Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas.

MOTIVATION: Digital therapeutics (DTX), i.e., health interventions that are provided through digital means, are increasingly available for use; in some countries, physicians can even prescribe selected DTX following a reimbursement by health insurances. This results in an increasing need for methodologies to consider and monitor DTX's negative consequences, their risks to patient safety, and possible adverse events. However, it is completely unknown which aspects should be subject to surveillance given the missing experiences with the tools and their negative impacts. OBJECTIVE: Our aim is to develop a tool-the DTX Risk Assessment Canvas-that enables researchers, developers, and practitioners to reflect on the negative consequences of DTX in a participatory process. METHOD: Taking the well-established business model canvas as a starting point, we identified relevant aspects to be considered in a risk assessment of a DTX. The aspects or building blocks of the canvas were constructed in a two-way process: first, we defined the aspects relevant for discussing and reflecting on how a DTX might bring negative consequences and risks for its users by considering ISO/TS 82304-2, the scientific literature, and by reviewing existing DTX and their listed adverse effects. The resulting aspects were grouped into thematic blocks and the canvas was created. Second, six experts in health informatics and mental health provided feedback and tested the understandability of the initial canvas by individually applying it to a DTX of their choice. Based on their feedback, the canvas was modified. RESULTS: The DTX Risk Assessment Canvas is organized into 15 thematic blocks which are in turn grouped into three thematic groups considering the DTX itself, the users of the DTX, and the effects of the DTX. For each thematic block, questions have been formulated to guide the user of the canvas in reflecting on the single aspects. Conclusions: The DTX Risk Assessment Canvas is a tool to reflect the negative consequences and risks of a DTX by discussing different thematic blocks that together constitute a comprehensive interpretation of a DTX regarding possible risks. Applied during the DTX design and development phase, it can help in implementing countermeasures for mitigation or means for their monitoring.

Integration of Annotated Phenotype, Gene and Chemical Text Data to Advance Exposome Informatics.

The field of phenomics has a range of biomedical informatics tools such as the Human Phenotype Ontology, providing a structured vocabulary with relationships between abnormal phenotype terms. Artificial intelligence has been widely used for entity extraction and tagging large corpora of text from PubMed and is reflected in applications such as PheneBank and PubTator. Phexpo is a tool for predicting chemical - phenotype relationships and vice-versa, although lacks the ability to decipher known relationships from unknown. Integration of these three resources can provide new meaningful relationships between phenotypes, genes and chemicals and has yet to be fully leveraged. Here we present a methodology to construct two new datasets for phenotype - gene and phenotype - chemical relationships and showcase how these datasets can be used to enhance exposome informatics.

Construction of a Physical Factor Resource for Exposome Informatics Research.

Exposome research is focused on all the exposures individuals experience during their lifetime and how it shapes their health and development of disease. The chemical and biological aspects of the exposome are readily available in data formats. In comparison there is a lack of data frameworks available for physical factors (e.g. noise, lighting, electromagnetic fields) and their biological relationships which would allow a greater understanding of the contribution of the physical environment on disease development and burden. We present the construction of a prototype that captures knowledge on physical factors and their interactions with genes and diseases derived from the biomedical literature to reflect the physical exposome.

An "in silico" Bench to Bedside Approach to Investigating Sepsis Biomarkers.

Sepsis results in various patient complications and is due to a heightened inflammatory response against infection. This condition requires further exploration of biomarkers. We employed an "in silico" method comprised of text-mining and additionally clinical validation through the Medical Information Mart for Intensive Care. We highlight that Factor VIII shows potential as a pertinent septic shock biomarker.

Linking Genome and Exposome: Computational Analysis of Human Variation in Chemical-Target Interactions.

The growing amount of available public data repositories containing a plethora of rich chemical and biomedical information is enabling new in silico research avenues. In this project we aim to link human genome variations and the exposome applying in silico biomedical informatics approaches to analyse the potential effects of those variants in the interactions with different chemicals.

Person-generated Data in Self-quantification. A Health Informatics Research Program.

OBJECTIVES: The availability of internet-connected mobile, wearable and ambient consumer technologies, direct-to-consumer e-services and peer-to-peer social media sites far outstrips evidence about the efficiency, effectiveness and efficacy of using them in healthcare applications. The aim of this paper is to describe one approach to build a program of health informatics research, so as to generate rich and robust evidence about health data and information processing in self-quantification and associated healthcare and health outcomes. METHODS: The paper summarises relevant health informatics research approaches in the literature and presents an example of developing a program of research in the Health and Biomedical Informatics Centre (HaBIC) at the University of Melbourne. The paper describes this program in terms of research infrastructure, conceptual models, research design, research reporting and knowledge sharing. RESULTS: The paper identifies key outcomes from integrative and multiple-angle approaches to investigating the management of information and data generated by use of this Centre's collection of wearable, mobiles and other devices in health self-monitoring experiments. These research results offer lessons for consumers, developers, clinical practitioners and biomedical and health informatics researchers. CONCLUSIONS: Health informatics is increasingly called upon to make sense of emerging self-quantification and other digital health phenomena that are well beyond the conventions of healthcare in which the field of informatics originated and consolidated. To make a substantial contribution to optimise the aims, processes and outcomes of health self-quantification needs further work at scale in multi-centre collaborations for this Centre and for health informatics researchers generally.

