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Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage 预防前哨出血的脑动脉瘤分割方法的发展
IF 2.3 Q2 Mathematics Pub Date : 2023-03-16 DOI: 10.1007/s13721-023-00412-7
Yousra Regaya, A. Amira, S. Dakua
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引用次数: 1
Deciphering the lysine acetylation pattern of leptospiral strains by in silico approach 用硅片法破译钩端螺旋体菌株赖氨酸乙酰化模式
IF 2.3 Q2 Mathematics Pub Date : 2023-01-22 DOI: 10.1007/s13721-023-00411-8
Vibhisha Vaghasia, K. Lata, Saumya K. Patel, Jayashankar Das
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引用次数: 1
Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services 基于机器学习的远程医疗框架,优先考虑患有多种慢性疾病的远程患者的紧急医疗服务
IF 2.3 Q2 Mathematics Pub Date : 2023-01-03 DOI: 10.1007/s13721-022-00407-w
Sara Yahya Kadum, O. Salman, Z. Taha, Amal Bati Said, Musab A. M. Ali, Q. Qassim, M. Aal-Nouman, Duraid Y. Mohammed, Baraa M. Al baker, Z. A. Abdalkareem
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引用次数: 2
Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding. 基于图嵌入的SARS-CoV-2刺突蛋白变异结构分析
IF 2.3 Q2 Mathematics Pub Date : 2023-01-01 DOI: 10.1007/s13721-022-00397-9
Pietro Hiram Guzzi, Ugo Lomoio, Barbara Puccio, Pierangelo Veltri

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of this virus has led to the insurgence of many variants that impact protein sequence and structure that need continuous monitoring and analysis of the sequences to understand the genetic evolution and to prevent possible dangerous outcomes. Some variants causing the modification of the structure of the proteins, such as the Spike protein S, need to be monitored. Protein contact networks (PCNs) have been recently proposed as a modelling framework for protein structures. In such a framework, the protein structure is represented as an unweighted graph whose nodes are the central atoms of the backbones (C- α ), and edges connect two atoms falling in the spatial distance between 4 and 7 Å. PCN may also be a data-rich representation since we may add to each node/atom biological and topological information. Such formalism enables the possibility of using algorithms from graph theory to analyze the graph. In particular, we refer to graph embedding methods enabling the analysis of such graphs with deep learning methods. In this work, we explore the possibility of embedding PCN using Graph Neural Networks and then analyze in the embedded space each residue to distinguish mutated residues from non-mutated ones. In particular, we analyzed the structure of the Spike protein of the coronavirus. First, we obtained the PCNs of the Spike protein for the wild-type, α , β , and δ variants. Then we used the GraphSage embedding algorithm to obtain an unsupervised embedding. Then we analyzed the point of mutation in the embedded space. Results show the characteristics of the mutation point in the embedding space.

自2019年12月以来,严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)几乎影响了所有国家。这种病毒前所未有的传播导致了许多影响蛋白质序列和结构的变异的爆发,需要对这些序列进行持续监测和分析,以了解遗传进化并防止可能的危险后果。一些引起蛋白质结构修饰的变异,如Spike蛋白S,需要监测。蛋白质接触网络(PCNs)最近被提出作为蛋白质结构的建模框架。在这种框架中,蛋白质结构被表示为一个未加权的图,其节点是骨架(C- α)的中心原子,边缘连接在4和7之间的空间距离上的两个原子Å。PCN也可以是数据丰富的表示,因为我们可以向每个节点/原子添加生物和拓扑信息。这种形式主义使得使用图论中的算法来分析图成为可能。特别地,我们引用了图嵌入方法,可以使用深度学习方法分析这些图。在这项工作中,我们探索了使用图神经网络嵌入PCN的可能性,然后在嵌入空间中分析每个残基,以区分突变残基和非突变残基。我们特别分析了冠状病毒刺突蛋白的结构。首先,我们获得了野生型、α型、β型和δ型Spike蛋白的pcn。然后使用GraphSage嵌入算法得到无监督嵌入。然后对嵌入空间中的突变点进行分析。结果显示了嵌入空间中突变点的特征。
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引用次数: 1
Policy analysis and data mining tools for controlling COVID-19 policies. 用于控制 COVID-19 政策的政策分析和数据挖掘工具。
IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-01-01 Epub Date: 2022-12-05 DOI: 10.1007/s13721-022-00400-3
Yoshiyasu Takefuji

Much research has been done on the efficacy of vaccines against the COVID-19 pandemic, but the claims have not yet been realized in the real world. This paper proposes three COVID-19 policy outcome analysis tools such as jpscore for scoring and revealing the best prefecture policy in Japan, scorecovid for scoring and revealing the best country policy in the world, and finally hiscovid for visualizing and identifying when policymakers made mistakes in time-series scores. Poorly scored countries or prefectures can learn good strategies from the best country or prefecture with excellent scores. Three tools are based on a single metric dividing the number of COVID-19 deaths by the population in millions. Three tools suggest us that the sustainable mandatory test-isolation strategy should be adopted in the world for mitigating the pandemic. This paper also addresses what is lacking in Japan for scientific evidence-based research for mitigating the pandemic. Visualization tools and sorted and time-series scores of policy outcomes help policymakers make the right decisions.

