Toward rapid real-time molecular diagnostic to guide smart use of antimicrobials

Current microbial diagnostic is too slow to provide clinically useful information for the physician to prescribe, only when needed, the appropriate antibiotics. New advances in real-time PCR promise results that will come fast enough to revolutionise the practice of medicine. These novel, fully integrated devices, coupled with appropriate databases, will insure better management of patients, should reduce health costs, and could impact on the spread of antibiotic resistance.     

Biomarkers and clinical staging in psychiatry

Personalized medicine is rapidly becoming a reality in today's physical medicine. However, as yet this is largely an aspirational goal in psychiatry, despite significant advances in our understanding of the biochemical, genetic and neurobiological processes underlying major mental disorders. Preventive medicine relies on the availability of predictive tools; in psychiatry we still largely lack these. Furthermore, our current diagnostic systems, with their focus on well‐established, largely chronic illness, do not support a pre‐emptive, let alone a preventive, approach, since it is during the early stages of a disorder that interventions have the potential to offer the greatest benefit. Here, we present a clinical staging model for severe mental disorders and discuss examples of biological markers that have already undergone some systematic evaluation and that could be integrated into such a framework. The advantage of this model is that it explicitly considers the evolution of psychopathology during the development of a mental illness and emphasizes that progression of illness is by no means inevitable, but can be altered by providing appropriate interventions that target individual modifiable risk and protective factors. The specific goals of therapeutic intervention are therefore broadened to include the prevention of illness onset or progression, and to minimize the risk of harm associated with more complex treatment regimens. The staging model also facilitates the integration of new data on the biological, social and environmental factors that influence mental illness into our clinical and diagnostic infrastructure, which will provide a major step forward in the development of a truly pre‐emptive psychiatry.     

A case study in pathway knowledgebase verification

Background  

Biological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledgebase as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference.     

Hantavirus Infection during Pregnancy

Hantavirus infection is a global health challenge, causing widespread public concern. In recent years, cases of hantavirus infection in pregnant women have been reported in many countries. The infected pregnant women and their fetuses appear to have more severe clinical symptoms and worse clinical outcomes. Hence, to study the prevalence of hantavirus infection in pregnant women, this study will focus on the epidemiological distribution of the virus, different virus species penetrating the placental barrier, and factors affecting the incidence and clinical outcome of the infection in pregnant women and their fetuses. In addition, this review will also discuss the diagnostic tools and treatments for pregnant patients and provide an overview of the relevant future research.     

Using multiple ontologies to integrate complex biological data

The strength of the rat as a model organism lies in its utility in pharmacology, biochemistry and physiology research. Data resulting from such studies is difficult to represent in databases and the creation of user-friendly data mining tools has proved difficult. The Rat Genome Database has developed a comprehensive ontology-based data structure and annotation system to integrate physiological data along with environmental and experimental factors, as well as genetic and genomic information. RGD uses multiple ontologies to integrate complex biological information from the molecular level to the whole organism, and to develop data mining and presentation tools. This approach allows RGD to indicate not only the phenotypes seen in a strain but also the specific values under each diet and atmospheric condition, as well as gender differences. Harnessing the power of ontologies in this way allows the user to gather and filter data in a customized fashion, so that a researcher can retrieve all phenotype readings for which a high hypoxia is a factor. Utilizing the same data structure for expression data, pathways and biological processes, RGD will provide a comprehensive research platform which allows users to investigate the conditions under which biological processes are altered and to elucidate the mechanisms of disease.     

环境耐药组及其健康风险的宏基因组学研究策略和方法

抗生素耐药性在环境中的发展和传播对人体健康造成潜在风险。随着高通量测序技术和生物信息学方法的不断发展,宏基因组学技术被广泛应用于不同环境样本的抗生素耐药组研究。本文介绍了两种针对环境耐药组筛查的宏基因组学分析方法,总结了当前主流的生物信息学软件和数据库,并阐述了环境耐药组的风险评估框架和基于宏基因组学技术的相关实践,以期为环境耐药组的监测、风险评估和管控提供可行的路线图。     

Text mining for literature review and knowledge discovery in cancer risk assessment and research

Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB - a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future.     

