Integrated data mining and network pharmacology to discover a novel traditional Chinese medicine prescription against diabetic retinopathy and reveal its mechanism

Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.

Simplified Inception Module Based Hadamard Attention Mechanism for Medical Image Classification

Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.

Application and mechanisms of Sanhua Decoction in the treatment of cerebral ischemia-reperfusion injury

Cerebral ischemia-reperfusion is a process in which the blood supply to the brain is temporarily interrupted and subsequently restored.However,it is highly likely to lead to further aggravation of pathological damage to ischemic tissues or the nervous system.,and has accordingly been a focus of extensive clinical research.As a traditional Chinese medicinal formulation,Sanhua Decoction has gradually gained importance in the treatment of cerebrovascular diseases.Its main constituents include Citrus aurantium,Magnolia officinalis,rhubarb,and Qiangwu,which are primarily used to regulate qi.In the treatment of neurological diseases,the therapeutic effects of the Sanhua Decoction are mediated via different pathways,including antioxidant,anti-inflammatory,and neurotransmitter regu-latory pathways,as well as through the protection of nerve cells and a reduction in cerebral edema.Among the studies conducted to date,many have found that the application of Sanhua Decoction in the treatment of neurological diseases has clear therapeutic effects.In addition,as a natural treatment,the Sanhua Decoction has received widespread attention,given that it is safer and more effective than traditional Western medicines.Consequently,research on the mechanisms of action and efficacy of the Sanhua Decoctions in the treatment of cerebral ischemia-reperfusion injury is of considerable significance.In this paper,we describe the pathogenesis of cerebral ischemia-reperfusion injury and review the current status of its treatment to examine the therapeutic mechanisms of action of the Sanhua Decoction.We hope that the findings of the research presented herein will contribute to a better understanding of the efficacy of this formulation in the treatment of cerebral ischemia-reperfusion,and provide a scientific basis for its application in clinical practice.

ATFF: Advanced Transformer with Multiscale Contextual Fusion for Medical Image Segmentation

Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.

Diabetes and cancer: Associations, mechanisms, and implications for medical practice

Both diabetes mellitus and cancer are prevalent diseases worldwide. It is evident that there is a substantial increase in cancer incidence in diabetic patients. Epidemiologic studies have indicated that diabetic patients are at significantly higher risk of common cancers including pancreatic, liver, breast, colorectal, urinary tract, gastric and female reproductive cancers. Mortality due to cancer is moderately increased among patients with diabetes compared with those without. There is increasing evidence that some cancers are associated with diabetes, but the underlying mechanisms of this potential association have not been fully elucidated. Insulin is a potent growth factor that promotes cell proliferation and carcinogenesis directly and/or through insulin-like growth factor 1(IGF-1). Hyperinsulinemia leads to an increase in the bioactivity of IGF-1 by inhibiting IGF binding protein-1. Hyperglycemia serves as a subordinate plausible explanation of carcinogenesis. High glucose may exert direct and indirect effects upon cancer cells to promote proliferation. Also chronic inflammation is considered as a hallmark of carcinogenesis. The multiple drugs involved in the treatment of diabetes seem to modify the risk of cancer. Screening to detect cancer at an early stage and appropriate treatment of diabetic patients with cancer are important to improve their prognosis. This paper summarizes the associations between diabetes and common cancers, interprets possible mechanisms involved, and addresses implications for medical practice.

Medical Radiation Exposure and Human Carcinogenesis-Genetic and Epigenetic Mechanisms

Ionizing radiation （IR） is a potential carcinogen. Evidence for the carcinogenic effect of IR radiation has been shown after long-term animal investigations and observations on survivors of the atom bombs in Hiroshima and Nagasaki. However, IR has been widely used in a controlled manner in the medical imaging for diagnosis and monitoring of various diseases and also in cancer therapy. The collective radiation dose from medical imagings has increased six times in the last two decades, and grow continuously day to day. A large number of evidence has revealed the increased cancer risk in the people who had frequently exposed to x-rays, especially in childhood. It has also been shown that secondary malignancy may develop within the five years in cancer survivors who have received radiotherapv, because of IR-mediated damage to healthy cells. In this article, we review the current knowledge about the role of medical x-ray exposure in cancer development in humans, and recently recognized epigenetic mechanisms in IR-induced carcinogenesis.

