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2,515 results for “serum”
Characterization of Metabolism Associated with Outcomes in Severe Acute Pancreatitis: Insights from Serum Metabolomic Analysis
<p>1H NMR spectra data of SAP patients (Survivors/ Non-survivors). The spectra were binned as 0.02 ppm spectral buckets. The chemical shift regions corresponding to the water region and TSP were excluded to avoid spectral interference. This dataset was used for the metabolomics related study to highlight the dysregulation of metabolites in the study group.</p> <p> </p>
Training dataset: Mass spectrometry based proteomics of healthy human serum samples
<p>The two raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>Serum of a healthy person was obtained by centrifugation of full blood in a serum-gelmonovette. One serum sample was depleted for high abundant proteins, the other not.<br> For the non-depleted sample: 5µl of serum was diluted with 0.1% Rapigest, resulting in a concentration of 1mg/ml.<br> Depletion was performed with the Seppro IgY14 Spin columns which are able to deplete 14 high abundent blood proteins by immunoaffinity. For the depleted sample 9µl of serum was diluted with TBS/HCl/NaCl buffer and added to the Seppro IgY14 spin column. After depletion the sample was buffered with Hepes pH 8.0 and Rapigest was added to a final 0.1% Rapigest concentration. From here on, both samples were reduced by adding TCEP, alkylated by IAA and quenched with DTT in solution. Digestion was performed by adding trypsin in a ratio of 1:50 to the samples. After incubation at 37°C, 600rpm, over night, the sample clean-up was performed with the PreOmics desalting columns. iRT peptides were added and the sample was measured with a Q-Exactive Plus mass spectrometer. Besides the two raw files, we uploaded a fasta file that serves as human protein sequence database and the Galaxy MaxQuant training result files: protein groups, peptides, mqpar and PTXQC.</p>
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
An exposome atlas of serum reveals risk of chronic diseases in Chinese population
<p>Although adverse environmental exposures are considered to be a major cause to chronic diseases, current studies have provided limited knowledge on real-world chemical exposures and related risks. Here, we collected serum samples from 5696 healthy people and patients, including 12 chronic diseases in China, and completed serum biomonitoring containing 267 chemicals using gas and liquid chromatography-tandem mass spectrometry. 74 high-frequently detected exposures were used for exposure characterization and risk analysis. Results showed that region was the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances were associated with multiple chronic diseases, and some of them exceeded safe ranges. Mixture effect models showed significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provided a comprehensive human serum exposure atlas and its disease risk, which could guide subsequent more in-depth cause-and-effect studies between environmental exposures and human health. The R codes and related example data for statistical analysis and figure production have been deposited to the GitHub (https://github.com/youlei2023/ExposomeAtlas).</p>
Effect of older age and/or ACL injury on the dose–response relationship between ambulatory load magnitude and immediate load-induced change in serum cartilage oligomeric matrix protein
<p>The data presented here was used in the models in the pulication doi <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jshs.2024.100993" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jshs.2024.100993</span></span></a>.</p> <p>The purpose of this study was to assess the influence of age, anterior cruciate ligament (ACL) injury, and sex on resting sCOMP concentration, on the immediate load-induced sCOMP kinetics after a 30-minute treadmill walking stress, and on the dose-response relationship between ambulatory load magnitude and the load-induced sCOMP change.</p> <p>Overall, data of 85 participants in four groups (20–30 years healthy, HEA<sub>20–30</sub>, n=24; 20–30 years ACL-injured, ACL<sub>20–30</sub>, n=23; 40–60 years healthy, HEA<sub>40–60</sub>, n=23; 40–60 years ACL-injured, ACL<sub>40–60</sub>, n=15) were included in this dataset. ACL injured participants suffered from an ACL injury 2-10 years prior to inclusion. The dateaset includes, patient data and serum cartilage oligomeric matrix protein (sCOMP) concentration measured on three testdays (m1, m2, m3) immediately before (t0) and immediately after 30 minutes of treadmill walking (t1) where the ambulatory loads were 80% bodyweight (BW), 100% BW or 120% BW (block randomized order). This dateset represents a subset of data collected in the parent study.</p> <p>The detailed experimental protocol of the parent study has been described in Herger, S., Vach, W., Nüesch, C., Liphardt, A. M., Egloff, C., & Mündermann, A. (2022). Dose-response relationship of in vivo ambulatory load and mechanosensitive cartilage biomarkers—The role of age, tissue health and inflammation: A study protocol. <em>PLoS One, 17</em>(8), e0272694. <a href="https://doi.org/10.1371/journal.pone.0272694">https://doi.org/10.1371/journal.pone.0272694</a></p>
Fig. 1 in Diversity of intestinal protozoa and clinical signs associated in wild-caught Phoneutria nigriventer kept in captivity for the anti-arachnid serum production
Fig. 1. Phoneutria nigriventer kept in glass containers with a humidified cotton ball and a cardboard substrate.
