DESIGN: This was a retrospective cohort study.
SETTING: We used administrative claims data from April 2014 to March 2017.
PARTICIPANTS: We included 18 347 residents of Fukuoka Prefecture, Japan, who received home care during the period, and aged ≥75 years with certified care needs of at least level 3. Participants were categorised based on home care facility use (ie, general clinics, Home Care Support Clinics/Hospitals (HCSCs), enhanced HCSCs with beds and enhanced HCSCs without beds).
PRIMARY AND SECONDARY OUTCOME MEASURES: We used generalised linear models (GLMs) to estimate care utilisation and the incidence of medical institutional death, as well as the potential influence of sex, age, care needs level and Charlson comorbidity index as risk factors.
RESULTS: The results of GLMs showed the inpatient days were 54.3, 69.9, 64.7 and 75.0 for users of enhanced HCSCs with beds, enhanced HCSCs without beds, HCSCs and general clinics, respectively. Correspondingly, the numbers of home care days were 63.8, 51.0, 57.8 and 29.0. Our multivariable logistic regression model estimated medical institutional death rate among participants who died during the study period (n=9919) was 2.32 times higher (p<0.001) for general clinic users than enhanced HCSCs with beds users (relative risks=1.69, p<0.001).
CONCLUSIONS: Participants who used enhanced HCSCs with beds had a relatively low inpatient utilisation, medical institutional deaths, and a high utilisation of home care and home-based end-of-life care. Findings suggest enhanced HCSCs with beds could reduce hospitalisation days and medical institutional deaths. Our study warrants further investigations of home care as part of community-based integrated care.
METHODS: This study involves a comprehensive search of different databases like Web of Science, PubMed, Embase, EBSCOhost, Cochrane, and Scopus. Specific criteria are established for the selection process to make sure the relevant literature included. The quality assessment of the included researches is conducted based on the guidelines outlined in the Cochrane 5.1 handbook. Review Manager 5.3 software is employed to synthesis the effect sizes. Additionally, bias is assessed using funnel plots, and to identify potential sources of heterogeneity, subgroup analyses are performed.
RESULTS: A total of 1907 academic papers, out of which 2 articles were identified via other data sources. The present study examined the impact of a pedagogical intervention involving physical education games on the enjoyment experienced by children and adolescents. The results indicated a significant positive effect (MD = 0.53, 95%CI:[0.27,0.79], P
RESULTS: In this study, G6PDH was identified as a target for algal strain improvement, wherein G6PDH gene was successfully overexpressed and antisense knockdown in P. tricornutum, and systematic comparisons of the photosynthesis performance, algal growth, lipid content, fatty acid profiles, NADPH production, G6PDH activity and transcriptional abundance were performed. The results showed that, due to the enhanced G6PDH activity, transcriptional abundance and NAPDH production, overexpression of G6PDH accompanied by high-CO2 cultivation resulted in a much higher of both lipid content and growth in P. tricornutum, while knockdown of G6PDH greatly decreased algal growth as well as lipid accumulation. In addition, the total proportions of saturated and unsaturated fatty acid, especially the polyunsaturated fatty acid eicosapentaenoic acid (EPA; C20:5, n-3), were highly increased in high-CO2 cultivated G6PDH overexpressed strains.
CONCLUSIONS: The successful of overexpression and antisense knockdown of G6PDH well demonstrated the positive influence of G6PDH on algal growth and lipid accumulation in P. tricornutum. The improvement of algal growth, lipid content as well as polyunsaturated fatty acids in high-CO2 cultivated G6PDH overexpressed P. tricornutum suggested this G6PDH overexpression-high CO2 cultivation pattern provides an efficient and economical route for algal strain improvement to develop algal-based biodiesel production.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.