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Objective To determine if natural language processing (NLP) and machine learning (ML) techniques accurately identify interview-based psychological stress and meaning/purpose data in child/adolescent cancer survivors. Materials and Methods Interviews were conducted with 51 survivors (aged 8-17.9 years; ≥5-years post-therapy) from St Jude Children’s Research Hospital. Two content experts coded 244 and 513 semantic units, focusing on attributes of psychological stress (anger, controllability/manageability, fear/anxiety) and attributes of meaning/purpose (goal, optimism, purpose). Content experts extracted specific attributes from the interviews, which were designated as the gold standard. Two NLP/ML methods, Word2Vec with Extreme Gradient Boosting (XGBoost), and Bidirectional Encoder Representations from Transformers Large (BERTLarge), were validated using accuracy, areas under the receiver operating characteristic curves (AUROCC), and under the precision-recall curves (AUPRC). Results BERTLarge demonstrated higher accuracy, AUROCC, and AUPRC in identifying all attributes of psychological stress and meaning/purpose versus Word2Vec/XGBoost. BERTLarge significantly outperformed Word2Vec/XGBoost in characterizing all attributes (P <.05) except for the purpose attribute of meaning/purpose. Discussion These findings suggest that AI tools can help healthcare providers efficiently assess emotional well-being of childhood cancer survivors, supporting future clinical interventions. Conclusions NLP/ML effectively identifies interview-based data for child/adolescent cancer survivors. 

Ferritin is a 24-mer protein nanocage that stores iron and regulates intracellular iron homeostasis. The nuclear receptor coactivator-4 (NCOA4) binds specifically to ferritin H subunits and facilitates the autophagic trafficking of ferritin to the lysosome for degradation and iron release. Using isothermal titration calorimetry (ITC), we studied the thermodynamics of the interactions between ferritin and the soluble fragment of NCOA4 (residues 383–522), focusing on the effects of the recently identified Fe–S cluster bound to NCOA4, ferritin subunit composition, and ferritin-iron loading. Our findings show that in the presence of the Fe–S cluster, the binding is driven by a more favorable enthalpy change and a decrease in entropy change, indicating a key role for the Fe–S cluster in the structural organization and stability of the complex. The ferritin iron core further enhances this association, increasing binding enthalpy and stabilizing the NCOA4-ferritin complex. The ferritin subunit composition primarily affects binding stoichiometry of the reaction based on the number of H subunits in the ferritin H/L oligomer. Our results demonstrate that both the Fe–S cluster and the ferritin iron core significantly affect the binding thermodynamics of the NCOA4-ferritin interactions, suggesting regulatory roles for the Fe–S cluster and ferritin iron content in ferritinophagy.