Suchergebnisse

IntroductionCorHealth Ontario, formerly Cardiac Care Network (CCN), maintains a registry of patients undergoing select cardiac procedures/surgeries in Ontario, Canada. This population-based database contains over 35 datasets with complex structure, linked by unique primary key or multiple keys.
Objectives and ApproachWe aimed to simplify the complex CorHealth database so that research analysts could create study cohorts more efficiently and effectively, and to enrich the study cohort by getting more clinical information through database linkage. Through internal linkage, we could combine clinical fields from multiple CorHealth datasets. While the CorHealth dataset may not have all the clinical information needed for a given study, we may link the CorHealth study cohort externally to other administration databases to obtain additional fields via the probability matching (i.e., identical patient ID, hospital ID and procedure/surgery date).
ResultsAfter identifying the primary keys on the relational database flowchart, we designed new data structures by combining similar topic datasets. The total number of datasets was reduced from 35 to 13. This simplified CorHealth dataset includes one main CorHealth dataset (including demographic information, referral data, comorbidities) plus 12 other linkable specific datasets (including stent, vessel, TAVI, STEMI). Through internal linkage, we can get the stent numbers, lengths and types of Percutaneous Coronary Interventions from the Stent dataset. Linking to Discharge Abstract Database (DAD), we can get the hospital length of stay and the episode of care of hospital transfer for each procedure; linking to The Ontario Health Insurance Plan database (OHIP), we can find the graft numbers and vessel types of Coronary Artery Bypass Graft.
Conclusion/ImplicationsTo improve the research capacity and increase the value of the CorHealth database, analysts could create enhanced cardiovascular study cohorts derived from the simplified CorHealth database, plus internal linkage from other CorHealth datasets, and external data linkage from population-based administrative sources. We have accomplished three reports (PCI/CABG/TAVI) accordingly in 2017/18.

Qinshan nuclear power plant is the first large Chinese-designed nuclear power station based on pressurized water reactor, and the second generation main stream active safety injection system is adopted for Qinshan nuclear power plant. In this paper, a novel passive safety injection system (PSIS) has been proposed for ocean-based Qinshan Phase One nuclear power plant to replace the original active one. The PSIS contains high-pressure, medium-pressure, and lower-pressure safety injection systems, and a two-stage automatic depressurization system. To evaluate the system performance, small-break LOCA has been investigated using RELAP5. Various break sizes and locations including 2-inch, 10-inch cold leg break, and double-ended direct vessel injection line break were analyzed. Key safety parameters such as safe injection mass flow rates, coolant level of the core, system pressure, and fuel cladding temperature were monitored during the accident process. The results illustrate that the performance of the safety injection system can guarantee the effective core cooling and submerged under different LOCA even with only half of the safety injection system, which can fulfill the single failure criteria. The thermal-hydraulic analysis for the Qinshan passive safety injection system is significant to master the related technologies for Chinese engineer and develop the Chinese-designed third-generation nuclear power plants, and the PSIS can guarantee the reactor submerged under LOCA even plus the station block out accident.

ABSTRACTObjectiveEfforts to enable persons with dementia to remain at home longer, and to reduce use of costly acute care resources, are at the forefront of policy agendas internationally. Foundational to planning appropriate health system supports is the ongoing, comparable and accurate estimation of the prevalence and incidence of dementia across regions, as well as associated patterns of health services use and cost. Our objective was to explore emerging approaches to using population data in dementia research and demonstrate the policy contribution of the resulting new knowledge.
ApproachUsing population-based health administrative data and an algorithm that was validated using electronic medical records, we developed a series of repeated, cross-sectional cohort studies to examine trends in dementia prevalence, incidence and publicly-funded health service use and costs between 2004/05 and 2013/14 among adults aged 65 years and older in Ontario, Canada. Trends in yearly rates of health service use were assessed using regression models for serially correlated data and compared to a 1:1 matched control group based on age, sex, geographic region and comorbidity level.
ResultsOver time, age- and sex-adjusted prevalence of dementia increased by 18.2% (from 63.0 to 74.5 per 1,000 persons; p-value < 0.001) and age- and sex-adjusted incidence decreased slightly (from 18.2 to 17.0 per 1,000 persons; p-value = 0.05). Community-dwelling persons with dementia were more likely than matched controls to be placed in long-term care (11.8% vs. 1.5% in 2013; p<0.001) and use home care (45.8% vs 23.2%; p<0.001) but equally likely to visit family physicians (93.9% vs. 94.8% in 2013) and specialists (87.1% vs. 89.4%). Median costs associated with one year of health system use were $19,468 (interquartile range (IQR) $4,490 to $47,726) for prevalent cases in 2012/13 and $16,549 (IQR $5,070 to $47,899) for incident cases. Long-term care and hospital care accounted for the largest portion of total costs in both groups.
ConclusionThe prevalence of dementia has increased in Ontario, Canada over time and, given slightly declining incidence rates, is likely attributable to improved survival. Surveillance of dementia with health administrative data is a cost-effective tool for describing and monitoring trends in incidence and prevalence over time, and for supporting health system capacity planning. This comparative information is critical to understanding the impact of policy decisions designed to address dementia-related health care needs at a population level.

