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40 results for “urine analysis”
Problems with nanoparticle tracking analysis (NTA) of urine extracellular vesicles (uEVs)
<p>Urinary extracellular vesicle (uEV) proteins may be used as specific markers of kidney damage in various pathophysiological conditions. The nanoparticle-tracking analysis (NTA) appears to be the most useful method for the analysis of uEVs due to its ability to analyze particles below 300 nm. The NTA method has been used to measure the size and concentration of uEVs and also allows for a deeper analysis of uEVs based on their protein composition using fluorescence measurements. However, despite much interest in the clinical application of uEVs, their analysis using the NTA method is poorly described and requires meticulous sample preparation, experimental adjustment of instrument settings, and above all, an understanding of the limitations of the method. We present the problems encountered during analysis with possible solutions: the choice of sample dilution, the method of the presentation and comparison of results, photobleaching, and the adjustment of instrument settings for a specific analysis.</p> <p> </p> <p>Figure 1. Expressions of specific markers CD63 in protein-standardized samples detected with Western blot analysis; anti-CD 63 (HPA010088, Sigma-Aldrich, Saint Louis, MO, USA, 1:1000); secondary antibodies conjugated to horseradish peroxidase (554021, BD Pharmingen (BD Biosciences, San Jose, CA, USA) 1:10000).</p> <p> </p> <p>Nanoparticle-Tracking Analysis of uEVs. A NanoSight NS300 instrument (Malvern Panalytical, Malvern, UK) was used to determine the concentrations and sizes of the uEVs in the samples. The total number of extracellular vesicles was measured during the continuous flow of samples delivered from a syringe pump.</p> <p>Figure 2. Determination of the size and concentration of uEVs: dilution factor—1:100; laser—405 nm.</p> <p>Figure 3. Effect of dilution on total number of particles per milliliter and size of uEVs in nanoparticle tracking analysis: sample dilutions—1:100, 1:500, and 1:1000; laser—488 nm.</p> <p>Figure 5. Fluorescence-based nanoparticle-tracking analysis of CD 63 expression in uEVs: without 500 nm long-pass filter; with 500 nm long-pass filter; comparison of sizes and concentrations of uEVs without and with 500 nm long-pass filter; dilution factor—1:100; laser—488 nm; anti-CD 63 (HPA010088, Sigma-Aldrich); secondary antibodies conjugated to Alexa Fluor 488 fluorescent dye (ab150073-500, Abcam, Cambridge, MA, USA).</p> <p>Figure 6. Fluorescence-based nanoparticle-tracking analysis of podocin expression in uEVs: without 500 nm long-pass filter; with 500 nm long-pass filter; comparison of sizes and concentrations of uEVs without and with 500 nm long-pass filter; dilution factor—1:100; laser—488 nm; anti-podocin (P0372, Sigma-Aldrich); secondary antibodies conjugated to Alexa Fluor 488 fluorescent dye (ab150073-500, Abcam, Cambridge, MA, USA).</p>
Fig. 8 Urine miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 8 Urine miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs
Fig. 6 Serum miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 6 Serum miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs. FDR represents false discovery rate
Fig. 7 Serum piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 7 Serum piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs. FDR represents false discovery rate
Fig. 9 Urine piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 9 Urine piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected group and control group. c The volcano plot shows the individual statistically significant piRNA between acutely infected group and chronically infected group. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs
Data analysis of an LC-MS dataset from a human urine biofluid cohort study
<p>Supplementary dataset and tutorials for the "<strong>Statistical analysis in metabolic phenotyping"</strong></p> <p> </p> <p>This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed<sup>1</sup> and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and <sup>1</sup>H NMR, using the methods described by Lewis <em>et al</em><sup>2</sup> and Dona <em>et al</em>. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE<sup>3</sup> repository, which can be accessed via the Sage BioNetworks portal (<a href="https://doi.org/10.7303/syn22252881">https://doi.org/10.7303/syn22252881</a>). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: <a href="https://www.ebi.ac.uk/metabolights/MTBLS719">https://www.ebi.ac.uk/metabolights/MTBLS719</a>.</p> <p> </p> <p>1. Lovestone, S. <em>et al.</em> AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in <em>Annals of the New York Academy of Sciences</em> (2009). doi:10.1111/j.1749-6632.2009.05064.x</p> <p>2. Lewis, M. R. <em>et al.</em> Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. <em>Anal. Chem.</em> <strong>88</strong>, acs.analchem.6b01481 (2016).</p> <p>3. Birkenbihl, C. <em>et al.</em> ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. <em>medRxiv</em> (2020). doi:10.1101/2020.08.04.20168229</p>
Methods for Extracting and Characterizing RNA from Urine: for downstream PCR and RNAseq Analysis
<p>Readily accessible samples such as urine or blood are seemingly ideal for differentiating and stratifying patients, however, it has proven a daunting task to identify reliable biomarkers in such samples. Noncoding RNA holds great promise as a source of biomarkers distinguishing physiologic wellbeing or illness.</p>
Risk Factors for Pediatric Emergence Agitation and Analysis of Serum or Urine Metabonomics in Children With Agitation
ClinicalTrials.gov study NCT04807998. IPD Sharing: NO. Countries: 1. Publications: 1.
