrome.

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction.

Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis.

Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients.

Privacy preserving image registration.

Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content.

A retrospective Italian Society of Veterinary Oncology (SIONCOV) study of 56 cats with appendicular osteosarcoma.

Osteosarcoma is the most common malignant primary bone cancer, but it is infrequently reported in cats. Feline appendicular osteosarcoma typically exhibits good prognosis when treated with surgery alone. A retrospective multi-institutional study was conducted to identify possible prognostic factors.

Evaluation of Prosthetic Marginal Fit and Implant Survival Rates for Conventional and Digital Workflows in Full-Arch Immediate Loading Rehabilitations: A Retrospective Clinical Study.

Digital impression provides several advantages in implant prosthodontics; however, its use in full-arch rehabilitations, especially immediately after surgery, has yet to be validated. The aim of this study was to retrospectively analyse the fit of immediate full-arch prostheses, fabricated using conventional or digital impressions. Patients requiring a full-arch immediate loading rehabilitation were divided into three groups: T1 (digital impression taken immediately after surgery), T2 (Preoperative digital impression, guided surgery-prefabricated temporary bridge) and C (conventional impression taken immediately after surgery).

MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer's disease progression modelling.

The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information.

Investigating the role of the strong field ligands in [FeFe] hydrogenase: spectroscopic and functional characterization of a semi-synthetic mono-cyanide active site.

Artificial maturation of hydrogenases provides a path towards generating new semi-synthetic enzymes with novel catalytic properties. Here enzymes featuring a synthetic asymmetric mono-cyanide cofactor have been prepared using two different hydrogenase scaffolds. Their structure and reactivity was investigated in order to elucidate the design rationale behind the native di-cyanide cofactor, and by extension the second coordination sphere of the active-site pocket.

Light-Driven [FeFe] Hydrogenase Based H Production in : A Model Reaction for Exploring Based Semiartificial Photosynthetic Systems.

Biohybrid technologies like semiartificial photosynthesis are attracting increased attention, as they enable the combination of highly efficient synthetic light-harvesters with the self-healing and outstanding performance of biocatalysis. However, such systems are intrinsically complex, with multiple interacting components. Herein, we explore a whole-cell photocatalytic system for hydrogen (H) gas production as a model system for semiartificial photosynthesis.

Polymer Dots as Photoactive Membrane Vesicles for [FeFe]-Hydrogenase Self-Assembly and Solar-Driven Hydrogen Evolution.

A semiartificial photosynthesis approach that utilizes enzymes for solar fuel production relies on efficient photosensitizers that should match the enzyme activity and enable long-term stability. Polymer dots (Pdots) are biocompatible photosensitizers that are stable at pH 7 and have a readily modifiable surface morphology. Therefore, Pdots can be considered potential photosensitizers to drive such enzyme-based systems for solar fuel formation.

Hydride state accumulation in native [FeFe]-hydrogenase with the physiological reductant H supports its catalytic relevance.

Small molecules in solution may interfere with mechanistic investigations, as they can affect the stability of catalytic states and produce off-cycle states that can be mistaken for catalytically relevant species. Here we show that the hydride state (H), a proposed central intermediate in the catalytic cycle of [FeFe]-hydrogenase, can be formed in wild-type [FeFe]-hydrogenases treated with H in absence of other, non-biological, reductants. Moreover, we reveal a new state with unclear role in catalysis induced by common low pH buffers.

Decoding Genetic Markers of Multiple Phenotypic Layers Through Biologically Constrained Genome-To-Phenome Bayesian Sparse Regression.

The applicability of multivariate approaches for the joint analysis of genomics and phenomics information is currently limited by the lack of scalability, and by the difficulty of interpreting the related findings from a biological perspective. To tackle these limitations, we present Bayesian Genome-to-Phenome Sparse Regression (G2PSR), a novel multivariate regression method based on sparse SNP-gene constraints. The statistical framework of G2PSR is based on a Bayesian neural network, were constraints on SNPs-genes associations are integrated by incorporating knowledge linking variants to their respective genes, to then reconstruct the phenotypic data in the output layer.

SimulAD: a dynamical model for personalized simulation and disease staging in Alzheimer's disease.

SimulAD is a computational disease progression model (DPM) originally developed on the ADNI database to simulate the evolution of clinical and imaging markers characteristic of AD, and to quantify the disease severity (DS) of a subject. In this work, we assessed the validity of this estimated DS, as well as the generalization of the DPM., by applying SimulAD on a new cohort from the Geneva Memory Center (GMC).

Stability of the H-cluster under whole-cell conditions-formation of an H-like state and its reactivity towards oxygen.

Hydrogenases are metalloenzymes that catalyze the reversible oxidation of molecular hydrogen into protons and electrons. For this purpose, [FeFe]-hydrogenases utilize a hexanuclear iron cofactor, the H-cluster. This biologically unique cofactor provides the enzyme with outstanding catalytic activities, but it is also highly oxygen sensitive.

Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

Purpose: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images.

Design: Development and evaluation of diagnostic technology.

Methods: Two deep learning ensemble models to predict pointwise VF sensitivity from SD-OCT images (model 1: RNFLT profile only; model 2: RNFLT profile plus SD-OCT image) and 2 reference models were developed.

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation.

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.

Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease.

Background And Objectives: Longitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD).

Methods: We use data from the 2 largest cohort imaging studies in HD-284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)-to train and test the model.

Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data.

In this study, we investigate SimulAD, a novel quantitative instrument for the development of intervention strategies for disease-modifying drugs in Alzheimer's disease. SimulAD is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative the method enables to generate hypothetical scenarios of amyloid lowering interventions.

Biophysics-based statistical learning: Application to heart and brain interactions.

Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements.