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3,259 results for “type studies”
Type 2 cytokine-dependent skin barrier regulation in personalized 2D- and 3D skin models of atopic dermatitis: A pilot study
GEO Series GSE243634. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes
GEO Series GSE116497. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Cell-type specific and combinatorial usage of diverse transcription factors revealed by genome-wide binding studies in multiple human cells
GEO Series GSE32883. Homo sapiens. 90 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Study of the role of c-Maf in type 2 innate lymphoid cells
GEO Series GSE166739. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Genome wide association study between segregate wild-type and homozygous mutant
GEO Series GSE197891. Arabidopsis thaliana. 2 samples. Type: Other.
Cell-Type-Specific Impact of Glucocorticoid Receptor Activation on the Developing Brain: A Cerebral Organoid Study [Wafergen iCell8]
GEO Series GSE189533. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
MCF7 wild-type RNA profiling for i-motif DNA studies
GEO Series GSE248441. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Studying RNA-DNA interactome by Red-C identifies noncoding RNAs associated with various chromatin types and reveals transcription dynamics
GEO Series GSE136141. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Other.
Transcriptome response study of arabidopsis wild type (Col6-3), gun1-9 mutant and MORF2 overexpression lines to norflurazon treatment in retrograde signaling
GEO Series GSE110125. Arabidopsis thaliana. 10 samples. Type: Expression profiling by high throughput sequencing.
Single-cell m6A profiling in the mouse brain uncovers cell type-specific RNA methylomes and age-dependent differential methylation [aged study]
GEO Series GSE268159. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Figure 3 from: Proćków J, Faltyn-Parzymska A, Jarzembowski P, Proćków M, Jakubska-Busse A (2020) How many type specimens can be stored in old lesser-known herbaria with turbulent histories? – A Juncus case study reveals their importance in taxonomy and biodiversity research. PhytoKeys 153: 85-110. https://doi.org/10.3897/phytokeys.153.50735
Figure 3 Collectors' names. Y-axis: number of herbarium labels analysed.
Data from: Ectopic fat obesity presents the greatest risk for incident type 2 diabetes: a population-based longitudinal study
Objectives: Obesity is a risk factor for type 2 diabetes mellitus. Among obesity, visceral fat obesity, and ectopic fat obesity, it has been unclear which has the greatest effect on incident diabetes. Methods: In this historical cohort study of 8430 men and 7034 women, we investigated the effect of obesity phenotypes on incident diabetes. Obesity, visceral fat obesity, and ectopic fat obesity were defined as body mass index ≥25 kg/m2, waist circumference ≥90 cm in men or ≥80 cm in women, and having fatty liver diagnosed by abdominal ultrasonography, respectively. We divided the participants into eight groups according to the presence or absence of the three obesity phenotypes. Results: During the median 5.8 years follow-up for men and 5.1 years follow-up for women, 286 men and 87 women developed diabetes. Compared to the non-obese group, the hazard ratios (HRs) of incident diabetes in the only-obesity, only-visceral fat obesity, only-ectopic fat obesity groups, and with all-three types of obesity group were 1.85 (95%CI 1.06–3.26, p = 0.05) in men and 1.79 (0.24–13.21, p = 0.60) in women, 3.41 (2.51–4.64, p < 0.001) in men and 2.30 (0.87–6.05, p = 0.12) in women, 4.74 (1.91–11.70, p < 0.001) in men and 13.99 (7.23–27.09, p < 0.001) in women and 10.5 (8.02–13.8, p < 0.001) in men and 30.0 (18.0–50.0, p < 0.001) in women. Moreover, the risk of incident diabetes of the groups with ectopic fat obesity were almost higher than that of the four groups without ectopic fat obesity. Conclusion: Ectopic fat obesity presented the greatest risk of incident type 2 diabetes.
