Endocannabinoid signaling as a synaptic circuit breaker in neurological disease

Cannabis sativa is one of the oldest herbal plants in the history of medicine. It was used in various therapeutic applications from pain to epilepsy, but its psychotropic effect has reduced its usage in recent medical practice. However, renewed interest has been fueled by major discoveries revealing that cannabis-derived compounds act through a signaling pathway in the human body. Here we review recent advances showing that endocannabinoid signaling is a key regulator of synaptic communication throughout the central nervous system. Its underlying molecular architecture is highly conserved in synapses from the spinal cord to the neocortex, and as a negative feed-back signal, it provides protection against excess presynaptic activity. The endocannabinoid signaling machinery operates on demand in a synapse-specific manner; therefore, its modulation offers new therapeutic opportunities for the selective control of deleterious neuronal activity in several neurological disorders.     

Plant Traditional Culture of Populus euphratica and Populus pruinosa of Uyghurs Folk ——A Case Study in Xinjiang Lopnur

The study on traditional knowledge and experience of using plants by Uyghurs folk people has a potential application in socio economic development and resource protection. Populus euphratica Oliv and Ppruinosa Schrenk are major plants in Taklimakan desert ecosystem of Xinjiang. This study aims to ascertain the Lopnur Uyghurs traditional knowledge on two Populus species and their cultural system related to these species. We investigated the key informants for interviews on using Populus plants. The Populus plants were used for various purposes. The white alkali crystals are using for the treatment of bloating, sore throat, stomach ulcers, duodenal ulcers and indigestion and also in soda, while black crystal is used for the treatment of back pain, leg pain, swelling and relieve pain. Alkali crystals are even used as shampoo after a simple processing and also as a laundry detergent. Populus plants water is applied as a treatment of neurasthenia. It is also used for anti aging, reducing blood pressure, high cholesterol and heart morbidity. Plants foliages are used as veterinary medicine such as bloating and rot of muscular and stomach of livestock. In addition, Lopnur people used the plants to build the Desert Ecological Park and controlling desertification. This article ascertained the two Populus speceis and Lopnur Uyghurs cultural system. Present study will improve the conservation of Lopnur Uyghurs folk indigenous/ traditional knowledge on Populus plants and to promote the sustainable development in the socio economic diversity.     

胡杨和灰叶胡杨的维吾尔族植物文化研究——以新疆尉犁县为例

研究维吾尔人利用植物的传统知识和经验,在社会经济发展和资源保护等方面具有潜在的应用价值。胡杨（Populus euphratica Oliv.）和灰叶胡杨（P.pruinosa Schrenk）是新疆沙漠生态系统的主要植物类群,研究采用关键人物访谈法,对胡杨和灰叶胡杨在新疆尉犁县维吾尔族民间的植物文化进行了研究。结果表明：胡杨和灰叶胡杨的胡杨碱白色结晶体在治疗胃胀、咽喉肿痛、胃溃疡、十二指肠溃疡及消化不良等方面有作用;胡杨碱黑色结晶体用于治疗腰疼、腿疼,有消肿及止痛等疗效;胡杨碱加工后作洗发膏和洗衣粉使用;胡杨水不仅治疗神经衰弱,有延缓衰老、降低高血压及高血脂的功效,还能降低心脏病发病率;胡杨和灰叶胡杨枝叶具有治疗家畜胃胀和肌胃腐烂病的作用。当地罗布人利用胡杨树来建设沙漠生态园及控制土壤沙漠化。研究初步揭示了两种胡杨植物与维吾尔人的植物文化体系,维吾尔族植物传统知识的研究,将对我国民族植物学的发展及多样性产生影响。     

广西龙胜红瑶传统药用植物的民族植物学知识

瑶族是一个拥有悠久历史和灿烂文化的民族,红瑶是其中一个分支,因妇女服饰上的花纹图案以大红色为主而得名,其生活习俗饱含民族特色,对植物尤其是药用植物的利用方式,与其他民族不同。该研究采用经典的民族植物学理论和方法,访问调查了广西龙胜红瑶传统药用植物种类,根据植物拉丁名、中文名、当地名、用途、用法及药用部位,对其进行民族植物学编目,并与《中国药典》作了比较。结果表明:共记录到药用植物95种,隶属于57科83属,其中蔷薇科、百合科种类最多,分别含有7种,显示龙胜红瑶传统药用植物资源的多样性;全株入药的植物种类41种,占总数的43.16%;根入药的种类为23种,占总数的24.21%;叶入药的种类为13种,占总数的13.68%。药用植物的药用部位以全株、根及叶为主;治疗风湿类疾病的药物比重最大,为23.47%,推测与其生活的环境有关;与《中国药典》比较,发现71种植物未被其收录,另有17种虽被收录但主治功效不同,有7种被收录且主治功效大致相同,为新型中药的研发提供了借鉴。同时,还探讨了龙胜红瑶传统药用植物的药用价值、资源现状及文化传承的问题。     

Time-frequency filtering of MEG signals with matching pursuit.

