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4,359 results for “Insulin”
Data from: mPRIME Study - Interaction of Insulin Resistance with Cognition, Lifestyle, and Mental Health
<p>The presented datasets were collected within the <em>m</em>PRIME study, a prospective, observational study of the H2020 project Prevention and Remediation of Insulin Multimorbidity in Europe (PRIME) (grant No. 847879). The study investigates the interaction of insulin resistance with cognition, lifestyle, and mental health by combining traditional methods with ambulatory assessment and sensor-based data collection. Recruitment took place between March 2021 and March 2023 at the University Hospital Frankfurt, Germany.</p> <p>The eligibility criteria for the study were as follows: Age above 18 years, no intake of antidiabetic medication, insulin or glucocorticoids, no existing type 1 diabetes mellitus or gestational diabetes, no diagnoses of bipolar I disorder, schizophrenia, organically caused mental disorders and substance dependence, no severe neurological disorders, no current pregnancy or breastfeeding, no non-correctable visual impairments, no participation in medication-related studies within the last 6 months, no use of weight-reducing medications or a diet within the last 3 months, sufficient proficiency in German to complete questionnaires and neuropsychological tests.</p> <p>All participants in the <em>m</em>PRIME study provided written informed consent. The study protocol and procedures were approved by the local ethics committee.</p> <p><strong>Study Design</strong></p> <p>Individuals completed a baseline assessment and a one-week ambulatory assessment. The baseline assessment included: socio-demographic information, blood samples, anthropometric measures, neuropsychological tests, and several questionnaires. In addition, individuals were introduced to smartphone-based ecological momentary assessment (EMA), food protocols, and the use of sensors (continuous glucose monitor, accelerometer). Food protocols and EMA were conducted on three consecutive days, including two weekdays and one weekend day (Thursday to Saturday or Sunday to Tuesday). Several times a day, individuals were prompted via their smartphone to complete a working memory task and answer questions about stress, affect, and food intake. The continuous glucose monitor and accelerometer were worn continuously for 1 week.</p> <p> </p>
In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells
<p>Dataset of the publication "In vivo treatment with insulin-like growth factor 1 reduces CCR5 expression on vaccine-induced activated CD4+ T-cells" by Bissa et al. on the journal Vaccines. </p><p>Each folder contains the original files reporting the data used to generate the manuscript.</p><p>For flowcytometry based assays the Flow panel is included in the folders. </p><p>For ELISA based assays the schemes of the plates are included in the folders. </p><p>The excel table "Bissa et al._Vaccines_2023_Animal IDs and viral acquisition" reports the IDs and grouping of the animals together with their viral acquisition</p><p>The excel table "Bissa et al._Vaccines_2023_Master table" reports each data used to generate the figures and supplemental materials included in the publication </p>
Diffraction data from cubic insulin at room temperature
<p>X-ray diffraction data from bovine insulin (space group I 21 3) collected at the Cornell High Energy Synchrotron Source (CHESS) beamline F1.</p> <p>The dataset is intended for a diffuse scattering data reduction tutorial at the <a href="https://crystalerice.org/2022/">2022 Erice International School of Crystallography</a>. The tutorial is available on GitHub: <a href="https://github.com/ando-lab/erice-2022-data-reduction">github.com/ando-lab/erice-2022-data-reduction</a></p> <p>The dataset includes:</p> <ul> <li>Diffraction images (500 frames, 0.1 degree per frame)</li> <li>Background images (50 frames, 1 degree per frame)</li> <li>Metrology files (x and y corrections)</li> </ul>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
Modelica Models and Jupyter Notebooks for System Analysis of Glucose Insulin Regulation