Fig. 2. – A and B in Diversity of intestinal protozoa and clinical signs associated in wild-caught Phoneutria nigriventer kept in captivity for the anti-arachnid serum production
Fig. 2. – A and B, Diarrheal stools, without differentiation of solid and liquid portion. C, Normal stools of Phoneutria nigriventer (red arrow). The white arrow indicates the urine portion, white in color due to urate. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Dataset related to article "SMA-miRs (miR-181a-5p, -324-5p, and -451a) are overexpressed in spinal muscular atrophy skeletal muscle and serum samples"
<p>mice survival after treatment with anti-miR-181a-5p; mice weight after treatment with anti-miR-324-5p</p>
Datasets and code for manuscript: Age, breed, sex, and diet influence serum metabolite profiles of 2000 pet dogs
<p>Physiology affects metabolism, but there is a lack of large-scale studies investigating the effects of different physiological factors on canine metabolism. We utilised generalised linear models to study how age, breed, sex, sterilisation status, size, diet type, and fasting time before blood sampling affect serum concentrations of 119 metabolite measurands in over 2000 pet dogs. This dataset contains input files and code for the analyses. </p>
Hypothyroidism Weight and serum thyroid hormones
<p>Dataset on 50 healthy controls (HC), 25 patients before starting treatment (Hyp1), 15 out of these 25 patients before starting treatment (Hyp12) and 15 patients (same 15 patients) after 6 months of treatment with L-thyroxine (Hyp2). Variables are: ID number, patient group (HC, Hyp1,Hyp12,Hyp2), sex (1=male), age (years), weight (kg), length (m), BSA (body surface area, m^2), BMI (kg/m^2), serum-TPO (IU/ml), serum-TSH (mIU/l), serum-free T3 (pmol/l), serum-free T4 (pmol/l), ratio serum-fT3/fT4, Zulewski score (points) and difference in weight before and after treatment (Hyp2-Hyp12), and difference in TSH (Hyp2-Hyp12) and dosage of L-thyroxine (Levaxin, mg).</p>
Extracellular Vesicles Analysis in the COVID-19 Era: Insights on Serum Inactivation Protocols towards Downstream Isolation and Analysis
<p>Representative AFM images of the samples analyzed in the relative manuscript. Raw data just imported from the AFM multimode native format to the Gwyddion Open source data analysis software</p>
LipidMS v3.0.3: source code and example of lipidomics dataset for human serum
<p>Source code and example dataset for LipidMS v3.0.3: a commercially available pooled human serum sample was analyzed in positive and negative detection modes and using MS1, DIA and DDA approaches. The obtained datasets were processed using LipidMS v3.0, MS-DIAL v4.80 or a combination of data pre-processing in XCMS v3.16 and lipid annotation in LipidMS v3.0.</p> <p>This repository contains:</p> <p>- Raw data for positive and negative polarities using MS scan, DIA and DDA acquisition modes.</p> <p>- R scripts for processing with LipidMS v3.0.3 and XCMS v3.16.1 and parameters used for processing with MS-DIAL v4.80.</p> <p>- Source code for LipidMS v3.0.3.</p> <p>- Results obtained for the 3 different softwares employed.</p> <p>- Tutorials for LipidMS R package and online application.</p> <p>- Human pooled serum analysis</p> <ul> <li>Raw data for positive and negative polarities using MS scan, DIA and DDA acquisition modes for a human pooled serum sample with or without the addition of 68 lipid standars</li> <li>Results for the data processing and annotation of the lipid standards using LipidMS 3.0, XCMS 3.16 and MS-DIAL 4.80</li> <li>Results for the manual curation of the total lipid annotations provided by both LipidMS 3.0 and MS-DIAL 4.80</li> </ul>
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology
<p>Contact prediction data generated by EPC-map used in the paper "Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology" by Rappsilber et al.</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
ScienceDex guides
Understand access before you commit
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.