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43 results for “UHPLC”
Dental, pathological, and UHPLC data from Middenbeemster archaeological site
<p>Datasets used in 'Multiproxy analysis exploring patterns of diet and disease in dental calculus and skeletal remains from a 19th century Dutch population' (<a href="https://doi.org/10.24072/pcjournal.414">https://doi.org/10.24072/pcjournal.414</a>).</p> <p><strong>Changes</strong></p> <p>v1.0.1: added <em>data-dictionary.md</em> file</p> <p>Newest v1.0.0: Upload the correct <em>LICENSE</em> file</p>
UHPLC-MS and MS/MS spectra
<p>The sets consist of raw data files generated during investigations into the racemization mechanism of the signaling molecule valdiazen and the stereoselective enzyme responsible for producing fragin.</p>
Exhaled breath condensate samples (mzML files) analysed by uHPLC-ESI-OrbitrapMS
<p>LC-MS raw data files<strong> </strong>were converted to mzML using MSConvert (Version: 3.0.20279, ProteoWizard)</p> <p>- QC_pos_xxx: QC samples analysed in positive polarity (11 files)<br> - ACOS_XXX_pos_01 to 03: Asthmatic patients (3 replicates) randomly injected (15 files)<br> - Ctrl_XXX_pos_01 to 03: Control (15 files)<br> - DPOC_XXX_pos_01 to 03: COPD (15 files)</p> <p> </p> <p><strong>EBC Samples and Clinical Assessment</strong></p> <p>EBC samples were collected from 15 individuals randomly selected (five controls, five with asthma medical diagnosis, and five with COPD medical diagnosis, as assessed by the OLDER Study – Obstructive Lung Diseases in Elders). The Ethics Committee of Nova Medical School approved this study.</p> <p>Asthma was assigned when the patient reported respiratory symptoms, was a nonsmoker, and presented a positive reversibility test (FEV1 > 12% and 200 mL). COPD disease was attributed to those who also reported being current smokers, had a post-bronchodilator FEV1/FVC < 0.70, and had a negative reversibility test.</p> <p><strong>LC–MS Analysis</strong></p> <p>Samples were analyzed in triplicate by LC–MS using an Orbitrap Q Exactive Focus (Thermo Scientific) coupled to an Ultimate 3000 UHPLC (Thermo Scientific). A pooled quality control (QC) sample was used to compensate for any possible time-dependent batch effects. The QC samples were created using a small aliquot from each sample. The QC samples were reinjected at regular intervals to bracket the samples. The separation was performed using a Waters XBridge column C18 (2.1 × 150 mm, 3.5 μm particle size, P/N 186003023). The mobile phase A was water with 0.1% formic acid (v/v), and mobile phase B was acetonitrile with 0.1% formic acid (v/v) (Optima LC–MS Grade, Fisher Scientific). The gradient program was as follows: 1 min at 1% B; 1–13 min from 1 to 99% B, 13–15 min at 99% B, 15–16 min from 99 to 1% B, and 4 min at 1% B. The column temperature was maintained at 30 °C, and a flow rate of 400 μL/min was used.</p> <p>The Q Exactive Focus MS method consisted of several cycles of full MS scan (<em>R</em> = 70000) followed by three ddMS2 scans (<em>R</em> = 17500), with a (N)CE of 30 and in positive mode. External calibration was performed using LTQ ESI Positive Ion Calibration Solution (Thermo Scientific) and the lock mass enabled internal calibration. Data were obtained using the Xcalibur software v.4.0.27.19 (Thermo Scientific). The raw MS files, as recorded by the instrument, are available from the corresponding author upon reasonable request.</p>
Validation of highly sensitive method based on UHPLC-ESI-MS/MS for the quantification of progestogens and androgens in plant material.
<p>We prepared a highly sensitive method for the quantification of progestogens and androgens in plant materials. This method is based on UHPLC-ESI-MS/MS. We show here the data used for the method validation. This includes the determination of linearity, recovery, precision, limits of detection and limits of quantification. </p> <p>The general procedure can be found in the txt or pdf file. </p> <p>The resulting data are collected in the excel file and can be found in the csv files, additonally.</p>
Full-scale MBR coupled with PAC for the removal of 232 OMPs: operating conditions, conventional pollutants, concentration of OMPs, UHPLC-QTOF-MS analysis
<p>The spreadsheet contains data regarding the operation and performance of a full-scale MBR coupled with powdered activated carbon (PAC) added inside the reactor. This hybrid system is chosen to evaluate the removal of 232 organic micropollutants (OMPs) and the potential enhancement in the removal efficiencies with the addition of PAC at a concentration of 0.1 g/L and 0.2 g/L</p><ul><li>First worksheet contains minimum, maximum, and average concentrations of conventional pollutants (COD, BOD, SST, SSV, DOC, UV254, nitrogen compounds, phosphorous, <i>E. coli</i>) in the influent and effluent of the WWTP. Methodologies adopted are also reported.</li><li>Second worksheet reports the operating conditions of the full-scale MBR as well as information about the tubular UF membranes installed. Characteristics of the PAC purchased to perform the experiments regarding the removal of OMPs are also described.</li><li>Third worksheet reports minimum, maximum and average concentration of 232 OMPs in the influent and effluent during:<ol><li>The monitoring period considering only the MBR</li><li>The experimental periods where PAC is added and maintained at a concentration of 0.1g/L and 0.2g/L inside the MBR</li></ol></li><li>Fourth worksheet contains metadata regarding the UHPLC–QTOF–MS analysis performed to evaluate the occurrence of OMPs in the influent and effluent of the WWTP. Sampling, storage and sample preparation is described, followed to LC-ESI-tandem MS analysis. LOD and LOQ are reported.</li></ul>
Untargeted metabolomic approach using UHPLC-HRMS to unravel the impact of fermentation on color and phenolic composition of Rosé wines
<p>Color is a major quality trait of rosé wines due to their packaging in clear glass bottles. This color is due to the presence of phenolic pigments extracted from grapes to wines and products of reactions taking place during the wine-making process. This study focuses on changes occurring during alcoholic fermentation of Syrah, Grenache and Cinsault musts, conducted at laboratory (250 mL) and pilot (100 L) scales. Color and phenolic composition of the musts and wines were analyzed using UV-visible spectrophotometry and metabolomics fingerprints were acquired by Ultra-High Performance Liquid Chromatography−High Resolution Mass Spectrometry. The acquisition dataset protocol is available in the affiliated publication on Molecules</p>
Raw data for the submitted manuscript entitled "Novel strategies for the determination of plastic additives derived from agricultural plastics in soil using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)"
Open the record for dataset details and reuse information.