Primary documentation on the scientific study of indicators of continuous monitoring and flash monitoring of glycemia in children and adolescents with type 1 diabetes mellitus
<p>The main purpose of creating an electronic database was to evaluate the performance of continuous glucose monitoring (CGM) and flash monitoring (FMS) in children and adolescents diagnosed with type 1 diabetes mellitus. The database is intended for entering, systematizing, storing and displaying patient data (date of birth, age, date of diagnosis of type 1 diabetes mellitus, length of illness, date of first visit to an endocrinologist, with the installation of a CGM or FMS), glycated hemoglobin indicators initially , during the study and ultimately, as well as CGM, FMS data (average glucose level, glycemic variability, percentage of cases above the target range, percentage of cases within the target range, percentage of cases below the target range, number of hypoglycemic episodes and their average duration, frequency of daily scans and frequency of sensor readings).</p><p>The database is the basis for comparative statistical analysis of dynamic monitoring indicators in groups of patients with the presence or absence of diabetic complications (neuropathy, retinopathy and nephropathy). The database presents the results of a prospective, open, controlled, clinical study obtained over a year and a half. The database includes information on 307 patients (adolescent children) aged 3 to 17 years inclusive. During the study, the observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and with no diabetic complications, 155 people. All registrants of the database were assigned individual codes, which made it possible to exclude personal data (full name) from the database.</p><p>The database is executed in the Microsoft Office Excel program and has the character of a depersonalized summary table, which consists of two blocks-sheets: patients of groups 1 and 2 and is structured according to the following sections: "Patient number"; "Patient code"; "Date of birth"; "Age of the patient"; section "Date of diagnosis of DM1" indicates the date of the official diagnosis of type 1 diabetes mellitus at the first hospitalization of the patient, this information is borrowed from medical information systems; section "Length of service DM1" reflects information about the duration of the patient's illness; the section "Date of the first visit" contains information about the date of the registrant's visit to the endocrinologist with the installation of FMS / CGM devices; the section "Frequency of self-monitoring with a glucometer" contains information about the frequency of measuring blood glucose levels by the patient at home using a glucometer until the establishment of FMS / CGM.</p><p>Sections "HbA1c initially (GMI)", "HbA1c (GMI)", "HbA1c final (GMI)", display the indicators of the level of glycated hemoglobin from the total for the period of the beginning of the study, at the intermediate stages of the study and at the end of observation.</p><p>The database structure has a number of sections accumulating information obtained with CGM/FMS, in particular: the section "Average glucose level"; the section "% above the target range", reflecting the percentage of the patient's stay with glycemia above the target indicators during the day; the section "% within the target range", reflecting the percentage of the patient's stay within the target glycemia indicators per day; the section "% below the target range", reflecting the percentage of the patient's stay with glycemia below the target indicators during the day; the section "Hypoglycemic phenomena", reflecting the number of cases of hypoglycemia in patients within 2 weeks; the section "Average duration", reflecting the average duration of hypoglycemic phenomena registered in the patient; the section "Sensor data received", indicating the percentage of time the patient was with an active device sensor; the section "Daily scans" show the frequency of scans of the patient's glycemic level (once a day); the section "%CV" displays the variability of the patient's glycemia recorded by the device. The listed sections are repeated in the database in accordance with the number of follow-up visit.</p><p>Also in the database there is a section "Mid. values", which contains indicators of the average values of patient data for all of the above sections, both in the first and in the second group of patients.</p><p>When working with the database, the use of filters (in the "Data" tab) containing the names of indicators allows you to enter information about new registrants in a convenient form or correct existing data, as well as sort and search for one or more specified indicators.</p><p>The electronic database allows you to systematize a large volume of results, distribute data into categories, search for any field or set of fields in the input format, systematize the selected array, makes it possible to directly use this data for statistical analysis, as well as to view and print information on specified conditions with the location of fields in a convenient sequence.</p>
