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646 results for “Clinical data”
Data from: The clinical, histological, and genotypic spectrum of SEPN1-related myopathy: a case series
<p><b><span>Objective: </span></b><span>To clarify the prevalence, long-term natural history and severity determinants of SEPN1-related myopathy (SEPN1-RM), we analyzed a large international case series. </span></p> <p><b>Methods: </b>Retrospective clinical, histological and genetic analysis of 132 pediatric and adult patients (2-58 years) followed-up for several decades.</p> <p><b><span>Results: </span></b><span>The clinical phenotype was marked by severe axial muscle weakness, spinal rigidity and scoliosis (86.1%, from 8.9±4 years), with relatively-preserved limb strength and previously-unreported ophthalmoparesia in severe cases. All patients developed respiratory failure (from 10.1±6 years), 81.7% requiring ventilation while ambulant. Histopathologically, 79 muscle biopsies showed large variability, partly determined by site of biopsy and age. Multi-minicores were the most common lesion (59.5%), often associated with mild dystrophic features and occasionally with eosinophilic inclusions. Identification of 65 SEPN1 mutations, including 32 novel ones and the first pathogenic CNV, unveiled exon 1 as the main mutational hotspot and revealed the first genotype-phenotype correlations, bi-allelic null mutations being significantly associated with disease severity (<i>p</i>=0.017). SEPN1-RM was more severe and progressive than previously thought, leading to loss of ambulation in 10% cases, systematic functional decline from the end of the third decade and reduced lifespan even in mild cases. The main prognosis determinants were scoliosis/respiratory management, <i>SEPN1</i> mutations and body mass abnormalities, which correlated with disease severity. Finally, we propose a set of severity criteria, provide quantitative data for outcome identification and establish a need for age stratification.</span></p> <p><b><span>Conclusion</span></b><span>: Our results inform clinical practice, improving diagnosis and management, and represent a major breakthrough for clinical trial readiness in this not-so-rare disease.</span></p>
Data from: Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
Objective: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. Results: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Clinical trial generalizability assessment in the big data era: a review
<p><span><span><span>Clinical studies, especially randomized controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic scoping review to understand the practice of generalizability assessment. We identified 187 relevant papers and systematically organized these studies in a taxonomy with three dimensions: (1) data availability (i.e., before or after trial [<i>a priori</i> vs <i>a posteriori</i> generalizability]), (2) result outputs (i.e., score vs non-score), and (3) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but less than 30% of studies reported positive generalizability results. As <i>a priori</i> generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, less than 40% of the studies in our review assessed <i>a priori</i> generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.</span></span></span></p>
(Extended Data) Amplicon deep sequencing of ama1 and mdr1 to track within-host P. falciparum diversity throughout treatment in a clinical drug trial
<p>These extended data accompany the manuscript: Targeted Amplicon deep sequencing of ama1 and mdr1 to track within-host <em>P. falciparum</em> diversity throughout treatment in a clinical drug trial</p> <p><strong>Table S1: Concentration ratios and resulting parasitemia in artificial dna mixtures of P. falciparum Lab Isolates 3D7 and Dd2.</strong> This table presents the parasitemia for the artificial mixtures of P. falciparum lab isolates 3D7 and Dd2. Each mixture was prepared at varying ratios of 3D7 to Dd2, starting from equal proportions to a complete presence of only 3D7. The original concentration of each isolate was approximately 50,000 parasites per microliter (pf/μl), and the table displays the proportion of each strain in the mixture and the resulting total parasitemia concentration.</p> <p><strong>Table S2. List of PCR and deep sequencing primers.</strong> This table shows the list of forward and reverse primers used for deep sequencing. In boldface are the MID tags, while in the regular face are the forward primers</p> <p><strong>Table S3. The relative frequencies of each ama1 variant and the number of samples with each variant.