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controlledIPD-YESFeb 2026View details →

Carbamazepine in Severe Liver Disease Due to Alpha-1 Antitrypsin Deficiency

ClinicalTrials.gov study NCT01379469. IPD Sharing: YES. Countries: 1. Publications: 44.

A 4 Week Study to Investigate the Safety and Tolerability of AZD5069 in Patients With Moderate to Severe Chronic Obstructive Pulmonary Disease (COPD)

ClinicalTrials.gov study NCT01233232. IPD Sharing: Not stated. Countries: 4. Publications: 1.

Induction Study #2 of Oral Ozanimod as Induction Therapy for Moderately to Severely Active Crohn's Disease

ClinicalTrials.gov study NCT03440385. IPD Sharing: Not stated. Countries: 31. Publications: 1.

controlledIPD-YESFeb 2026View details →

Study to Evaluate the Safety and Antiviral Activity of Remdesivir (GS-5734™) in Participants With Severe Coronavirus Disease (COVID-19)

ClinicalTrials.gov study NCT04292899. IPD Sharing: YES. Countries: 15. Publications: 4.

publicJan 2025View details →

Data from: Whole plant disease severity is associated with reduced polyphenolic concentrations in lesioned but not green tissue in eelgrass

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publicSep 2025View details →

Disease severity in coinfected hosts: the importance of infection order

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publicNov 2020View details →

Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal disease

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publicJul 2020View details →

The effect of parasite dose on disease severity in the rodent malaria Plasmodium chabaudi

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opennotspecifiedMar 2020View details →

History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2

History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively.

opencc-zeroJun 2020View details →

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.

opencc-zeroMar 2020View details →

Modelling the impact of antibody-dependent enhancement on disease severity of ZIKV and DENV sequential and co-infection

Human infections with viruses of the genus Flavivirus, including dengue virus (DENV) and Zika virus (ZIKV), are of increasing global importance. Due to antibody dependent enhancement, secondary infection with one Flavivirus following primary infection with another {\it Flavivirus} can result in a significantly larger peak viral load with a much higher risk of severe disease. Although several mathematical models have been developed to quantify the virus dynamics in the primary and secondary infections of DENV, little progress has been made regarding secondary infection of DENV after a primary infection of ZIKV, or DENV-ZIKV co-infection. Here, we address this critical gap by developing compartmental models of virus dynamics. We first fitted the models to published data on dengue viral loads of the primary and secondary infections with the observation that the primary infection reaches its peak much more gradually than the secondary infection. We then quantitatively show that antibody dependent enhancement (ADE) is the key factor determining a sharp increase/decrease of viral load near the peak time in the secondary infection. In comparison, our simulations of DENV and ZIKV co-infection (simultaneous rather than sequential) show that ADE has very limited influence on the peak DENV viral load. This indicates pre-existing immunity to ZIKV is the determinant of a high level of ADE effect. Our numerical simulations show that 1) in the absence of ADE effect, a subsequent co-infection is beneficial to the second virus; 2) if ADE is feasible, then a subsequent co-infection can induce greater damage to the host with a higher peak viral load and a much earlier peak time for the second virus, and for the second peak for the first virus.

opencc-zeroDec 2012View details →

Data from: Relatedness severely impacts accuracy of marker- assisted selection for disease resistance in hybrid wheat

The accuracy of genomic selection depends on the relatedness between the members of the set in which marker effects are estimated based on evaluation data and the types for which performance is predicted. Here, we investigate the impact of relatedness on the performance of marker-assisted selection for fungal disease resistance in hybrid wheat. A large and diverse mapping population of 1,739 elite European winter wheat inbred lines and hybrids was evaluated for powdery mildew, leaf rust, and stripe rust resistance in multi-location field trials and fingerprinted with 9k and 90k SNP arrays. Comparison of the accuracies of prediction achieved with data sets from the two marker arrays revealed a crucial role for a sufficiently high marker density in genome-wide association mapping. Cross- validation studies using test sets with varying degrees of relationship to the corresponding estimation sets unraveled that close relatedness leads to a substantial increase in the proportion of total genotypic variance explained by the identified QTL and, consequently, to an overoptimistic judgment of the prospected precision of marker-assisted selection.

