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21
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ShareScore release 0.9.0
Dataset results
21 results for “Biosensor detection”
Detecting Changes in the Caenorhabditis elegans Intestinal Environment Using an Engineered Bacterial Biosensor
<p>Data for the figures in the manuscript <br> <a href="https://pubs.acs.org/doi/10.1021/acssynbio.9b00166">https://pubs.acs.org/doi/10.1021/acssynbio.9b00166</a></p> <p>Abstract:<br> <em>Caenorhabditis elegans</em> has become a key model organism within biology. In particular, the transparent gut, rapid growing time, and ability to create a defined gut microbiota make it an ideal candidate organism for understanding and engineering the host microbiota. Here we present the development of an experimental model that can be used to characterize whole-cell bacterial biosensors <em>in vivo</em>. A dual-plasmid sensor system responding to isopropyl β-d-1-thiogalactopyranoside was developed and fully characterized <em>in vitro</em>. Subsequently, we show that the sensor was capable of detecting and reporting on changes in the intestinal environment of <em>C. elegans</em> after introducing an exogenous inducer into the environment. The protocols presented here may be used to aid the rational design of engineered bacterial circuits, primarily for diagnostic applications. In addition, the model system may serve to reduce the use of current animal models and aid in the exploration of complex questions within general nematode and host–microbe biology.</p>
DNA origami book biosensor for multiplex detection of cancer-associated nucleic acids
<p>This dataset contains the raw data that were used for the publication entitled, "DNA origami book biosensor for multiplex detection of cancer-associated nucleic acids" published in Nanoscale.</p> <p> </p> <p>Abstract</p> <p>DNA nanotechnology provides a promising approach for the development of biomedical point-of-care diagnostic nanoscale devices that are easy to use and cost-effective, highly sensitive and thus constitute an alternative to expensive, complex diagnostic devices. Moreover, DNA nanotechnology-based devices are particularly advantageous for applications in oncology, owing to being ideally suited for the detection of cancer-associated nucleic acids, including circulating tumor-derived DNA fragments (ctDNAs), circulating microRNAs (miRNAs) and other RNA species. Here, we present a dynamic DNA origami book biosensor that is precisely decorated with arrays of fluorophores acting as donors and acceptors and also fluorescence quenchers that produce a strong optical readout upon exposure to external stimuli for the single or dual detection of target oligonucleotides and miRNAs. This biosensor allowed the detection of target molecules either through the decrease of Förster resonance energy transfer (FRET) or an increase in the fluorescence intensity profile owing to a rotation of the constituent top layer of the structure. Single-DNA origami experiments showed that detection of two targets can be achieved simultaneously within 10 min with a limit of detection in the range of 1–10 pM. Overall, our DNA origami book biosensor design showed sensitive and specific detection of synthetic target oligonucleotides and natural miRNAs extracted from cancer cells. Based on these results, we foresee that our DNA origami biosensor may be developed into a cost-effective point-of-care diagnostic strategy for the specific and sensitive detection of a variety of DNAs and RNAs, such as ctDNAs, miRNAs, mRNAs, and viral DNA/RNAs in human samples.</p>
A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus
<p>Raw data associated to the study entitled:</p> <p><em>A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus</em><strong> </strong></p> <p><em>- </em>Metadata file</p> <p>- Computational data</p> <p>- Extraction data</p> <p>- Fluorescence detection</p> <p>- Fluorescence imaging</p> <p>- Labbooks</p> <p>- Surface characterization</p>
Label-free multiplexed detection of diabetic retinopathy biomarkers using fiber optic biosensors: towards lab-in-the-tear
<p>Raw experimental data on label-free detection of diabetic retinopathy biomarkers using fiber optic biosensors. This data contains information on the multiplexed and separate detection of LCN1 and VEGF diabetic retinopathy biomarkers in artificial tears. </p>
Mode characterization and sensitivity evaluation of an ultra-high-frequency surface acoustic wave (UHF-SAW) resonator biosensor: application to the glial-fibrillary-acidic-protein (GFAP) biomarker detection
<p>Biosensors detect specific bio-analytes by generating a measurable signal from the interaction between the sensing element and the target molecule. Surface acoustic wave (SAW) biosensors offer unique advantages due to their high sensitivity, real-time response capability, and label-free detection. The typical SAW modes are the Rayleigh mode and the shear-horizontal mode. Both present pros and cons for biosensing applications and generally need different substrates and device geometries to be efficiently generated. This study investigates and characterizes ultra-high-frequency (UHF-) SAW resonator biosensors. It reveals the simultaneous presence of the two typical SAW modes, clearly separated in frequency, called slow and fast. The two modes are studied by numerical simulations and biosensing experiments with the glial-fibrillary-acidic-protein (GFAP) biomarker. The slow mode is generally more sensitive to changes in surface properties, such as temperature and mass changes, by a factor of about 1.4 with respect to the fast mode.</p>
Porous Silicon Biosensor for the Detection of Bacteria Through Their Lysate: Data
<p>This file contains:</p> <p>- Characterization data of porous silicon layer based on different etching parameters</p> <p>- Relative EOT shift on PSi layers for different analytes</p> <p>- Relative EOT shift on PSi membranes for different analytes</p> <p>- EOT shift on PSi membranes with respect to refractive index changes</p>
Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
Background <p>Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. </p> Methods and Findings <p>A single-molecule fluorescence immunosorbent assay was used to detect the Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.</p> Conclusions <p>The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</p>
Molecular Biosensors for Detection of Bladder Cancer
ClinicalTrials.gov study NCT02957370. IPD Sharing: Not stated. Countries: 1. Publications: 38.
Detection of Gastrointestinal Bleeding in Intensive Care Patients Via Biosensor Watch
ClinicalTrials.gov study NCT03874169. IPD Sharing: NO. Countries: 1. Publications: 1.
Droplet Digital PCR and PCR-free BIOSensors for the Detection of Resistance-associated SNPs in Pneumocystis Jirovecii
ClinicalTrials.gov study NCT06431958. IPD Sharing: YES. Countries: 1. Publications: 7.
Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
Open the record for dataset details and reuse information.
Smart Catheter: A Novel Biosensor for Early Detection of Catheter Associated Urinary Tract Infection
ClinicalTrials.gov study NCT04315129. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Rapid detection of cocaine using aptamer-based biosensor on an evanescent wave fibre platform
Open the record for dataset details and reuse information.
Validation of a Purine Biosensor in Detecting Acute Cerebral Ischaemia: Carotid Endarterectomy Model in SMARTChip
ClinicalTrials.gov study NCT02545166. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Target Urine VOCs Biosensor for Genitourinary Malignancy Detection
ClinicalTrials.gov study NCT06211010. IPD Sharing: NO. Countries: 1. Publications: 0.
Detecting Systemic Carbon Dioxide Levels With a Novel Biosensor
ClinicalTrials.gov study NCT06367686. IPD Sharing: NO. Countries: 1. Publications: 0.
Rapid Detection of COVID-19 by Portable and Connected Biosensor : Biological Proof of Concept
ClinicalTrials.gov study NCT04780334. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detecting Tissue Ischemia in Reconstruction Flaps by a Novel CO2 Biosensor (DIMENSION-study)
ClinicalTrials.gov study NCT05487820. IPD Sharing: NO. Countries: 1. Publications: 0.
The Biosensor Using the Target Urine VOCs for Detecting DKD Diabetic Kidney Disease
ClinicalTrials.gov study NCT07137351. IPD Sharing: NO. Countries: 1. Publications: 0.
Rapid Detection of COVID-19 by Portable and Connected Biosensor
ClinicalTrials.gov study NCT04367142. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.