Antibody-secreting cells (ASCs) play a fundamental role in humoral immunity. The aberrant function of ASCs is related to a number of disease states, including autoimmune diseases and cancer. Recent insights into activated B cell subsets, including naïve B cell to ASC stages and their resultant cellular disturbances, suggest that aberrant ASC differentiation occurs during autoimmune diseases and is closely related to disease severity. However, the mechanisms underlying highly active ASC differentiation and the B cell subsets in autoimmune patients remain undefined. Here, we first review the processes of ASC generation. From the perspective of novel therapeutic target discovery, prediction of disease progression, and current clinical challenges, we further summarize the aberrant activity of B cell subsets including specialized memory CD11c^hiT-bet⁺ B cells that participate in the maintenance of autoreactive ASC populations. An improved understanding of subgroups may also enhance the knowledge of antigen-specific B cell differentiation. We further discuss the influence of current B cell therapies on B cell subsets, specifically focusing on systemic lupus erythematosus, rheumatoid arthritis, and myasthenia gravis.

Background. Tacrolimus is a second-line immunosuppressant in myasthenia gravis (MG) therapy, which is mainly used in combination with corticosteroids to reduce steroid dose and maintain the effect of immunotherapy. However, few studies have focused on the effect of tacrolimus as single-agent immunotherapy on achieving minimal manifestation status (MMS). Thus, this study is aimed at exploring the efficacy and influencing factors of tacrolimus as single-agent immunotherapy in MG. Methods. Clinical data of 75 nonthymoma MG patients treated with tacrolimus single-agent as initial immunotherapy were retrospectively analyzed. The therapeutic effect was evaluated by Myasthenia Gravis Foundation of America postintervention status. Clinical factors affecting the achievement of MMS and treatment reactivity of different MG subtypes were determined by Cox regression analysis. Results. Tacrolimus was generally safe, with only two patients (2.7%) switching medications due to side effects. 32% of patients had improved symptoms after 1 month of treatment. 69.2% of patients achieved MMS or better after one year. The years old, points, and were indicative of a favorable response, which was independent of gender, course of the disease. As for MG subtypes, ocular and seronegative MG showed better treatment sensitivity. Conclusions. Tacrolimus as single-agent immunotherapy takes effect quickly and can effectively enable nonthymoma MG patients to achieve MMS. Tacrolimus can be used alone for the initial immunotherapy of MG patients, especially for young, mild, and low antibody titer patients.

Background: Elderly patients are more likely to suffer from severe ischemic stroke (IS) and have worse outcomes, including death and disability. We aimed to develop and validate predictive models using novel machine learning algorithms for the 3-month mortality in elderly patients with IS admitted to the intensive care unit (ICU). Methods: We conducted a retrospective cohort study. Data were extracted from Medical Information Mart for Intensive Care (MIMIC)-IV and International Stroke Perfusion Imaging Registry (INSPIRE) database. Ten machine learning algorithms including Categorical Boosting (CatBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Gradient Boosting Machine (GBM), K-Nearest Neighbors (KNNs), Multi-Layer Perceptron (MLP), Naive Bayes (NB), eXtreme Gradient Boosting (XGBoost) and Logistic Regression (LR) were used to build the models. Performance was measured using area under the curve (AUC) and accuracy. Finally, interpretable machine learning (IML) models presenting as Shapley additive explanation (SHAP) values were applied for mortality risk prediction. Results: A total of 1826 elderly patients with IS admitted to the ICU were included in the analysis, of whom 624 (34.2%) died, and endovascular treatment was performed in 244 patients. After feature selection, a total of eight variables, including minimum Glasgow Coma Scale values, albumin, lactate dehydrogenase, age, alkaline phosphatase, body mass index, platelets, and types of surgery, were finally used for model construction. The AUCs of the CatBoost model were 0.737 in the testing set and 0.709 in the external validation set. The Brier scores in the training set and testing set were 0.12 and 0.21, respectively. The IML of the CatBoost model was performed based on the SHAP value and the Local Interpretable Model-Agnostic Explanations method. Conclusion: The CatBoost model had the best predictive performance for predicting mortality in elderly patients with IS admitted to the ICU. The IML model would further aid in clinical decision-making and timely healthcare services by the early identification of high-risk patients.