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728 results for “urine”
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>
Rafael et al., 2018: Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy (Dataset)
<p>Dataset containing 1um polystyrene beads, urine samples, urine samples mixed with ecoli and homogenous ecoli. Dataset is the post-processing version of the images to remove static background. Original model was trained on the post-processed images exclusiviely.</p>
2D NMR HSQC spectra of proteins and mouse urine with peaks picked by DEEP Picker
<p>2D 15N-1H HSQC NMR spectra of Im7 and a-synuclein with peak lists produced by DEEP Picker.</p> <p>2D 13C-1H HSQC NMR spectrum of mouse urine with peak lists determined by DEEP Picker.</p>
A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.
<p><strong>Contents of the dataset</strong></p> <p>The dataset includes the set of urine samples in .mea format, which can be<br> read using the GCIMS R package.</p> <p>It also contains analytical standards in the same format, used for quality<br> control of the equipment and as a retention time alignment reference.</p> <p>If you want to preview the data, you do not need to download the full Urines.zip<br> and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and<br> AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole<br> dataset.</p> <p>Besides the actual measurements, you will find the annotations.csv and<br> reference_peaks.csv files, with sample annotations and some reference peaks<br> identified in the samples.</p> <p>See further details below.</p> <p><br> <strong>Sample collection</strong></p> <p>Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects<br> were diagnosed with colorectal cancer, 14 subjects were controls. The study<br> protocol was approved by the Ethics Committee of Hospital de Reus (study<br> approval no. 074/2018).</p> <p>Samples were aliquoted and frozen at -80ºC for storage.</p> <p><strong>Sample preparation</strong><br> </p> <p>Sample preparation improves urine preservation by blocking bacterial growth in<br> the urine, and favours volatile extraction. It also adds an internal standard<br> for verification of instrument variability.</p> <p><em>Stock solution preparation</em></p> <p>Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium<br> azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with<br> deionized water until the final volume is 50mL. The HCl 5M is used to obtain<br> an acid pH. The pH is controlled with a pH test paper. The final pH level must<br> be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits<br> the bacterial growth in the urine.</p> <p><em>Internal standard solution preparation</em><br> </p> <p>The 4-flurobenzaldehyde is located in retention time around 200 seconds and<br> can be used as an internal standard.</p> <p>Prepare a methanol stock solution using 100 ml of methanol grade for<br> preparative chromatography and 200 ml of distilled water.</p> <p>Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution.</p> <p>Dilute the previous mixture in 400 mL of mili-Q water.</p> <p><br> <em>Sample preparation</em><br> </p> <p>Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution<br> were added to the urine sample, and 1.5 ml of the acidified urine sample were<br> transferred into a 20ml vial, ensuring only the supernatant of the sample<br> is transferred.</p> <p>Finally, 20 mL of the internal standard solution is added to the sample.</p> <p><strong>GC-IMS Analysis</strong></p> <p>Samples were analyzed with a GC-IMS FlavourSpec® instrument from<br> G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes<br> at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier<br> gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC<br> and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes.</p> <p>Besides the urines, a set of measurements of a ketone mixture was also analyzed<br> at least once per day as an analytical standard control of the equipment. The mixture<br> included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone,<br> 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as<br> the urine samples.</p> <p>Samples are provided in the native instrument format (.mea format), that can be<br> read with the GCIMS R package or with the instrument software.</p> <p><strong>Sample annotations</strong></p> <p>The dataset includes a CSV file with sample annotations.</p> <p>The annotations include the following information:</p> <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> <p><br> <strong>Reference peaks</strong></p> <p>Some peaks were manually annotated to ease the alignment of the samples and explore<br> alignment solutions. While manual peak labelling is not generally required, we<br> attach those reference peaks as well and their locations, in case they are of<br> interest.</p> <p>These reference peaks are found at reference_peaks.csv.</p> <p> </p>
Magnetic Soft Robotic Bladder for Assisted Urination
<p>The poor contractility of the detrusor muscle in underactive bladders (UABs) fails to increase the pressure inside the UAB, leading to strenuous and incomplete urination. However, existing therapeutic strategies by modulating/repairing detrusor muscles, e.g., neurostimulation and regenerative medicine, still have low efficacy and/or adverse effects. Here, we present an implantable magnetic soft robotic bladder (MRB) that can directly apply mechanical compression to the UAB to assist urination. Composed of a biocompatible elastomer composite with optimized magnetic domains, the MRB enables on-demand contraction of the UAB when actuated by magnetic fields. A representative MRB for an UAB in a porcine model is demonstrated and MRB-assisted urination is validated by in situ computed tomography imaging after 14-day implantation. The urodynamic tests show a series of successful urination with a high pressure increase and fast urine flow. Our work paves the way for developing MRB to assist urination for humans with UABs.</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. 10 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 10 Venn diagrams showing the common and unique DE miRNAs (a) and DE piRNAs (b) in both serum and urine between the acutely and chronically infected rabbits versus uninfected rabbits