</strong> The relative frequencies (%) of the 33 AMA1 variants in pre-and post-treatment samples (n = 330) are shown as a 33 amino acid sequence. The frequencies were calculated by dividing the number of reads of each microhaplotype by the total number of reads obtained per sample (116,187,131).</p> <p><strong>Table S4. Distribution of microhaplotypes among samples.</strong> This table shows the occurrence of microhaplotypes across all participants, both with monoclonal and multiclonal ama1 infections. It presents the ama1 clonality – monoclonal or multiclonal (column 1) - participant IDs (column 2), microhaplotype IDs (column 3), and the relative frequencies of these microhaplotypes across timepoints from 0 to 1008 hours (day 42) (column 3). Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S5. Distribution of rare microhaplotypes among samples.</strong> This table shows the occurrence of rare microhaplotypes in various samples. It presents participant IDs (column 1), microhaplotype IDs (column 2), and the relative frequencies of these microhaplotypes across time points from 0 to 1008 hours (day 42) (column 3). Samples containing rare microhaplotypes - specifically from PID10, PID32, PID38, PID40, PID49, PID60, PID63, and PID65 - are shown in orange, along with the corresponding rare microhaplotypes and their time points of occurrence. Furthermore, participants are categorised by shared microhaplotypes to indicate instances of rarity and commonality. Except for one microhaplotype unique to PID30, rare microhaplotypes were detected in several samples, frequently exceeding a 5% relative frequency. Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S6. The parasitemia levels associated with each ama1 microhaplotype per timepoint.</strong> This table shows the parasitemia for each ama1 microhaplotype per timepoint and each participant. “Patient ID” represents the patient ID, “AMA1 COI at 0h” represents the complexity of infection (COI) for each participant at baseline, based on ama1 while subsequent columns represent the parasitemia for each ama1 microhaplotype from timepoint 0h to 1008h. Parasitemia was back-calculated using the COI and total parasitemia for each time point. For time points with a COI > 1, parasitemia for the respective ama1 microhaplotypes are separated by commas, cells in red indicate timepoints without sequencing data (ND = not determined). In contrast, cells in grey indicate time points where microhaplotypes were detected below 10 parasites/μl, hence at risk of falling below the sampling limit.</p> <p><strong>Figure S1. Performance of AmpSeq in the sequencing controls.</strong> Six aliquots were prepared for each control set to ensure sufficient control data in case of PCR or sequencing failure. The median read depth in the lab controls was 5,658 (range 4,310 – 12,603) and 704 (291 – 1,676). The x-axis represents the aliquot identifier across the five mixtures, starting from 1 to 6, while the y-axis represents the proportions of each variant across all aliquots. For ama1 (A), two variants (3D7 and Dd2) were detected, whereas in mdr1 (B), two variants were detected YY, FY and NY following amplification of Dd2 Copy I, Dd2 Copy II and 3D7, respectively. For ama1, sequencing failed for aliquot 6 of control set 1, while for mdr1, sequencing failed for aliquot 2 and 6 of control set 3, aliquots 1 and 6 of control set 4 and aliquots 1 and 5 of control set 5. Under the mdr1 control set 4, the Dd2 copy II (86F, 184Y) was not identified, possibly due to having very low concentrations that were not picked up in this aliquot. Based on our control mixtures, the minimum variant frequency we could detect was 5%.</p> <p><strong>Figure S2. Heatmaps of the successfully PCR amplified and sequenced samples for ama1 (A) and mdr1 (B).</strong> The rows represent the study participants, while the columns represent time in hours. Successfully sequenced samples are shown in blue, those that failed PCR are shown in red and those that failed sequencing are in black. The timepoint “ Rec” represents unscheduled visits where a recurrent sample was collected. The unshaded areas with "-" are time points where samples were not collected. For each time point, the number of samples successfully sequenced (n Successful) is indicated in the last row of each panel. The table in panel C shows the groupings of samples based on parasitemia, high (> 5,000), moderate (100-5,000) and low (< 100 parasites per microlitre). Many samples collected between 0h-12h had high parasitemia, samples collected between 18h–30h had moderate parasitemia, while samples collected after 30h were primarily of low parasitemia.</p> <p><strong>Figure S3. The mean complexity of infection (COI) by AMA1 throughout treatment.</strong> The mean COI (red diamonds) appeared to be stable (between 1.5 - 2) from baseline (0h) up to 72h and thereafter fluctuated due to the small sample sizes (<5) in the post-treatment samples. The black dots represent the COI per sample.</p> <p> </p>
COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'
<p>Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.</p>
Data for article "Personal approach for cancer treatment: a meta-analysis of Phase II Clinical trials"
<p>We conducted systematic review and meta-analysis to provide a comprehensive overview of outcomes in patients who underwent personalized genomics-based versus non-personalized treatment in oncology. The PubMed searches detected 803 studies based on phase II clinical trials’ results published from 2010 to 2021. We selected 50 studies, having 81 arms and 6536 patients for the analysis. We compared Response Rate (RR), medians and 1-year rates of Overall Survival (OS) and Progression-Free Survival (PFS) between genomics-based personalized and non-personalized arms. This repository contains final dataset (Dataset file) and t<span lang="EN-US">he information on 803 studies identified in the literature search (803 studies description file)</span>. </p> <p>The searches, study selection, data extraction and synthesis were performed in accordance to PRISMA (preferred reporting items for systematic review and meta-analysis) guidelines. The research protocol was registered in PROSPERO (International prospective register of systematic reviews, <a href="https://www.crd.york.ac.uk/PROSPERO" rel="nofollow">https://www.crd.york.ac.uk/PROSPERO</a>), record ID CRD42024504021. </p> <p>We performed proportional meta-analysis using the RStudio program, utilizing the R programming language and packages "meta", "metafor," and "tidyverse", the code is available at github: https://github.com/MikhailPot/PreciseOnco_meta-analysis </p>
The indirect impact of COVID-19 on major clinical outcomes of people with Parkinson's disease or atypical parkinsonism: a cohort study. Raw data
<p>Raw dataset of the study "The indirect impact of COVID-19 epidemic on major clinical outcomes of people with Parkinson’s disease (PD) or atypical parkinsonism: a cohort study"</p>
Raw Data for the article: A retrospective molecular epidemiological scenario of carbapenemase-producing Klebsiella pneumoniae clinical isolates in a Sicilian transplantation hospital shows a swift polyclonal divergence among sequence types, resistome and virulome
<p>In this work, we assessed and characterized the epidemiological scenario of carbapenem-resistant Klebsiella pneumoniae strains (CR-Kp) at IRCCS-ISMETT, a transplantation hospital in Palermo, Italy, from 2008 to 2017. A total of 288 K. pneumoniae clinical isolates were selected based on their resistance to carbapenems. Molecular characterization was also done in terms of the presence of virulence and resistance genes. All patients were inpatients from our facility and clinical isolates were collected from several sources, either from infection or colonization cases. We observed that, in agreement with the Italian epidemiological scenario, initially only ST258 and ST512 clade II (but not from clade I) were identified from 2008 to 2011. From 2012 onwards, other STs have been observed, including the clinically relevant ST101 and ST307, but also others not previously observed in other Italian health settings, such as ST220 and ST753. The presence of genes involved in resistance and virulence was confirmed, and a heterogeneous genetic resistance profile throughout the years was observed. Our work highlights that resistance genes are rapidly disseminating between different and novel K. pneumoniae clones which, combined with resistance to multiple antibiotics, can derive into more aggressive and pathogenic multidrug-resistant strains of clinical importance. Our results stress the importance of continuous surveillance of CR Enterobacterales in health facilities so that novel STs carrying resistance and virulence genes that may become increasingly pathogenic can be identified and adequate therapies to adopted to avoid their dissemination and derived pathologies.</p>
Raw Data for the article: Clinical and Molecular-Based Approach in the Evaluation of Hepatocellular Carcinoma Recurrence after Radical Liver Resection
