Leveraging interpretable machine learning to identify sarcopenia in middle-aged and older adults with intrinsic capacity decline: an analysis of CHARLS data under AWGS 2025.

Dental hard tissue non-carious diseases, including enamel hypoplasia, dentinogenesis imperfecta, dental fluorosis, abfraction, erosion, and attrition, are vital in Endodontics and Operative Dentistry. Despite being frequently underestimated, they significantly impact oral health, highlighting the need for early diagnosis, effective treatment, and comprehensive patient management strategies [, ]. Mastering these diseases is essential for improving patient outcomes and advancing oral health. However, their intricate nature poses challenges in diagnosis and instruction. Traditional lecture-based teaching often fails to inspire creativity, nurture critical thinking, or fully engage students, making it hard for them to grasp the nuances of these disorders. To address this, this study aims to integrate Rain Classroom with the Case-based Learning (CBL) model to create a more dynamic and effective learning environment. Rain Classroom, co-developed by Tsinghua University and XuetangX, is a widely used e-learning platforms that seamlessly integrates Microsoft PowerPoint with WeChat []. Teachers can share course QR codes in WeChat groups for students to join sessions. By embedding the software into PowerPoint, teachers can launch real-time quizzes, monitor responses, and gather feedback instantly, enabling them to adapt teaching strategies promptly. Its core mission is to foster seamless teacher-student interaction through smart devices, enhancing learning experiences across pre-class, in-class, and post-class activities, and promoting educational reform [, ].