AF-Net:A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods.

Spatial Differentiation and Influencing Mechanism of Medical Care Accessibility in Beijing:A Migrant Equality Perspective

Spatial equality of access to basic public services, especially medical care services which are directly related to life safety, is the first step to achieve the goal of equalization of basic public services for all the people proposed by central government of China. Using the spatial analysis and the statistical analysis, this study evaluates the spatial differentiation of medical care facilities accessibility by constructing STT(Shortest Travel-Time) and SAI(Spatial Accessibility Index). And then this study explores the neighborhood effects on the medical care facilities accessibility in Beijing, with a particular focus on the effect of neighborhood migrant proportion by constructing spatial dependent regression model. The spatial accessibility analysis of medical care facilities show that the spatial distribution of medical care facilities was basically consistent with administrative regions but not with population demands. Bivariate LISA cluster maps identify that suburban areas are the overlapped clusters of high percent of migrants and limited medical care services. This is associated with the public service allocation rule in China, which stresses equality within urban areas and within rural areas but overlooks equality between urban areas and rural areas; and stresses local resident demands but overlooks migrant demands. To estimate the effects on medical care accessibility of neighborhood migrant proportion, spatial dependence models are applied due to spatial dependence of accessibility of medical care facilities. The regression results show that neighborhoods with high percent of migrants, even conditioning on neighborhood SES, are related to limited spatial accessibility of medical care services. Besides neighborhood characteristics, another important factor influencing spatial accessibility of medical care services is the process of spatial spillover effects. This indicates that the attenuate accessibility of medical care services for migrants is not only because of their own constraints but also because of their proximity to other disadvantaged neighborhoods. Therefore, it is urgently needed to increase the medical facilities in the suburban areas, to take into account migrants' demands and to reduce residential segregation between local residents and migrants for local governments to achieve the goal of equalization of medical care service.

Analysis on the medication rules and mechanisms of action of traditional Chinese medicine in treating cervical cancer

Objective: To summarize the rule of application of traditional Chinese medicine (TCM) in the prevention and treatment of cervical cancer, and to explore the molecular mechanism of the compatibility of core herbs. Methods: Collect relevant literatures on cervical cancer in Chinese National Knowledgey Ifrastructure (CNKI), use TCM inheritance platform system (TCMISSV2.5) for association rules and complex system entropy clustering analysis;use BATMAN-TCM online analysis tools to construct target-pathway-disease network to reveal the underlying mechanisms of action. Results: Among the 78 prescriptions selected, a total of 172 Chinese medicines were used, and the five most frequently used herbs were Huang-bo (Phellodendri Chinrnsis Cortex), Fu-ling (Poria Cocos), Huang-qi (Hedysarum Multijugum Maxim.), Bai-hua-she-she-cao (Hedyotis Diffusae Herba) and Gan-cao (Licorice). Bai-hua-she-she-cao (Hedyotis Diffusae Herba) and Huang-qi (Hedysarum Multijugum Maxim.), Bai-hua-she-she-cao (Hedyotis Diffusae Herba) and Fu-ling (Poria Cocos), Bai-zhu (Atractylodes Macrocephala Koidz.) and Fu-ling (Poria Cocos) are the three most commonly used medicine match. In addition, through cluster analysis, a total of 4 core herbs compatibility and 2 new prescriptions were excavated. Herbs in the new prescriptions which are used to clearing heat-toxin and removing dampness were most frequently used. Through the analysis of the signal pathway of high frequency Chinese medicines, we found that neuroactive ligand-receptor interaction pathway may play important roles. Conclusion: The core Chinese medicines for the prevention and treatment of cervical cancer are mainly clearing heat-toxin and removing dampness. The core Chinese medicines may play their anti-cervical cancer by interfering with the neuroactive ligand-receptor interaction signaling pathway.

