Rapid Acceleration of Diagnostics - Digital Health (RADx-DH): Personalized Analytics and Wearable Biosensor Platform for Early Detection of Covid-19 Decompensation (DECODE)
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PROVIDER: PRJNA751640 | ENA |
REPOSITORIES: ENA
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Project description:Rapid Acceleration of Diagnostics - Digital Health (RADx-DH): Personalized Analytics and Wearable Biosensor Platform for Early Detection of Covid-19 Decompensation (DECODE)
| PRJNA751639 | ENA
Project description:CRISPR-Analytics (CRISPR-A): a platform for precise analytics and simulations for gene editing
| PRJEB53901 | ENA
Project description:BackgroundDuring the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets.ObjectiveTo this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ's end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed.MethodsThis is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on the development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study.ResultsOur target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1-β]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time-time to predict decompensation-and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale.ConclusionsUsing machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19.Trial registrationClinicalTrials.gov NCT04575532; https://www.clinicaltrials.gov/ct2/show/NCT04575532.International registered report identifier (irrid)DERR1-10.2196/27271.
| S-EPMC8158529 | biostudies-literature
Project description:DeCODE Genetics of Nicotine Dependence
| PRJNA75873 | ENA
Project description:To investigate whether PS19 brain lysate transfection changes ER stress gene expression in Biosensor cell
2022-12-22 | GSE217525 | GEO
Project description:Metabolic heterogeneity modulates productivity, antibiotic resistance and cancer aggressiveness. Since metabolic fluxes represent the functional output of metabolism, with glycolytic flux correlating with highly-productive phenotypes and cancer, such flux map will be indicative of the cellular metabolic state. Therefore, the quantification of metabolic fluxes is vital to identify the existence of metabolic subpopulations and to understand the process of their emergence at the single-cell level. However, so far inference of metabolic fluxes in individual cells is not possible as no method is available. Here, we developed a biosensor for glycolytic flux measurements in single yeast cells drawing on the robust correlation between fructose-1,6-bisphosphate (FBP) and flux levels in yeast, and using the B. subtilis FBP-binding transcription factor CggR. We followed a systematic engineering approach starting from promoter design, computational protein design and protein engineering, accompanied by strict characterization of the biosensor using different biochemical methods, proteomics, metabolomics and physiological analyses. As proof of principle, we applied the biosensor in vivo in the search for metabolic subpopulations in yeast cultures and, using fluorescence microscopy, we demonstrated that quiescent yeast cells have low glycolytic fluxes in comparison to coexisting dividing cells. We anticipate that our biosensor will contribute with unprecedented resolution for the study of metabolic subpopulations, to understand how and why metabolic subpopulations emerge and, very importantly, give clues on how to counteract the undesirable effects of such.
2019-12-17 | PXD012964 | Pride
Project description:Biosensor-based growth-coupling as an evolutionary strategy to improve heme export in Corynebacterium glutamicum
| PRJEB79651 | ENA
Project description:A Novel Progesterone Biosensor Derived from Microbial Screening
| PRJNA594030 | ENA
Project description:To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.
| S-EPMC7806725 | biostudies-literature
Project description:HYlight is a genetically encoded fluorescent biosensor that ratiometrically monitors fructose 1,6-bisphosphate (FBP), a key glycolytic metabolite. Given the role of glucose in liver cancer metabolism, we expressed HYlight in human liver cancer cells and primary mouse hepatocytes. Through in vitro, in silico, and in cellulo experiments, we showed HYlight's ability to monitor FBP changes linked to glycolysis, not gluconeogenesis. HYlight's affinity for FBP was ~1 µM and stable within physiological pH range. HYlight demonstrated weak binding to dihydroxyacetone phosphate, and its ratiometric response was influenced by both ionic strength and phosphate. Therefore, simulating cytosolic conditions in vitro was necessary to establish a reliable correlation between HYlight’s cellular responses and FBP concentrations. They were found to be in the lower micromolar range, far lower than previous millimolar estimates. Altogether, this biosensor approach offers real-time monitoring of FBP concentrations at single-cell resolution, potentially revolutionizing our understanding of cancer metabolism.
2024-11-14 | PXD051879 | Pride