Propuesta de desarrollo de una unidad de oncogeriatría. Tiempos de cambio: nuestra realidad

Age is one of the main risk factors for the development of cancer. It is expected that the progressive aging of the population will have an unprecedented impact on the incidence of various tumours. In fact, the management of elderly cancer patients is already a major public health problem in developed countries. However, elderly patients have systematically been excluded from cancer drug studies or protocol development. This has left health professionals in uncharted territory, without proper tools to address the multiple difficulties that arise in the treatment of these patients. A comprehensive geriatric assessment may serve as an ideal tool for the correct detection of hidden problems, facilitating treatment decisions in these complex patients, and integrating the care of patients with comorbidities.     

Parasitic mites of medical and veterinary importance – is there a common research agenda?

There are an estimated 0.5–1 million mite species on earth. Among the many mites that are known to affect humans and animals, only a subset are parasitic but these can cause significant disease. We aim here to provide an overview of the most recent work in this field in order to identify common biological features of these parasites and to inform common strategies for future research. There is a critical need for diagnostic tools to allow for better surveillance and for drugs tailored specifically to the respective parasites. Multi-‘omics’ approaches represent a logical and timely strategy to identify the appropriate mite molecules. Recent advances in sequencing technology enable us to generate de novo genome sequence data, even from limited DNA resources. Consequently, the field of mite genomics has recently emerged and will now rapidly expand, which is a particular advantage for parasitic mites that cannot be cultured in vitro. Investigations of the microbiota associated with mites will elucidate the link between parasites and pathogens, and define the role of the mite in transmission and pathogenesis. The databases generated will provide the crucial knowledge essential to design novel diagnostic tools, control measures, prophylaxes, drugs and immunotherapies against the mites and associated secondary infections.     

Has the Time Come for Big Science in Wildlife Health?

The consequences of wildlife emerging diseases are global and profound with increased burden on the public health system, negative impacts on the global economy, declines and extinctions of wildlife species, and subsequent loss of ecological integrity. Examples of health threats to wildlife include Batrachochytrium dendrobatidis, which causes a cutaneous fungal infection of amphibians and is linked to declines of amphibians globally; and the recently discovered Pseudogymnoascus (Geomyces) destructans, the etiologic agent of white nose syndrome which has caused precipitous declines of North American bat species. Of particular concern are the novel pathogens that have emerged as they are particularly devastating and challenging to manage. A big science approach to wildlife health research is needed if we are to make significant and enduring progress in managing these diseases. The advent of new analytical models and bench assays will provide us with the mathematical and molecular tools to identify and anticipate threats to wildlife, and understand the ecology and epidemiology of these diseases. Specifically, new molecular diagnostic techniques have opened up avenues for pathogen discovery, and the application of spatially referenced databases allows for risk assessments that can assist in targeting surveillance. Long-term, systematic collection of data for wildlife health and integration with other datasets is also essential. Multidisciplinary research programs should be expanded to increase our understanding of the drivers of emerging diseases and allow for the development of better disease prevention and management tools, such as vaccines. Finally, we need to create a National Fish and Wildlife Health Network that provides the operational framework (governance, policies, procedures, etc.) by which entities with a stake in wildlife health cooperate and collaborate to achieve optimal outcomes for human, animal, and ecosystem health.     

A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory

Systems biology and the digital revolution are together transforming healthcare to a proactive P4 medicine that is predictive, preventive, personalized and participatory. Systems biology - holistic, global and integrative in approach - has given rise to systems medicine, a systems approach to health and disease. Systems medicine promises to (1) provide deep insights into disease mechanisms, (2) make blood a diagnostic window for viewing health and disease for the individual, (3) stratify complex diseases into their distinct subtypes for a impedance match against proper drugs, (4) provide new approaches to drug target discovery and (5) generate metrics for assessing wellness. P4 medicine, the clinical face of systems medicine, has two major objectives: to quantify wellness and to demystify disease. Patients and consumers will be a major driver in the realization of P4 medicine through their participation in medically oriented social networks directed at improving their own healthcare. P4 medicine has striking implications for society - including the ability to turn around the ever-escalating costs of healthcare. The challenge in bringing P4 medicine to patients and consumers is twofold: first, inventing the strategies and technologies that will enable P4 medicine and second, dealing with the impact of P4 medicine on society - including key ethical, social, legal, regulatory, and economic issues. Managing the societal problems will pose the most significant challenges. Strategic partnerships of a variety of types will be necessary to bring P4 medicine to patients.     

Literature-aided interpretation of gene expression data with the weighted global test

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.     