Primary documentation on the scientific study of glycemic variability indicators in children and adolescents with diabetes mellitus type 1 diabetes
<p>The main purpose of creating an electronic database was to assess indicators of glycemic variability in adolescents and children diagnosed with diabetes mellitus type 1. The database presents the results of prospective, open, controlled, clinical study obtained over a period of one and a half years. Base data includes information on 307 patients (children and adolescents) aged 3 to 17 years inclusive. The observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and without diabetic complications, 155 people. All database registrants were assigned individual codes, which made it possible to exclude personal data (full name) from the database.</p><p>During the study, an analysis of carbohydrate metabolism was carried out with an assessment HbA1c (glycated hemoglobin), CGMS (continuous glucose monitoring), FMG (flash glucose monitoring) with face-to-face consultations with an endocrinologist and analysis of the data obtained. Obtained monitoring results were processed using a specialized variability calculator glycemia (EasyGV®, ver. 9), the following indicators and indices were calculated variability: average glycemic level (Mean), standard deviation (SD); index prolonged increase in glycemia (CONGA); glycemic lability index (LI); index risk of hypoglycemia (LBGI); hyperglycemia risk index (HBGI), average value overall risk (ADRR), average amplitude of glycemic fluctuations (MAGE); grade inter-day GV (MODD), rate of change in glycemia (MAG), J-index - indicator quality of glycemic control. The database is intended for entering, organizing, storing and displaying all of the above data.</p>
DEVELOPMENT OF NEW TYPES OF SHORTENINGS, COMPOSITION AND STUDY OF CHARACTERISTICS
Open the record for dataset details and reuse information.
Increasing Contribution of Adolescent Type 1 Diabetes Drives Incidence Rates in Poland - a 40-year-long Observational Study
<div> <div> <p><span><span>Aims/hypothesis</span></span><span><span>: </span><span>40-year-long longitudinal observation</span> <span>of long-term trends of type 1 diabetes</span><span> incidence</span><span> and prevalence</span> <span>in children </span><span>in Central P</span><span>o</span><span>land </span></span><span> </span></p> </div> <div> <p><span><span>Methods</span></span><span><span>: This was a prospective observational study performed </span><span>by </span><span>a reference </span><span>regional </span><span>center</span><span> for pediatric diabetes care for Lodz Province (currently 2</span><span>·</span><span>4M </span><span>inhabitants</span><span>, 360K children). </span><span>We </span><span>registered </span><span>e</span><span>ach case of new-onset </span><span>type 1 </span><span>diabetes </span><span>admitted</span> <span>to regional pediatric diabetes centers </span><span>between </span><span>the </span><span>years 1983 and 2022 in children between 0 and 14 </span><span>y.o.</span> <span>The diagnosis</span><span> was based on </span><span>currently </span><span>available guidelines</span><span>.</span> <span>C</span><span>ases of other types of diabetes (e.g.</span><span>,</span><span> monogenic) were excluded</span><span> upon identification from incidence and prevalence rates</span><span>. Yearly data on the at</span><span>-</span><span>risk</span> <span>population were </span><span>acquired</span><span> from Poland`s General Statistical Office. Sex-specific data </span><span>on population structure </span><span>were available from 1989 onwards.