Supplementary files for "Fast and sensitive quantification of AccQ-Tag derivatized amino acids and biogenic amines by UHPLC-UV from complex biological samples"
<p>Supplementary files for "Fast and sensitive quantification of AccQ-Tag derivatized amino acids and biogenic amines by UHPLC-MS from complex biological samples"</p>
A Rapid and Simple UHPLC-MS/MS Method for Quantification of Plasma Globotriaosylsphingosine (lyso-Gb3)
<p>Fabry disease (FD) is a rare X-linked lysosomal storage disorder caused by α-galactosidase A gene (GLA) mutations, resulting in loss of activity of the lysosomal hydrolase, α-galactosidase A (α-Gal A). As a result, the main glycosphingolipid substrates, globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3), accumulate in plasma, urine, and tissues. Here, we propose a simple, fast, and sensitive method for plasma quantification of lyso-Gb3, the most promising secondary screening target for FD. Assisted protein precipitation with methanol using Phree cartridges was performed as sample pre-treatment and plasma concentrations were measured using UHPLC-MS/MS operating in MRM positive electrospray ionization. Method validation provided excellent results for the whole calibration range (0.25–100 ng/mL). Intra-assay and inter-assay accuracy and precision (CV%) were calculated as <10%. The method was successfully applied to 55 plasma samples obtained from 34 patients with FD, 5 individuals carrying non-relevant polymorphisms of the GLA gene, and 16 healthy controls. Plasma lyso-Gb3 concentrations were larger in both male and female FD groups compared to healthy subjects (<em>p </em>< 0.001). Normal levels of plasma lyso-Gb3 were observed for patients carrying non-relevant mutations of the GLA gene compared to the control group (<em>p </em>= 0.141). Dropping the lower limit of quantification (LLOQ) to 0.25 ng/mL allowed us to set the optimal plasma lyso-Gb3 cut-off value between FD patients and healthy controls at 0.6 ng/mL, with a sensitivity of 97.1%, specificity of 100%, and accuracy of 0.998 expressed by the area under the ROC curve (C.I. 0.992 to 1.000, <em>p</em>-value < 0.001). Based on the results obtained, this method can be a reliable tool for early phenotypic assignment, assessing diagnoses in patients with borderline GalA activity, and confirming non-relevant mutations of the GLA gene.</p>
Fig. 3 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 3. Structures of five isomeric compounds with the molecular formula C30H42O7 and molecular mass 514.2931 found in the G44 sample.
Fig. 2 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 2. General chemical structure of a typical triterpene showing the position of various side chains and list of side chains typically found in Ganoderma.
Fig. 1 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 1. (a) Total ion chromatogram of G44 mushroom extract in ESI negative mode (b) Extracted ion chromatogram (EIC) of G44 mushroom extract in ESI negative mode.
Fig. 5 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 5. The hierarchical clustering of the differentially abundant metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.
Fig. 2 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 2. Total ion current (TIC) chromatogram of healthy flowers (HF) and petal blight flowers (PBF) of R. agastum using GC-TOF-MS.
Fig. 3 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 3. The data analysis of the metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA). (B) Orthogonal projections to latent structures discriminant analysis (OPLS-DA). The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 1. The petal blight of R. agastum. (A) R. agastum grown in field habitat. (B) The healthy flower of R. agastum. (C) The petal blight flower of R. agastum (white arrow).
Fig. 4 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 4. The data analysis of the metabolites based on UHPLC-QTOF-MS/MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA) analysis in positive ion modes. (B) Principal component analysis (PCA) analysis in negative ion modes. (C) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in positive ion modes. (D) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in negative ion modes. The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 6. The hierarchical clustering of the differentially abundant metabolites in the positive ion modes (A) and in the negative ion modes (B) based on UHPLC-QTOFMS/MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.
Figure 2 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
Figure 2 MS2 spectra and fragmentation analysis of the three basic HCA-I conjugates in negative ionization mode. The characteristic difference of 149.048 Da, indicating a neutral loss of the 5,6-dihydroxyindole is indicated. The fragment ion corresponding to 5,6-dihydroxyindole is 148.04 m/z.
Figure 3 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
Figure 3 Proposed fragmentation behavior and diagnostic fragment ions of the basic components of oleraindoles: 5,6-dihydroxyindole, and coumaroyl, caffeoyl, and feruloyl moieties. (A): negative ionization mode; (B): positive ionization mode.
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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.