A study published in BMCPsychiatry provides novel insight into the development and implementation of leisure-based activities known as the Wellness Program into integrated youth services that can be leveraged as a health promotion tool.
]. The program was designed and implemented over six phases and evaluated using mixed methods primarily in Phase 5 and Phase 6. To support implementation, a multidisciplinary team of researchers, a research and evaluation associate, youth peer evaluators, and various leadership team members was created at Foundry Central Office , Foundry’s central administrative and implementation team.
Centres worked with their community members to develop and draft an outline for their Wellness Program and co-design a custom curriculum to meet their unique needs. The project coordinator sent initial draft curriculum content to centres. Centres then sought input from Youth Advisory Committees, peer support workers, and Indigenous youth and families. Content was also reviewed by FCO staff.
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KATZNCP: a miRNA–disease association prediction model integrating KATZ algorithm and network consistency projection - BMC BioinformaticsBackground Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA–disease associations predicted by computational methods are the best complement to biological experiments. Results In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA–disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA–disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA–disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. Conclusion A new computational model KATZNCP was proposed for predicting potential miRNA–drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA–disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.
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