</span><span> </span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: In the </span><span>analyzed</span> <span>period, </span><span>the </span><span>incidence rate</span><span> of type 1 diabetes</span><span> increased </span><span>tenfold </span><span>from 3</span><span>·</span><span>29/100</span><span>,</span><span>000 (95%CI: </span><span>1</span><span>·</span><span>85</span><span>-</span><span>4</span><span>·</span><span>73</span><span>) in 1983 to 3</span><span>2.43</span><span> (2</span><span>6</span><span>·</span><span>42-38</span><span>·</span><span>44</span><span>) in 2022, with </span><span>an </span><span>average annual percentage change of 5</span><span>·</span><span>73</span><span>% (95%CI: 4</span><span>·</span><span>9</span><span>9</span><span>%-6</span><span>·</span><span>44</span><span>%). Joinpoint analysis detected two distinct periods of increase</span><span>:</span> <span>rapid in 1983-200</span><span>5</span><span> (annual percentage increase </span><span>of </span><span>7</span><span>·</span><span>38</span><span>%</span><span>, 95%CI: </span><span>6</span><span>·</span><span>30</span><span>-1</span><span>0</span><span>·</span><span>52</span><span>%</span><span>) and </span><span>a </span><span>slower </span><span>one</span><span> in </span><span>200</span><span>5</span><span>-2022 (3</span><span>·</span><span>65</span><span>%</span><span>, 95%CI: -0</span><span>·</span><span>86</span><span>-</span><span>5</span><span>·</span><span>13</span><span>%</span><span>). </span><span>I</span><span>ncidence rates among the youngest children (0-4</span> <span>y.o.</span><span>) were significantly lower than in 5-9</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>5</span><span>67</span><span>±</span><span>0</span><span>·</span><span>059</span><span>, p<0</span><span>·</span><span>0001</span><span>)</span><span> and 10-14</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>520</span><span>±</span><span>0</span><span>·</span><span>0</span><span>60</span><span>, p<0</span><span>·</span><span>0001)</span><span>. </span><span>The </span><span>incidence </span><span>growth dynamic </span><span>for</span> <span>the two older groups showed </span><span>a </span><span>consistent </span><span>increase</span><span>, </span><span>whereas</span><span> the incidence in </span><span>0-4 year-olds</span><span> plateaued after 2007.</span> <span>Incidence rates varied </span><span>seasonally,</span><span> with the </span><span>most cases diagnosed </span><span>during the </span><span>winter months (December, January, </span><span>and </span><span>February</span><span>;</span> <span>mean difference from remaining seasons of 29</span><span>±11</span><span>·</span><span>6 percentage points</span><span>, p</span><span><0</span><span>·</span><span>0001</span><span>). </span><span>Corresponding with increasing incidence rate, estimated prevalence of type 1 diabetes increased over the years and reached </span><span>177</span><span>·</span><span>21</span><span>/100</span><span>,</span><span>000 (95%CI: 163</span><span>·</span><span>18-191</span><span>·</span><span>24)</span><span> for children 0-14 </span><span>y.o.</span><span>,</span><span> and</span><span> </span> <span>1</span><span>7</span><span>·</span><span>11</span> <span>(95%CI:</span><span> 9</span><span>·</span><span>2</span><span>-</span><span>2</span><span>5</span><span>·</span><span>02</span><span>),</span><span> 1</span><span>90</span><span>·</span><span>54</span><span> (95%CI:</span> <span>16</span><span>5</span><span>·</span><span>03</span><span>-</span><span>21</span><span>5</span><span>·</span><span>75</span><span>),</span><span> 2</span><span>38</span><span>·</span><span>73</span><span> (95%CI: </span><span>21</span><span>1</span><span>·</span><span>7</span><span>-26</span><span>5</span><span>·</span><span>76</span><span>)</span><span> for 0-4</span><span>,</span><span> 5-9</span><span>, </span><span>and 10-14</span> <span>y.o.</span><span>, respectively.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusion/i</span><span>nterpretation</span></span><span><span>: </span><span>Over the past 40 years, the incidence of </span><span>type 1 diabetes</span><span> in </span><span>children in </span><span>Central Poland </span><span>has increased significantly, but the rate of increase </span><span>appears to be</span><span> slowing.</span><span> As</span> <span>majority</span><span> of patients with type 1 diabetes are 10 years old or older, with the </span><span>most new</span><span> cases occurring in that age group</span><span> the healthcare systems should prepare for care of young adults who are extensive users of new diabetes technologies</span><span>.</span></span><span> </span></p> </div> </div>
A Study Of Rosiglitazone Plus Insulin To Treat Type 2 Diabetes Mellitus Patients
ClinicalTrials.gov study NCT00349427. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multicenter Study of Fulminant Type 1 Diabetes in China
ClinicalTrials.gov study NCT05593081. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Evaluate the Efficacy and Safety of DBPR108 100 mg in Type 2 Diabetes Mellitus Patients
ClinicalTrials.gov study NCT04161430. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Observational Study to Evaluate the Safety and Efficacy of NovoMix® 70 in Type 2 Diabetes
ClinicalTrials.gov study NCT00704223. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.