Influence of Surface Topography on the Tactile Friction of Medical Compression Textiles against a Mechanical Skin Model

Influence of surface topography on the tactile friction of medical compression textiles was studied in this paper. The friction behavior was investigated for three kinds of medical textiles with various structures and compositions by using a textile friction analyzer under dry condition. In order to simulate the contacts of textiles/human finger, a mechanical skin model with similar texture to the skin and applied normal load oflN were selected. Meanwhile, the 3D surface topography of textiles was measured using a digital microscope. The topographical data were analyzed concerning height distribution and material ratio, and the real contact area was estimated as a function of penetration depth. Results showed that the investigated textiles revealed a significant variation on the friction coefficients, which were （ 0.41 ± 0.01 ） （ polyamide, jersey 2 × 2 ）, （0.56 ± 0. 01） （cotton, jersey 2×2） and （0. 47 ± 0. 01） （polyamide, jersey 1 × 1 ）, respectively. The textile with higher friction coefficient was found to own a relative compact and homogenous surface and larger contact area, vice versa.

Analysis of herbs use law and mechanism of traditional Chinese medicine in preventing and treating postoperative complications of breast cancer

Objective:Excavate the compatibility rules of traditional Chinese medicine for preventing and treating postoperative complications of breast cancer,study the potential mechanism of prevention and treatment with traditional Chinese medicine.Methods:Collate and analyze the relevant prescriptions collected from CNKI and the core drug combination and the new formula are obtained by adopting the traditional Chinese medicine inheritance auxiliary system(TCMISSV2.5).Use BATMAN-TCM online analysis tool to analyze KEGG pathways of high frequency drugs and predict the potential signal pathways.Results:Data mining has selected 10 pairs of core herb combinations and 5 new recipes of 133 prescriptions.In the network pharmacology analysis of bai zhu,huang qi,fu ling,the potential signaling pathways of its action are the neuroactive ligand-receptor interaction pathway and calcium signaling pathway.Conclusion:Herbs for invigorating spleen and qi and herbs for nourishing blood and activating blood are commonly used to prevent and treat postoperative complications of breast cancer.Core herbs may play their role by interfering with the neuroactive ligand-receptor interaction pathway and calcium signaling pathway.

Mechanical Design of Long-Term Body-Adhered Medical Devices to Maximize On-Body Survival

Long-term, body-adhered medical devices rely on an adhesive interface to maintain contact with the patient. The greatest threat to on-body adhesion is mechanical stress imparted on the medical device. Several factors contribute to the ability of the device to withstand such stresses, such as the mechanical design, shape, and size of the device. This analysis investigates the impact that design changes to the device have on the stress and strain experienced by the system when acted on by a stressor. The analysis also identifies the design changes that are most effective at reducing the stress and strain. An explicit dynamic finite element analysis method was used to simulate several design iterations and a regression analysis was performed to quantify the relationship between design and resultant stress and strain. The shape, height, size, and taper of the medical device were modified, and the results indicate that, to reduce stress and strain in the system, the device should resemble a square in shape, be short in height, and small in size with a large taper. The square shape experienced 17.5% less stress compared to the next best performing shape. A 10% reduction in device height resulted in a 21% reduction in stress and 24% reduction in strain. A 20% reduction in device size caused a 7% reduction in stress and 2% reduction in strain. A 20% increase in device taper size led to a negligible reduction in stress and a 6% reduction in strain. The height of the device had the greatest impact on the resultant stress and strain.

Analysis of drug use law and mechanism of prostate cancer based on data mining and network pharmacology

目的:挖掘中药治疗前列腺癌的用药规律,并预测高频药物配伍的生物分子水平作用机制。方法:系统搜集与整理中国知网、万方医学网和维普中文生物医学期刊数据库,Pubmed和EMbase中的相关文献,采用中医传承辅助平台(TCMISSV2.5)进行频次统计、关联规则分析和新方挖掘,采用BATMAN-TCM分析高频药物靶标间相互作用关系及相关通路。结果:在纳入标准的102首处方中,黄芪为使用最多的单药。关联规则分析中置信度较高的组合有6对。系统熵聚类分析得到20个核心药物组合和9首新处方。对黄芪、茯苓、甘草、地黄进行KEGG通路分析,发现神经活性配体受体相互作用通路和嘌呤代谢通路潜在靶标个数最多。结论:前列腺癌的治疗以补虚药为主,配伍活血化瘀、清热利湿、解毒散结之药。高频中药作用机制可能是通过干预神经活性配体受体相互作用通路和嘌呤代谢通路实现的。

