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In this randomized, placebo-controlled trial, treatment with nintedanib, an intracellular inhibitor of multiple tyrosine kinases, led to a reduced rate of loss of forced vital capacity in patients with idiopathic pulmonary fibrosis. Idiopathic pulmonary fibrosis is a fatal lung disease characterized by worsening dyspnea and progressive loss of lung function. 1 A decline in forced vital capacity (FVC) is consistent with disease progression and is predictive of reduced survival time. 1 – 6 Idiopathic pulmonary fibrosis is believed to arise from an aberrant proliferation of fibrous tissue and tissue remodeling due to the abnormal function and signaling of alveolar epithelial cells and interstitial fibroblasts. 7 The activation of cell-signaling pathways through tyrosine kinases such as vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), and platelet-derived growth factor (PDGF) has been implicated in the pathogenesis of . . .

In patients with a progressive interstitial lung disease, 62% of whom had a CT pattern of usual interstitial pneumonia, those who received nintedanib had a lower annual rate of decline in the forced vital capacity than those who received placebo at 52 weeks.

Crohn's disease (CD) is a chronic progressive destructive disease. Currently available instruments measure disease activity at a specific point in time. An instrument to measure cumulative structural damage to the bowel, which may predict long-term disability, is needed. The aim of this article is to outline the methods to develop an instrument that can measure cumulative bowel damage. The project is being conducted by the International Program to develop New Indexes in Crohn's disease (IPNIC) group. This instrument, called the Crohn's Disease Digestive Damage Score (the Lémann score), should take into account damage location, severity, extent, progression, and reversibility, as measured by diagnostic imaging modalities and the history of surgical resection. It should not be “diagnostic modality driven”: for each lesion and location, a modality appropriate for the anatomic site (for example: computed tomography or magnetic resonance imaging enterography, and colonoscopy) will be used. A total of 24 centers from 15 countries will be involved in a cross-sectional study, which will include up to 240 patients with stratification according to disease location and duration. At least 120 additional patients will be included in the study to validate the score. The Lémann score is expected to be able to portray a patient's disease course on a double-axis graph, with time as the x-axis, bowel damage severity as the y-axis, and the slope of the line connecting data points as a measure of disease progression. This instrument could be used to assess the effect of various medical therapies on the progression of bowel damage. (Inflamm Bowel Dis 2011)

Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the biomarker transition from normal to pathological stages along the disease time axis. In this work we reformulated DPM within a probabilistic setting to quantify the diagnostic uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to missing measurements, biomarkers, and follow-up information. We show that the staging provided by the model on 582 amyloid positive testing individuals has high face validity with respect to the clinical diagnosis. Using follow-up measurements largely reduces the prediction uncertainties, while the transition from normal to pathological stages is mostly associated with the increase of brain hypo-metabolism, temporal atrophy, and worsening of clinical scores. The proposed formulation of DPM provides a statistical reference for the accurate probabilistic assessment of the pathological stage of de-novo individuals, and represents a valuable instrument for quantifying the variability and the diagnostic value of biomarkers across disease stages.

Hereditary transthyretin amyloidosis is caused by the deposition of misfolded transthyretin proteins in peripheral nerves and other tissues. This phase 3 trial tested patisiran, a small interfering RNA targeting transthyretin messenger RNA, to treat the disease.

While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself. [Display omitted] ► A computational neurodegenerative disease progression score (DPS) is proposed ► The DPS combines measurements from multiple biomarkers ► Validation with the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort ► An Alzheimer's DPS (ADPS) is computed for each subject and time-point in ADNI ► Evidence for a common Alzheimer's disease progression within ADNI subjects

•A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed model was evaluated on synthetic and real datasets.•Model outperforms a set of baselines for missing data reconstruction across modalities. [Display omitted] 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. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.

