Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options

Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging

Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues

Efficacy and safety of remimazolam besilate for sedation in outpatients undergoing impacted third molar extraction: a prospective exploratory study

Abstract Background Dental treatments often cause anxiety, fear, and stress in patients. Intravenous sedation is widely used to alleviate these concerns, and various agents are employed for sedation. However, it is important to find safer and more effective sedation agents, considering the adverse effects associated with current agents. This study aimed to investigate the efficacy and safety of remimazolam besilate (hereinafter called “remimazolam”) and to determine the optimal dosages for sedation in outpatients undergoing dental procedures. Methods Thirty-one outpatients aged 18–65 years scheduled for impacted third molar extraction were included in the study. Remimazolam was administered as a single dose of 0.05 mg/kg followed by a continuous infusion at a rate of 0.35 mg/kg/h, with the infusion rate adjusted to maintain a sedation level at a Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) score of 2–4. The primary endpoint was the sedation success rate with remimazolam monotherapy, and the secondary endpoints included induction time, recovery time, time until discharge, remimazolam dose, respiratory and circulatory dynamics, and frequency of adverse events. Results The sedation success rate with remimazolam monotherapy was 100%. The remimazolam induction dose was 0.08 (0.07–0.09) mg/kg, and the anesthesia induction time was 3.2 (2.6–3.9) min. The mean infusion rate of remimazolam during the procedure was 0.40 (0.38–0.42) mg/kg/h. The time from the end of remimazolam administration to awakening was 8.0 (6.7–9.3) min, and the time from the end of remimazolam administration to discharge was 14.0 (12.5–15.5) min. There were no significant respiratory or circulatory effects requiring intervention during sedation. Conclusions Continuous intravenous administration of remimazolam can achieve optimal sedation levels without significantly affecting respiratory or circulatory dynamics. The study also provided guidance on the appropriate dosage of remimazolam for achieving moderate sedation during dental procedures. Additionally, the study findings suggest that electroencephalogram monitoring can be a reliable indicator of the level of sedation during dental procedural sedation with remimazolam. Trial registration The study was registered in the Japan Registry of Clinical Trials (No. jRCTs061220052) on 30/08/2022