Atrial fibrillation is a common cardiac arrhythmia with an annually increasing global prevalence. Ablation of atrial  fibrillation is a minimally invasive procedure that treats atrial fibrillation by using a catheter to deliver radiofrequency energy to heart tissues generating abnormal electrical potentials. The success of this procedure relies significantly on the adhesion between the catheter and the heart tissue, presenting a challenge in accurately assessing  the contact force (CF) during surgery. To improve the safety and success rate of surgery, researchers are committed to developing various methods to evaluate or detect catheter-tissue CF. Among these, some studies integrated  optical fibers or magnetic elements into the catheter tip to create CF sensing catheters that monitor CF in real  time; other studies used impedance measurement, electrical coupling index, local impedance and other methods  to evaluate the CF between the catheter and the tissue by measuring changes in electrical signals. These methods  have achieved certain success in clinical practice, offering new ways to improve the effectiveness and safety of  cardiac radiofrequency ablation surgery.

Radiofrequency ablation (RFA) is a minimally invasive clinical treatment that uses radiofrequency energy to generate heat, resulting in the thermal necrosis of targeted tissues. To enhance the therapeutic benefits of traditional  RFA, cooled RFA (CRFA) technology has been developed. CRFA incorporates cooling technology to prevent thermal  damage and rapid impedance changes caused by tissue overheating. This review article provides a comprehensive overview of various types of cooling electrode needles used in CRFA, as well as an evaluation of their efficacy  and clinical applications. We discuss the advantages of CRFA, including its minimally invasive nature, improved  safety profile, and highly effective treatment outcomes. Nevertheless, certain problems and limitations are also  addressed to optimize the potential of CRFA as a clinical treatment option. Overall, CRFA has promising prospects.  With continued advancements in technology and further research, this innovative treatment modality is expected  to significantly impact the treatment of a wide range of medical conditions.

Breast diseases pose a significant threat to women's health, so early detection and treatment are extremely important. In this context, early disease identification has become crucial in the diagnosis and treatment of breast diseases. This paper begins by outlining the pivotal role of mammography in the early diagnosis of breast cancer, comparing the structural similarities and differences between normal and diseased breast tissues. This comparison underscores the primary role of mammography in the diagnosis and treatment of breast diseases. Additionally, our paper provides an overview of fundamental concepts related to breast cancer detection, diagnosis, and prediction systems. It delves into the latest research developments in auxiliary diagnostic detection, examination, and risk prediction systems associated with breast cancer. Our objective is to offer a comprehensive understanding of the role of computer-aided detection, diagnosis, and prediction systems in breast diseases, fostering further development and application. This work aims to explore and drive innovation in the field, enhance early detection rates of breast diseases, and guide readers towards novel directions, thus contributing to female healthcare management.

Objective: This study presents the design of an innovative lower limb exoskeleton featuring dynamic self-balancing capabilities. It employs Zero Moment Point and Model Predictive Control online gait algorithms to plan stable walking patterns, thereby facilitating precise gait training for individuals with lower limb motor impairments and enhancing the overall training effectiveness. Methods: The positive and negative kinematic solutions of the lower limb exoskeleton were determined using the Denavit-Hartenberg method and the geometric method, respectively. The geometric relationships of the exoskeleton’s linkage components were employed to derive workspace expres sions for various gait phases. By utilizing Zero Moment Point and Model Predictive Control online gait algorithms, simulation experiments were conducted to validate the dynamic self-balancing capability of the exoskeleton while walking on flat terrain. Results: In the evaluation of the online gait generation algorithm’s validity, the generated gait trajectory aligned with the planned trajectory. When examining dynamic self-balancing walking capability, the trajectories from initial simulation experiments on flat terrain closely matched the intended trajectories. Conclusion: The online gait generation algorithm presented in this study is capable of producing a stable walking pattern for continuous bipedal gait. This newly designed lower limb exoskeleton can achieve stable dynamic self-balancing walking.

Recent years have witnessed unprecedented advances in medical device industry. New technologies, such as 3D printing, organ-on-a-chip, and surgical robots, have emerged and iterated constantly. Innovation is the cornerstone in the development of medicine. Though we still need robust clinical data to evaluate the safety and effectiveness of these new technologies, we never stop our pursuit of more effective technologies and devices with a cautious attitude. With these intentions, we are pleased to introduce this new quarterly journal, Progress in Medical Devices.

