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872 results for “disease progression”
PROSTVAC (PSA-TRICOM) in Preventing Disease Progression in Patients With Localized Prostate Cancer Undergoing Active Surveillance
ClinicalTrials.gov study NCT02326805. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Association of Nutrition and T Cell Immune Activity With Disease Progression in Nontuberculous Mycobacterial Pulmonary Disease
ClinicalTrials.gov study NCT07363798. IPD Sharing: NO. Countries: 1. Publications: 17.
Putative resistance and tolerance mechanisms have little impact on disease progression for an emerging salamander pathogen
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PEMT-mediated phospholipid imbalance promotes age-associated metabolic dysfunction-associated steatotic liver disease progression
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Data from: Steatotic liver disease induced by TCPOBOP-activated hepatic constitutive androstane receptor: Primary and secondary gene responses with links to disease progression
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Advanced 4-chamber echocardiography techniques enable clinically matched precise characterization of heart disease progression in mice
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Plasma neurofilament light for prediction of disease progression in familial frontotemporal lobar degeneration
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Data from: Blood NfL: a biomarker for disease severity and progression in Parkinson's disease
Objective: To examine whether plasma neurofilament light chain (NfL) levels were associated with motor and cognitive progression in Parkinson's disease (PD). Methods: This prospective follow-up study enrolled 178 participants, including 116 with PD, 22 with multiple system atrophy (MSA), and 40 healthy controls. We measured plasma NfL levels with electrochemiluminescence immunoassay. Patients with PD received evaluations of motor and cognition, at baseline and at a mean follow-up interval of 3 years. Changes in the unified Parkinson's disease rating scale (UPDRS) part III motor score and Mini-Mental State Examination (MMSE) score were used to assess motor and cognition progression. Results: Plasma fL levels were significantly higher in MSA than in PD and healthy groups (35.8±6.2 pg/ml, 17.6±2.8 pg/ml, and 10.6±2.3 pg/ml, respectively; P<0.001). In the PD group, NfL levels were significantly elevated in patients with advanced Hoehn-Yahr (H-Y) stage and patients with dementia (PDD) (P<0.001). NfL levels were modestly correlated with UPDRS part III scores (r=0.42, 95% CI: 0.46-0.56, P<0.001). After a mean follow-up of 3.4±1.2 years, a Cox regression analysis adjusted for age, sex, disease duration and baseline motor or cognitive status showed that higher baseline NfL levels were associated with higher risks for either motor or cognition progression (P=0.029 and P=0.015, respectively). Conclusions: Plasma NfL levels correlated with disease severity and progression in terms of both motor and cognitive functions in PD. Classification of evidence: This study provides Class III evidence that plasma NfL levels distinguish PD and MSA, and is a surrogate biomarker for PD progression.
Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
<p>Infectious diseases, including transmissible cancers, can have a broad range of impacts on host behaviour, particularly in the latter stages of disease progression. However, the difficulty of early diagnoses makes the study of behavioural influences of disease in wild animals a challenging task. Tasmanian devils (<i>Sarcophilus harrisii</i>) are affected by a transmissible cancer, devil facial tumour disease (DFTD), in which tumours are externally visible as they progress. Using telemetry and mark-recapture data sets, we quantify the impacts of cancer progression on the behaviour of wild devils by assessing how interaction patterns within the social network of a population change with increasing tumour load. DFTD negatively influences devils' likelihood of interaction within their network, an effect which increases with increasing tumour load. Infected devils were more active within their network late in the mating season, a pattern with repercussions for DFTD transmission. Our study provides a rare opportunity to quantify and understand the behavioural feedbacks of disease in wildlife and how they may affect transmission and population dynamics in general.</p>
Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) - code and data sets
<p>This repository contains the scripts for the paper in revision to the AAPS J: Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) </p> <p>Authors: Niels Hendrickx, MSc, France Mentré, MD, PhD, Andreas Traschütz, MD, PhD, Cynthia Gagnon, PhD, Rebecca Schüle, MD, ARCA Study Group, EVIDENCE-RND consortium, Matthis Synofzik, MD, Emmanuelle Comets, PhD</p> <p>A simulated dataset (<strong>simulated_arsacs.csv</strong>) has been included in the repository to make the code executable as a standalone. Four main scripts have been provided in addition with the present Readme describing the files. The repository also includes 3 R objects and 2 folders which will be overwritten when the scripts are run, and are included as examples of the expected outputs. The main scripts are:</p> <p>- <strong>Script_imputation_selection.R</strong>: runs the covariate selection method. It uses a simulated dataset provided in the depot. The multiple imputation model is hardcoded as an input to the mice package to generate 10 imputed datasets, saved in current_directory/imputed_data_sets/df_arsacs_mi_i.csv. The script then runs the covariate selection method. The script prints out the list of selected covariates and returns a saemixObject containing the fit of the selected covariate model.<br> After the script executes, a list will be saved with the name of the selected covariates in the current directory (an example is included under the name "cov_matrix_model.RData" in the repository), the output of the selection, containing the whole history of runs will be saved under "final_covariate_model.RData", the list of selected covariate names will be saved under "list_covariates.RData".</p> <p>- <strong>source_mi.R</strong>: contains the functions used by Script_imputation_selection.R</p> <p>- <strong>script_bootstrap_indfit.R</strong>: This script loads "cov_matrix_model.RData" containing the matrix of covariate effects (used by saemix) and "list_covariates.RData", the list of covariates included, fits the model on the imputed data sets and computes its bootstrap distribution for each imputed data set (in the script, using only 20 samples for computation time, saved in current_directory/bootstrap/boot.arsacs.case.mi.i). It then computes the mean parameter and relative standard error of each parameter. It then computes the conditional distribution of each patient in each bootstrap samples and returns a data frame of individual predictions. The script will then plot 4 indivudal predictions. </p> <p>-<strong> source_bootstrap.R</strong>: contains the functions used by script_bootstrap_indfit.R</p> <p>Both scripts need the saemix package to run, which we haven’t included in the repository as it is freely available on the CRAN (https://cran.r-project.org/web/packages/saemix/index.html). Additional libraries we make use of in the code (MICE, tidyverse, ggplot2) also need to be installed prior to execution. <br>The R code provided can be further customised to be adapted to different scenarios.</p> <p>For the code to run, it is preferable to unzip the whole folder and set the working directory to the source file location as the script uses the "bootstrap" and "imputed_data_sets" sub-folders</p> <p>To execute this code, assuming the required libraries are available in the local R installation, please open an R session and run:<br>source("Script_imputation_selection.R") # for the covariate selection method (runtime: 3h on a i7-8565U laptop)<br>source("script_bootstrap_indfit.R") # to obtain individual trajectories (runtime: 1h on a i7-8565U laptop)</p>
Polyfunctionality and breadth of HIV-1 antibodies are associated with delayed disease progression
