Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease

Connectome, traits and behavior data for APOE234 mice. <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>

opencc-zeroApr 2024View details →

Data from: A qualitative analysis of an Aβ-monomer model with inflammation processes for Alzheimer's disease

We introduce and study a new model for the progression of Alzheimer's disease incorporating the interactions of Aβ-monomers, oligomers, microglial cells and interleukins with neurons through different mechanisms such as protein polymerization, inflammation processes and neural stress reactions. In order to understand the complete interactions between these elements, we study a spatially-homogeneous simplified model that allows to determine the effect of key parameters such as degradation rates in the asymptotic behavior of the system and the stability of equilibriums. We observe that inflammation appears to be a crucial factor in the initiation and progression of Alzheimer's disease through a phenomenon of hysteresis, which means that there exists a critical threshold of initial concentration of interleukins that determines if the disease persists or not in the long term. These results give perspectives on possible anti-inflammatory treatments that could be applied to mitigate the progression of Alzheimer's disease. We also present numerical simulations that allow to observe the effect of initial inflammation and concentration of monomers in our model.

opencc-by-4.0Jun 2024View details →

Supplementary data: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer's disease

Supplementary data for: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer’s disease.  Supplementary_Table1_QC: Excel sheet containing QC metrics for the RNA-seq data and the other containing QC metrics for the ATAC-seq data.  Supplementary_Table2_DEGs: Excel sheet containing DeSeq2 differential expression analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3.  Supplementary_Table3_MAGMA_geneset_analysis_res: CSV file containing MAGMA gene set analysis results using the differentially expressed genes (FDR < 0.05) for the comparisons outlined in Supplementary_Table2_DEGs and three independent AD GWAS.  Supplementary_Table4_DARs: Excel sheet containing DeSeq2 differential accessibility analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3.  Supplementary_Table5_sLDSC_res.csv: CSV file containing s-LDSC results using the consensus set of ATAC-seq peaks with three brain disorder GWAS (Alzheimer's disease, autism spectrum disorder, and amyotrophic lateral sclerosis).  Supplementary_Table6_WGCNA_clusterProfiler_pathway_enrichment.csv: CSV file containing pathway enrichment results using two WGCNA-identified modules that were significantly upregulated in APOE2-expressing microglia.  Supplementary_Table7_homer_motifEnrichment_res.xlsx: Excel sheet containing Homer motif enrichment analysis results using top 100 peaks with increased and decreased chromatin accessibility for APOE2 vs APOE3, APOE4 vs APOE3, and APOE4 vs APOE2.

opencc-by-4.0Sep 2021View details →

The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer's Disease: A Review- Screening

The Potential of Naturalistic Eye Movement tasks in the  Diagnosis of Alzheimer’s Disease: A Review- Screening file

opencc-zeroFeb 2022View details →

Processed FDG-PET data from: A computational model of neurodegeneration in Alzheimer's disease

Disruption of mental functions in Alzheimer's disease (AD) and related disorders is accompanied by selective degeneration of brain regions. These regions comprise large-scale ensembles of cells organized into systems for mental functioning, however the relationship between clinical symptoms of dementia, patterns of neurodegeneration, and functional systems is not clear. We developed a model of the association between dementia symptoms and degenerative brain anatomy using F18-fluorodeoxyglucose (FDG) PET and dimensionality reduction techniques patients with AD. This data and code package contains preprocessed FDG-PET images from 423 subjects across the Alzheimer's disease spectrum and the MATLAB code to produce eigenbrains from this data.

opencc-zeroJul 2022View details →

DYRK1a inhibitor mediated rescue of Drosophila models of Alzheimer's disease-Down Syndrome phenotypes

