Childhood obesity is becoming one of the 21st century’s most important public health problems. Nowadays, the main treatment of childhood obesity is behavior intervention that aims at improve children’s lifestyle to arrest the disease. Information and communication technologies (ICTs) have not been widely employed in this intervention, and most of existing ICTs systems are not having a long-term effect. The purpose of this paper is to define a system to support family-based intervention through a state-of-the-art analysis of family-based interventions and related technological solutions first, and then using the analytic hierarchy process to derive a childhood obesity family-based behavior intervention model, and finally to provide a prototype of a system called OB CITY. The system makes use of applied behavior analysis, affective computing technologies, as well as serious game and gamification techniques, to offer long term services in all care dimensions of the family-based behavioral intervention aiming to provide positive effects to the treatment of childhood obesity.

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Background This study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model. Methods We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes. Results Seventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1 , RRM2 , HELLS , FANCD2 , AURKA , GABARAPL2 , SLC7A11 , KRAS , ACSL4 , MAPK3 , HMGB1 , CXCL2 , ATG7 , DDIT4 , NOX1 , PLIN4 , and STEAP3 . A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems. Conclusion We constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.

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