Chronic liver damage leads to pathological accumulation of ECM proteins (liver fibrosis). Comprehensive characterization of the human ECM molecular composition is essential for gaining insights into the mechanisms of liver disease. To date, studies of ECM remodeling in human liver diseases have been hampered by the unavailability of purified ECM. Here, we developed a decellularization method to purify ECM scaffolds from human liver tissues. Histological and electron microscopy analyses demonstrated that the ECM scaffolds, devoid of plasma and cellular components, preserved the three-dimensional ECM structure and zonal distribution of ECM components. This method has been then applied on 57 liver biopsies of HCV-infected patients at different stages of liver fibrosis according to METAVIR classification. Label-free nLC-MS/MS proteomics and computation biology were performed to analyze the ECM molecular composition in liver fibrosis progression, thus unveiling protein expression signatures specific for the HCV-related liver fibrotic stages. In particular, the ECM molecular composition of liver fibrosis was found to involve dynamic changes in matrix stiffness, flexibility and density related to the dysregulation of predominant collagen, elastic fibers and minor components with both structural and signaling properties. This study contributes to the understanding of the molecular bases underlying ECM remodeling in liver fibrosis and suggests new molecular targets for fibrolytic strategies.

The gut microbiota has been causally linked to cancer, yet how intestinal microbes influence progression of extramucosal tumors is poorly understood. Here we provide evidence implying that Prevotella heparinolytica promotes the differentiation of Th17 cells colonizing the gut and migrating to the bone marrow (BM) of transgenic Vk*MYC mice, where they favor progression of multiple myeloma (MM). Lack of IL-17 in Vk*MYC mice, or disturbance of their microbiome delayed MM appearance. Similarly, in smoldering MM patients, higher levels of BM IL-17 predicted faster disease progression. IL-17 induced STAT3 phosphorylation in murine plasma cells, and activated eosinophils. Treatment of Vk*MYC mice with antibodies blocking IL-17, IL-17RA, and IL-5 reduced BM accumulation of Th17 cells and eosinophils and delayed disease progression. Thus, in Vk*MYC mice, commensal bacteria appear to unleash a paracrine signaling network between adaptive and innate immunity that accelerates progression to MM, and can be targeted by already available therapies. The mechanisms through which gut microbiota affect extramucosal tumors are poorly understood. Here the authors show that the gut microbiota promotes multiple myeloma by inducing differentiation and migration of Th17 cells in the bone marrow resulting also in increased recruitment of pro-tumorigenic eosinophils.

Current models of progression in neurodegenerative diseases use neuroimaging measures that are averaged across pre-defined regions of interest (ROIs). Such models are unable to recover fine details of atrophy patterns; they tend to impose an assumption of strong spatial correlation within each ROI and no correlation among ROIs. Such assumptions may be violated by the influence of underlying brain network connectivity on pathology propagation – a strong hypothesis e.g. in Alzheimer's Disease. Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. DIVE clusters vertex-wise (i.e. point-wise) biomarker measurements on the cortical surface that have similar temporal dynamics across a patient population, and concurrently estimates an average trajectory of vertex measurements in each cluster. DIVE uniquely outputs a parcellation of the cortex into areas with common progression patterns, leading to a new signature for individual diseases. DIVE further estimates the disease stage and progression speed for every visit of every subject, potentially enhancing stratification for clinical trials or management. On simulated data, DIVE can recover ground truth clusters and their underlying trajectory, provided the average trajectories are sufficiently different between clusters. We demonstrate DIVE on data from two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Dementia Research Centre (DRC), UK. The DRC cohort contains patients with Posterior Cortical Atrophy (PCA) as well as typical Alzheimer's disease (tAD). DIVE finds similar spatial patterns of atrophy for tAD subjects in the two independent datasets (ADNI and DRC), and further reveals distinct patterns of pathology in different diseases (tAD vs PCA) and for distinct types of biomarker data – cortical thickness from Magnetic Resonance Imaging (MRI) vs amyloid load from Positron Emission Tomography (PET). We demonstrate that DIVE stages have potential clinical relevance, despite being based only on imaging data, by showing that the stages correlate with cognitive test scores. Finally, DIVE can be used to estimate a fine-grained spatial distribution of pathology in the brain using any kind of voxelwise or vertexwise measures including Jacobian compression maps, fractional anisotropy (FA) maps from diffusion tensor imaging (DTI) or other PET measures. [Display omitted] •DIVE estimates fine-grained spatial patterns of pathology in neurodegenerative diseases, not available with ROI-based methods.•Further estimates the temporal evolution of brain pathology and subject specific disease stages.•Forecasts the evolution of pathology at population- and subject-level.•Finds plausible patterns of pathology in four distinct datasets comprising different diseases and modalities.•DIVE, available online, can be applied to any registered voxelwise images (e.g.MRI, PET, DTI).