中国医疗器械杂志最新文献

A method for determining volatile organic compounds (VOCs) emitted from medical molecular sieve oxygen concentrators was developed using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The oxygen concentrator gas was sampled at a flow rate of 0.5 L/min through a branched sampling system onto Tenax GR/carbopack B adsorption tubes. The adsorbed compounds were desorbed and introduced using a programmed temperature vaporization inlet system, followed by chromatographic separation on an SH-I-624Sil MS column. Four VOCs (BHT-Q, PTBP, BHT-quinol, and EHB) were detected in the medical oxygen concentrator using this method. Calibration curves for these compounds exhibited excellent linearity ( R ²>0.99) within the range of 3~100 ng. With a sampling volume of 20 L, the detection limit of the four VOCs ranged from 0.003 9 to 0.022 2 μg/m ³. Spike recovery rates for the four VOCs were between 95% and 115%, with relative standard deviations (RSDs) below 5% ( n=6). The method is simple, rapid, highly sensitive, and accurate, making it suitable for VOCs detection in medical molecular sieve oxygen concentrators.

The article introduces the formation process of bacterial infection and encrustation on the surface of ureteral stents, elaborates in detail on six surface modification methods for ureteral stents, namely impregnation, spraying, vapor deposition, chemical grafting, self-assembly, and electrospinning, then analyzes the advantages and limitations of each technique during application. Meanwhile, it introduces three commonly used materials for ureteral stents, namely polymers, metals, and biodegradable materials, and further explains the commonly used surface modification methods corresponding to different materials. Looking ahead, with the continuous strengthening of the multi-technology integration trend and the continuous advancement of new material research and development, it is expected that a more ideal ureteral stent can be developed.

Ultra micro angiography (UMA) can effectively improve the sensitivity and spatial resolution of blood flow imaging. To further meet the clinical requirements of quantitative measurements for microvascular imaging, this article demonstrated technical features and performance verification results of the microflow quantitative analysis function, which was developed based on the UMA function. This article provided preliminary validation of the measurement of Synchronous Arbitrary Gate-PW (SAG-PW) using a Doppler flow phantom and a moving string phantom. The Doppler flow phantom results demonstrated that the SAG-PW measurement values were close to the traditional PW results, and the average error of multiple sets of measurement results under different conditions was 3.9%. The results of SAG-PW and PW were strongly linearly correlated, with the slope and correlation coefficient approaching 1. The results of the moving string phantom demonstrated that the relative errors of TAMean and SAG-PW with different phantom set values were within 10%.

Objective: To investigate the impact of different iterative cone beam CT (iCBCT) scanning beam currents from a ring-mounted linac on synthetic CT image quality for lung adaptive radiotherapy under lung scanning protocol.

Methods: The CIRS lung motion phantom was configured to simulate conventional respiratory motion pattern, followed by 4D-CT simulation. After transferring the radiotherapy plan to the ring-mounted Halcyon 3.0 linac, three groups of typical iCBCT scans with different beam currents [ I _low (160 mA), I _middle (282 mA), and I _high (491 mA)] were performed and corresponding image reconstructions were completed. Synthetic CT (sCT) images were subsequently obtained based on the deformable registration algorithm.

Results: Compared to the corresponding CBCT images, the sCT images exhibited a significant reduction in artifacts. The fine structure of the planning CT (pCT) image was preserved for sCT images corresponding to different scanning beam currents, with Dice similarity coefficients exceeding 0.90 for all cases.

Conclusion: The image quality of sCT corresponding to different iCBCTs is comparable to that of pCT, and changes in iCBCT beam parameters have a negligible impact on sCT image quality. Taking into account both image quality and imaging dose factors associated with the beam currents, iCBCT with a lower beam current on the ring-mounted Halcyon linac offers greater clinical value in lung adaptive radiotherapy.

The minimally invasive thermal ablation technology differs from traditional surgical operations, which requires auxiliary equipment to evaluate ablation results. However, the ultrasound and CT currently used in clinical practice have shortcomings such as artifacts and radiation. Therefore, this paper proposes a design for a minimally invasive thermal ablation evaluation system based on the principle of electrical impedance tomography technology to monitor the ablation range. At the same time, the innovative introduction of a programmable gain feedforward signal as the parameter signal of the multiplier demodulator in the electrical impedance tomography system design can effectively solve the problem of weak signals being submerged in noise and improve imaging accuracy. The system controls the amplitude of the excitation current signal and the acquisition / processing of boundary voltages via an STM32, uploads the collected data to an upper computer, and reconstructs the conductivity distribution using the Newton-Raphson algorithm to map the size of the ablation area. Experimental results show that the system can effectively reflect the size of the microwave ablation area. Under the same minimally invasive ablation parameters, the average imaging errors are 0.6 mm for the long diameter, 0.8 mm for the short diameter, and 1.75% for the axial ratio (long diameter / short diameter), demonstrating high consistency. This verifies the technical potential of electrical impedance tomography in minimally invasive thermal ablation.

