The patients with type 1 diabetes (T1D) lack natural insulin secretion and need daily insulin injections to keep their blood glucose (BG) within an appropriate range. Artificial pancreas (AP) system is a promising therapeutic method to solve that problem, which comprised three parts, namely, a continuous glucose monitor (CGM), an insulin pump, and an intelligent controller. The intelligent controller plays a key role because it calculates the appropriate insulin amount for the insulin pump. We have developed an AP controller based on generalized predictive control (GPC). But it only uses linear equations and could not imitate the complex blood glucose-insulin dynamics. The nonlinear autoregressive moving average (NARMA-L2) is a simple neural model but an effective way to represent nonlinear systems. Here, a NARMA-L2 controller was proposed for AP system. Tests results showed that it effectively regulated the BG of 9 in-silico patients with an average percentage of time within an appropriate range of 77.43

Low-dose CT (LDCT) effectively reduces radiation exposure in patients; however, the inherent noise and artifacts can degrade image quality and hinder accurate clinical diagnosis. To mitigate these issues, numerous researchers have proposed LDCT denoising algorithms based on conventional convolutional neural networks (CNNs), achieving remarkable success. However, traditional CNNs use fixed convolutional filters for feature extraction at all pixel locations, making it difficult to adapt to varying image regions. This often leads to excessive smoothing in denoised results, causing the loss of critical texture details. To overcome these limitations, this paper proposes a novel LDCT image denoising method based on multi-scale feature fusion and detail enhancement. The proposed approach integrates multi-scale feature extraction, a detail enhancement module, the convolutional block attention module (CBAM), and a high-frequency enhancement module to achieve more effective noise reduction while preserving rich texture and structural details. Specifically, multi-scale feature extraction captures fine-grained, medium-scale, and large-scale image features, while the fusion of these features, combined with the attention mechanism, facilitates enhances detail preservation during denoising. Experimental results demonstrate that the proposed method significantly improves PSNR and SSIM compared to other approaches while reducing computational complexity. Additionally, it enhances the visual quality of low-dose CT images, providing more reliable imaging support for clinical diagnosis.

Blood glucose prediction plays a key role in the automated treatment of diabetic patients. In order to improve the prediction accuracy of blood glucose concentration, this paper proposes a short-term blood glucose concentration prediction model that combines the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with a Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) neural network. Firstly, the CEEMDAN algorithm is used to decompose the blood glucose concentration time series into sub-sequence components. Then, each sub-sequence component is predicted using the PSO-LSTM neural network separately. Finally, the predicted results of each Intrinsic Mode Function (IMF) component are accumulated to obtain the final prediction value of the blood glucose concentration. Experimental analysis shows that the proposed model achieves the best performance in terms of the four prediction evaluation indicators (R2, RMSE, MAE, SMAPE), validating the superiority of the CEEMDAN-PSO-LSTM model.

Wearing hearing aids can help hearing impaired patients enter the sound world, so as to improve their quality of life. Hearing aid fitting is highly professional and requires long-term after-sales service. This paper takes 300 impaired patients and 50 hearing aid fitters of Siemens hearing aid fitting Engineer as the research object to analyze the main influencing factors of hearing impaired patients' purchase of hearing aids and the key factors to improve long-term satisfaction. Hearing aid effect, wearing comfort and service quality significantly affect the long-term satisfaction of hearing-impaired patients. Brand, price, refined customer relationship maintenance and technical personification have a positive impact on customer repeat purchase and recommendation.

Artificial pancreas (AP) is an important therapeutic method for patients with type 1 diabetes (T1D), which is composed of a continuous glucose monitor (CGM), an insulin pump, and an intelligent controller. The intelligent controller plays a key role because it receives the blood glucose data from the CGM and calculates the appropriate insulin amount. We have developed an AP controller based on generalized predictive control (GPC). But the test results with the UVA/Padova type 1 diabetes mellitus simulator (T1DMS) containing 10 in-silico child patients were not satisfying. The reason might be that the younger patients have high-fluctuating blood glucose (BG) levels and high insulin resistance. Thus, the parameter settings of the GPC controller, like control weighting parameter (), for the child patients should be different from those for adults. But it has not been illustrated clearly yet. Here, different values were tested with 10 in-silico child subjects to identify the reasonable setting for child group with T1D. Three indexes provided by the T1DMS software, including the percentage of time in the euglycemic region (TIR), low blood glucose index (LBGI) and high blood glucose index (HBGI), were used to evaluate the efficacy and the security of the child patients with different settings. Test results showed that the valid range of is 0.9 to 4.0, in which the TIRs of child patients were above 80%. The should be set above 0.5 to prevent the hyperglycemia of child patients (HBGI<10), and should be set below 1 to prevent hypoglycemia events (LBGI<2.5). Therefore, the selection range of for child patients with GPC-based AP system should be 0.9∼1.0. Those results are helpful to the AP design and parameter settings for the children with T1D, which will further improve the BG control effect and the security of the AP system.

