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467 results for “Trial Data”
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>
Study protocol and data dictionary: Effectiveness of a GP delivered medication review in reducing polypharmacy and potentially inappropriate prescribing in older patients with multimorbidity in Irish primary care: a cluster randomised controlled trial (SPPiRE study)
<p><strong>Methods</strong></p> <p><strong>Study design and participants</strong></p> <p>The methods for the SPPiRE cluster RCT have been described in the trial protocol (21). This study is reported in line with the CONSORT 2010 cluster RCT checklist (22), see Appendix 1, and was approved by the Irish College of General Practitioners Research Ethics Committee. In brief, SPPiRE was a pragmatic two arm cluster RCT, with the intervention delivered to GP clusters and analysis of outcomes at the patient level. Information about the trial was publicised through a variety of GP research, teaching and training networks throughout Ireland. Eligible practices expressing an interest were formally invited. Practices were eligible to participate if they had at least 300 registered patients aged ≥65 years (based on the need to identify a sufficient number of eligible participants) and used either of the two Irish GP practice management systems (PMS) with over 80% national cover; this enabled use of a SPPiRE patient finder tool which was developed and embedded into these systems. Practices were excluded if they were currently involved in a medication management or prescribing trial or if they were unable to recruit at least five participants.</p> <p>Eligible patients were aged ≥65 years and prescribed ≥15 repeat medicines. A repeat medicine was defined as any unique item with a World Health Organisation Anatomical Therapeutic Chemical code on the patient’s current repeat prescription. Patients were excluded if they had been recruited into a practice that was unable to recruit at least four other participants, they were judged by their GP as unable to give informed consent or they were unable to attend the practice for a face to face medication review, (e.g. nursing home residents and house bound patients). Recruited GPs ran the SPPiRE patient finder tool and screened the generated list to ensure only eligible patients were invited. Practices who identified more than 40 eligible patients were supported in selecting a random sample of 30 patients to invite. All recruited practices and patients gave fully informed consent and baseline data was collected prior to practice allocation, to reduce the likelihood of selection bias.</p> <p><strong>Randomisation and masking</strong></p> <p>Recruited practices were allocated to intervention or control groups by minimisation using Minimpy software (23) by the trial statistician (FB) who had no knowledge of participating practices. Minimisation variables included practice size (number of GP sessions per week, 0-14, 14-28 and 28 or more) and location (urban, rural or mixed). Considering the nature of the intervention, it was not possible to blind GPs or patients to the intervention, however to reduce the risk of detection bias the two primary outcome measures; the number of repeat medicines and whether a PIP was present were assessed by an independent blinded pharmacist (MF).</p> <p><strong>Procedures</strong></p> <p>Intervention GPs received unique login details to the SPPiRE website where they had access to five training videos and a template for performing the SPPiRE medication review. The training videos provided background information on multimorbidity and polypharmacy, PIP, eliciting patient treatment priorities and conducting a brown bag medication review. GPs were instructed to book a double appointment and to ask their patients to bring all their medicines in to the medication review visit with them. The SPPiRE medication review process had two main components; gather and record information and then to discuss and agree changes with their patient based on the recorded information, with a focus on deprescribing medicines that were potentially inappropriate, figure 1. The website provided suggested treatment alternatives for identified PIP but all treatment decisions were ultimately at the discretion of the individual GP, based on their clinical judgement and their patients’ individual priorities.</p> <p>Control GPs delivered usual care during the six to twelve month study period. At the time of intervention delivery there was no structured chronic disease management programme in Irish primary care and many patients with multimorbidity attended multiple hospital specialists. In Ireland, the majority of people aged ≥70 years of age have access to free GP visits and medicines with some prescription charge co-payments. In the 65 – 69 year old age category a lower proportion have access to both free GP visits and prescription medicines. Access to specialists and diagnostics in secondary care is free for the entire population.