中国医疗器械杂志最新文献

First of all, the overall framework of 3D printing is briefly introduced, including the basic principles of the additive manufacturing process, the classification and summary of the seven processes. Secondly, the common negative Poisson's ratio structure is introduced. Compared with the conventional structure, the negative Poisson's ratio structure has stronger energy absorption capacity, better fracture resistance and better indentation resistance, which are its advantages in printing manufacturing. Finally, 3D printing, the application of negative Poisson's ratio structure and the combination of the two are introduced from the different perspective of medical field, for example, the application of cardiovascular stent, biomedical material structure preparation, and lumbar disc implants. This paper suggests that the structural design of negative Poisson's ratio in 3D printing guides the development of new application directions in the medical field. Negative Poisson's ratio materials have a wide range of applications, not only in the medical field but also in mechanical equipment, automotive manufacturing, aerospace, and other high-tech industries.

In order to improve the biological effect of proton therapy, the authors first propose a new method of boron-based proton-enhanced radiotherapy in a " ternary " radiotherapy mode, based on the existing sensitizing effect of proton radiotherapy: i.e, Boron-based mediators (¹¹B and ¹⁰B) induce the proton-hydrogen-boron fusion reaction of the low-energy protons arriving at the Bragg peak region of the tumor target area (p+¹¹B→3α) and thermal neutron capture (¹⁰B+n→⁷Li³⁺(0.84 MeV)+⁴He²⁺(1.47 MeV)+$\text{γ}$(0.477 MeV)), which release low-energy α-particles with high LETs to enhance the biological effect of proton dose in the target area, thus improve the clinical effect of proton therapy. Then, the advantages and disadvantages of the "ternary" model were analyzed from the theoretical basis and current research status, and finally, the "ternary" model is summarized and prospected.

Objective: To introduce a locating device for the entry point of intramedullary nail based on the inertial navigation technology, which utilizes multi-dimensional angle information to assist in rapid and accurate positioning of the ideal direction of femoral anterograde intramedullary nails' entry point, and to verify its clinical value through clinical tests.

Methods: After matching the locating module with the developing board, which are the two components of the locating device, they were placed on the skin surface of the proximal femur of the affected side. Anteroposterior fluoroscopy was performed. The developing angle corresponding to the ideal direction of entry point was selected based on the X-ray image, and then the yaw angle of the locating module was reset to zero. After resetting, the locating module was combined with the surgical instrument to guide the insertion angle of the guide wire. The ideal direction of entry point was accurately located based on the angle guidance. By setting up an experimental group and a control group for clinical surgical operations, the number of guide wire insertion times, surgical time, fluoroscopy frequency, and intraoperative blood loss with or without the locating device was recorded.

Results: Compared to the control group, the experimental group showed significant improvement in the number of guide wire insertion times, surgical time, fluoroscopy frequency, and intraoperative blood loss, with a statistically significant difference (P<0.01).

Conclusion: The locating device can assist doctors in quickly locating the entry point of intramedullary nail, effectively reducing the fluoroscopy frequency and surgical time by improving the success rate of the guide wire insertion with one shot, improving surgical efficiency, and possessing certain clinical value.

The medical electron linear accelerator（LINAC） has the characteristics of complex system structure, many core components and high precision control requirements, which puts forward higher requirements for product quality control and regulation. This study puts forward the main points of field inspection through the analysis of the technical characteristics and production risk of LINAC, combined with the requirements of the good manufacturing practice of medical devices. It has certain reference significance for quality management personnel and field inspectors.

Population aging trend is taking place in our country, and low back pain is a symptom of neuromuscular diseases of concern in the elderly. Accurately analyzing the disease of low back pain is important for both timely intervention and rehabilitation of patients. As a kind of bioelectrical signal, the acquisition and analysis of lumbar electromyography (EMG) signal is an important direction for the study of low back pain. The study reviews the acquisition of lumbar EMG by different types of sensors, introduces the signal characteristics of needle electrodes, surface electromyography electrodes and array electrodes, describes the use of signal algorithms, points out that wireless sensors and the use of deep learning algorithms are the direction of development, and puts forward prospects for its further development.

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

The concentration of end-tidal carbon dioxide is one of the important indicators for evaluating whether the human respiratory system is normal. Accurately detecting of end-tidal carbon dioxide is of great significance in clinical practice. With the continuous promotion of the localization of end-tidal carbon dioxide monitoring technology, its application in clinical practice in China has become increasingly widespread in recent years. The study is based on the non-dispersive infrared method and comprehensively elaborates on the detection principle, gas sampling methods, key technologies, and technological progress of end-tidal carbon dioxide detection technology. It comprehensively introduces the current development status of this technology and provides reference for application promotion and further improvement.

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r²≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.

The study developed a memory task training system using functional near-infrared spectroscopy (fNIRS) and neurofeedback mechanisms, and acquired and analyzed subjects' EEG signals. The results showed that subjects participating in the neurofeedback task had higher correlated brain network node degrees and average cluster coefficients in the right hemisphere brain region of the prefrontal lobe, with relatively lower dispersion of mediator centrality. In addition, the subjects' left hemisphere brain region of the prefrontal lobe section had increased centrality in the neurofeedback task. Classification of brain data by the channel network model and the support vector machine model showed that the classification accuracy of both models was higher in the task state and resting state than in the feedback task and the control task, and the classification accuracy of the channel network model was higher. The results suggested that subjects in the neurofeedback task had distinct brain data features and that these features could be effectively recognized.

In order to realize the diagnosis of slit lamp in cross-regional patients and improve the real-time and convenience of diagnosis, a remote slit lamp diagnosis platform based on Internet of Things (IoT) technology is designed. Firstly, the feasibility of remote slit lamp is analyzed. Secondly, the IoT platform architecture of doctor/server/facility (D/S/F) is proposed and a remote slit lamp is designed. Finally, the performance of the remote slit lamp diagnostic platform is tested. The platform solves the communication problem of distributed slit lamps and realizes respectively numerical control of multi-area slit lamp by multi-eye experts. The test results show that the remote control delay of the platform is less than 20 ms, which supports multiple experts to diagnose multiple patients separately.