Time-frequency signal analysis based on various decomposition techniques is widely used in biomedical applications. Matching Pursuit is a new adaptive approach for time-frequency decomposition of such biomedical signals. Its advantage is that it creates a concise signal approximation with the help of a small set of Gabor atoms chosen iteratively from a large and redundant set. In this paper, the usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed. The technique was validated on artificial signals and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.     

Development and application of microwave-assisted extraction technique in biological sample preparation for small molecule analysis

Microwave-assisted extraction (MAE) has emerged as an efficient extraction technique for various kinds of biological samples due to its low usage of extraction solvents and shorter extraction time. This review will focus on the recent developments and advantages of incorporating MAE in sample preparation protocols for the analysis of small molecules in plant, food and clinical samples in recent years. The operating principles of this technique and the key parameters influencing its extraction efficiency, including the nature of solvent, temperature, power and extraction time and their limitations are first mentioned. This is followed by a discussion on the advantages of applying MAE to extract organic contaminants in food for routine food safety analysis and active ingredients recovery. The successful application of MAE technique to recover bioactive compounds from plants in drug discovery studies and quality control purposes is then described. Additionally, the feasibility of using green solvents such as water, micelle and ionic liquids with MAE for plant metabolite profiling studies is evaluated and the associated challenges discussed. Finally, the application of MAE in clinical samples is highlighted. The use of MAE in this field is currently limited to the targeted detection of small molecules in human samples, due to a lack of knowledge of its effects on thermally labile metabolites. Consequently, the need for additional studies on how MAE impacts the recoveries of different metabolite classes in mammalian samples is discussed. The outcome of these studies can potentially broaden MAE applications in the clinical field.     

Chapter 1: Biomedical Knowledge Integration

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

What to Learn in This Chapter

• Understand basic knowledge types and structures that can be applied to biomedical and translational science;
• Gain familiarity with the knowledge engineering cycle, tools and methods that may be used throughout that cycle, and the resulting classes of knowledge products generated via such processes;
• An understanding of the basic methods and techniques that can be used to employ knowledge products in order to integrate and reason upon heterogeneous and multi-dimensional data sets; and
• Become conversant in the open research questions/areas related to the ability to develop and apply knowledge collections in the translational bioinformatics domain.

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

An e-learning application on electrochemotherapy

Background  

Electrochemotherapy is an effective approach in local tumour treatment employing locally applied high-voltage electric pulses in combination with chemotherapeutic drugs. In planning and performing electrochemotherapy a multidisciplinary expertise is required and collaboration, knowledge and experience exchange among the experts from different scientific fields such as medicine, biology and biomedical engineering is needed. The objective of this study was to develop an e-learning application in order to provide the educational content on electrochemotherapy and its underlying principles and to support collaboration, knowledge and experience exchange among the experts involved in the research and clinics.     

Reflexive Biomedicalization and Alternative Healing Systems

The utilization of alternative medical therapies and practitioners has increased dramatically in the U.S. in the last two to three decades. This trend seems paradoxical when one considers the rapid advances taking place in biomedical knowledge and technology during this same time period. Observers both inside and outside of the medical profession have attempted to explain the rising popularity of alternative medicine by proposing that it signals a growing sense of dissatisfaction and disenchantment with professional biomedical practices on the part of the lay public. This paper challenges this thesis and offers an alternative explanation, arguing that the rise of alternative medicine is a consequence of the success and expanding influence of biomedicine rather than its failure and declining authority. The argument presented draws primarily on Ulrich Beck’s “risk society” perspective and theory of “reflexive modernization,” with specific attention to his analysis of the “reflexive scientization” process. The application of this perspective allows us to understand the emergence and development of alternative medicine as an unanticipated consequence of the process of reflexive biomedicalization in the late modern era.     

Phosphorus-containing polymers: a great opportunity for the biomedical field

This Review is focused on the growing interest brought to phosphorus-containing organic materials for applications in the biomedical field, mainly because of their properties such as biocompatibility, hemocompatibility, and protein adsorption resistance. It mainly describes relevant works achieved on these materials for various applications: dentistry, regenerative medicine, and drug delivery. Special attention was given to 2-methacryloyloxyethyl phosphorylcholine (MPC) monomer as the latter appeared of great importance because of its biomimetic structure due to the presence of the phospholipid group on its structure. As a result, much research effort is currently concentrated on the development of phosphorylcholine-containing (co)polymers that represent a promising class of materials.     

medplot: A Web Application for Dynamic Summary and Analysis of Longitudinal Medical Data Based on R