<p><strong>Background: </strong>Hepatic resection remains the treatment of choice for patients with early-stage HCC with preserved liver function. Unfortunately, however, the majority of patients develop tumor recurrence. While several clinical factors were found to be associated with tumor recurrence, HCC pathogenesis is a complex process of accumulation of somatic genomic alterations, which leads to a huge molecular heterogeneity that has not been completely understood. The aim of this study is to complement potentially predictive clinical and pathological factors with next-generation sequencing genomic profiling and loss of heterozygosity analysis.</p> <p><strong>Methods: </strong>124 HCC patients, who underwent a primary hepatic resection from January 2016 to December 2019, were recruited for this study. Next-generation sequencing (NGS) analysis and allelic imbalance assessment in a case-control subgroup analysis were performed. A time-to-recurrence analysis was performed as well by means of Kaplan-Meier estimators.</p> <p><strong>Results: </strong>Cumulative number of HCC recurrences were 26 (21%) and 32 (26%), respectively, one and two years after surgery. Kaplan-Meier estimates for the probability of recurrence amounted to 37% (95% C.I.: 24-47) and to 51% (95% C.I.: 35-62), after one and two years, respectively. Multivariable analysis identified as independent predictors of HCC recurrence: hepatitis C virus (HCV) infection (HR: 1.96, 95%C.I.: 0.91-4.24, <em>p</em> = 0.085), serum bilirubin levels (HR: 5.32, 95%C.I.: 2.07-13.69, <em>p</em> = 0.001), number of nodules (HR: 1.63, 95%C.I.: 1.12-2.38, <em>p</em> = 0.011) and size of the larger nodule (HR: 1.11, 95%C.I.: 1.03-1.18, <em>p</em> = 0.004). Time-to-recurrence analysis showed that loss of heterozygosity in the <em>PTEN</em> loci (involved in the PI3K/AKT/mTOR signaling pathway) was significantly associated with a lower risk of HCC recurrence (HR: 0.35, 95%C.I.: 0.13-0.93, <em>p</em> = 0.036).</p> <p><strong>Conclusions: </strong>multiple alterations of cancer genes are associated with HCC progression. In particular, the evidence of a specific AI mutation presented in 20 patients seemed to have a protective effect on the risk of HCC recurrence.</p>
Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility
<p>Objective: Determine the clinical safety and feasibility of implementing a telemedicine and medication delivery service (TMDS) to address gaps in nighttime healthcare access for children in low-resource settings.</p> <p>Results: A total of 391 cases were enrolled from September 9th, 2019 to January 19th, 2021; 89% (347) received a household visit. Most cases were triaged as mild or moderate (92%; 361). Among the severe cases, 83% (20) sought subsequent referred care. The most common complaint was a respiratory problem (63%; 246). At 10-days, 95% (329) of parents reported their child's condition as "improved" or "recovered". Ninety-nine percent (344) rated the TMDS as "good" or "great". The median phone consultation was 20 minutes, time to arrival at the household was 73 minutes and total workflow per case was 114 minutes.</p> <p>Conclusion: The TMDS was a feasible healthcare delivery model with high rates of improved clinical status at 10-days.</p>
Single-cell expression and TCR data from CD19-specific CAR T cells in a phase I/II clinical trial
<p><span>By leveraging single-cell transcriptome and T cell receptor (TCR) sequencing, we aimed to track the transcriptional signatures of CAR T cell clonotypes throughout the course of treatment and furthermore identify molecular patterns leading to potent CAR T cell cytotoxicity. The data presented in this study encompass blood and bone marrow samples from patients ≤ 21 years of age with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) participating in the SJCAR19 phase I/II clinical trial (<a href="https://clinicaltrials.gov/ct2/show/NCT03573700">NCT03573700</a>). In brief, patients enrolled in the clinical trial received either 1 x 10^6 (dose level 1) or 3 x 10^6 (dose level 2) per kilogram of body weight following successful generation of autologous CAR T cell products and lymphodepleting chemotherapy. Peripheral blood was drawn from each participant every week until week 4 post-infusion, at week 6 or 8, and month 3 or 6 if feasible. At week 4 post-infusion, blood marrow was also collected from participants. Total T cells (CD3+) were sorted from each post-infusion sample, as well as the pre-infusion CAR T cell products, and processed through 10x Genomics' single-cell gene expression and V(D)J sequencing platform using the standard protocol. We identified a unique and unexpected transcriptional signature in a subset of pre-infusion CAR T cells that shared TCRs with post-infusion cytotoxic effector CAR T cells. Functional validation of cells with even a subset of these pre-effector markers demonstrated their immediate cytotoxic potential and resistance to exhaustion.</span></p>
Data and scripts from Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020
<p>Raw data and scripts used in the publication "<em>Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020 separator commenting unavailable</em>" in Eurosurveillance in 2022.</p> <p> </p> <p>https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.6.2100406</p>