Long COVID and its Management

The pandemic of COVID-19 is the biggest public health crisis in 21^st Century. Besides the acute symptoms after infection, patients and society are also being challenged by the long-term health complications associated with COVID-19, commonly known as long COVID. While health professionals work hard to find proper treatments, large amount of knowledge has been accumulated in recent years. In order to deal with long COVID efficiently, it is important for people to keep up with current progresses and take proactive actions on long COVID. For this purpose, this review will first introduce the general background of long COVID, and then discuss its risk factors, diagnostic indicators and management strategies. This review will serve as a useful resource for people to understand and prepare for long COVID that will be with us in the foreseeable future.     

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.     

Continuing Medical Education in Midpassage

Mandatory continuing education for physicians and other health professionals raises numerous problems. Issues that were considered academic when continuing education was voluntary now take on major practical and political significance. There is the risk that future legislation will mandate activities and methodologies which have not been proven. Research and development in continuing education must be accelerated so that decisions can be based on proper data. Centers of research and development should be established to encourage research and provide a sound foundation for the future of continuing education.     

New approaches to antibiotic discovery

New antibiotics are urgently required by human medicine as pathogens emerge with developed resistance to almost all antibiotic classes. Pioneering approaches, methodologies and technologies have facilitated a new era in antimicrobial discovery. Innovative culturing techniques such as iChip and co-culturing methods which use ‘helper’ strains to produce bioactive molecules have had notable success. Exploiting antibiotic resistance to identify antibacterial producers performed in tandem with diagnostic PCR based identification approaches has identified novel candidates. Employing powerful metagenomic mining and metabolomic tools has identified the antibiotic’ome, highlighting new antibiotics from underexplored environments and silent gene clusters enabling researchers to mine for scaffolds with both a novel mechanism of action and also few clinically established resistance determinants. Modern biotechnological approaches are delivering but will require support from government initiatives together with changes in regulation to pave the way for valuable, efficacious, highly targeted, pathogen specific antimicrobial therapies.     

Farm animal genome databases

The requirements for bioinformatics resources to support genome research in farm animals is reviewed.The resources developed to meet these needs are described. Resource databases and associated tools have been developed to handle experimental data. Several of these systems serve the needs of multinational collaborations. Genome databases have been established to provide contemporary summaries of the status of genome maps in a range of farm and domestic animals along with experimental details and citations. New resources and tools will be required to address the informatics needs of emerging technologies such as microarrays. However, continued investment is also required to maintain the currency and utility of the current systems, especially the genome databases.     

The Passaic River Creel/Angler Survey: Expert Panel Review,Findings, and Recommendations

A Creel/Angler Survey (CAS) was conducted to provide site-specific information on recreational fishing in the lower six miles of the Passaic River (Study Area). Information collected during the CAS will be used to develop site-specific exposure factors, including fish consumption rates, for use in the human health risk assessment required by an Administrative Order on Consent as part of the Remedial Investigation/Feasibility Study for the Study Area. An expert panel was convened to provide an independent opinion regarding the need for, design of, and implementation of the CAS. The expert panel was charged with evaluating whether the conduct of a CAS is necessary to support an accurate risk assessment for the Study Area and whether the proposed CAS is sufficient to characterize local fish consumption behavior for risk assessment purposes. The expert panel agreed that a CAS is necessary and concluded that the proposed CAS, with specific modifications to the study design and data analysis, would provide the information necessary to estimate site-specific fish consumption rates. Revision of the CAS to accommodate the expert panel recommendations enhanced the quality of the data collected and ensured that the data will support the assessment of human health risks from consumption of fish from the Study Area.     

Metabolomics: building on a century of biochemistry to guide human health

Medical diagnosis and treatment efficacy will improve significantly when a more personalized system for health assessment is implemented. This system will require diagnostics that provide sufficiently detailed information about the metabolic status of individuals such that assay results will be able to guide food, drug and lifestyle choices to maintain or improve distinct aspects of health without compromising others. Achieving this goal will use the new science of metabolomics – comprehensive metabolic profiling of individuals linked to the biological understanding of human integrative metabolism. Candidate technologies to accomplish this goal are largely available, yet they have not been brought into practice for this purpose. Metabolomic technologies must be sufficiently rapid, accurate and affordable to be routinely accessible to both healthy and acutely ill individuals. The use of metabolomic data to predict the health trajectories of individuals will require bioinformatic tools and quantitative reference databases. These databases containing metabolite profiles from the population must be built, stored and indexed according to metabolic and health status. Building and annotating these databases with the knowledge to predict how a specific metabolic pattern from an individual can be adjusted with diet, drugs and lifestyle to improve health represents a logical application of the biochemistry knowledge that the life sciences have produced over the past 100 years.