Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.

OBJECTIVE: The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics. METHODS: We retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation. RESULTS: Among all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5-25%), normal (25-75%), high (75-95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE. CONCLUSION: Machine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

Size, composition, and surface capping-dependent catalytic activity of spherical gold nanoparticles.

Gold nanostructures are used as catalysts in heterogeneous catalytic processes and have intrigued chemists and materials scientists. Isotropic spherical gold nanoparticles (AuNPs) are ideal for catalysis due to their simple preparation process, controllable surface-active site, tunable size, and composition-dependent catalytic activity. In this study, spherical AuNPs with different size, composition, and surface capping agents have been prepared, and their catalytic activity in reduction of 4-nitrophenol (4-NP) is evaluated. The catalytic activity of AuNPs decreases as their size increases. Meanwhile, the catalytic activity of AuNPs with tartrate as the reducing agent show no evident changes because of containing anisotropic AuNPs. Moreover, silver not only improves monodisperse and spherical AuNPs, but also increases the catalytic activity of small AuNPs. Since the molecular structures of tartrate and citrate are similar, there is no remarkable difference in the catalytic activity of AuNPs using tartrate and citrate as capping agents. These results demonstrate the influence of size, composition, and surface capping on the catalytic activity of AuNPs. Overall, this study facilitates the applicability of gold-based catalyst and AuNPs in plasmonics, nanophotonics, biomedical photonics, and photocatalysis.