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INTRODUCTION: Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory infections (ALRI) in children under one year of age. In high-income countries, RSV infections cause a significant overload of care every winter, imposing a significant burden to the healthcare system, which has made the development of prevention strategies a major global health priority. In this context, a new bivalent RSV prefusion F protein-based vaccine (RSVpreF) has recently been approved. The objective of this study was to evaluate the cost-effectiveness of vaccinating pregnant women with the RSVpreF vaccine to prevent RSV in infants from the Spanish National Healthcare System (NHS) perspective. METHODS: A hypothetical cohort framework and a Markov-type process were used to estimate clinical outcomes, costs, quality-adjusted life years (QALY) and cost-per-QALY gained (willingness-to-pay threshold: 25,000/QALY) for newborn infants born to RSV-vaccinated versus unvaccinated mothers over an RSV season. The base case analysis was performed from the NHS perspective including direct costs (2023) and applying a discount of 3% to future costs and outcomes. To evaluate the robustness of the model, several scenarios, and deterministic and probabilistic analyses were carried out. All the parameters and assumptions were validated by a panel of experts. RESULTS: The results of the study showed that year-round maternal vaccination program with 70% coverage is a dominant option compared to no intervention, resulting in direct cost savings of 1.8 million each year, with an increase of 551 QALYs. Maternal vaccination could prevent 38% of hospital admissions, 23% of emergency room visits, 19% of primary care visits, and 34% of deaths due to RSV. All scenario analyses showed consistent results, and according to the probabilistic sensitivity analysis (PSA), the probability of maternal vaccination being cost-effective versus no intervention was 99%. CONCLUSIONS: From the Spanish NHS perspective, maternal vaccination with bivalent RSVpreF is a dominant alternative compared with a non-prevention strategy.
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The digital extraction of detailed neuronal morphologies from microscopy data is an essential step in the study of neurons. Ever since Cajal's work, the acquisition and analysis of neuron anatomy has yielded invaluable insight into the nervous system, which has led to our present understanding of many structural and functional aspects of the brain and the nervous system, well beyond the anatomical perspective. Obtaining detailed anatomical data, though, is not a simple task. Despite recent progress, acquiring neuron details still involves using labor-intensive, error prone methods that facilitate the introduction of inaccuracies and mistakes. In consequence, getting reliable morphological tracings usually needs the completion of post-processing steps that require user intervention to ensure the extracted data accuracy. Within this framework, this paper presents NeuroEditor, a new software tool for visualization, editing and correction of previously reconstructed neuronal tracings. This tool has been developed specifically for alleviating the burden associated with the acquisition of detailed morphologies. NeuroEditor offers a set of algorithms that can automatically detect the presence of potential errors in tracings. The tool facilitates users to explore an error with a simple mouse click so that it can be corrected manually or, where applicable, automatically. In some cases, this tool can also propose a set of actions to automatically correct a particular type of error. Additionally, this tool allows users to visualize and compare the original and modified tracings, also providing a 3D mesh that approximates the neuronal membrane. The approximation of this mesh is computed and recomputed on-the-fly, reflecting any instantaneous changes during the tracing process. Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. The edited morphology can then be stored, together with the corresponding 3D mesh that approximates the neuronal membrane.
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INTRODUCTION: Atopic dermatitis (AD) is a chronic, inflammatory skin disease characterized by itchy, painful, and dry skin. Despite the great number of available therapies, economic evaluations are still needed to provide evidence on their cost efficiency. This research aimed to evaluate the cost effectiveness of the Janus kinase (JAK) inhibitor abrocitinib (200 mg) compared with dupilumab (300 mg), tralokinumab (300 mg), baricitinib (2 and 4 mg), and upadacitinib (15 and 30 mg) for the treatment of patients with severe AD from the Spanish National Health System (NHS) perspective. METHODS: A hybrid model consisting of a decision tree linked to a Markov model was developed to estimate costs, quality-adjusted life-years (QALYs), total years in response and incremental cost-per-QALY gained (willingness-to-pay [WTP] threshold: 25,000/QALY). Adults with severe AD entered the decision tree and response (75% reduction in baseline Eczema Area and Severity Index score, EASI-75) was considered at 16 and 52 weeks. After this time, patients entered the Markov model (remainder of the 10-year time horizon), which consisted of three health states: maintenance with active therapy, subsequent treatment, or death. All costs were presented in 2022 euros (). Additionally, cost per number-needed-to-treat (NNT) was calculated for abrocitinib and dupilumab based on a head-to-head post-hoc analysis. RESULTS: Abrocitinib 200 mg was dominant (i.e., lower incremental costs and higher incremental benefit) compared with all studied alternatives (dupilumab 300 mg, tralokinumab 300 mg, baricitinib 2 and 4 mg, upadacitinib 15 and 30 mg) with a QALYs gain of 0.49, 0.60, 0.64, 0.43, 0.45, and 0.08, respectively, and per-person costs savings of 22,097, 24,140, 14,825, 7,116, 12,805, and 45,189, respectively. Considering the WTP threshold, abrocitinib was dominant or cost effective compared with all alternatives for most simulations. Additionally, abrocitinib was dominant compared with all alternatives when evaluating the cost effectiveness over a 5-year time horizon. NNT showed that abrocitinib was dominant versus dupilumab. CONCLUSIONS: The results of the study show that abrocitinib is a cost-effective therapy compared with other JAK inhibitors and biological therapies from the Spanish NHS perspective.
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An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels-ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.
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Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools-Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool-Neuronize-giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.
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The field of neuroanatomy has progressed considerably in recent decades, thanks to the emergence of novel methods which provide new insights into the organization of the nervous system. These new methods have produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data. In other disciplines, such as in many engineering areas, scientists and engineers are dealing with increasingly complex systems, using hierarchical decompositions, graphical models and simplified schematic diagrams for analysis and design processes. This approach makes it possible for users to simultaneously combine global system views and very detailed representations of specific areas of interest, by selecting appropriate representations for each of these views. In this way, users can concentrate on specific details while also maintaining a general system overview - a capability that is essential for understanding structure and function whenever complexity is an issue. Following this approach, this paper focuses on a graphical tool designed to help neuroanatomists to better understand and detect morphological characteristics of neuronal cells. The method presented here, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has proven to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies. A software tool has been developed to generate graphical representations of neurons from 3D computer-aided reconstruction files.
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Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells' overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma's morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes.
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This study presents a tool, Neuronize, for building realistic three-dimensional models of neuronal cells from the morphological information extracted through computer-aided tracing applications. Neuronize consists of a set of methods designed to build 3D neural meshes that approximate the cell membrane at different resolution levels, allowing a balance to be reached between the complexity and the quality of the final model. The main contribution of the present study is the proposal of a novel approach to build a realistic and accurate 3D shape of the soma from the incomplete information stored in the digitally traced neuron, which usually consists of a 2D cell body contour. This technique is based on the deformation of an initial shape driven by the position and thickness of the first order dendrites. The addition of a set of spines along the dendrites completes the model, building a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.