Comparing mail-in self-collected specimens sent via United States Postal Service versus clinic-collected specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae in extra-genital sites data set
<p>This data set was used to evaluate the concordance between clinic-collected extra-genital specimens and self-collected mailed-in extra-genital specimens among participants seeking sexually transmitted infection testing at a free clinic in Hollywood, CA. The newest version of the file reflects sample adequacy control (SAC) cycle threshold values for each participant.</p>
Raw Data to manuscript on "Oxidation of a Zirconium Nitride Multilayer Covered Knee Implant after Two Years in Clinical Use"
<p>This dataset contains the raw data for the manuscript "Oxidation of a Zirconium Nitride Multilayer Covered Knee Implant after Two Years in Clinical Use", especially TEM, APT and SIMS data</p>
FHIRed MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data has been standardized in Fast Healthcare Interoperability Resources (FHIR) data standard. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2).</p> <p>Authors express their gratitude to all the AlmaHealthDB Team.</p> <h2>Instruction MOTU-to-FHIR Importer</h2> <div> <div> <p>The repository includes a Docker Compose setup for importing the MOTU dataset into a HAPI FHIR server, formatted as NDJSON following the HL7 FHIR R4 standards.</p> <h3>Prerequisites</h3> <p>Before you begin, ensure you have the following installed:</p> <ul> <li><a href="https://www.docker.com/get-started/">Docker </a></li> <li><a href="https://docs.docker.com/compose/install/">Docker Compose</a></li> <li><a href="https://www.python.org/downloads/">Python >=3.7</a> </li> <li><a href="https://pypi.org/project/requests/">Requests Python Library</a></li> </ul> </div> <div> <h3>How to run</h3> <ol> <li>First, unzip the <code>dataset</code> directory containing the NDJSON files.</li> <li>Open a terminal or command prompt in the root directory of this repository.</li> <li>Run the command <code>docker-compose up</code> in the terminal to start the Docker containers.</li> <li>Once the containers are up and running, open another terminal window in the root directory of this repository.</li> <li>Run the command <code>python main.py</code> in the terminal to start the data import process.</li> <li>After the import process is complete, you can access the HAPI FHIR server by opening a web browser and navigating to <a href="http://localhost:8082" target="_blank" rel="nofollow noreferrer noopener">http://localhost:8082</a>.</li> </ol> <p> </p> </div> </div>
Source data for publication "A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory" in Journal of Hepatology
<p>Processed genomic and transcriptomic data for the publication <a href="https://doi.org/10.1016/j.jhep.2024.05.017">https://doi.org/10.1016/j.jhep.2024.05.017</a>.</p> <p>cnv_segmentation.tsv: CNV segmentation file from Sequenza.</p> <p>cnv_arm.tsv: Significant arm level CNV events called by GISTIC, from broad_values_by_arm.txt file.</p> <p>cnv_gene.tsv: Gene level CNV events called by GISTIC, from all_threshold_by_genes.txt file. </p> <p>RNA_raw_counts.tsv: Raw RNA-seq read counts from featureCounts.</p> <p>snv_indel.tsv: All SNV and Indel called with annotation from Funcotator.</p> <p> </p>
Predicting Publication of Clinical Trials Using Structured and Unstructured Data
<p>This dataset (N=76,950) links metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a bibliographic database of academic journal articles), and can be used to model whether a clinical trial will get published or not.</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples - Time course data
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes only the 10X Flex time course data and analysis.</strong></p>
A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies
<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done. </p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies. </p>
Data for Bernabeu-Herrero et al, Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT
<p>This dataset is for the 2024 manuscript<strong>: </strong></p> <p><strong>Bernabéu-Herrero ME, Patel D, Bielowka A, Zhu J, Jain K, Mackay IS, Chaves Guerrero P, Emanuelli G, Jovine L, Noseda M, Marciniak SJ, Aldred MA, Shovlin CL. </strong></p> <p><strong>Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT. </strong></p> <p><strong>Blood. 2024 May 30;143(22):2314-2331. </strong></p> <p><strong>doi: 10.1182/blood.2023021777. PMID: 38457357; PMCID: PMC11181359.</strong></p> <p>It was originally uploaded in 2021 ahead of an earlier manuscript submission<br>- see https://www.biorxiv.org/content/10.1101/2021.12.05.471269v1</p>
ScienceDex guides
Understand access before you commit
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.