In biomedical studies the patients are often evaluated numerous times and a large number of variables are recorded at each time-point. Data entry and manipulation of longitudinal data can be performed using spreadsheet programs, which usually include some data plotting and analysis capabilities and are straightforward to use, but are not designed for the analyses of complex longitudinal data. Specialized statistical software offers more flexibility and capabilities, but first time users with biomedical background often find its use difficult. We developed medplot, an interactive web application that simplifies the exploration and analysis of longitudinal data. The application can be used to summarize, visualize and analyze data by researchers that are not familiar with statistical programs and whose knowledge of statistics is limited. The summary tools produce publication-ready tables and graphs. The analysis tools include features that are seldom available in spreadsheet software, such as correction for multiple testing, repeated measurement analyses and flexible non-linear modeling of the association of the numerical variables with the outcome. medplot is freely available and open source, it has an intuitive graphical user interface (GUI), it is accessible via the Internet and can be used within a web browser, without the need for installing and maintaining programs locally on the user’s computer. This paper describes the application and gives detailed examples describing how to use the application on real data from a clinical study including patients with early Lyme borreliosis.     

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.     

Fluorescent quantum dots: An insight on synthesis and potential biological application as drug carrier in cancer

Quantum dots (QDs) are nanocrystals of semiconducting material possessing quantum mechanical characteristics with capability to get conjugated with drug moieties. The particle size of QDs varies from 2 to 10 nm and can radiate a wide range of colours depending upon their size. Their wide and diverse usage of QDs across the world is due to their adaptable properties like large quantum yield, photostability, and adjustable emission spectrum. QDs are nanomaterials with inherent electrical characteristics that can be used as drug carrier vehicle and as a diagnostic in the field of nanomedicine. Scientists from various fields are aggressively working for the development of single platform that can sense, can produce a microscopic image and even be used to deliver a therapeutic agent. QDs are the fluorescent nano dots with which the possibilities of the drug delivery to a targeted site and its biomedical imaging can be explored. This review is mainly focused on the different process of synthesis of QDs, their application especially in the areas of malignancies and as a theranostic tool. The attempt is to consolidate the data available for the use of QDs in the biomedical applications.     

The dragon on the gold: myths and realities for data mining in biomedicine and biotechnology using digital and molecular libraries

To develop bioscience and personalized medicine in the post-genomic era, the biggest problem may be how to extract knowledge from the rich libraries of biomedical data. A particular dragon protects the gold therein: the dragon is the "curse of dimensionality" and its formidable fire weapon, which is burning researchers, is the "combinatorial explosion". This arises because many genomic, proteomic, clinical, and lifestyle factors may interact that cannot necessarily be considered on a simple pairwise or additive basis. A suggested theoretical solution--or at least "road map" that ameliorates management of these problems--borrows from several disciplines. It is undertaken also in the hope might also lead to research with broader impact on several unresolved issues in biotechnology: conversely, mathematical understanding of processes involving molecular libraries, such as cDNA libraries and DNA in the living cell itself, may open the opportunities to use biotechnology to construct nanotechnological storage and query systems.     

Laboratory scale medicinal plants mediated green synthesis of biocompatible nanomaterials and their versatile biomedical applications

Nanoparticles and its green synthesis with plants have become an important field of nanoscience due it is great benefits provided to humanity through it and its cost effective, least harm to humans and the environment also, it offering a lot of application in biomedical research, diagnostics, and medicine as well as, drug manufacturing, improvement, or drug discovery. In this work, I focused on green synthesis nanoparticles with antioxidant involve in plants and the method for preparing them also the factors on which the extraction process depends on, spectroscopic techniques like UV–Visible, (TEM), (XRD), (IR), (EDX), (SEM), (HPLC), and zeta potential are use here.     

Perspectives on an Education in Computational Biology and Medicine

The mainstream application of massively parallel, high-throughput assays in biomedical research has created a demand for scientists educated in Computational Biology and Bioinformatics (CBB). In response, formalized graduate programs have rapidly evolved over the past decade. Concurrently, there is increasing need for clinicians trained to oversee the responsible translation of CBB research into clinical tools. Physician-scientists with dedicated CBB training can facilitate such translation, positioning themselves at the intersection between computational biomedical research and medicine. This perspective explores key elements of the educational path to such a position, specifically addressing: 1) evolving perceptions of the role of the computational biologist and the impact on training and career opportunities; 2) challenges in and strategies for obtaining the core skill set required of a biomedical researcher in a computational world; and 3) how the combination of CBB with medical training provides a logical foundation for a career in academic medicine and/or biomedical research.     

Translational Bioinformatics:Past,Present, and Future

Though a relatively young discipline, translational bioinformatics (TBI) has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contex-tualization of the term TBI, describes the discipline’s brief history and past accomplishments, as well as current foci, and concludes with predictions of future directions in the field.