Objective: Accurately capturing signals from healthcare devices worn on the body is vital for health monitoring, a field of growing interest. A flexible polyvinylidene fluoride (PVDF)-based piezoelectric sensor has been chosen for wearable applications. This study aims to optimize PVDF sensor performance by focusing on structural design. Methods: According to piezoelectric theory, this paper presents a mathematical expression system centered on the first-order piezoelectric equation, which describes the relationship between the electric displacement, the quantity of charge, and the output voltage of the PVDF film under strain. The study focuses on the features of weak physiological signals in the human body to establish a multi-layer shell structure finite element model using COMSOL Multiphysics. The analysis covers the electric displacement field mode, output charge, electric potential, and uniformity of stress distribution. We systematically investigated the impact of PVDF piezoelectric layer area, shape, thickness, and encapsulation materials on sensor performance using the controlled variable method. Results: As the area increases, the sensitivity and stress uniformity improve. Rectangular shapes exhibit more uniform stress distribution than circular shapes, and raising the aspect ratio within a certain range further enhances stress uniformity while maintaining comparable sensitivity. Increasing the thickness of the piezoelectric layer raises the electrical potential. Flexible encapsulation materials can provide higher sensitivity than rigid materials but are more susceptible to stress concentration. Conclusion: This paper finds the best structural form and parameter adjustment range of the PVDF sensor, which can provide a theoretical basis and numerical references for designing high-performance wearable sensors.

Since the beginning of the 21st century, artificial intelligence (AI) has been profoundly reshaping medical research, propelling its transition from the traditional "hypothesis-verification" paradigm towards a “data-driven, generative” cognitive structure. Leveraging deep learning and generative pre-trained models, AI is not only transforming research workflows in areas such as literature review, image recognition, clinical trial design, and drug development, but also challenging the philosophical foundations, interpretability, ethical considerations, and evaluation mechanisms of medical research. This paper systematically analyzes the multifaceted evolution of AI’s role in medical research—from a tool to a collaborator, and from an accelerator to a paradigm architect. It proposes that a framework of “trustworthy, transparent, and controllable” AI should serve as the institutional cornerstone for reconstructing future research paradigms. By examining representative case studies and emerging trends under AI’s influence, the paper emphasizes that human-AI collaboration will become the new norm in medical knowledge production. It further calls for establishing interdisciplinary consensus mechanisms to ensure the harmonious progression of scientific rigor, ethical integrity, and innovative capacity in medical research.

Key Words: artificial intelligence; paradigm of medical research; data-driven science; generative pre-training model;  research ethics

Artificial intelligence (AI) tools show great potential in knowledge innovation and dissemination. However, the application of AI tools has also exposed many ethical and compliance issues, including academic integrity risk, data privacy and security risks, lack of accuracy of the content, the definition of ethical responsibility and the fuzziness of academic norms. This paper discusses in detail the advantages and disadvantages of the application of AI tools in medical research and medical paper writing, as well as the reasons. The paper also gives some concrete solutions to the problems of data protection, content verification and ethical education. In addition, we focuses on the transformation of medical knowledge innovation and dissemination paradigm in the era of AI, in order to promote the standardized application of AI technology in medical research.

Key Words: medical research; paper writing; artificial intelligence; ethical issues; academic norms

Safeguarding the physical and mental health of senior secondary students is the cornerstone of all-round development, which has a profound impact on learning effectiveness, mindset shaping and future life trajectory. However, there are many challenges, such as high academic pressure, lack of sleep, lack of exercise, and weak psychological care. To solve this dilemma, GPT technology came into being, with its powerful natural language processing capabilities to help. GPT can collect physical and mental health data such as students' sleep, diet, exercise, and academic stress to achieve accurate fine-tuning of personalized guidance, making suggestions more intimate and practical. At the same time, a convenient dialogue interface is built so that students can interact with GPT at any time and enjoy instant feedback and long-term companionship. In the face of potential risks, GPT can quickly warn and propose intervention suggestions to protect students' mental health. At the psycho-educational level, GPT acts as an intelligent dialogue partner, providing students with emotional comfort, professional advice and resource links. It can also generate a variety of educational materials to meet the differentiated needs of students and improve self-adjustment through interactive applications. In psychological counseling, GPT is a powerful assistant, from the initial screening of psychological problems to the guidance of help, to the assistance of counseling, and the monitoring of social media to identify crises, to protect the mental health of students in an all-round way. Home-school cooperation is also indispensable. GPT serves as a bridge to generate mental health education content for parents, promote home-school co-education, and jointly protect students' physical and mental health. Of course, GPT applications also face challenges such as data privacy and information accuracy, so it is necessary to strengthen data protection, integrate expert wisdom to ensure that the information is correct, and continuously optimize guidance strategies. To give full play to the effectiveness of GPT, educators, technology developers, parents and all sectors of society need to work together to explore and ensure that technological progress and humanistic care are equally important, to protect the physical and mental health of students.

Key Words: generative pre-trained transformer; artificial intelligence; high school students; physical and mental health