The four adenosine receptors (ARs) A1AR, A2AAR, A2BAR, and A3AR are G protein-coupled receptors (GPCRs) for which an exceptional amount of experimental and structural data is available. Still, limited success has been achieved in getting new chemical modulators on the market. As such, there is a clear interest in the design of novel selective chemical entities for this family of receptors. In this work, we investigate the selective recognition of ISAM-140, a recently reported A2BAR reference antagonist. A combination of semipreparative chiral HPLC, circular dichroism and X-ray crystallography was used to separate and unequivocally assign the configuration of each enantiomer. Subsequently affinity evaluation for both A2A and A2B receptors demonstrate the stereospecific and selective recognition of (S)-ISAM140 to the A2BAR. The molecular modeling suggested that the structural determinants of this selectivity profile would be residue V2506.51 in A2BAR, which is a leucine in all other ARs including the closely related A2AAR. This was herein confirmed by radioligand binding assays and rigorous free energy perturbation (FEP) calculations performed on the L249V6.51 mutant A2AAR receptor. Taken together, this study provides further insights in the binding mode of these A2BAR antagonists, paving the way for future ligand optimization ; This work was financially supported by the Consellería de Cultura, Educacioń e Ordenacioń Universitaria of the Galician Government: (grant: ED431B2017/70), Centro singular de Investigacioń de Galicia accreditation 2016−2019 (ED431G/ 09), Xunta de Galicia (ED431C 2018/21) and the European Regional Development Fund (ERDF). Xuesong Wang thanks the China Scholarship Council (CSC) for her PhD scholarship. The authors are part of the EU COST action ERNEST (CA 18133) and the Swedish strategic research program eSSENCE (H.G.dT., W.J.) ; SI

ObjectivesIndividual risk factors for dementia are well known, but the influence of co-occurring chronic conditions has not been considered. We identified clusters of chronic conditions using an unsupervised machine learning approach and examined associations with incident dementia.
ApproachUsing linked population-based administrative databases, we followed all community-dwelling adults aged 40-54 years in Ontario, Canada from April 2002 until March 2019 for incident dementia. We estimated the prevalence of 29 chronic conditions using validated algorithms and/or diagnosis codes. We reduced dataset dimensionality using multiple correspondence analysis and a fuzzy c-means clustering algorithm identified the optimal number of clusters (between 3-6 tested). Associations between clusters and incident dementia were examined using a cause-specific hazard model adjusted for sociodemographic characteristics and accounting for the competing risk of death.
ResultsWe identified 82,359 eligible individuals (random 3% sample of total eligible individuals; mean age 46.5 years; 50.4% female). Regression analyses were based on 5 comorbidity clusters (fuzzy silhouette index:0.69). Compared to the low comorbidity cluster, persons in the cerebrovascular disease/metabolic (HRadj=3.06, 95%CI[2.42,3.86]) and neuro-related/mental health clusters (HRadj=2.51, 95%CI[2.05,3.07]) had the highest rates of incident dementia, followed by the cardiovascular risk factor cluster (HRadj=1.66,95%CI[1.32,2.09]). Persons in the cancer cluster did not have an increased incidence of dementia (HRadj=0.96,95%CI[0.77,1.20]).
ConclusionsWe found significant associations between machine learning-derived clusters of chronic conditions and dementia.
ImplicationsUnsupervised machine learning approaches to identify clusters of chronic conditions may be a useful tool for considering the impact of multimorbidity on dementia risk.