<p>This dataset contains source code of Modelica models of Glucose-Insulin regulation using different techniques.</p> <p>Accompanying Jupyter notebook is demo for system analysis (parameter estimation) of artificial data and to match model simulation able to be used in Teaching class.</p> <ul> <li><strong>ModelicaIdentification.ipynb</strong> - default notebook - code contains ellipsis which needs to be replaced as per instruction in text</li> <li><strong>ModelicaIdentificationResolution.ipynb - </strong>notebook - code with exemplar solution to default notebook</li> <li><strong>glucoseinsulin.mo - </strong>Modelica source code</li> <li><strong>PatientInsulinConcentration.csv</strong> - sample data to be fitted against model</li> <li><strong>seminar11hw.GIExperiment.fmu</strong> - FMU exported from Modelica in order to run simulation in Python and PyFMI library</li> </ul> <p>Thanks to the MYBINDER service, the Jupyter notebook can be viewed and executed as</p> <ul> <li><a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/">https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/</a> note that you need to launch terminal first in Jupyter -> New -> Terminal and install pyfmi and matplotlib by:</li> </ul> <pre><code class="language-bash">conda install -c conda-forge pyfmi matplotlib</code></pre> <ul> <li>Most recent version with other models and notebooks <a href="https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/">https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/</a></li> </ul>
Gestational Cd exposure in the CD-1 mouse induces sex-specific hepatic insulin insensitivity, obesity and metabolic syndrome in adult female offspring
<p>There is compelling evidence that developmental exposure to some toxic metals increases risk for obesity and obesity-related morbidity including cardiovascular disease and type 2 diabetes in adults. To explore the hypothesis that developmental Cd exposure increased risk of obesity later in life, male and female CD-1 mice were maternally exposed to 500 ppb CdCl<sub>2</sub> in drinking water during a human gestational equivalent period (GD0 - PND10). Hallmark indicators of metabolic disruption, hepatic steatosis, and metabolic syndrome were evaluated prior to birth through adulthood. Blood Cd levels in dams were similar to those observed in human pregnancy cohorts. There were no observed impacts of exposure on dams or pregnancy-related outcomes. Results of glucose and insulin tolerance testing revealed that Cd-exposure impaired glucose homeostasis in young adult offspring. Exposure-related increases in circulating triglycerides and hepatic steatosis were apparent only in females. By PND120, Cd-exposed females had become 30% heavier with 700% more perigonadal fat than unexposed control females. There was no evidence of dyslipidemia, steatosis, increased weight gain, nor increased adiposity in Cd-exposed male offspring. Hepatic transcriptome analysis at PND1, PND21, and PND42 revealed evidence for female-specific increases in oxidative stress and mitochondrial dysfunction with significant early disruption of retinoic acid signaling and altered insulin receptor signaling consistent with hepatic insulin sensitivity in adult females. The observed steatosis and metabolic syndrome-like phenotypes resulting from exposure to 500 ppb CdCl<sub>2</sub> during the pre- and perinatal period of development equivalent to human gestation indicate that Cd acts developmentally as a sex-specific delayed obesogen.</p>
Analysis of insulin glulisine at the molecular level by X-ray crystallography and biophysical techniques
<p>Raw diffraction images for the study:- Gillis, R.B., Solomon, H.V., Govada, L. <em>et al.</em> Analysis of insulin glulisine at the molecular level by X-ray crystallography and biophysical techniques. <em>Sci Rep</em> <strong>11, </strong>1737 (2021). https://doi.org/10.1038/s41598-021-81251-2 </p> <p>PDB code 6GV0.</p>
Short wavelength/ high energy (14.2 keV) Cubic Insulin helical dataset, beamline ID23-EH2 ESRF
<p>Short wavelength/ high energy (14.2 keV) Cubic Insulin helical dataset, beamline ID23-EH2 ESRF. CBF images from a Pilatus3 2M detector at 100K</p>
1440° (4 turn) data set from cubic insulin, with slight radiation damage
<p>X-ray diffraction data set recorded on beamline i03 at Diamond Light Source, as part of a training workshop. Data were collected from a cubic insulin crystal prepared following standard methods with an Eiger 2XE 16M detector at 250Hz.</p> <p> </p> <p>Data Collection Parameters</p> <table> <tbody> <tr> <td>Wavelength</td> <td>1.2399Å</td> </tr> <tr> <td>Oscillation angle</td> <td>0.1°</td> </tr> <tr> <td>Nominal resolution (inscribed circle)</td> <td>1.5</td> </tr> <tr> <td>Total rotation</td> <td>1440°</td> </tr> <tr> <td>Nominal flux</td> <td>5e11 ph/s</td> </tr> <tr> <td>Beam size</td> <td>80x20µm</td> </tr> </tbody> </table> <p> </p> <p>Merging stats from xia2 / DIALS automated online processing</p> <pre>For mx30951v8/xins162/SAD Overall Low High High resolution limit 1.32 3.58 1.32 Low resolution limit 54.95 55.00 1.34 Completeness 98.7 100.0 83.3 Multiplicity 132.9 152.8 33.7 I/sigma 41.7 200.6 0.4 Rmerge(I) 0.072 0.044 4.027 Rmerge(I+/-) 0.071 0.043 3.977 Rmeas(I) 0.072 0.044 4.088 Rmeas(I+/-) 0.071 0.044 4.094 Rpim(I) 0.006 0.004 0.675 Rpim(I+/-) 0.008 0.005 0.940 CC half 1.000 1.000 0.360 Wilson B factor 19.890 Anomalous completeness 98.4 100.0 79.3 Anomalous multiplicity 69.1 84.3 17.7 Anomalous correlation 0.760 0.646 -0.050 Anomalous slope 0.613 dF/F 0.026 dI/s(dI) 1.013 Total observations 2426172 151841 26018 Total unique 18257 994 772 Assuming spacegroup: I 2 3 Unit cell (with estimated std devs): 77.71580(7) 77.71580(7) 77.71580(7) 90.0 90.0 90.0 </pre> <p> </p> <p>Data are made available for any purpose including methods developers trying to optimize their software for higher multiplicity data sets. </p>