In order to deeply understand the research status and development trend of tyrosinase inhibitors in China, the literatures related to tyrosinase inhibitors from 2002 to 2022 were achieved from the CNKI database. The literatures were analyzed by using the visual analysis function of the CNKI database. The results represented that the annual number of papers on tyrosinase inhibitors in China in the past 20 years showed an overall upward trend. Colleges and universities are the main force in the research of tyrosinase inhibitors. The high-frequency keywords of tyrosinase inhibitor in the papers are tyrosinase, inhibitor, tyrosinase inhibition, tyrosinase inhibitor activity, chemical composition, etc., while the literature on the physiological function mechanism of tyrosinase inhibitors is less. This study provides a scientific basis for the follow-up research of tyrosinase inhibitors, and provides a strong reference for their application in the medical field.

We report a novel method of continuous online microsphere/particle separation and sorting that integrated techniques of microsphere-imaging, microfluidic chip, moving micro-object super-resolution analysis, and AI image recognition into a simple system. It is the first method with the ability to determine the size of moving micro-objects with nanoscale resolution and separate/sort them simultaneously according to their size, shape, and refractive index. In the experiment, the separation and sorting of polystyrene, silica, and PMMA microspheres from the mixture solutions of several different microspheres using a microfluidic device demonstrated that the sorting was accurate and highly efficient and could classify micro-objects with a nanoscale size difference of similar to 10 nm and a refractive index difference of 10(-5). The method is suitable for coupling with various micro-object separation/sorting instruments for different micro-object separations. It can also divide microobjects either with or without coating.

秦山第三核电厂低功率和停堆工况一级概率安全评价可以对秦山第三核电厂停堆大修期间的堆芯损坏频率进行定量化的分析,本文使用基于风险指引的秦山第三核电厂低功率和停堆工况一级概率安全评价的模型对该厂1号机组第九次停堆大修期间不同的电厂配置和各类维修活动的不同组合下的风险进行分析比较,对高风险的维修活动给于特别的关注和限制,根据风险大小优化大修计划.

Aiming at the complexity and instability of blood glucose data of diabetic patients, this paper introduces an extreme learning machine algorithm (ELM) based on improved particle swarm optimization (IPSO) into the prediction of blood glucose concentration in patients with type I diabetes. First, the blood glucose concentration time series of diabetes patients collected by dynamic blood glucose monitoring is smoothed and normalized to improve the smoothness of the blood glucose data sequence and weaken the randomness of the original blood glucose data sequence. Then, the extreme learning machine is optimized, the improved particle swarm optimization algorithm is introduced to select the appropriate parameters required by the extreme learning machine, and the optimized algorithm is applied to the prediction of blood glucose concentration of diabetic patients. The experimental results show that the extreme learning machine algorithm based on improved particle swarm optimization has higher accuracy for short-term blood glucose concentration prediction of patients.

一、前言 近年来,随着国家对医疗卫生费用的投入不断增多,医疗卫生机构数量不断增加,医疗器械制造、发展政策持续发布等因素刺激着国内CT设备市场持续增长.[1]另一方面, 2020年突发的新型冠状病毒给我们带来了巨大的灾难,严重威胁着人类的健康及生命, CT设备在新冠肺炎病例的筛查及诊断中发挥了巨大作用[2,3] ,常规 CT 设备及方舱CT设备需求日益增多.综上所述,未来几年中国CT设备市场仍会处于稳定发展阶段.随之而来, CT设备的安装、维护、保养的专业性人才的需求亦会持续增加.

Objective: To analyze the volatile constituents in the leaves of Trifolium pratense L. and to explore the effects of different factors on the extraction efficiency. Methods: The volatile components were extracted by the headspace solid phase microextraction (HS-SPME) method and analyzed by gas chromatography-mass spectrometry (GC-MS), and the relative content of the components was calculated by the area normalization method. Results: The best headspace solid phase microextraction conditions of the volatile components of the leaves of Trifolium pratense L. were as follows: 50 μm DVB/CAR/PDMS extraction head was used, the extraction temperature was 70 °C, and extraction time was 40min. 82 compounds were identified from Trifolium pratense L. leaves by GC-MS, accounting for 93.02% of the total volatile components, and the total peak area of volatile components was 2.31x109. The compounds with higher content and active activity were beta.-Caryophyllene (6.48%) and Naphthalene, decahydro-4a-methyl-1-methylene-7-(1-methylethenyl) (4.11%). Conclusion: The type of extraction head, the extraction temperature and the extraction time have great influence on the extraction effect. The HS-SPME-GC-MS method is suitable for the rapid extraction, separation and analysis of the volatile components of the leaves of Trifolium pratense L.