<p><span>HIV-1 infection leads to chronic disease requiring life-long treatment and therefore alternative therapeutics, a cure and/or a protective vaccine are needed. Antibody-mediated effector functions could have a role in the fight against HIV-1. However, the properties underlying the potential beneficial effects of antibodies during HIV-1 infection are poorly understood. </span></p> <p><span>To identify a specific profile of antibody features associated with delayed disease progression, we studied antibody polyfunctionality during untreated HIV-1 infection in the well-documented Amsterdam Cohort Studies. Serum samples were analyzed from untreated individuals with HIV-1 at approximately 6 months (n = 166) and 3 years (n = 382) post-seroconversion (post-SC). A Luminex antibody Fc array was used to profile 15 different Fc features for serum antibodies against 20 different HIV-1 envelope glycoprotein antigens and the resulting data was also compared with data on neutralization breadth. </span></p> <p><span>We found that high HIV-1 specific IgG1 levels and low IgG2 and IgG4 levels at 3 years post-SC were associated with delayed disease progression. Moreover, delayed disease progression was associated with a broad and polyfunctional antibody response. Specifically, the capacity to interact with all Fc γ receptors (FcγRs) and C1q, and in particular with FcγRIIa, correlated positively with delayed disease progression. There were strong correlations between antibody Fc features and neutralization breadth and several antibody features that were associated with delayed disease progression were also associated with the development of broad and potent antibody neutralization. </span></p> <p><span>In summary, we identified a strong association between broad, polyfunctional antibodies and delayed disease progression. These findings contribute new information for the fight against HIV-1, especially for new antibody-based therapy and cure strategies. </span></p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Contrasting disease progression, microglia reactivity, tolerance, and resistance to Toxoplasma gondii infection in two mouse strains
<p><strong><span>Figure S1</span></strong><span>. Stereological Sampling: Low-power photomicrographs (A and B) displaying the molecular layer of the dentate gyrus, the area of interest, alongside the sampling grid (C). Additionally, a high-power photomicrograph (D) showcases IBA-1 immunolabeled microglia (the object of interest). Scale bar: A - 250µm, and D: 25µm.</span></p> <p> </p> <p><strong><span>Figure S2</span></strong><span>. The recovery of BALB/c microglia in the molecular layer of dentate gyrus was observed 43 days after infection. At this point, only minor morphological changes were observed, and all the morphological changes induced by <em>T. gondii</em> infection at 22 dpi have disappeared. The analysis methods used included hierarchical cluster analysis (A), discriminant function analysis (C and E), morphological complexity (B), and convex hull volume (D).</span></p>
Cerebrovascular disease progression in patients with ACTA2 Arg179 pathogenic variants supplementary materials
<p><span><span><span><span><span><span><span><span><span><span><span>Our study entitled "Cerebrovascular disease progression in patients with ACTA2 Arg179 Pathogenic Variants" examines progression of imaging biomarkers of stroke, arterial steno-occlusive disease and white matter injury in patients with smooth muscle dysfunction syndrome caused by mutations in the ACTA2 gene (ACTA2 Arg179 pathogenic variants). We found characteristic patterns of acute white matter ischemic injury and progressive internal carotid artery stenosis on MRI. Longitudinal analysis showed both stable and progressive lesion entities. The data was collected on various scanner setups. To eliminate potential bias related to improving imaging technology we re-analyzed imaging markers for those patients with available longitudinal imaging on an identical scanner setups and present the data in supplementary table e-1. Supplementary table e-2 shows results of a comparison of imaging markers between male and female sex.</span></span></span></span></span></span></span></span></span></span></span></p>
Prediction of Progression of Coronary Artery Disease (CAD) Using Vascular Profiling of Shear Stress and Wall Morphology
ClinicalTrials.gov study NCT01316159. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Risk Stratification of Rapid Disease Progression in Children With Crohn's Disease
ClinicalTrials.gov study NCT00790543. IPD Sharing: NO. Countries: 2. Publications: 1.
A Study of Disease Progression and Anti-HIV Treatments
ClinicalTrials.gov study NCT00001068. IPD Sharing: Not stated. Countries: 3. Publications: 1.
Ibrutinib in Previously Untreated Binet Stage a Chronic Lymphocytic Leukemia with Risk of Disease Progression
ClinicalTrials.gov study NCT02863718. IPD Sharing: NO. Countries: 1. Publications: 2.
COVID-19 Study to Evaluate Safety, Tolerability, and Efficacy of REGN14256+Imdevimab for the Treatment of COVID-19 Adult and Adolescent Patients Without Risk Factors for Progression to Severe Disease
ClinicalTrials.gov study NCT05081388. IPD Sharing: YES. Countries: 1. Publications: 0.
Supplemented Very Low Protein Diet and the Progression of Chronic Kidney Disease
ClinicalTrials.gov study NCT02031224. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.