Photoperiod controls wing polyphenism in a water strider independently of insulin receptor signaling
Insect wing polyphenism has evolved as an adaptation to changing environments and a growing body of research suggests that the nutrient sensing insulin receptor signaling pathway is a hot spot for the evolution of polyphenisms, as it provides a direct link between increased growth and the available nutrients in the environment. However, little is known about the role of insulin receptor signaling in polyphenisms which are controlled by seasonal variation in photoperiod. Here, we demonstrate that wing length polyphenism in the water strider Gerris buenoi is determined by photoperiod and nymphal density, but not by nutrient availability. Exposure to a long-day photoperiod is highly inducive of the short-winged morph whereas high nymphal densities moderately promote development of long wings. Using RNA interference we demonstrate that, unlike in several other species where wing polyphenism is controlled by nutrition, there is no detectable role of insulin receptor signaling in wing morph induction. Our results indicate that the multitude of possible cues that trigger wing polyphenism can be mediated through multiple genetic pathways in insects.
Multi-crystal cubic insulin example data set recorded on i24
<p>Data collected as part of routine commissioning on Diamond Light Source beamline i24 15th August 2022 with samples prepared by Felicity Bertram at Diamond following standard techniques. Data are individually incomplete but combined make for a reasonably complete and reasonable data set. </p> <p> </p> <p>Purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p>
Figure S1. Mediation analysis on the effect of insulin resistance on intraocular pressure. Figure S2. Forest plot showing the OR (95% CI) for EIOP of ALD versus NAFLD and the OR (95% CI) for EIOP of drinkers versus non-drinkers. Abbreviations: OR, odds ratio; CI, confidence interval; ALD, alcoholic liver disease; NAFLD, non-alcoholic fatty liver disease.
<p>Figure S1. Mediation analysis on the effect of insulin resistance on intraocular pressure.</p> <p>Figure S2. Forest plot showing the OR (95% CI) for EIOP of ALD versus NAFLD and the OR (95% CI) for EIOP of drinkers versus non-drinkers. Abbreviations: OR, odds ratio; CI, confidence interval; ALD, alcoholic liver disease; NAFLD, non-alcoholic fatty liver disease.</p>
Metallomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metallomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
Metabolomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metabolomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
Recognition of symptoms, mitigating mechanisms and self-care experiences of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia
<p>Compliance of patients with self-care practices is the mainstay of measures to manage diabetes. Thus, the study explored self-care practices of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia.</p>
Slightly widely sliced cubic insulin data sets for Diamond / CCP4 workshop tutorials
<p>A 450 x 0.2° image data set from a cubic insulin crystal recorded at Diamond Light Source beamline i03 for the 2022 CCP4 workshop. This is to accompany tutorials so that students can download data and work through tutorials at home</p> <p> </p> <p>Tutorial home space is https://github.com/graeme-winter/dials_tutorials</p> <p> </p>
Study of Semaglutide for Non-Alcoholic Fatty Liver Disease (NAFLD), a Metabolic Syndrome With Insulin Resistance, Increased Hepatic Lipids, and Increased Cardiovascular Disease Risk (The SLIM LIVER St
ClinicalTrials.gov study NCT04216589. IPD Sharing: YES. Countries: 2. Publications: 2.
Comparison of SAR341402 to NovoLog in Adult Patients With Type 1 Diabetes Mellitus Also Using Insulin Glargine
ClinicalTrials.gov study NCT03874715. IPD Sharing: YES. Countries: 1. Publications: 2.
Comparison of a New Formulation of Insulin Glargine With Lantus in Patients With Type 2 Diabetes Mellitus on Basal Plus Mealtime Insulin
ClinicalTrials.gov study NCT01499082. IPD Sharing: YES. Countries: 13. Publications: 3.
Efficacy and Safety of the Insulin Glargine/Lixisenatide Fixed Ratio Combination (FRC) Versus GLP-1 Receptor Agonist in Patients With Type 2 Diabetes, With a FRC Extension Period
ClinicalTrials.gov study NCT02787551. IPD Sharing: YES. Countries: 9. Publications: 5.
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