opencc-by-4.0Dec 2023View details →

Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity

Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity ' Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.nbd.2023.106382</a> Dataset description Data\data2publish\sub- : 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs, Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal  Data\data2publish\EA_info.xlsx: number of epileptiform activities automatically detected for each animal at d28 and d29 Data\data2publish\derivatives\eeg_preprocessing: results of the script A_EEG_preprocessing.m, for each animal and session Data\data2publish\derivatives\elec_layout: different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4 Data\data2publish\derivatives\network_metrics_wpli: results of network analyses (script C_network_analyses.m) Data\data2publish\derivatives\wpli: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit Code for analyses available here:  <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a> Abbreviations: EEG= electroencephalography

opencc-by-4.0Mar 2022View details →

Genetic mutations and decreased protein expression of ABCG2 urate transporters are associated with susceptibility to gout, disease severity and renal-overload hyperuricemia

We are releasing the data and code of our study of clinically defined gouty patients.

opencc-by-4.0Jul 2022View details →

The baseline immunological and hygienic status of pigs impact disease severity of African swine fever

Primary data of manuscript

opencc-by-4.0Oct 2024View details →

Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures

The repository contains all the data and scripts required to perform the analysis and generate the figures and tables presented in the article: "Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures". 0. DATA FOLDER: 0_Data This folder contains the raw phenotypic and genotypic data. I. FILE PREPARATION FOLDER: 1_FilePrep_Design_Fig The R script prepares the phenotypic and genotypic data for analysis and generates the kinship matrix. It also creates a figure illustrating the pairs of phenotyped genotypes. II. PHENOTYPIC ANALYSIS  FOLDER: 2_GeneticEffects_PhenoCorr_Residus The R script tests the significance of genetic effects, calculates the proportion of phenotypic variance explained for each phenotype, and checks for correlations between phenotypes. It also controls the residuals in the models. III. A. DGE-BASED GWAS FOLDER: 3_DGE_GWAS This folder contains three subfolders for each phenotype (GWAS_DGE_B, GWAS_DGE_N, GWAS_DGE_P). For example, the pycnidia folder contains the script 3.A_GWAS_DGE_P_AsREML_cluster.R, which performs DGE-based GWAS for the pycnidia phenotype. III. B. Analysis of GWAS Results for DGE FOLDER: 3_DGE_GWAS Within the same folder, the script 3.B_Results_GWAS_DGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs. IV. A. IGE-BASED GWAS FOLDER: 4_IGE_GWAS This folder contains three subfolders for each phenotype (GWAS_IGE_B, GWAS_IGE_N, GWAS_IGE_P). For example, the pycnidia folder contains the script 4.A_GWAS_IGE_P_AsREML_cluster.R, which performs IGE-based GWAS for the pycnidia phenotype. IV. B. Analysis of GWAS Results for IGE FOLDER: 4_IGE_GWAS Within the same folder, the script 4.B_Results_GWAS_IGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs. V. PREPARATION OF FILES FOR CO-GWAS FOLDER: 5_FilePrep_coGWAS The script 5.A_FilePrep_coGWAS.Rmd prepares the phenotypic and genotypic data for analysis after SNP pruning. The script 5.B_FilePrep_SNP_Pruning.R performs SNP pruning. The script 5.C_Plot_SNP_Pruning_position.R plots the positions of SNPs before and after pruning. VI. CO-GWAS FOLDER: 6_coGWAS This folder contains three subfolders for each phenotype (coGWAS_B, coGWAS_N, and coGWAS_P).  For example, the pycnidia folder contains the script 6.A_coGWAS_DGEIGE_P_Sommer_cluster.R, which performs the co-GWAS for the pycnidia phenotype. The other scripts in this folder combine the result files. VII. HEATMAPS AND QQPLOT OF CO-GWAS RESULTS FOLDER: 7_Heatmaps_qqplots_coGWAS This folder contains three subfolders for each phenotype (Heatmaps_qqplots_B, Heatmaps_qqplots_N, and Heatmaps_qqplots_P). For example, the pycnidia subfolder includes two scripts: 7.A_coGWAS_P_Heatmaps.R generates heatmaps for the pycnidia phenotype and 7.B_coGWAS_P_qqplots.R produces the QQ plot. VIII. BOXPLOTS - 3D PLOTS - PHYSICAL MAPS FOLDER: 8_3Dplots_PhysicalMaps_Boxplots_coGWAS This folder contains three subfolders for each phenotype (Boxplots_PhysicalMaps_3Dplots_B, Boxplots_PhysicalMaps_3Dplots_N, and Boxplots_PhysicalMaps_3Dplots_P). For example, the pycnidia folder contains the script 8_coGWAS_P_Boxplots_PhysicalMaps_3Dplots.R, which generates 3D plots, boxplots and physical maps for the significant interactions.  IX. CIRCULAR PLOTS   FOLDER: 9_CircularPlots_coGWAS This folder contains three subfolders for each phenotype (CircularPlots_B, CircularPlots_N, and CircularPlots_P). For example, the pycnidia folder contains the script 9_coGWAS_P_CircularPlot.R, which generates the necessary files to create the circular plot.  The script circos.conf creates the circular plot.  X. GO ENRICHMENT ANALYSIS  FOLDER: 10_GOterms_coGWAS This folder contains three subfolders for each phenotype (GOenrichments_B, GOenrichments_N, and GOenrichments_P). For example, the pycnidia folder contains the script 10_coGWAS_P_GOenrichments.Rmd, which generates GO enrichment plots.

