Rheumatoid arthritis (RA) is an autoimmune disease that often causes progressive joint dysfunction, even disability and death in severe cases. The radical improvement of inflammatory cell infiltration and the resulting disorder in oxygen supply is a novel therapeutic direction for RA. Herein, a near-infrared-absorbing metal/semiconductor composite, polyethylene glycol-modified ceria-shell-coated gold nanorod (Au@CeO 2 ), is fabricated for topical photothermal/oxygen-enriched combination therapy for RA in a mouse model. Upon laser irradiation, the photothermal conversion of Au@CeO 2 is exponentially enhanced by the localized surface plasma resonance-induced light focusing. The elevated temperature can not only remarkably obliterate hyperproliferative inflammatory cells gathered in diseased joints but also vastly increase the catalase-like activity of ceria to accelerate the decomposition of H 2 O 2 to produce much oxygen, which relieves hypoxia. Significantly, RA-induced lesions are improved, and the expression of proinflammatory cytokines and hypoxia-inducible factors is effectively repressed under the cooperation of heat and oxygen. Overall, the core/shell-structured Au@CeO 2 is a promising nanotherapeutic platform that can well realize light-driven heat/ oxygen enrichment to completely cure RA from the perspective of pathogenesis.

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Early diagnosis, standardized treatment, and regular monitoring are the clinical treatment principle of rheumatoid arthritis (RA). The overarching principles and recommendations of treat‐to‐target (T2T) in RA advocate remission as the optimum aim, especially for patients with very early disease who are initiating therapy with anti‐RA medications. However, traditional anti‐RA drugs cannot selectively target the inflammatory areas and may cause serious side effects due to its short biological half‐life and poor bioavailability. These limitations have significantly driven the research and application of nanomaterial‐based drugs in theranostics of RA. Nanomedicines have appropriate sizes and easily modified surfaces which can enhance their biological compatibility and prolong circulation time of drug‐loading systems in vivo. Traditional T2T regimens cannot evaluate the efficacy of drugs in real time, while clinical drug nanosizing can realize the integration of diagnosis and treatment of RA. This review bridges clinically proposed T2T concepts and nanomedicine in an integrated system for RA early‐stage diagnosis and treatment. The most advanced progress in various nanodrug delivery systems for theranostics of RA is summarized, establishing a clear path and a new perspective for further optimization of T2T. Finally, the key facing challenges are discussed and prospects are addressed.

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Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.