</p> <p><strong>Outcomes</strong></p> <p>The two primary outcomes were the number of repeat medicines and the proportion of patients with any PIP, from a list of 34 pre-specified indicators (see Appendix 2). A series of secondary prescribing related outcome measure were pre-specified to allow a more in depth analysis of the effect of the intervention on prescribing. These were:</p> <ul> <li>The number of medicines stopped and started</li> <li>The proportion of patients with a reduction in significant polypharmacy (defined as ≥15 repeat medicines)</li> <li>The number of PIP</li> <li>The proportion of patients with a high risk PIP (see Appendix 2)</li> <li>The proportion of patients with any reduction in PIP</li> </ul> <p>Secondary patient reported outcomes measures were included to capture the effectiveness of the intervention from the patients’ perspective. These were:</p> <ul> <li>Health related Quality of life (EQ5D-5L)(24)</li> <li>Revised Patients' attitudes towards deprescribing (rPATD) (25)</li> <li>Multimorbidity Treatment Burden Questionnaire (MTBQ) (26)</li> </ul> <p>Health care utilisation data was collected to assess the effect of the intervention on health care usage and for the trial’s economic evaluation.</p> <p>Outcomes were collected at baseline and at six months after intervention delivery. Patient reported measures were collected by postal questionnaires. Data for all other measures including prescribed medicines, medical and investigations history and healthcare utilisation were collected by participating GPs and submitted to the study manager (CMC). This was a deviation from the original protocol, which indicated this data would be collected by the research team. This deviation related to changes in data protection and national health research regulations during the study period, which precluded research team access to the patients’ full clinical record.</p> <p><strong>Adverse events</strong></p> <p>Information on adverse events such as mortality, ED presentations and hospital admissions was collected at follow up. Given the deprescribing approach of the intervention a safety protocol for identifying and reporting any suspected adverse drug withdrawal events (ADWEs) was developed. An ADWE is defined as either recurrence of the condition for which the drug was prescribed (e.g. recurrence of angina after stopping a beta blocker) or a physiologic reaction to drug withdrawal (e.g. SSRI withdrawal syndrome) (27, 28). Although discontinuing medicines in older people has been demonstrated to be safe (29), given the paramount importance of the principle of “do no harm” in research ethics a vigorous and detailed method was established to ensure that any potential ADWEs precipitated by deprescribing in a SPPiRE medication review were captured. Intervention GPs were asked to report any possible ADWE following the SPPiRE medication review. The Naranjo ADR probability scale (30) has been adapted in other studies to assess the likelihood a reaction is related to drug withdrawal (27, 28). This tool was further adapted for SPPiRE and used to make an assessment on the causality of the ADWE. To ensure the patient perspective was included, self-reported possible ADWEs were also collected from patient follow up questionnaires. </p> <p> </p> <p><strong>Sample size</strong></p> <p>As outlined in the trial protocol (21), the study was designed with 90% power to detect a 20% reduction in the proportion with PIP and a mean difference of one medicine between intervention and control groups (based on a mean of 17.4 medicines SD (2.6)) and the sample size inflated to incorporate the effects of clustering (using an ICC of 0.025). The sample size was recalculated when it became apparent during early recruitment that it would not be possible to recruit clusters with an average of 15 participants, as was initially planned in the protocol. An average cluster size of eight was anticipated which inflated the original sample size from 30 practices (450 patients) to 50 practices (400 patients).</p> <p><strong>Statistical analysis</strong></p> <p>Descriptive statistics were used to describe baseline characteristics of recruited practices and participants. All analyses were conducted under the intention-to-treat principle and those lost to follow up had their baseline data carried forward. The primary analysis was carried out using multi-level modelling. The first primary outcome measure, number of repeat medications, was assessed using mixed effects Poisson regression with the individual as the unit of analysis and the practice included as the random effect to control for the effects of clustering and results presented using incidence rate ratios (IRR) and 95% confidence intervals (CI). The baseline number of medicines, GP size (number of GP sessions per week) and GP location (urban/rural) were included in the analysis as fixed effects. The second outcome measure, proportion of patients with a PIP, was analysed in a similar manner using mixed effects logistic regression, including PIP at baseline, GP size and location, and results presented using odd ratios (OR) and 95% CIs. A number of pre-specified sensitivity analyses were conducted; complete case analysis, per protocol analysis and including “presence of a repeat prescribing policy” as a covariate. All secondary outcomes were analysed in a similar manner to the primary outcomes, using appropriate mixed effects regression methods (i.e. linear, logistic, Poisson).