Alzheimer's disease (AD) is the most common neurodegenerative disease which is becoming increasingly prevalent due to ageing populations resulting in huge social, economic, and health costs to the community. Despite the pathological processing of genes such as Amyloid Precursor Protein (APP) into Amyloid-b and Microtubule Associated Protein Tau (MAPT) gene, into hyperphosphorylated Tau tangles being known for decades, there remains no treatments to halt disease progression. One population with increased risk of AD are people with Down syndrome (DS), who have a 90% lifetime incidence of AD, due to trisomy of human chromosome 21 (HSA21) resulting in three copies of APP and other AD-associated genes, such as DYRK1A (Dual specificity tyrosine-phosphorylation-regulated kinase 1A) overexpression. This suggests that blocking DYRK1A might have therapeutic potential. However, it is still not clear to what extent DYRK1A overexpression by itself leads to AD-like phenotypes and how these compare to Tau and Amyloid-b mediated pathology. Likewise, it is still not known how effective a DYRK1A antagonist may be at preventing or improving any Tau, Amyloid-b and DYRK1a mediated phenotype. To address these outstanding questions, we characterised Drosophila models with targeted overexpression of human Tau, human Amyloid-b or the fly orthologue of DYRK1A, called minibrain (mnb). We found targeted overexpression of these AD-associated genes caused degeneration of photoreceptor neurons, shortened lifespan, as well as causing loss of locomotor performance, sleep, and memory. Treatment with the experimental DYRK1A inhibitor PST-001 decreased pathological phosphorylation of human Tau (at serine (S) 262). PST-001 reduced degeneration caused by human Tau, Amyloid-b or mnb lengthening lifespan as well as improving locomotion, sleep and memory loss caused by expression of these AD and DS genes. This demonstrated PST-001 effectiveness as a potential new therapeutic targeting AD and DS pathology.

opencc-by-4.0Jul 2024View details →

Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics

Supplementary material to the article: Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer’s Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics,These data are available to researchers.

opencc-zeroJul 2024View details →

Data from: Flortaucipir PET uncovers relationships between tau and β-amyloid in aging, primary age related tauopathy, and Alzheimer disease

[18F]-Flortaucipir PET is considered a good biomarker of Alzheimer's disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by β-amyloid. It is also unclear whether flortaucipir can detect tau in definite primary age-related tauopathy (PART). We identified 248 individuals at Mayo Clinic that had undergone [18F]-flortaucipir PET during life, had died, and undergone an autopsy, 239 cases of which also had β-amyloid PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage and Thal β-amyloid phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with high β-amyloid phase. Flortaucipir uptake linearly increased with β-amyloid phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer's disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART and imaging defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored histopathological tau distribution, were influenced by β-amyloid phase, and were useful for distinguishing different ADNC scores and PART.

opencc-by-4.0Dec 2017View details →

Noscapine treatment effect in transgenic mouse model of Alzheimer's disease

Cerebrovascular dysfunction and neuroinflammation play key roles in the pathophysiology of Alzheimer’s disease (AD). The kinin-kallikrein system involving bradykinin receptor has been proposed at the nexus of beta-amyloid, vascular pathology and inflammation in patients with AD and in animal models. Here, we evaluated the effect of blocking the bradykinin receptors 1 and 2 by treatment with the bradykinin antagonist noscapine on cerebrovascular dysfunction, inflammation and amyloid pathology in a transgenic mouse model of amyloidosis. Transgenic arcAβ mice, and wild-type littermates of 14 months-of-age were either treated with noscapine (3 g/L, acidified drinking water) or received drinking water as control for three months (n = 8-11 per group). Arterial spin labeling magnetic resonance imaging showed alleviated regional hypoperfusion in noscapine-treated arcAb compared to control arcAb mice. Functional magnetic resonance imaging showed mitigated reduced regional cerebral vascular reactivity in noscapine-treated arcAb compared to control arcAb mice.

opencc-by-2.0Oct 2019View details →

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]

