McManus Kure (meatmodem42)

Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. compound library chemical A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https//oncopdss.capitalbiobigdata.com . OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https//oncopdss.capitalbiobigdata.com . A sizeable body of research has demonstrated a relationship between organizational change and increased sickness absence. However, fewer studies have investigated what factors might mitigate this relationship. The aim of this study was to examine if and how the relationship between unit-level downsizing and sickness absence is moderated by three salient work factors temporary contracts at the individual-level, and control and organizational commitment at the work-unit level. We investigated the association between unit-level downsizing, each moderator and both short- and long-term sickness absence in a large Norwegian hospital (n = 21,085) from 2011 to 2016. Data pertaining to unit-level downsizing and employee sickness absence were retrieved from objective hospital registers, and moderator variables were drawn from hospital registers (temporary contracts) and the annual work environment survey (control and organizational commitment). We conducted a longitudinal multilevel random effects regression analysizing. The results from this study suggest that the relationship between unit-level downsizing and sickness absence varies according to the stage of change, and that work-related factors moderate this relationship, albeit in different directions. The identification of specific work-factors that moderate the adverse effects of change represents a hands-on foundation for managers and policy-makers to pursue healthy organizational change. The results from this study suggest that the relationship between unit-level downsizing and sickness absence varies according to the stage of change, and that work-related factors moderate this relationship, albeit in different directions. The identification of specific work-factors that moderate the adverse effects of change represents a hands-on foundation for managers and policy-makers to pursue healthy organizational change. Re