METHODS: The theoretical model was validated by SPSS 26.0 and smartPLS4.0. To evaluate the measurement and structural models, structural equation modeling (SEM) was carried out using the partial least squares (PLS) method.
FINDINGS: (a) Teaching presence positively predicts academic buoyancy, and academic buoyancy positively predicts social presence and cognitive presence; (b) academic buoyancy mediates teaching presence and social presence, as well as teaching presence and cognitive presence; and (c) academic buoyancy acts as a chain mediator between teaching presence and cognitive presence through social presence.
DISCUSSION: The results of this study fill a gap in the multiple roles of individual positive psychological construct-academic buoyancy in blended learning communities, extend the Community of Inquiry theoretical framework, and provide empirical evidence for blended learning quality and practical improvement strategies.
METHODS: Using measures of discrimination and calibration, we tested the performance of the NL-IHRS (n=100 475) and FC-IHRS (n=107 863) for predicting incident CVD in a community-based, prospective study across seven geographic regions: South Asia, China, Southeast Asia, Middle East, Europe/North America, South America and Africa. CVD was defined as the composite of cardiovascular death, myocardial infarction, stroke, heart failure or coronary revascularisation.
RESULTS: Mean age of the study population was 50.53 (SD 9.79) years and mean follow-up was 4.89 (SD 2.24) years. The NL-IHRS had moderate to good discrimination for incident CVD across geographic regions (concordance statistic (C-statistic) ranging from 0.64 to 0.74), although recalibration was necessary in all regions, which improved its performance in the overall cohort (increase in C-statistic from 0.69 to 0.72, p<0.001). Regional recalibration was also necessary for the FC-IHRS, which also improved its overall discrimination (increase in C-statistic from 0.71 to 0.74, p<0.001). In 85 078 participants with complete data for both scores, discrimination was only modestly better with the FC-IHRS compared with the NL-IHRS (0.74 vs 0.73, p<0.001).
CONCLUSIONS: External validations of the NL-IHRS and FC-IHRS suggest that regionally recalibrated versions of both can be useful for estimating CVD risk across a diverse range of community-based populations. CVD prediction using a non-laboratory score can provide similar accuracy to laboratory-based methods.
METHODS: Total 14 eligible articles published before March 2019 involving 35 studies, of which 21 studies (16,109 cases and 26,378 controls) for rs2205960 G > A, 8 studies (2,424 cases and 3,692 controls) for rs704840 T > G, and 6 studies (3,839 cases and 5,867 controls) for rs844648 G > A were included. Effects of the three respective polymorphisms on the susceptibility to ADs were estimated by pooling the odds ratios (ORs) with their corresponding 95% confidence interval (95% CI) in allelic, dominant, recessive, heterozygous and homozygous models.
RESULTS: The overall analysis revealed that all the rs2205960 G > A, rs704840 T > G and rs844648 G > A polymorphisms could increase the risk of ADs in allelic, dominant, recessive, heterozygous and homozygous models. Furthermore, subgroup analysis showed that both rs2205960 G > A and rs704840 T > G were significantly associated with the susceptibility to systemic lupus erythematosus (SLE). What's more, statistically significant association between rs2205960 G > A polymorphism and primary Sjögren's syndrome (pSS) susceptibility was also observed in allelic, dominant and heterozygous models.
CONCLUSIONS: This current meta-analysis suggested that all of the three TNFSF4 polymorphisms may be associated with ADs susceptibility in Asians.
PATIENTS AND METHODS: A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses.
RESULTS: Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively.
CONCLUSIONS: The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.