This file contains the pretrained models and the evaluation of the pipelines described in the paper Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation. Source code can be downloaded at: <a href="https://github.com/aramis-lab/AD-DL">https://github.com/aramis-lab/AD-DL</a> Also, single files can be obtained at: <a href="https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/">https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/</a> The structure of the compressed file is as follows: clinicadl_models/ ├── 2D_slice │   ├── baseline │   │   ├── AD_CN │   │   │   ├── best_model │   │   │   └── performances │   │   └── AD_CN_dataleakage │   │       ├── best_model │   │       └── performances │   └── longitudinal │       └── AD_CN │           ├── best_model │           └── performances ├── 3D_patch │   ├── baseline │   │   ├── AD_CN │   │   │   ├── best_model │   │   │   └── performances │   │   └── sMCI_pMCI │   │       ├── best_model │   │       └── performances │   └── longitudinal │       ├── AD_CN │       │   ├── best_model │       │   └── performances │       └── sMCI_pMCI │           ├── best_model │           └── performances ├── 3D_ROI_based │   ├── baseline │   │   ├── AD_CN │   │   │   ├── best_model │   │   │   └── performances │   │   └── sMCI_pMCI │   │       ├── best_model │   │       └── performances │   └── longitudinal │       ├── AD_CN │       │   ├── best_model │       │   └── performances │       └── sMCI_pMCI │           ├── best_model │           └── performances ├── 3D_subject │   ├── baseline │   │   ├── AD_CN │   │   │   ├── best_model │   │   │   └── performances │   │   └── sMCI_pMCI │   │       ├── best_model │   │       └── performances │   └── longitudinal │       ├── AD_CN │       │   ├── best_model │       │   └── performances │       └── sMCI_pMCI │           ├── best_model │           └── performances ├── autoencoders │   ├── 3D_patch │   │   ├── baseline │   │   │   └── best_model │   │   └── longitudinal │   │       └── best_model │   ├── 3D_ROI_based │   │   ├── baseline │   │   │   └── best_model │   │   └── longitudinal │   │       └── best_model │   └── 3D_subject │       └── baseline │           ├── extensive │           └── minimal └── svm     ├── baseline     │   ├── AD_CN     │   │   ├── all_subjects.tsv     │   │   └── classifier     │   └── sMCI_pMCI     │       ├── all_subjects.tsv     │       └── classifier     └── longitudinal         ├── AD_CN         │   ├── all_subjects.tsv         │   └── classifier         └── sMCI_pMCI             ├── all_subjects.tsv             └── classifier We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a .pth.tar file (Pytorch format) inside the best_model folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The performances folder contains the computed metrics for the correponding model (ACC, BA, etc).  For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, tsv files with the subject list.

opencc-zeroJun 2021View details →

Supplemental Materials Eikelboom et al. Neuropsychiatric and cognitive symptoms across the Alzheimer's disease clinical spectrum

The objective of this study was to investigate the prevalence and trajectories of neuropsychiatric symptoms (NPS) in relation to cognitive functioning in a cohort of amyloid-β positive individuals across the Alzheimer's disease (AD) clinical spectrum. In this single-center observational study, we included all individuals who visited the Alzheimer Center Amsterdam and had 1) a clinical diagnosis of subjective cognitive decline (SCD), mild cognitive impairment (MCI), or probable AD dementia, and 2) were amyloid-β positive (A+). We measured NPS with the Neuropsychiatric Inventory (NPI), examining total scores and the presence of specific NPI domains. Cognition was assessed across five cognitive domains and with the MMSE. We examined trajectories including model based trends for NPS and cognitive functioning over time. We used linear mixed models to relate baseline NPI scores to cognitive functioning at baseline (whole-sample) and longitudinal time-points (subsample n=520, Mean=1.8 [SD=0.7] years follow-up). We included 1,524 amyloid-β positive individuals from the Amsterdam Dementia Cohort with A+ SCD (n=113), A+ MCI (n=321), or A+ AD dementia (n=1,090). NPS were prevalent across all clinical AD stages (≥1 NPS 81.4% in SCD, 81.2% in MCI, 88.7% in dementia; ≥1 clinically relevant NPS 54.0% in SCD, 50.5% in MCI, 66.0% in dementia). Cognitive functioning showed an uniform gradual decline; while in contrast, large intra-individual heterogeneity of NPS was observed over time across all AD groups. At baseline, we found associations between NPS and cognition in dementia that were most pronounced for NPI total scores and MMSE (range β=0.18 to 0.11, FDR-adjusted p<0.05), while there were no cross-sectional relationships in SCD and MCI (range β=-0.32 to 0.36, all FDR-adjusted p>0.05). There were no associations between baseline NPS and cognitive functioning over time in any clinical stage (range β=-0.13 to 0.44, all FDR-adjusted p>0.05). NPS and cognitive symptoms are both prevalent across the AD continuum, but show a different evolution during the course of the disease.

