Selenium-Containing Nanoparticles Combine the NK Cells Mediated Immunotherapy with Radiotherapy and Chemotherapy

Considering the limited clinical benefits of individual approaches against malignancy, natural killer (NK) cell-mediated immunotherapy is increasingly utilized in combination with radiotherapy and target therapeutics. However, the interplay of targeted agents, immunotherapy, and radiotherapy is complex. An improved understanding of the effect of chemotherapy or radiotherapy on specific molecular pathways in immune cells would help to optimize the synergistic antitumor efficiency. In this study, the selenium-containing nanoparticles (NPs) could deliver the chemotherapeutic drug doxorubicin (DOX) to tumor sites by systemic administration. Radiation stimuli facilitate DOX release and enhance chemotherapy efficiency. Moreover, radiation could oxidize diselenide-containing NPs to seleninic acid, which have both synergistic antitumor effect and immunomodulatory activity through enhancing NK cells function. These results indicate that the selenium-containing NPs would be a potential approach to achieve simultaneous treatments of immunotherapy, chemotherapy, and radiotherapy by a simple but effective method.     

A Sensitive Aptasensor Based on a Hemin/G‐Quadruplex‐Assisted Signal Amplification Strategy for Electrochemical Detection of Gastric Cancer Exosomes

Emerging evidence indicates that exosomes derived from gastric cancer cells enhance tumor migration and invasion through the modulation of the tumor microenvironment. However, it remains a major problem to detect cancer‐specific exosomes due to technical and biological challenges. Most of the methods reported could not achieve efficient detection of tumor‐derived exosomes in the background of normal exosomes. Herein, a label‐free electrochemical aptasensor is presented for specific detection of gastric cancer exosomes. This platform contains an anti‐CD63 antibody modified gold electrode and a gastric cancer exosome specific aptamer. The aptamer is linked to a primer sequence that is complementary to a G‐quadruplex circular template. The presence of target exosomes could trigger rolling circle amplification and produce multiple G‐quadruplex units. This horseradish peroxidase mimicking DNAzyme could catalyze the reduction of H₂O₂ and generate electrochemical signals. This aptasensor exhibits high selectivity and sensitivity toward gastric cancer exosomes with a detection limit of 9.54 × 10² mL^?1 and a linear response range from 4.8 × 10³ to 4.8 × 10⁶ exosomes per milliliter. Therefore, this electrochemical aptasensor is expected to become a useful tool for the early diagnosis of gastric cancer.     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation

Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real-time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd–Au synthetic alloys are reported for mass-spectrometry-based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core–shell structure is designed using magnetic core particles to support Pd–Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application-driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future.     

Wearable Electronics Based on 2D Materials for Human Physiological Information Detection

Recently, advancement in materials production, device fabrication, and flexible circuit has led to the huge prosperity of wearable electronics for human healthcare monitoring and medical diagnosis. Particularly, with the emergence of 2D materials many merits including light weight, high stretchability, excellent biocompatibility, and high performance are used for those potential applications. Thus, it is urgent to review the wearable electronics based on 2D materials for the detection of various human signals. In this work, the typical graphene‐based materials, transition‐metal dichalcogenides, and transition metal carbides or carbonitrides used for the wearable electronics are discussed. To well understand the human physiological information, it is divided into two dominated categories, namely, the human physical and the human chemical signals. The monitoring of body temperature, electrograms, subtle signals, and limb motions is described for the physical signals while the detection of body fluid including sweat, breathing gas, and saliva is reviewed for the chemical signals. Recent progress and development toward those specific utilizations are highlighted in the Review with the representative examples. The future outlook of wearable healthcare techniques is briefly discussed for their commercialization.     

