Carbon Free Radical (R∙) Inactivates NF-κB for Radical Capping Therapy.

Inactivating hyperactivated transcription factors can overcome tumor therapy resistance, but their undruggable features limit the development of conventional inhibitors. Here, we report that carbon-centered free radicals (R∙) can inactivate NF-κB transcription by capping the active sites in both NF-κB and DNA. We construct a type of thermosensitive R∙ initiator loaded amphiphilic nano-micelles to facilitate intracellular delivery of R∙. At a temperature of 43°C, the generated R∙ engage in electrophilic radical addition towards double bonds in nucleotide bases, and simultaneously cap the sulfhydryl residues in NF-κB through radical chain reaction. As a result, both NF-κB nuclear translocation and NF-κB-DNA binding are suppressed, leading to a remarkable NF-κB inhibition of up to 94.1%. We have further applied R∙ micelles in a clinical radiofrequency ablation tumor therapy model, showing remarkable NF-κB inactivation and consequently tumor metastasis inhibition. Radical capping strategy not only provides a method to solve the heat-sink effect in clinic tumor hyperthermia, but also suggests a new perspective for controllable modification of biomacromolecules in cancer therapy.

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules.

OBJECTIVES: The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS: In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS: The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION: The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT: As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.