opencc-by-4.0Dec 2018View details →

Relative cerebral flow from dynamic PIB scans as an alternative for FDG scans in Alzheimer's disease PET studies

In Alzheimer’s Disease (AD) dual-tracer positron emission tomography (PET) studies with 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) and 11C-labelled Pittsburgh Compound B (PIB) are used to assess metabolism and cerebral amyloid-β deposition, respectively. Regional cerebral metabolism and blood flow (rCBF) are closely coupled, both providing an index for neuronal function. The present study compared PIB-derived rCBF, estimated by the ratio of tracer influx in target regions relative to reference region (R1) and early-stage PIB uptake (ePIB), to FDG scans. Fifteen PIB positive (+) patients and fifteen PIB negative (-) subjects underwent both FDG and PIB PET scans to assess the use of R1 and ePIB as a surrogate for FDG. First, subjects were classified based on visual inspection of the PIB PET images. Then, discriminative performance (PIB+ versus PIB-) of rCBF methods were compared to normalized regional FDG uptake. Strong positive correlations were found between analyses, suggesting that PIB-derived rCBF provides information that is closely related to what can be seen on FDG scans. Yet group related differences between method’s distributions were seen as well. Also, a better correlation with FDG was found for R1 than for ePIB. Further studies are needed to validate the use of R1 as an alternative for FDG studies in clinical applications. The enclosed dataset refers to the work developed at the University Medical Center Groningen and consists of all the data retrieved from the PET images and from clinical assessment used in this work.

opencc-by-4.0Feb 2023View details →

Data for "Brain cell-type shifts in Alzheimer's disease, autism and schizophrenia interrogated using methylomics and genetics"

Data for "Genetic and methylomic interrogation of brain cell-type shifts in autism, schizophrenia, and Alzheimer’s disease" (Yap et al. 2023). Source data from ROSMAP, LIBD and UCLA_ASD post-mortem brain datasets. This data repository contains 3 files: 220819_Supplementary_Tables.xlsx Supplementary Tables for the manuscript: <ol> <li>Supplementary Table 1: Comparison of CTP deconvolution methods in the ROSMAP dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 2: Comparison of CTP deconvolution methods in the LIBD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 3: Comparison of CTP deconvolution methods in the UCLA_ASD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 4: Deconvolved brain CTPs (raw), with covariates.</li> <li>Supplementary Table 5: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and raw PGS for all ancestries. Comp* indicates compositionally-aware principal components of the CTP data; *pgs_raw indicates PGS calculated for using genotyping across all ancestries. This table has a total of n=1,098 across all ancestries, including n=885 EUR. After applying a rel<0.05 threshold on the n=885 EUR, there were n=878 EUR which were used in the PGS analysis so that population stratification PCs did not simply capture family structure.</li> <li>Supplementary Table 6: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and standardised PGS for n=878 Europeans. *pgs_raw indicates unstandardised PGS, PC* indicates genotyping PCs within the European dataset, *_PGS indicates standardised PGS within the European subset.</li> <li>Supplementary Table 7: Deconvolved brain CTPs (clr-transform, offset 1e-3), adjusted for oligodendrocyte proportions, with covariates.</li> <li>Supplementary Table 8: Deconvolved brain CTPs (raw), adjusted for oligodendrocyte proportions, with covariates.</li> </ol> 20220108_maf05_gwas_ctp.tar.gz GWAS summary statistics for the 7 brainCTPs (clr-transformed): Exc, Inh, Astro, Endo, Micro, Oligo, OPC METAL output format: MarkerName: SNP Allele1 Allele2 Freq1: Allele1 frequency FreqSE: frequency standard error MinFreq: minimum Allele1 frequency in meta-analysis MaxFreq: maximum Allele1 frequency in meta-analysis Effect: effect size StdErr: standard error of effect size P-value: calculated in inverse variance weighted meta-analysis Direction: directions of effects across the 3 datasets HetISq: heterozygosity I-squared HetChiSq: heterozygosity chi-squared HetDf: heterozygosity degress of freedom HetPVal: heterozygosity test p-value 20220108_maf05_gwas_ctp_pc.tar.gz GWAS summary statistics for the 5 CTP_PCs METAL output format (see above)   Source data: ROSMAP: Raw methylation .idat files were obtained from Synapse accession syn7357283. Whole genome sequencing .vcf files (variants jointly called with MSBB and Mayo studies) were obtained from Synapse accession syn11707420. LIBD: Raw methylation .idat files were obtained from GEO accession GSE74193. SNP genotypes were downloaded from dbGaP accession phs000417.v2.p1. UCLA-ASD: The processed methylation beta matrix was downloaded from Synapse accession syn8263588. SNP genotypes were downloaded from Synapse accession syn10537134. GWAS summary statistics are available at: 10.5281/zenodo.7604231 Code is available on GitHub: gandallab/brain_CTP_deconv