Cancer is a major cause of morbidity and mortality worldwide and constitutes a considerable burden on society. The inhibitors targeting the programmed death receptor–1 (PD-1)/programmed death ligand 1 protein (PD-L1) pathway are the most promising approaches for cancer treatment. Toripalimab, a humanized IgG4 antibody targeting PD-1, was approved by the China National Medical Products Administration in 2018. Although the crystal structure of the toripalimab/PD-1 complex was reported in 2019, it was just a frozen structure and needs to be further studied dynamically to fully understand the blockade mechanism of the antibody toripalimab. Thus, long-time molecular dynamics (MD) simulations were performed for toripalimab/PD-1 and PD-1/PD-L1 complexes. Nine residues were predicted to be epitope and paratope residues for toripalimab, including PD-1PRO130, PD-1LYS131, PD-1ALA132, PD-1ILE134 and GLU99H, GLY100H, THR102H, TYR111H, HSE31L. The PD-1ALA132 locating on the FG loop is the common binding site for PD-L1 and toripalimab. Thus, antibody toripalimab block PD-1/PD-L1 interaction through direct competitive binding of the FG loop of PD-1.

目的:分析国内生物医学工程的研究热点及研究前沿,为相关研究提供参考.方法:以CNKI数据库为检索库,检索条件设置为"文献来源=中国生物医学工程学报and生物医学工程学杂志",使用CiteSpace 5.7.R2软件对2010年1月1日至2021年5月1日收录的相关文献进行分析并绘制可视化图谱.结果:共检索到3367篇文献;分析结果显示支持向量机、脑电、有限元分析是最近10年出现频次最高的关键词.结论:生物医学工程学科进入多领域快速发展时期,深度学习、机器学习、卷积神经网络、脑机接口、生物材料等是当前国内的研究热点与前沿.

In order to make full use of Trifolium pratense L. resources, GC-MS was used to evaluate the effects of two extraction methods on the volatile oil components of Trifolium pratense L. A total of 96 compounds were identified by GC-MS from the volatile oil of Trifolium pratense L. extracted by petroleum ether (PE) method and by steam distillation (SD) method. 67 and 42 compounds were identified respectively, with a total peak area of 2.14 × 109 and 1.83 × 108 respectively. The relative percentage of the volatile oil extracted from Trifolium pratense L. by PE method was higher than that of the others, which were Tetracontane (19.70%), Oleic acid (14.21%), 9,12-Octadecadienoic acid (12.51%) and 1 - (+) - Ascorbic acid 2,6-dihydroxadecanoate (12.13%). The relative percentage of the volatile oil distilled from Trifolium pratense L. by SD method were 13 -docosenamide (28.52%), 7,9-di-tert-butyl-1-oxaspiro (4,5) deca-6,9-diene-2,8-dione (3.34%) and L - (+) - ascorbic acid 2,6-dihexadecanoate (2.34%). There were rich compounds in the volatile oil of Trifolium pratense L. Compared with SD method, PE method has higher kinds of compounds and total peak area.

The inhibitors targeting the programmed death receptor–1 (PD-1)/programmed death ligand 1 protein (PD-L1) pathway are the most promising approaches for cancer treatment. BMS-936559 is a fully human IgG4 antibody blocking PD-L1. Although the crystal structure of the BMS-936559/PD-L1 complex was reported in 2016, providing us the complex interface at atom level, it was just a frozen structure and some key interaction residues might be missed. Thus, molecular dynamics (MD) simulation is used to map the epitope to paratope residues for BMS-936559 dynamically. Two residues, including PD-L1ASP49 on PD-L1 and TYR32L on the light chain of BMS-936559, were newly sorted out in simulations and there are altogether eight residues predicted critical in the complex interface, including PD-L1ASP49, PD-L1TYR56, PD-L1GLU58, PD-L1HIS69 on PD-L1, and LYS57H, HIS59H, SER106H, TYR32L on both heavy and light chains of BMS-936559.

Avelumab, approved by the US Food and Drug Administration (FDA) for the treatment of Merkel cell carcinoma in adults and paediatric patients in 2017, is an investigational fully human anti–PD-L1 IgG1 antibody that inhibits PD-1/PD-L1 interactions. Although the crystal structure of the avelumab/PD-L1 complex was reported in 2017, which provided us the interface information at atom level, the dynamics information of the complex is missed, and some key residues could not be detected in that static crystal structure. Here, molecular dynamics simulations were performed for the avelumab/PD-L1 complex to map the epitope to paratope residues. The results showed that the epitope residues locating on the C strand (PD-L1TYR56 and PD-L1GLU58), CC’ loop (PD-L1GLU60, PD-L1ASP61 and PD-L1LYS62), C’ strand (PD-L1ASN63), and C'D loop (PD-L1HIS69) of PD-L1 mainly form the interface with avelumab. The paratope residues on avelumab include TYR52H, SER54H, GLY102H, THR105H, TYR34L, ASP52L and ARG99L. The C’ strand of PD-L1 is also a binding region for PD-1. Thus, antibody avelumab block PD-1/PD-L1 interaction through direct competitive binding of the C’ strand of PD-L1.