opencc-zeroJul 2021View details →

Data from: Association of serum neurofilament light (sNfL) and disease severity in patients with spinocerebellar ataxia type 3

<a name="_Hlk22462511">Objective:</a> To investigate serum neurofilament light protein (sNfL) levels in patients with spinocerebellar ataxia type 3 (SCA3) and to determine whether they are associated with disease severity. Methods: This cross-sectional study enrolled 185 healthy controls and 235 ATXN3 mutation carriers (17 asymptomatic stage, 20 preclinical stage, and 198 ataxic stage). We measured sNfL levels with the single molecule array (Simoa) platform. Clinical disease severity was assessed using the scale of assessment and rating of ataxia (SARA) and the inventory of non-ataxia signs (INAS). In a subgroup of 50 ataxic stage patients, we further evaluated the gray matter volume and the integrity of white matter fibers by magnetic resonance imaging (MRI). Results: sNfL concentrations were elevated in asymptomatic, preclinical, and ataxic ATXN3 mutation carriers compared to controls (12.18 [10.20-13.92], 21.84 [18.37-23.45], 36.06 [30.04-45.90] and 8.24 [5.92-10.84] pg/mL, median [interquartile range], respectively, P < 0·001). sNfL correlated with SARA (r=0.406, 95% confidence interval 0.284-0.515, P < 0.0001) and INAS (r=0.375, 95% confidence interval 0.250-0.487, P < 0.0001), and remained significant after adjustment for age and CAG repeats. In addition, we observed negative correlations of the sNfL with gray matter volume in the left precentral gyrus and the left paracentral lobule as well as with the mean diffusivity in widespread white matter tracts. Conclusion: Our results demonstrate that sNfL levels are increased in SCA3 and are associated with clinical disease severity, which supports sNfL as a biomarker for disease severity in SCA3.

ClinicalTrials.gov32/100

Assessment Of Disease Pathology And Key Therapeutic Targets In Severe Asthma

ClinicalTrials.gov study NCT00327197. IPD Sharing: Not stated. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov32/100

Placebo Controlled Study of Preladenant in Participants With Moderate to Severe Parkinson's Disease (P07037)

ClinicalTrials.gov study NCT01227265. IPD Sharing: YES. Countries: 0. Publications: 1.

ClinicalTrials.gov32/100

A Clinical Trial of TQC3721 Suspension for Inhalation in Patients With Moderate to Severe Chronic Obstructive Pulmonary Disease

ClinicalTrials.gov study NCT06527144. IPD Sharing: Not stated. Countries: 1. Publications: 0.