中医特色疗法在肝硬化腹水防治中的应用及机制研究进展

肝硬化腹水是肝硬化失代偿期最常见的并发症之一,也是疾病进展的重要标志。目前西医对此尚无特效的治疗方法,而中医特色疗法在本病治疗或辅助治疗中具有确切的疗效。通过总结近10年来中医特色疗法在肝硬化腹水中的应用和作用机制研究,指出目前临床应用和机制研究中存在的问题,为进一步临床应用和机制研究提供思路和方向。

可吸收载药手术缝合线制备技术研究进展

可吸收载药手术缝合线已在医学缝合领域得到广泛应用,在业界展现了巨大的实践价值,并呈现出巨大潜力与广阔前景。回顾了可吸收缝合线的发展历程,详细介绍了可吸收载药手术缝合线的原材料、制备方法和载药方式。研究表明,可吸收载药手术缝合线是未来手术缝合线的发展方向,它们具有一定的强度并能递送药物,兼具可降解的特性,虽然可吸收载药缝合线性能优良,但仍需要在材料、工艺上改进以获得更好的发展。

基于结构功能交叉神经网络的多模态医学图像融合

针对多模态医学图像融合中存在纹理细节模糊和对比度低的问题,提出了一种结构功能交叉神经网络的多模态医学图像融合方法。首先,根据医学图像的结构信息和功能信息设计了结构功能交叉神经网络模型,不仅有效地提取解剖学和功能学医学图像的结构信息和功能信息,而且能够实现这两种信息之间的交互,从而很好地提取医学图像的纹理细节信息。其次,利用交叉网络通道和空间特征变化构造了一种新的注意力机制,通过不断调整结构信息和功能信息权重来融合图像,提高了融合图像的对比度和轮廓信息。最后,设计了一个从融合图像到源图像的分解过程,由于分解图像的质量直接取决于融合结果,因此分解过程可以使融合图像包含更多的细节信息。通过与近年来提出的7种高水平方法相比,本文方法的AG,EN,SF,MI,QAB/F和CC客观评价指标分别平均提高了22.87%,19.64%,23.02%,12.70%,6.79%,30.35%,说明本文方法能够获得纹理细节更清晰、对比度更好的融合结果,在主观视觉和客观指标上都优于其他对比算法。

基于多样化标签矩阵的医学影像报告生成

医学影像在医学诊断中具有重要作用,而准确描述的文本报告对于理解图像以及后续疾病诊断是必不可少的。目前在医学影像报告生成领域,基于模式化方法生成规范的文本报告成为近年的研究热点。但正负样本数量差距较大导致的数据偏差问题,使得生成的报告内容普遍倾向于描述正常状况,难以准确捕捉异常信息。为解决这一问题,提出了一种基于多样化标签矩阵的医学报告生成方法,可以对不同的疾病进行差异化学习,生成多样化的医疗报告;设计文本-矩阵特征损失函数,优化多样化标签矩阵;增加特征交叉模块改进Transformer网络,加强图像与文本的映射,提升疾病描述的准确性。在IU-X-Ray和MIMIC-CXR两个数据集上进行实验,实验结果表明,与目前的主流方法相比,所提方法在BLEU,METEOR等多个指标上取得了最优的效果。

价值支付视角下门诊按人头付费的理论内涵和改革建议

按人头付费是医保门诊统筹改革的核心任务之一。价值支付视角下按人头付费应具有控制医疗成本、提升服务质量、改善患者健康结果的价值目标,其核心要素包括:以整合型医疗卫生服务组织和基层家庭医生签约团队为供方主体,为特定疾病人群或签约人群提供连续性医疗服务;支付标的为固定时间范围内发生的医疗费用总额,通过合理设定人头基础费率和风险调整因子,建立以质量和健康结果为核心的绩效支付和结余分享、风险分担机制等,建立起支付方、供方、患者三方价值均衡且相容的激励约束机制。

利益群体视角下江西省国家区域医疗中心建设探讨

建设国家区域医疗中心是促进优质医疗资源均衡布局的重大举措。江西省国家区域医疗中心建设数量多,且病种选择准、会商调度勤、思想统一快。但从政府、输出医院、依托医院等利益群体视角看,仍存在顶层设计不清晰、参与医院动力不足等问题,需要加快健全协调机制、明晰三方权责、完善考评和专班制度、优化绿色通道,强化输出医院动力,支持依托医院错位发展,打造可持续的双方合作与项目医院自我发展模式。