opencc-by-4.0May 2023View details →

Proteomics analysis for: The platelet transcriptome and proteome in Alzheimer's disease and aging: an exploratory cross-sectional study

Alzheimer’s disease (AD) and aging are associated with platelet hyperactivity. However, the mechanisms underlying abnormal platelet function in AD and aging are yet poorly understood. To explore the molecular profile of AD and aged platelets, we investigated platelet activation (i.e., CD62P expression), proteome and transcriptome in AD patients, non-demented elderly, and young individuals as controls. AD, aged and young individuals showed similar levels of platelet activation based on CD62P expression. However, AD and aged individuals had a proteomic signature suggestive of increased platelet activation compared with young controls. Transcriptomic profiling suggested the dysregulation proteolytic machinery involved in the regulation of platelet function, particularly in the ubiquitin-proteasome system in AD and autophagy in aging. The functional implication of these transcriptomic alterations remains unclear and requires further investigations.

opencc-by-4.0Aug 2023View details →

Identification of Potential JNK3 Inhibitors Through Virtual Screening, Molecular Docking And Molecular Dynamics Simulation as Therapeutics for Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurological disorder without effective treatment. One factor in its development is c-Jun N-terminal kinases (JNKs), a type of protein related to brain function. JNK3, found mainly in the brain, contributes to AD by promoting brain abnormalities. Current research aims to create new JNK3 inhibitors for AD treatment using a virtual screening method. A database of compounds was filtered, and five potential compounds were identified with better scores than a reference. These compounds underwent simulations and energy calculations, showing stability and potential as JNK3 inhibitors.

opencc-by-4.0Oct 2023View details →

Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

In the present research, we propose a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against AD. The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (<a href="https://coconut.naturalproducts.net/download">https://coconut.naturalproducts.net/download</a>) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to enhance the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Moreover, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.

Statin Effects on Beta-Amyloid and Cerebral Perfusion in Adults at Risk for Alzheimer's Disease

ClinicalTrials.gov study NCT00939822. IPD Sharing: Not stated. Countries: 1. Publications: 2.

A Phase 2 Study to Evaluate Efficacy and Safety of AL002 in Participants With Early Alzheimer's Disease

ClinicalTrials.gov study NCT04592874. IPD Sharing: UNDECIDED. Countries: 12. Publications: 2.

Study Evaluating The Efficacy And Safety Of Bapineuzumab In Alzheimer Disease Patients

ClinicalTrials.gov study NCT00667810. IPD Sharing: Not stated. Countries: 26. Publications: 2.

Effects of Methylene Blue in Healthy Aging, Mild Cognitive Impairment and Alzheimer's Disease

ClinicalTrials.gov study NCT02380573. IPD Sharing: Not stated. Countries: 1. Publications: 15.