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1,921 results for “incidence”
Transcriptional profiling of Granny Smith apple peel (Malus x domestica Borkh) at harvest between contrasted years for scald incidence
GEO Series GSE135863. Malus domestica. 12 samples. Type: Expression profiling by array.
Genome-Scale Patterns in The Loss of Heterozygosity Incidence in Saccharomyces cerevisiae
GEO Series GSE192479. Saccharomyces cerevisiae. 12 samples. Type: Expression profiling by high throughput sequencing.
Masculinizing Testosterone Therapy Reduces the Incidence of PIK3CA-Mutant/ER⁺ Breast Cancer but Not BRCA1-Associated Triple-Negative Breast Cancer
GEO Series GSE306236. Homo sapiens; Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
In vitro fertilization does not increase the incidence of de novo copy number alterations in fetal and placental lineages
GEO Series GSE93353. Homo sapiens. 460 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
Early detection of food safety and spoilage incidents based on live microbiome profiling and qPCR-based monitoring of indicators
GEO Series GSE255249. food metagenome. 145 samples. Type: Other.
Incidence of microvascular dysfunction is increased in hyperlipidemic mice, reducing cerebral blood flow and impairing remote memory.
GEO Series GSE247400. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Machine readable code lists for an algorithm to identify incident lung cancer in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident lung cancer in United States healthcare claims data</p>
Data from: Association of CD14 with incident dementia and markers of brain aging and injury
Objective To test the hypothesis that the inflammatory marker plasma soluble CD14 (sCD14), associates with incident dementia and related endophenotypes in two community-based cohorts. Methods. Our samples included the prospective community-based Framingham Heart Study (FHS) and Cardiovascular Health Study (CHS) cohorts. Plasma sCD14 was measured at baseline and related to the incidence of dementia, domains of cognitive function, and MRI defined brain volumes. Follow-up for dementia occurred over a mean of 10 years (SD, 4) in the FHS and a mean of 6 years (SD, 3) in the CHS. Results We studied 1588 participants from the FHS (mean age 69±6 years, 47% male,131 incident events) and 3129 participants from the CHS (mean age 72±5 years, 41% male, 724 incident events) for the risk of incident dementia. Meta-analysis across the two cohorts showed that each standard deviation unit increase in sCD14 was associated with a 12% increase in the risk of incident dementia (95% confidence interval [CI], 1.03 to 1.23; P=0.01) following adjustments for age, sex, APOE ε4 status, and vascular risk factors. Higher levels of sCD14 were associated with various cognitive and MRI markers of accelerated brain aging in both cohorts and with a greater progression of brain atrophy and a decline in executive function in the FHS. Conclusion. sCD14 is an inflammatory marker related to brain atrophy, cognitive decline, and incident dementia.
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.
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>
Figure 1 from: Deliberalli W, Cansian RL, Mielniczki Pereira AM, Loureiro RC, Hepp LU, Restello RM (2018) The effects of heavy metals on the incidence of morphological deformities in Chironomidae (Diptera). Zoologia 35: 1-7. https://doi.org/10.3897/zoologia.35.e12947
Figure 1 Geographic location of the collection sites by the Tigre River, Erechim, RS, Brazil.
Prevalence, incidence and risk factors of white spot lesions associated with orthodontic treatment – systematic review ad meta-analysis
<p>These are data sets for a meta-analysis titled "<span><span>Prevalence, incidence and risk factors of white spot lesions associated with orthodontic treatment – systematic review ad meta-analysis. </span></span></p>
Incidence of Uveal Melanoma in USA between 2001 - 2018
<p>The nationwide Incidence of Uveal Melanoma in USA between 2001 - 2018, Data from NPCR</p>
Incidence of Urethrocutaneous Fistula With and Without Caudal Epidural Block
ClinicalTrials.gov study NCT03812731. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of the Incidence and Prognosis of Intrahospital Acute Kidney Injury
ClinicalTrials.gov study NCT06697730. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Arsenic Exposure and Lung Cancer Incidence
ClinicalTrials.gov study NCT04125381. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Antithrombin to Improve Thromboprophylaxis and Reduce the Incidence of Trauma-Related Venous Thromboembolism
ClinicalTrials.gov study NCT05794165. IPD Sharing: NO. Countries: 1. Publications: 0.
PRE-DELIRIC Prediction Model Plus SMART Care to Reduce the Incidence of Delirium in ICU Patients
ClinicalTrials.gov study NCT06279390. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Long-term Protease Inhibitors in Patients Living With HIV on the Incidence of COVID-19 ( COVIP )
ClinicalTrials.gov study NCT04357639. IPD Sharing: NO. Countries: 1. Publications: 0.
Study to Evaluate the Incidence of Gastric Ulcers Following Administration of Either PN 200 or Naproxen in Subjects Who Are at Risk for Developing NSAID-Associated Ulcers.
ClinicalTrials.gov study NCT00367211. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.