BIOchemical Urine Analysis of Adherence to Statins and Associated FACTorS in Coronary Artery Disease
ClinicalTrials.gov study NCT05814692. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of SARS-CoV2 Urine Viral Particles and Association With Proximal Tubular Dysfunction
ClinicalTrials.gov study NCT04937712. IPD Sharing: NO. Countries: 1. Publications: 1.
OUTREACH: Urine Analysis and Antihypertensive Treatment
ClinicalTrials.gov study NCT03293147. IPD Sharing: NO. Countries: 1. Publications: 0.
Prospective Analysis of Urine LAM to Eliminate NTM Sputum Screening
ClinicalTrials.gov study NCT04579211. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Analysis of Urine Composition Saturation and Dietary Intervention in Subjects Without Urinary Calculi
ClinicalTrials.gov study NCT05102279. IPD Sharing: NO. Countries: 1. Publications: 4.
Urine Gene Analysis for Pathogen Detection
ClinicalTrials.gov study NCT05591911. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Accuracy and Usability of the ACR | U.S. Urine Analysis Test System in the Lay User Hands
ClinicalTrials.gov study NCT04626271. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Anaplastic Thyroid Cancer and Follicular Thyroid Cancer-derived Exosomal Analysis Via Treatment of Lovastatin and Vildagliptin and Pilot Prognostic Study Via Urine Exosomal Biological Markers in Thyro
ClinicalTrials.gov study NCT02862470. IPD Sharing: NO. Countries: 0. Publications: 1.
Single-Cell RNA-Seq Analysis of cells from human urine
GEO Series GSE165396. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Single-cell RNAseq and longitudinal proteomic analysis of a novel semi-spontaneous urothelial cancer model reveals tumor cell heterogeneity and pretumoral urine protein alterations
GEO Series GSE174182. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Data from: Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease
Coronary heart disease (CHD) is top risk factor for health in modern society, causing high mortality rate each year. However, there is no reliable way for early diagnosis and prevention of CHD so far. So study the mechanism of CHD and development of novel biomarkers is urgently needed. In this study, metabolomics and metagenomics technology are applied to discover new biomarkers from plasma and urine of 59 CHD patients and 43 healthy controls and trace their origin. We identify GlcNAc-6-P which has good diagnostic capability and can be used as potential biomarkers for CHD, together with mannitol and 15 plasma cholines. These identified metabolites show significant correlations with clinical biochemical indexes. Meanwhile, GlcNAc-6-P and mannitol are potential metabolites originated from intestinal microbiota. Association analysis on species and function levels between intestinal microbes and metabolites suggest a close correlation between Clostridium sp. HGF2 and GlcNAc-6-P, Clostridium sp. HGF2, Streptococcus sp. M143, Streptococcus sp. M334 and mannitol. These suggest the metabolic abnormality is significant and gut microbiota dysbiosis happens in CHD patients.
Urinating in transponder-controlled feeding stations – Analysis of an undesirable behaviour in horses
<p>Urination in transponder-controlled roughage feeding stations is a widespread undesirable behaviour of group-housed horses. Urination on hard surfaces, such as the floor of the stations, is contrary to the natural elimination behaviour of horses because they prefer to urinate on soft, absorbent surfaces, and it increases ammonia emissions around the stations. The following aspects were analysed: a) urination as potential displacement activity during feed anticipation, b) absence of appropriate elimination areas in the stable and c) ammonia odour as a trigger stimulus. We observed a group of 33 horses in three different situations: 1) baseline situation, 2) provision of elimination areas containing an absorbent substrate in front of the feeding stations and 3) neutralisation of ammonia odour in the feeding stations. In the baseline situation all horses were observed, regardless whether they urinated in the feeding station or not. In the other two situations, only the urinating-horses were observed. We analysed 55 h of video per day, recorded on 4 days from seven feeding stations in each situation. We used an information theory approach, calculating three different (generalized) linear mixed effects models and all according sub-models. In the baseline situation, the horses showed that the horses urinated often after ‘ground exploration’, and there was more ground exploration in the urinating-horses than in the non-urinating-horses. In addition, a higher percentage of the mares than of the geldings urinated during at least one station visit, and mares urinated more often per visit than geldings. Before urination, the horses in most cases lowered the head toward the hay container to trigger the sensor and cause the station to open the partition and make the hay accessible. The time span between lowering the head and access to feed could be perceived as too long by the horses (maximum duration: 30 s) and lead to urination as a displacement activity. In addition, urination never occurred when the hay was accessible, only when the hay was inaccessible, closed, opening or closing. This leads us to conclude that urination is related to feed anticipation. The frequency of urination bouts was not reduced by installing additional elimination areas or neutralising the urine odour, so additional elimination areas and urine odour do not seem to have a role in the undesirable behaviour. Further research is needed to investigate a displacement activity or a classical conditioning in more detail to prevent urination by horses in automated feeding stations.</p>
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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.