</p> <p> </p> <p>Note: Version 3 (published 28 April 2025) updates Version 2 by removing Participant GP1P4 following consent withdrawal. This version should be used for all future analyses.</p> <p> </p>
Data from: A randomized controlled safety and feasibility trial of floatation-REST in anxious and depressed individuals
<div> <div> <div> <div> <p>Background: Reduced Environmental Stimulation Therapy via floatation (floatation- REST) is a behavioral intervention designed to attenuate exteroceptive sensory input to the nervous system. Prior studies in anxious and depressed individuals demonstrated that single sessions of floatation-REST are safe, well-tolerated, and associated with an acute anxiolytic and antidepressant effect that persists for over 48 hours. However, the feasibility of using floatation-REST as a repeated intervention in anxious and depressed populations has not been well-investigated.</p> <p>Methods: In this single-blind safety and feasibility trial, 75 individuals with anxiety and depression were randomized to complete six sessions of floatation-REST in different formats: pool-REST (weekly 1-hour float sessions), pool-REST preferred (float sessions with flexibility of duration and frequency), or an active comparator (chair- REST; weekly 1-hour sessions in a Zero Gravity chair). Feasibility (primary outcome) was assessed via an 80% rate of adherence to the assigned intervention; tolerability via study dropout and duration/frequency of REST utilization; and safety via incidence of adverse events and ratings about the effects of REST.</p> <p>Results: Of 1,715 individuals initially screened, 75 participants were ultimately randomized. Six-session adherence was 85% for pool-REST (mean, M=5.1 sessions; standard deviation, SD=1.8), 89% for pool-REST preferred (M=5.3 sessions; SD=1.6), and 74% for chair-REST (M=4.4 sessions; SD=2.5). Dropout rates at the end of the intervention did not differ significantly between the treatment conditions. Mean session durations were 53.0 minutes (SD=12.3) for pool-REST, 75.4 minutes (SD=29.4) for pool-REST preferred, and 58.4 minutes (SD=4.3) for chair-REST. There were no serious adverse events associated with any intervention. Positive experiences were endorsed more commonly than negative ones and were also rated at higher levels of intensity.</p> <p>Conclusions: Six sessions of floatation-REST appear feasible, well-tolerated, and safe in anxious and depressed individuals. Floatation-REST induces positively-valenced experiences with few negative effects. Larger randomized controlled trials evaluating markers of clinical efficacy are warranted.</p> <p>Clinical Trial Registration Identifier: NCT03899090</p> </div> </div> </div> </div>
(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>
Data from: Use of the lung flute ECO to assist in sputum collection for tuberculosis testing: a randomized crossover trial
<p>The Lung Flute ECO, a self-powered, low cost, oscillatory positive expiratory pressure (OPEP) device, assisted people with presumptive tuberculosis to produce an adequate sputum volume for diagnostic testing and was well-tolerated.</p>
Reduced optical data for Bayesian model trial
<p>The dataset is a subset of a few selected optical indices from the column experiment. </p> <p>The dataset contains all the observations after the column reversal, with day00 indicating the day before the reversal where all the columns are samplead and day0 indicating the day of the reversal. It does not contain any of the control measurements before day00. </p> <p>From the absorbance and fluoresence measurements, we have calculated several indices among which bix (biological index), fi (fluoresence index), hix, (humification index), a254 (decadal absorption coefficient at 254 nm) ,E2_E3 (ratio of absorbance E2 to E3) , SR (slope ratio). All of the indices are calcuated using the StaRdom package from the raw data. </p> <p>Sample name is in the samplingDay_replicate_columnNo format. </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>
Data from: Neurofeedback facilitation enhances post-stroke gait and balance recovery: a randomized trial