The blockade of immune checkpoints, such as programmed death receptor 1 (PD-1) and programmed death ligand 1 protein (PD-L1), is a promising therapeutic approach in cancer immunotherapy. Nivolumab, a humanized IgG4 antibody targeting PD-1, was approved by the US Food and Drug Administration for several cancers in 2014. Crystal structures of the nivolumab/PD-1 complex show that the epitope of PD-1 locates at the IgV domain (including the FG and BC loops) and the N-terminal loop. Although the N-terminal loop of PD-1 has been shown to play a dominant role in the complex interface of the static structure, its role in the dynamic binding process has not been illustrated clearly. Here, eight molecular systems were established for nivolumab/PD-1 complex, and long-time molecular dynamics simulations were performed for each. Results showed that the N-terminal loop of PD-1 prefers to bind with nivolumab to stabilize the interface between IgV and nivolumab. Furthermore, the binding of the N-terminal loop with nivolumab induces the rebinding between the IgV domain and nivolumab. Thus, we proposed a two-step binding model for the nivolumab/PD-1 binding, where the interface switches to a high-affinity state with the help of the N-terminal loop. This finding suggests that the N-terminal loop of PD-1 might be a potential target for anti-PD-1 antibody design, which could serve as an important gatekeeper for the anti-PD-1 antibody binding.

After the accident at the Three Mile Island nuclear power plant in the United States, the probabilistic safety assessment (Probabilistic Safety Assessment, referred to as PSA) in the nuclear industry began to boom. Although nuclear power plants have various safety designs, reactor core damage accidents can still occur. Using PSA technology can help nuclear power plants identify weak links and reduce the chance of core damage accidents within a reasonable range. The NRC believes it is necessary to establish a mechanism to monitor the effectiveness of maintenance operations at nuclear power plants to ensure that critical safety systems are able to perform their assigned safety functions. In July 1991, the NRC issued 10 CFR 50.65 Maintenance Rules (MR), titled "Requirements for Monitoring the Effectiveness of Nuclear Power Plant Maintenance." In 1993, the Electric Power Research Institute (EPRI) issued NUMARC 93-01 "Industry Guidelines for Monitoring the Effectiveness of Nuclear Power Plant Maintenance". On July 10, 1996, the maintenance rules came into effect. Effective maintenance closely related to nuclear power plant maintenance and operation and nuclear safety can minimize the number of transient events due to system, structure and component (SSC) failures. To evaluate and/or monitor the effectiveness of maintenance and operations, Maintenance Regulations (MR) use probabilistic safety assessment (PSA) techniques to ensure that each system is performing well. A peer-reviewed PSA model is an appropriate tool for conducting (a)(4) evaluations. In general, risk assessment staff should evaluate the risk of planned maintenance activities (such as preventive maintenance) on the previous day or two and ensure that potential risks are acceptable and controlled. If certain emergencies are likely to change the conditions of a previously (or planned) evaluation performed, PSA staff should re-evaluate the risk due to the change in conditions if it falls within the scope of the MR. Depending on the results of the evaluation, planned maintenance activities may need to be suspended or rescheduled.

背景:智能的血糖控制算法是闭环式人工胰腺的重要环节.目的:为了有效控制1型糖尿病患者的血糖浓度,降低患者高、低血糖事件的发生概率,提出基于活性胰岛素累积量模糊自适应比例微积分的闭环胰岛素控制算法.方法:根据实时监测的人体血糖数据,采用比例微积分算法模拟人体β细胞分泌胰岛素生理传输过程,然后不断优化比例-积分-微分控制器参数数值,利用活性胰岛素累积量进行修正模糊比例-积分-微分计算出来的胰岛素剂量,使得最终计算出适合该患者的最佳胰岛素泵给药量.结果与结论:该算法在美国弗吉尼亚大学与意大利帕多氏联合开发的UVa/Padova仿真平台进行了算法性能的测试,仿真实验结果表明该控制算法显著降低了低血糖事件,将血糖水平控制在设定的目标区间,提高了胰岛素注射疗法的精确性和有效性,显著降低了各种并发症的产生.

为了有效解决无法找到合格的核级供应商提供核级产品或服务的问题,美国核电行业开发了商品级物项替代的流程,并且该流程得到了美国核管会的认可.本文介绍了美国商品级物项替代管理流程的历史背景、法规依据、行业导则以及具体实施流程,为中国核电行业所遇到的相类似问题提供了有价值的参考.