mainstream digital health into oncology nursing practice. Telemedicine services go much further than simply digitizing traditionally analogue health care processes and services, they fundamentally reorganize processes, procedures, and services. Thus, in ARV-771 in vivo to training and education, nursing tele-oncology demands a service transformation. The prevalence of exercise as an adjunct therapy to cancer treatments including chemotherapy, radiation therapy, and surgery is growing rapidly and has been shown to improve health outcomes, treatment completion rates, and quality of life in people affected by cancer. Given the complexity of delivering cancer services during coronavirus disease (COVID-19), many people who are undergoing cancer treatment are unable to access exercise services. This review aims to investigate (1) the feasibility of exercise telehealth interventions for individuals diagnosed with cancer; and (2) the impact of exercise telehealth interventions for people affected by cancer on physical and psychosocial outcomes. The literature search was conducted in four electronic databases (CINAHL, Cochrane, Medline, and Psych Info) from January 1, 2010 until May 1, 2020. All peer-reviewed qualitative and quantitative studies were included irrespective of study design. Studies that investigated adults (aged ≥18 years) with a diagnosis of anmportant for people affected by cancer to continue to engage in and maintain regular exercise under the guidance of qualified health professionals in keeping with evidence-based clinical guidelines. Predictive risk models are advocated in psychosocial oncology practice to provide timely and appropriate support to those likely to experience the emotional and psychological consequences of cancer and its treatments. New digital technologies mean that large scale and routine data collection are becoming part of everyday clinical practice. Using these data to try to identify those at greatest risk for late psychosocial effects of cancer is an attractive proposition in a climate of unmet need and limited resource. In this paper, we present a framework to support the development of high-quality predictive risk models in psychosocial and supportive oncology. The aim is to provide awareness and increase accessibility of best practice literature to support researchers in psychosocial and supportive care to undertake a structured evidence-based approach. Statistical prediction risk model publications. In statistical modeling and data science different approaches are needed if the goal is to predict rather than explain. The deployment of a poorly developed and tested predictive risk model has the potential to do great harm. Recommendations for best practice to develop predictive risk models have been developed but there appears to be little application within psychosocial and supportive oncology care. Use of best practice evidence will ensure the development and validation of predictive models that are robust as these are currently lacking. These models have the potential to enhance supportive oncology care through harnessing routine digital collection of patient-reported outcomes and the targeting of interventions according to risk characteristics.Use of best practice evidence will ensure the development and validation of predictive models that are robust as these are currently lacking. These models have the potential to enhance supportive oncology care through harnessing routine digital collection of patient-reported outcomes and the targeting of interventions according to risk characteristics. Efficient analysis strategies for complex network with cardiovascular disease (CVD) risk stratification remain lacking. We sought to identify an optimized model to study CVD prognosis using survival conditional inference tree (SCTREE), a machine-learning method. We identified 5379 new onset CVD from 2006 (baseline) to May, 2017 in the Kailuan I study including 101,510 participants (the training dataset). The second cohort composing 1,287 CVD survivors was used to validate the algorithm (the Kailuan II study, n=57,511). All variables (e.g., age, sex, family history of CVD, metabolic risk factors, renal function indexes, heart rate, atrial fibrillation, and high sensitivity C-reactive protein) were measured at baseline and biennially during the follow-up period. Up to December 2017, we documented 1,104 deaths after CVD in the Kailuan I study and 170 deaths in the Kailuan II study. #link# Older age, hyperglycemia and proteinuria were identified by the SCTREE as main predictors of post-CVD mortality. CVD survivors in the high risk group (presence of 2-3 of these top risk factors), had higher mortality risk in the training dataset (hazard ratio (HR) 5.41; 95% confidence Interval (CI) 4.49-6.52) and in the validation dataset (HR 6.04; 95%CI 3.59-10.2), than those in the lowest risk group (presence of 0-1 of these factors). Older age, hyperglycemia and proteinuria were the main predictors of post-CVD mortality. ChiCTR-TNRC-11001489.ChiCTR-TNRC-11001489. Oxidative stress contributes to development of diabetic nephropathy. We implicated SH3YL1 in oxidative stress-induced inflammation and examined whether SH3YL1 could be used as a new biomarker of diabetic nephropathy. In this study, we investigated the relationship between plasma level of SH3YL1 and diabetic nephropathy in patients with type 2 diabetes. In addition, we examined the physiological role of SH3YL1 in db/db mice and cultured podocytes. Plasma SH3YL1 concentration was significantly higher in patients with diabetes than in controls, even in normoalbuminuric patients, and was markedly increased in the macroalbuminuria group. Plasma SH3YL1 level was positively correlated with systolic blood pressure, HOMA-IR, postprandial blood glucose, plasma level of retinol binding protein 4 (RBP 4), and urinary albumin excretion (UAE) and was inversely correlated with BMI. Regression analysis showed that plasma level of RBP 4, UAE, and BMI were the only independent determinants of plasma SH3YL1 concentration. In db/db mice, plasma and renal SH3YL1 levels were significantly increased in mice with diabetes compared with control mice. In cultured podocytes, high glucose and angiotensin II stimuli markedly increased SH3YL1 synthesis. These findings suggest that plasma level of SH3YL1 offers a promising new biomarker for diabetic nephropathy.These findings suggest that plasma level of SH3YL1 offers a promising new biomarker for diabetic nephropathy.