针对 COVID-19 大流行的疫苗疗效已进行了大量研究,但这些说法尚未在现实世界中实现。本文提出了三种 COVID-19 政策结果分析工具,如 jpscore(用于评分和揭示日本最佳都道府县政策)、scorecovid(用于评分和揭示世界最佳国家政策)和 hiscovid(用于可视化和识别政策制定者在时间序列评分中的失误)。得分较差的国家或县可以从得分优秀的国家或县那里学到好的策略。三种工具都基于一个单一指标,即用 COVID-19 死亡人数除以百万人口。三种工具建议我们在全球范围内采用可持续的强制检测隔离策略来缓解疫情。本文还论述了日本在缓解疫情的科学循证研究方面的不足之处。可视化工具以及政策结果的分类和时间序列评分有助于决策者做出正确决策。
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引用次数: 0
Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2. 建立人工神经网络模型,预测新型口服抗冠状病毒SARS-CoV-2药物的PAMPA有效渗透率。
IF 2.3 Q2 Mathematics Pub Date : 2023-01-01 DOI: 10.1007/s13721-023-00410-9
Chrysoula Gousiadou, Philip Doganis, Haralambos Sarimveis

Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design.

Supplementary information: The online version contains supplementary material available at 10.1007/s13721-023-00410-9.

为应对新冠肺炎大流行,科学界加大了抗疫药物研发力度。为了加强这些努力,“COVID登月计划”项目一直在接受公众对计算分类、合成和测试分子的建议。该项目旨在确定具有抗病毒活性的低分子量分子,用于口服治疗。药物穿过肠细胞膜并进入循环的能力决定性地影响其生物利用度,因此需要在药物发现的早期阶段优化通透性。在我们目前的工作中,作为对正在进行的科学努力的贡献,我们采用人工神经网络算法开发QSAR工具,用于模拟口服药物的PAMPA有效渗透性(被动扩散)。我们确定了61个与解释药物细胞渗透性最相关的特征,并利用它们开发了一个堆叠回归集合模型,随后用于预测通过COVID Moonshot项目提供的数据集中包含的分子的渗透性。我们的模型被证明是稳健的,并可能为预测尚未合成的分子的潜在渗透性提供一个有希望的框架,从而指导药物设计的过程。补充信息:在线版本包含补充资料,下载地址:10.1007/s13721-023-00410-9。
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引用次数: 2
A subunit vaccine against pneumonia: targeting Streptococcus pneumoniae and Klebsiella pneumoniae. 一种针对肺炎链球菌和肺炎克雷伯菌的肺炎亚单位疫苗。
IF 2.3 Q2 Mathematics Pub Date : 2023-01-01 DOI: 10.1007/s13721-023-00416-3
Md Oliullah Rafi, Khattab Al-Khafaji, Santi M Mandal, Nigar Sultana Meghla, Polash Kumar Biswas, Md Shahedur Rahman

Community-acquired pneumonia is primarily caused by Streptococcus pneumoniae and Klebsiella pneumoniae, two pathogens that have high morbidity and mortality rates. This is largely due to bacterial resistance development against current antibiotics and the lack of effective vaccines. The objective of this work was to develop an immunogenic multi-epitope subunit vaccine capable of eliciting a robust immune response against S. pneumoniae and K. pneumoniae. The targeted proteins were the pneumococcal surface proteins (PspA and PspC) and choline-binding protein (CbpA) of S. pneumoniae and the outer membrane proteins (OmpA and OmpW) of K. pneumoniae. Different computational approaches and various immune filters were employed for designing a vaccine. The immunogenicity and safety of the vaccine were evaluated by utilizing many physicochemical and antigenic profiles. To improve structural stability, disulfide engineering was applied to a portion of the vaccine structure with high mobility. Molecular docking was performed to examine the binding affinities and biological interactions at the atomic level between the vaccine and Toll-like receptors (TLR2 and 4). Further, the dynamic stabilities of the vaccine and TLRs complexes were investigated by molecular dynamics simulations. While the immune response induction capability of the vaccine was assessed by the immune simulation study. Vaccine translation and expression efficiency was determined through an in silico cloning experiment utilizing the pET28a(+) plasmid vector. The obtained results revealed that the designed vaccine is structurally stable and able to generate an effective immune response to combat pneumococcal infection.