基于视觉特性的LCD显示器光谱特征化方法

目的实现LCD显示器RGB颜色空间到颜色光谱高效的特征化。方法利用主成分分析法对光谱数据进行降维处理以及借助RBF神经网络研究输入变量数据范围、视觉加权函数和颜色数量对特征化模型的精度影响。结果主成分个数为6时可以很好地保留光谱原来的信息;输入变量范围为0到2.55,CIE1931视觉函数作为加权函数,颜色数量为364时特征化精度高,客观验证99个颜色转换的平均色差为0.36,最大色差为1.59,总样本的平均色差为0.17。结论输入变量数据范围对模型影响最大,视觉加权函数和颜色数量次之,因此在特征化时要考虑输入变量范围、视觉加权函数和颜色数量,这样可以提高模型的精度。文中提出的模型是一种精度较高的特征化模型,具有一定实际应用价值。     

基于人机交互的老年人产品设计

从影响老年人与产品交互的生理、心理、社会因素进行分析,提出了老年人产品交互设计的一般原则,构建了老年人交互产品的设计流程以及老年用户参与产品设计的模型。通过对老年手机用户的问卷调查,对得到的手机外观、功能反馈信息进行了数据分析和整理,得到老年用户手机设计模型。     

基于网络的虚拟三维环境中人机交互实现途径

VRML（虚拟现实建模语言）是网络三维技术中的代表，本文以VRML为例分析了网络虚拟三维环境中人机交互的实现模式。详细分析了具体节点接受鼠桔操作事件以实现交互的方式，并且介绍了通过Script节点和外部编程接口扩展交互功能的途径。     

基于结构熵的人力资源管理部门组织结构的评价

比较了职能式与矩阵式两种人力资源部门的组织架构,并基于结构熵模型,分析了人力资源管理部门作为一个组织系统的信息流的时效熵和质量熵,对矩阵式和职能式两种结构的有序度作出了评价,结果表明矩阵式组织结构具有更优的有序度。     

A New Reward System Based on Human Demonstrations for Hard Exploration Games

The main idea of reinforcement learning is evaluating the chosen action depending on the current reward. According to this concept, many algorithms achieved proper performance on classic Atari 2600 games. The main challenge is when the reward is sparse or missing. Such environments are complex exploration environments like Montezuma’s Revenge, Pitfall, and Private Eye games. Approaches built to deal with such challenges were very demanding. This work introduced a different reward system that enables the simple classical algorithm to learn fast and achieve high performance in hard exploration environments. Moreover, we added some simple enhancements to several hyperparameters, such as the number of actions and the sampling ratio that helped improve performance. We include the extra reward within the human demonstrations. After that, we used Prioritized Double Deep Q-Networks (Prioritized DDQN) to learning from these demonstrations. Our approach enabled the Prioritized DDQN with a short learning time to finish the first level of Montezuma’s Revenge game and to perform well in both Pitfall and Private Eye. We used the same games to compare our results with several baselines, such as the Rainbow and Deep Q-learning from demonstrations (DQfD) algorithm. The results showed that the new rewards system enabled Prioritized DDQN to out-perform the baselines in the hard exploration games with short learning time.     

基于B/S结构的人力资源管理系统设计与实现

论述了基于B/S结构的人力资源管理系统整体设计与应用方案设计,讨论了系统功能以及安全控制.应用表明,系统用户界面友好,操作灵活方便,可提高工作效率.     

A Paclitaxel‐Based Mucoadhesive Nanogel with Multivalent Interactions for Cervical Cancer Therapy

Cervical cancer treatment is subject to limited drug access to locally diseased targets and generally resistant to chemotherapy, thus it is essential to develop a local drug delivery system to overcome these problems, premised on guaranteeing drug efficacy. With this goal in mind, a multivalent interactions‐based mucoadhesive nanogel for vaginal delivery is proposed. Briefly, the nanogel is constructed with mucoadhesive poly(acrylic acid) as the backbone and multiple inclusions between β‐cyclodextrin and paclitaxel as the crosslinking points. The in vitro experiments demonstrate that nanogel exerts high cytotoxicity to cancer cells, reverses multidrug resistance effectively, and successfully promotes the permeation of drugs. More to the point, as proved in the in vivo experiments, the retention time in the vagina is prolonged and the tumor growth is effectively suppressed by the nanogel without any side effects in the orthotopic cervical cancer model. As mentioned above, this novel mucoadhesive nanogel is believed to be a useful tool toward designing drug delivery systems for cervical cancer treatment.