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183 results for “Base editing”
Base Editing for Mutation Repair in Hematopoietic Stem & Progenitor Cells for X-Linked Chronic Granulomatous Disease
ClinicalTrials.gov study NCT06325709. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Base Editing Hematopoietic Stem Cell and T Cell Gene Therapy for CD40L-HyperIgM Syndrome: Single Patient Study
ClinicalTrials.gov study NCT06959771. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Sequencing data from validation experiment for base editing of SCN2A
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Figure 9 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
Figure 2 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 2 - Mockup of audio recording annotation tool – Step 1: Selection. This figure shows a mockup of what an audio annotation Web application tool could look like. In this first step, (A) the Worker presses the Play icon to listen to the voice recording, (B) selects a problematic segment by clicking and dragging the mouse over the waveform, and (C) replays the recording if necessary and selects other problematic segments.
Figure 5 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 5 - DARPA-funded seedling project. This schematic represents our DARPA-funded seedling project to assess the feasibility of collecting phone voice recordings from PD patients for use in a competition.
Figure 4 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 4 - Audio recording annotation tool – Step 3: Rating. Following Figures 2 and 3, here the Worker rates how serious the problem is that is affecting the highlighted segment of the recording. In this example, the Worker indicates that the background noise (wind) is not good, but that it doesn't interfere with his/her ability to hear the voice in the recording.
Figure 7 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 7 - Example mPower patient voice data. In the mPower app, PD patients are prompted to perform the voice activity three times per day: once before taking their medication, a second time when they feel they are at their best after taking their medication, and a third "random" time. This figure shows example voice data for a single patient on medication (top) and at a "random" time, very likely off medication (bottom). On the left are waveforms, showing the acoustic voice signal over time (0-10 seconds), from which one can clearly see that the patient's voice trailed off to a minimum (bottom left) compared to after medication (top left). On the right are spectrograms, representing signal amplitude at different frequencies (0-5 kHz) over time (0-10 seconds). The spectrogram after medication (top right) has more uniform frequency bands across the recording compared to the rather "muddled" spectrogram recorded at the random time (bottom right).
Figure 3 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 3 - Audio recording annotation tool – Step 2: Annotation. Following Figure 2, here the Worker selects one or more categories describing why the highlighted segment in the audio waveform is problematic. In this example, there was a lot of background noise (wind).
Figure 6 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 6 - Android and iOS Parkinson app screenshots. Top: Android PD app screenshots showing instructions for the phonation (voice) task. Bottom: mPower PD app screenshots. Each participant in the mPower study is prompted to perform a voice activity three times a day. The rightmost screenshot demonstrates the visual feedback that is provided during audio recording, to try to keep the voice at the best amplitude for recording.
Identification of novel HPFH-like mutations by CRISPR base editing that elevate the expression of fetal hemoglobin
<p><span>Naturally occurring point mutations in the <i>HBG</i> promoter switch hemoglobin synthesis from defective adult beta-globin to fetal gamma-globin in sickle cell patients with hereditary persistence of fetal hemoglobin (HPFH) and ameliorate the clinical severity. Inspired by this natural phenomenon, we tiled the highly homologous<i> HBG1</i> and <i>HBG2 </i>proximal promoters using adenine and cytosine base editors that avoid the generation of large deletions. Among the novel regulatory region identified, base editing at -123 region induced HbF to a higher level than disruption of a well-known BCL11A binding site in erythroblasts derived from healthy donor CD34+ HSPC. We further demonstrated <i>in vitro</i> that the introduction of -123T>C and -124T>C HPFH-like mutations drives gamma-globin expression by creating a <i>de novo</i> binding site for the master erythroid regulator KLF1. Overall, our findings shed light on so far unknown regulatory elements within the <i>HBG</i> promoter and identified additional targets for therapeutic upregulation of fetal hemoglobin. </span></p>
Identification of novel HPFH-like mutations by CRISPR base editing that elevate the expression of fetal hemoglobin
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Base editing effectively prevents the early-onset hypertrophic cardiomyopathy in Mybpc3 mutant mice
GEO Series GSE239872. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Genotoxic effects of base and prime editing in human hematopoietic stem cells [BARseq_BaseE]
GEO Series GSE220753. Homo sapiens. 136 samples. Type: Other.
An Improved SNAP-ADAR Tool Enables Efficient RNA Base Editing to Interfere with Post-translational Protein Modification
GEO Series GSE264114. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
Base editing mediated correction of severe β0 thalassemia mutations
GEO Series GSE273814. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
CRISPR-based epigenome editing screens identify transcriptional and epigenetic regulators of human CD8 T cell function [Sorting-based CRISPR screens]
GEO Series GSE241933. Homo sapiens. 40 samples. Type: Other.
Expanded repertoire of RNA-editing-based detection for RNA binding protein interactions (1)
GEO Series GSE232513. Homo sapiens. 76 samples. Type: Other.
Transgenic mice for in vivo epigenome editing with CRISPR-based systems
GEO Series GSE146848. Mus musculus. 116 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Activation of the imprinted Prader-Willi Syndrome locus by CRISPR-based epigenome editing [bisulphite-seq]
GEO Series GSE285300. Homo sapiens. 18 samples. Type: Methylation profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.