Supplementary information: The online version contains supplementary material available at 10.1007/s13721-023-00416-3.

社区获得性肺炎主要由肺炎链球菌和肺炎克雷伯菌引起,这两种病原体的发病率和死亡率都很高。这主要是由于细菌对现有抗生素产生耐药性以及缺乏有效的疫苗。这项工作的目的是开发一种免疫原性多表位亚单位疫苗,能够引发对肺炎链球菌和肺炎克雷伯菌的强大免疫反应。目标蛋白为肺炎链球菌的肺炎球菌表面蛋白(PspA、PspC)、胆碱结合蛋白(CbpA)和肺炎克雷伯菌的外膜蛋白(OmpA、OmpW)。采用不同的计算方法和不同的免疫过滤器来设计疫苗。利用多种理化和抗原谱对疫苗的免疫原性和安全性进行了评价。为了提高结构稳定性,将二硫工程应用于高迁移率的部分疫苗结构。通过分子对接研究疫苗与toll样受体(TLR2和4)在原子水平上的结合亲和力和生物相互作用。此外,通过分子动力学模拟研究疫苗与TLRs复合物的动态稳定性。同时通过免疫模拟研究对疫苗的免疫反应诱导能力进行了评价。通过pET28a(+)质粒载体的硅克隆实验确定疫苗的翻译和表达效率。结果表明,所设计的疫苗结构稳定,能够产生有效的免疫应答来对抗肺炎球菌感染。补充信息:在线版本包含补充资料,下载地址:10.1007/s13721-023-00416-3。
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引用次数: 3
Mobile heath applications for self-management in chronic lung disease: a systematic review. 移动健康在慢性肺病自我管理中的应用:一项系统综述。
IF 2.3 Q2 Mathematics Pub Date : 2023-01-01 Epub Date: 2023-06-06 DOI: 10.1007/s13721-023-00419-0
Shirley Quach, Wade Michaelchuk, Adam Benoit, Ana Oliveira, Tara L Packham, Roger Goldstein, Dina Brooks

Integration of mobile health (mHealth) applications (apps) into chronic lung disease management is becoming increasingly popular. MHealth apps may support adoption of self-management behaviors to assist people in symptoms control and quality of life enhancement. However, mHealth apps' designs, features, and content are inconsistently reported, making it difficult to determine which were the effective components. Therefore, this review aims to summarize the characteristics and features of published mHealth apps for chronic lung diseases. A structured search strategy across five databases (CINAHL, Medline, Embase, Scopus and Cochrane) was performed. Randomized controlled trials investigating interactive mHealth apps in adults with chronic lung disease were included. Screening and full-text reviews were completed by three reviewers using Research Screener and Covidence. Data extraction followed the mHealth Index and Navigation Database (MIND) Evaluation Framework (https://mindapps.org/), a tool designed to help clinicians determine the best mHealth apps to address patients' needs. Over 90,000 articles were screened, with 16 papers included. Fifteen distinct apps were identified, 8 for chronic obstructive pulmonary disease (53%) and 7 for asthma (46%) self-management. Different resources informed app design approaches, accompanied with varying qualities and features across studies. Common reported features included symptom tracking, medication reminders, education, and clinical support. There was insufficient information to answer MIND questions regarding security and privacy, and only five apps had additional publications to support their clinical foundation. Current studies reported designs and features of self-management apps differently. These app design variations create challenges in determining their effectiveness and suitability for chronic lung disease self-management. Registration: PROSPERO (CRD42021260205).

Supplementary information: The online version contains supplementary material available at 10.1007/s13721-023-00419-0.

将移动健康(mHealth)应用程序集成到慢性肺病管理中越来越受欢迎。MHealth应用程序可能支持采用自我管理行为,以帮助人们控制症状和提高生活质量。然而,mHealth应用程序的设计、功能和内容报告不一致,很难确定哪些是有效的组件。因此,本综述旨在总结已发表的用于慢性肺部疾病的mHealth应用程序的特点和特点。在五个数据库(CINAHL、Medline、Embase、Scopus和Cochrane)中执行结构化搜索策略。包括在患有慢性肺病的成年人中调查交互式mHealth应用程序的随机对照试验。筛选和全文综述由三名评审员使用Research Screener和Covidence完成。数据提取遵循mHealth指数和导航数据库(MIND)评估框架(https://mindapps.org/),一个旨在帮助临床医生确定最佳mHealth应用程序以满足患者需求的工具。放映了90000多篇文章,其中包括16篇论文。确定了15种不同的应用程序,其中8种用于慢性阻塞性肺病(53%),7种用于哮喘(46%)自我管理。不同的资源为应用程序设计方法提供了依据,同时研究中也存在不同的质量和功能。常见的报告特征包括症状跟踪、药物提醒、教育和临床支持。没有足够的信息来回答MIND关于安全和隐私的问题,只有五款应用程序有额外的出版物来支持其临床基础。目前的研究报告了不同的自我管理应用程序的设计和功能。这些应用程序设计的变化在确定其对慢性肺病自我管理的有效性和适用性方面带来了挑战。注册:PROSPERO(CRD42021260205)。补充信息:在线版本包含补充材料,可访问10.1007/s13721-023-00419-0。
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引用次数: 0
Epidemic dynamics in census-calibrated modular contact network. 人口普查校准模块化接触网络中的流行病动力学。
IF 2.3 Q2 Mathematics Pub Date : 2023-01-01 DOI: 10.1007/s13721-022-00402-1
Kirti Jain, Vasudha Bhatnagar, Sharanjit Kaur

Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.

基于网络的模型适合于理解流行病动态,因为它们具有固有的能力,可以模拟人类紧密联系的当代世界中相互作用的异质性。我们提出了一个框架来创建一个线框,通过将其与人口统计信息捆绑在一起来模仿地理上人口的社会联系网络。该框架形成了一个具有小世界拓扑结构的模块化网络,可以适应密度变化,并模拟家庭、社会和工作空间中的人类互动。当装载了适当的经济、社会和城市数据来塑造人类联系模式时,网络就成为城市规划者、人口学家和社会科学家强有力的决策工具。我们使用合成网络在受控环境中进行实验,并使用SEIR模型的一种变体研究分区、密度变化和人口流动对流行病变量的影响。我们的研究结果表明,这些人口因素对社会接触模式具有特征性的影响,表现为独特的流行动态。随后,我们通过使用可用的人口普查数据创建相应的替代社会联系网络,为印度三个邦提供了一个真实的COVID-19案例研究。案例研究证实,人口统计学模块接触网络减少了流行病变量估计的误差。
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引用次数: 1
Optimal control of a two-group malaria transmission model with vaccination. 带疫苗接种的两群疟疾传播模型的最优控制。
IF 2.3 Q2 Mathematics Pub Date : 2023-01-01 DOI: 10.1007/s13721-022-00403-0
S Y Tchoumi, C W Chukwu, M L Diagne, H Rwezaura, M L Juga, J M Tchuenche

Malaria is a vector-borne disease that poses major health challenges globally, with the highest burden in children less than 5 years old. Prevention and treatment have been the main interventions measures until the recent groundbreaking highly recommended malaria vaccine by WHO for children below five. A two-group malaria model structured by age with vaccination of individuals aged below 5 years old is formulated and theoretically analyzed. The disease-free equilibrium is globally asymptotically stable when the disease-induced death rate in both human groups is zero. Descarte's rule of signs is used to discuss the possible existence of multiple endemic equilibria. By construction, mathematical models inherit the loss of information that could make prediction of model outcomes imprecise. Thus, a global sensitivity analysis of the basic reproduction number and the vaccination class as response functions using Latin-Hypercube Sampling in combination with partial rank correlation coefficient are graphically depicted. As expected, the most sensitive parameters are related to children under 5 years old. Through the application of optimal control theory, the best combination of interventions measures to mitigate the spread of malaria is investigated. Simulations results show that concurrently applying the three intervention measures, namely: personal protection, treatment, and vaccination of childreen under-five is the best strategy for fighting against malaria epidemic in a community, relative to using either single or any dual combination of intervention(s) at a time.

疟疾是一种病媒传播的疾病,对全球健康构成重大挑战,5岁以下儿童的负担最重。预防和治疗一直是主要的干预措施,直到最近世卫组织大力建议为五岁以下儿童接种具有突破性意义的疟疾疫苗。建立了一个按年龄构成的两组疟疾模型,并对5岁以下儿童接种疫苗进行了理论分析。当两组人群的疾病死亡率均为零时,无病平衡全局渐近稳定。利用笛卡儿的符号规则讨论了多重地方性均衡存在的可能性。通过构造,数学模型继承了可能导致模型结果预测不精确的信息损失。因此,使用拉丁超立方抽样结合部分等级相关系数对基本繁殖数和疫苗接种类别作为响应函数的全局敏感性分析进行了图形化描述。正如预期的那样,最敏感的参数与5岁以下的儿童有关。通过应用最优控制理论,探讨了缓解疟疾传播的干预措施的最佳组合。模拟结果表明,与一次使用单一或任何双重干预措施组合相比,同时采用个人保护、治疗和五岁以下儿童接种疫苗这三种干预措施是在社区防治疟疾流行的最佳战略。
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引用次数: 2
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Network Modeling and Analysis in Health Informatics and Bioinformatics
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