Fig. 5 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 5 Global view of temporal sRNA expression profiles in rabbit urine during T. gondii infection. a The sRNA correlation heatmap of sample clustering. b Principal component analysis of all identified urine sRNAs. c Unsupervised hierarchical clustering of sRNA profiling data. sRNA intensity is normalized so that blue represents low intensity and yellow represents high intensity. Columns were hierarchically clustered based on a complete linkage using Pearson correlation coefficients as the distance measure. Sample groups are acutely infected rabbits, chronically infected rabbits and uninfected control rabbits, which are labeled as AI, CI and Con, respectively
Fig. 4 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 4 First nucleotide bias of obtained small RNA in urine samples of rabbits. a First nucleotide bias of known miRNAs in rabbit urine. b First nucleotide bias of predicted miRNAs in rabbit urine. c First nucleotide bias of predicted piRNAs in rabbit urine
Fig. 1 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 1 Histological features of spleen section from healthy control rabbits and rabbits experimentally infected with Toxoplasma gondii. Images showing the H&E-stained spleen section at 100× (a, b). a Spleen section from healthy, uninfected rabbit. The structures of white pulp (WP) and red pulp (RP) were clearly identified with normal cell density. b Spleen section from a rabbit with acute T. gondii infection. The number and dimension of splenic nodule are increased, and more plasma cells are observed in the splenic cord (black triangle) of red pulp. Note that granulomas are present (big black arrow). Hemosiderin deposition (small black arrow) indicated red blood cell destruction. CA central arteriole. Scale bar = 100 μm
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. 3 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 3 Global view of temporal sRNA expression profiles in rabbit serum during T. gondii infection. a The sRNA correlation heatmap of sample clustering. b Principal component analysis of all identified serum sRNA. c Unsupervised hierarchical clustering of sRNA profiling data. sRNA intensity is normalized so that blue represents low intensity and yellow represents high intensity. Columns are hierarchically clustered based on a complete linkage using Pearson correlation coefficients as the distance measure. Sample groups including acutely infected rabbits, chronically infected rabbits and uninfected control rabbits are labeled as AI, CI and Con, respectively
Fig. 2 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 2 First nucleotide bias of obtained small RNA in serum samples of rabbits. a First nucleotide bias of known miRNAs in rabbit serum. b First nucleotide bias of predicted miRNAs in rabbit serum. c First nucleotide bias of predicted piRNAs in rabbit serum
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>
Raman spectra of urine from patients with diabetes mellitus and other pathologies
<p>This dataset contains raw (<strong>unprocessed Raman spectra</strong>) of urine from de-identified human patients. Analysis of this dataset is included in our journal article, "<em><strong>Analysis of urine Raman spectra differences from patients with diabetes mellitus and other pathologies</strong></em>." The dataset includes urine Raman spectra from (1) healthy volunteers, (2) patients with chronic kidney disease and diabetes mellitus, (3) patients with chronic kidney disease and without diabetes mellitus, (4) patients with biopsy-confirmed diabetic nephropathy, (5) patients with biopsy-confirmed immune-mediated nephropathy, (5) patients with biopsy-confirmed membranous nephropathy, (6) patients with biopsy-confirmed renal neoplasm, (7) patients with other glomerular pathologies, and (8) Surine (urinalysis control).</p> <p>Raman spectra were obtained with the following parameters:</p> <ul> <li>Raman spectrometer: Agiltron PeakSeeker PRO-785</li> <li>Mode: Bulk liquid scanning</li> <li>Wavelength: 785 nm</li> <li>Wavenumber range: 200-2000 cm-1</li> <li>Laser power: 30 mW</li> <li>Spectral resolution: 8 cm-1</li> <li>Laser spot size: 0.2 mm</li> <li>Excitation time: 30 s</li> </ul> <p>Raman spectral data and de-identified metadata are present in tab-separated value (tsv) files. Each urine sample is bar-code identified and linked to a disease state (or control) in the metadata tsv file. Ten (10) independent Raman scan replicates exist for each urine sample and are identified by bar-code in the spectral data tsv file. The study IRB approval and a sample blank patient consent form are also included here.</p>
FIGURE 2 Fitted HeV urine pool prevalence and 95 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
FIGURE 2 Fitted HeV urine pool prevalence and 95% confidence intervals from the most parsimonious GAMM with week, seasonal interactions with roost type and previous food shortages, and an adjustment for relative abundance of Pteropus alecto. Weekly data are overlaid, coloured by roost type, and sized by corresponding P. alecto relative abundance. Thin lines show the fitted curves from the random factor smooth including each roost per year
Elevated urine BMP phospholipids in LRRK2 and VPS35 mutation carriers with and without Parkinson's disease
<p><strong>Participant demographic and clinical characteristics, and urine BMP phospholipid levels. </strong></p> <p>For each participant, sample collection site is provided as: BCN (Barcelona), VIE (Vienna), DND (Dundee), or SSB (San Sebastian). Also provided are age at study participation, age at PD diagnosis (where applicable), sex (M, for male, and F, for female), experimental group (control, iPD –idiopathic PD –, LRRK2 G2019S, LRRK2 R1441G/C, VPS35 D620N, GBA, or other), and PD status (NMC for non-manifesting mutation carriers, or PD). Values for all measured BMP species presented as ng of BMP per mg of creatinine are provided. Additionally, urine creatinine (mg/ml) and non-normalized BMP levels are provided. BQL designates BMP levels that were below quantification level and NM designates values that were not measured for a particular individual.</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>
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.