<p><strong>Objective:</strong> To test the hypothesis that supplementary motor area (SMA) facilitation with functional near-infrared spectroscopy mediated neurofeedback (fNIRS-NFB) augments post-stroke gait and balance recovery using the 3-meter-Timed Up-and-Go (TUG) test, we conducted this two-center, double-blind, randomized controlled trial involving 54 Japanese patients.</p> <p><strong>Methods:</strong> Patients with subcortical stroke-induced mild-to-moderate gait disturbance more than 12 weeks from onset, underwent 6 sessions of SMA neurofeedback facilitation during gait- and balance-related motor imagery using fNIRS-NFB. Participants were randomly allocated to intervention (REAL: 28 patients) or placebo (SHAM: 26 patients) group. In the REAL group, the fNIRS signal contained participants' cortical activation information. Primary outcome was TUG improvement, 4 weeks post intervention.</p> <p><strong>Results: </strong>The REAL group showed greater improvement in the TUG test (12.84 ± 15.07 s, 95% CI: 7.00-18.68) than the SHAM group (5.51± 7.64 s, 95% CI: 2.43 – 8.60; group difference 7.33 s, 95% CI: 0.83 – 13.83; p = 0.028), even after adjusting for covariates (group × time interaction; F1.23,61.69 = 4.50, p = 0.030, partial η2 = 0.083). Only the REAL group showed significantly increased imagery-related SMA activation and enhancement of resting-state connectivity between SMA and ventrolateral premotor area. Adverse effects associated with fNIRS-mediated neurofeedback intervention were absent.</p> <p><strong>Conclusion:</strong> SMA facilitation during motor imagery using fNIRS neurofeedback may augment post-stroke gait and balance recovery by modulating the SMA and its related network.</p> <p><strong>Classification of Evidence:</strong> This study provides Class III evidence that for patients with gait disturbance from subcortical stroke, SMA neurofeedback facilitation improves TUG time. (UMIN000010723 at UMIN-CTR http://www.umin.ac.jp/english/)</p>
Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers"
<p>Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers" by Piccoli et al. (2021) Agronomy 2021, 11, 1472. https://doi.org/10.3390/agronomy11081472</p>
Data set from "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data"
<p>This data set contains the complete digitalized individual patient data that are used in the manuscript "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data", which is accepted from "Acta Diabetologica".<br> The data file is in CSV format and contains the five variables "OutcomeType" (with values "AllcauseMortality" or "PrimaryOutcome"), "Study" (denoting the respective cardiovascular outcome trial), "Treatment" (denoting the respective study treatment or placebo), "Event" (denoting if the respective has been observed (Event=1) or not (Event=0)), and "SurvivalTimeMonths" (denoting the respective time to the event or censoring in months).</p>
Data from on-farm trials of the InnovAfrica project in the South African case country
<p>The data set were generated through on-farm field trials that were established in Maluti-a-Phofung municipality, situated in the eastern Free State province of South Africa as part of a project entitled" Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym-InnovAfrica)". This project was implemented by a consortium of 16 institutions from Africa and Europe continents across six African countries (viz. Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania) from June 2017 to November 2021. Farmer-led trials were established for three planting seasons (2017/2018 to 2019/2020) to demonstrate, test and upscale the best-bet sustainable agricultural intensification practices for improved food and nutrition security in smallholder settings.</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>
ImPure Injection Molding Sensor Data - Trial 12th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 17th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 16th May
<p>ImPure project, open access data from PASCOE IM line. </p>
Data for: Open COVID Trials (OCT) Project
<p><span>The COVID-19 pandemic has brought substantial attention to the systems used to communicate biomedical research. In particular, the need to rapidly and credibly communicate research findings has led many stakeholders to encourage researchers to adopt open science practices such as posting preprints and sharing data. To examine the degree to which this has led to the actual adoption of such practices, we examined the "openness" of a sample of 539 published papers describing the results of randomized controlled trials testing interventions to prevent or treat COVID-19. The majority (56%) of the papers in this sample were free to read at the time of our investigation and 23.56% were preceded by preprints. However, there is no guarantee that the papers without an open license will be available without a subscription in the future, and only 49.61% of the preprints we identified were linked to the subsequent peer-reviewed version. Of the 331 papers in our sample with statements identifying if (and how) related datasets were available, only a paucity indicated that data was available in a repository that facilitates rapid verification and reuse. Our results demonstrate that, while progress has been made, there is still a significant mismatch between aspiration and actual practice in the adoption of open science in an important area of the COVID-19 literature.</span></p>
