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.
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.
METHODS: Through stratified random sampling, 1469 students, aged 18-19 years, were enrolled. Participants whose score achieved the aggressive evaluation standard were selected and then 60 participants were randomly divided into 2 groups: G-IPT and control. The participants in the G-IPT group received 16 sessions of treatment, whereas the participants in the control group did not receive any intervention. All participants completed the assessment three times: before, after, and tracking.
RESULTS: The results showed that the total score and the scores of all subscales of aggression dropped significantly (P
METHODS: A systematic literature search was performed in Scopus, Embase, Web of Science, and PubMed databases up to February 2020 for RCTs that investigated the effect of DHEA supplementation on testosterone levels. The estimated effect of the data was calculated using the weighted mean difference (WMD). Subgroup analysis was performed to identify the source of heterogeneity among studies.
RESULTS: Overall results from 42 publications (comprising 55 arms) demonstrated that testosterone level was significantly increased after DHEA administration (WMD: 28.02 ng/dl, 95% CI: 21.44-34.60, p = 0.00). Subgroup analyses revealed that DHEA increased testosterone level in all subgroups, but the magnitude of increment was higher in females compared to men (WMD: 30.98 ng/dl vs. 21.36 ng/dl); DHEA dosage of ˃50 mg/d compared to ≤50 mg/d (WMD: 57.96 ng/dl vs. 19.43 ng/dl); intervention duration of ≤12 weeks compared to ˃12 weeks (WMD: 44.64 ng/dl vs. 19 ng/dl); healthy participants compared to postmenopausal women, pregnant women, non-healthy participants and androgen-deficient patients (WMD: 52.17 ng/dl vs. 25.04 ng/dl, 16.44 ng/dl and 16.47 ng/dl); and participants below 60 years old compared to above 60 years old (WMD: 31.42 ng/dl vs. 23.93 ng/dl).
CONCLUSION: DHEA supplementation is effective for increasing testosterone levels, although the magnitude varies among different subgroups. More study needed on pregnant women and miscarriage.