The application of artificial intelligence (AI) technology in the field of radiation protection is gradually becoming a key force driving industrial development. This paper focuses on the applications of AI technology in radiation monitoring, shielding design, and nuclear science data mining, and prospects its future development. Through real-time monitoring and predictive analysis, AI technology has significantly improved the accuracy and efficiency of radiation monitoring, optimized the selection and configuration of shielding materials, and effectively reduced radiation exposure. Additionally, AI's data mining capabilities provide powerful tools for nuclear reactor design and optimization, promoting innovation in nuclear and radiological sciences. Despite technical challenges and ethical issues such as accuracy, data processing, and algorithmic transparency, the application prospects of AI in radiation protection remain broad. This paper emphasizes the critical role of AI technology in enhancing medical safety and efficiency, and foresees more future innovations and applications in the field of radiation protection.

Due to the wide variety and varying quality of medical wound dressings, as well as the current lack of unified national or industry standards for regulation, this paper proposes a research strategy for establishing a quality evaluation system for medical wound dressings‌. By developing a technical roadmap, this strategy clarifies the process flow and key points in the quality evaluation process, establishes evaluation methods for various types of medical wound dressings, and addresses important issues such as how to determine key performance indicators based on product characteristics and how to research and validate test methods for key items. This provides a detailed and feasible research strategy and evaluation method for medical wound dressing manufacturers, testing institutions, and regulatory authorities. It reduces the difficulty and cost of quality evaluation for medical wound dressings and has certain significance in standardizing and improving their quality level, ensuring their safety and effectiveness, and serving the quality and safety regulation of medical devices.‌.

Objective: To estimate the primary electron beam parameters (PEB), including energy, radial intensity distribution and average angular divergence, of the individual linear accelerator using the Monte Carlo method.

Methods: A model of the treatment head and a standard field were built by BEAMnrc, and the dose distribution was simulated in water phantoms by DOSXYZnrc to obtain the percentage depth dose curve and off-axis ratio. By debugging the parameters mentioned above until the simulation and measurement results could match.

Results: The simulation and measurement results could achieve the best match when the parameters mentioned above were 6.25 MeV, 0.95 mm and 0.1° respectively.

Conclusion: The PEB of a linear accelerator could have a significant impact on the output beam characteristics. Monte Carlo estimation is one of the most crucial steps in establishing an individual linear accelerator model.

The importance of gait assessment in the rehabilitation of cancer patients is gradually being recognized. However, quantitative analysis of balance ability in cancer patients is still limited. A total of 102 cancer patients meeting the inclusion criteria were recruited from Hefei Cancer Hospital, Chinese Academy of Sciences. Their balance ability was evaluated using the Berg Balance Scale (BBS). Gait data were collected by an electronic walkway and an IMU sensor system, including spatial-temporal and kinematic gait features such as step length, cadence, support time, and range of motion. Recursive feature elimination was used for feature selection. Ridge, Elastic Net, SVR, RF, and AdaBoost models were used to predict balance ability scores. Five-fold cross-validation was used to evaluate the performance of these models. Results show that the SVR model achieves the best performance with fifteen features (RMSE=3.22, R ²=0.91), followed by Ridge (RMSE=3.63, R ²=0.89). A method for evaluating balance ability based on gait features is proposed, providing a quantitative tool for personalized rehabilitation interventions in cancer patients.

In recent years, with the development of big data application technology, the real-world data and the corresponding generated real-world evidence have attracted the attention of healthcare regulatory authorities around the world. Regulators recognize that real-world research with specific purposes using real-world data can provide important evidence for regulatory decisions. A total of 90 instances of publicly released on the application of real-world evidence to support regulatory decisions of U. S. Food and Drug Administration are explored, and the positioning and value of real-world evidence in U. S. Food and Drug Administration regulatory decisions are summarized and analyzed, providing references for the use of real-world data and real-world evidence to promote medical devices whole cycle regulation in China.