Data from: Effect of information about the benefits and harms of mammography on women's decision making: the InforMa randomised controlled trial
<p>Background:</p> <p>In Spain, women invited to breast screening are not usually informed about potential harms of screening. The objective of the InforMa study is to assess the effect of receiving information about the benefits and harms of breast screening on informed choice and other decision making outcomes, in women approaching the age of invitation to mammography screening.</p> <p>Methods:</p> <p>Two-stage randomised controlled trial. In the first stage, 40 elementary territorial units of the public healthcare system were selected and randomised to intervention or control. In the second stage, women aged 49-50 years were randomly obtained. The target sample size was 400 women. Women in the intervention arm received a decision aid (DA) with detailed information on the benefits and harms of screening. Women in the control arm received a standard leaflet that did not mention harms and recommended accepting the invitation to participate in the Breast Cancer Screening Program (BCSP). The primary outcome was informed choice, defined as adequate knowledge and intentions consistent with attitudes. Secondary outcomes included decisional conflict, worry about breast cancer, time perspective, opinions about the DA, and participation in the BCSP.</p> <p>Results:</p> <p>In the intervention group, 23.2% of 203 women made an informed choice compared to only 0.5% of 197 women in the control group (p < 0.001). Attitudes and intentions were similar in both study groups with a high frequency of women intending to be screened, 82.8% vs 82.2% (p=0.893). Decisional conflict was significantly lower in the intervention group. No differences were observed in confidence in the decision, anxiety, and participation in BCSP.</p> <p>Conclusions:</p> <p>Women in Spain lack knowledge on the benefits and harms of breast screening. Providing quantitative information on benefits and harms has produced a considerable increase on knowledge and informed choice, with a high acceptance of the informative materials.</p>
Recent HIV infections among newly diagnosed individuals living with HIV in rural Lesotho: Secondary data from the VIBRA cluster-randomized trial
<p>These are pseudo-anonymised data from a secondary analysis of the VIBRA randomised trial.</p> <p>HIV recency assays are used to distinguish recently acquired infection from long-term infection among individuals newly diagnosed with HIV. Since 2015, the World Health Organisation recommends the use of an algorithm to assess recency of infections which is based on an HIV recency assay and viral load (VL) quantification. We determined the proportion of recent HIV infections among participants of the VIBRA (Village-Based Refill of Antiretroviral therapy) cluster-randomized trial in Lesotho and assessed risk factors for these recent infections.</p> <p>The VIBRA trial recruited individuals living with HIV and not taking antiretroviral therapy during a door-to-door HIV testing campaign in two rural districts (Butha-Buthe and Mokhotlong). Samples were collected from participants newly diagnosed and tested for HIV recency using the Asanté HIV-1 Rapid Recency Assay and VL using the Roche Cobas System. Clinical and socio-demographic data were extracted from the trial database. Univariate analysis was conducted to determine factors associated with recent compared to long-term infection.</p> <p>There is one dataset containing all data presented in this dtuy. The data codebook explains the data available in the dataset.</p>
Patient-reported outcomes via electronic health record portal vs. telephone: process and retention data in a pilot trial of anxiety or depression symptoms in epilepsy
<p>Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms.</p> <p>Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time.</p> <p>Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months.</p>
Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"
<p>Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"</p> <p>This folder contains 4 files:</p> <p>1) Database that contains the values for concentric and eccentric power measured with both iso-inertial and gravitational systems (Dataset_power.xlsx)</p> <p>2) Database that contains the values for the Short Physical Performance Battery (SPPB) and Get Up and Go (GUG) test (Dataset_SPPB_GUG.xlsx)</p> <p>3) R Software script used to analyse file 1 (Iso-inertial analysis_power.R)<br> <br>4) R Software script used to analyse file 2 (Iso-inertial analysis_SPPB_GUG.R)</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.