The Genomic Medicine Game.

With advancements in genomics technology, health care has been improving and new paradigms of medicine such as genomic medicine have evolved. The education of clinicians, researchers and students to face the challenges posed by these new approaches, however, has been often lagging behind. From this the Genomic Medicine Game, an educational tool, was created for the purpose of conceptualizing the key components of Genomic Medicine. A number of phenotype-genotype associations were found through a literature review, which was used to be a base for the concepts the Genomic Medicine Game would focus on. Built in Java, the game was successfully tested with promising results.

The New Role of Biomedical Informatics in the Age of Digital Medicine.

OBJECTIVES: To reflect on the recent rise of Digital Medicine, as well as to analyse main research opportunities in this area. Through the use of several examples, this article aims to highlight the new role that Biomedical Informatics (BMI) can play to facilitate progress in research fields such as participatory and precision medicine. This paper also examines the potential impact and associated risks for BMI due to the development of digital medicine and other recent trends. Lastly, possible strategies to place BMI in a better position to face these challenges are suggested. METHODS: The core content of this article is based on a recent invited keynote lecture delivered by one of the authors (Martin-Sanchez) at the Medical Informatics Europe conference (MIE 2015) held in Madrid in May 2015. Both authors (Lopez-Campos and Martin-Sanchez) have collaborated during the last four years in projects such as the ones described in section 3 and have also worked in reviewing relevant articles and initiatives to prepare this talk. RESULTS AND CONCLUSIONS: Challenges for BMI posed by the rise of technologically driven fields such as Digital Medicine are explored. New opportunities for BMI, in the context of two main avenues for biomedical and clinical research (participatory and precision medicine) are also emphasised. Several examples of current research illustrate that BMI plays a key role in the new area of Digital Medicine. Embracing these opportunities will allow academic groups in BMI to maintain their leadership, identify new research funding opportunities and design new educational programs to train the next generation of BMI scientists.

Comment on 'Discovering hospital admission patterns using models learnt from electronic hospital records'. The importance of using the right codes.

CONTACT: guillermo.lopez@unimelb.edu.au.

Is Precision Medicine different from Personalised Medicine? A Biomedical informatics perspective.

There is an open controversy in the use of the terms personalised and precision medicine and what they refer to in different contexts. In the present work we have considered the data types managed by each of them rather than their application and we have been able to identify commonalities but also differences in the types of data addressed in both approaches that would ultimately lead to include, from a data perspective, personalised medicine within the broader precision medicine term.

Mapping biomedical vocabularies: a semi-automated term matching approach.

Biomedical vocabularies vary in scope, and it is often necessary to utilize multiple vocabularies simultaneously in order to cover the full range of concepts relevant to a given biomedical application. However, as the number and size of these resources grow both redundancy (i.e., different vocabularies containing similar terms) and inconsistency (i.e., different terms in multiple vocabularies referring to the same entity) between the vocabularies increase. Therefore, there is a need for automatically aligning vocabularies. In this paper, we explore and propose new methods for detecting probable matches between two vocabularies. The methods build upon existing string similarity functions, enhancing these functions for the context of semi-automated vocabulary matching.

Merging personalized and participatory medicine: interpretation of individual genomes.

Interpretation-only providers are becoming increasingly prominent in the field of Direct-To-Consumer genomics. We examined the information obtained from two different providers (Interpretome and Promethease) when analysing the same personal genome. We found large discrepancies between the results from these services for the list of SNPs included in the analysis, but a high level of concordance in their interpretation when the SNPs were coincident.

Proposal for a Standardised Reporting Guideline to Annotate Health-related Self-Quantification Experiments.

Self-monitoring experiments are becoming increasingly common as it is the case in other complex environments their interpretation and reproducibility relies heavily in the amount of associated meta-data available. In this work we propose a standardised reporting guideline to annotate these experiments and facilitate their interpretation. The existence of such reporting guideline may lead the development of future standards that would facilitate platform interoperability, data sharing and the improvement in the interpretation of such experiments as well as their reproducibility.

Global transcriptome analysis of Lactococcus garvieae strains in response to temperature.

Lactococcus garvieae is an important fish and an opportunistic human pathogen. The genomic sequences of several L. garvieae strains have been recently published, opening the possibility of global studies on the biology of this pathogen. In this study, a whole genome DNA microarray of two strains of L. garvieae was designed and validated. This DNA microarray was used to investigate the effects of growth temperature (18°C and 37°C) on the transcriptome of two clinical strains of L. garvieae that were isolated from fish (Lg8831) and from a human case of septicemia (Lg21881). The transcriptome profiles evidenced a strain-specific response to temperature, which was more evident at 18°C. Among the most significant findings, Lg8831 was found to up-regulate at 18°C several genes encoding different cold-shock and cold-induced proteins involved in an efficient adaptive response of this strain to low-temperature conditions. Another relevant result was the description, for the first time, of respiratory metabolism in L. garvieae, whose gene expression regulation was temperature-dependent in Lg21881. This study provides new insights about how environmental factors such as temperature can affect L. garvieae gene expression. These data could improve our understanding of the regulatory networks and adaptive biology of this important pathogen.

Characterization of plasmids in a human clinical strain of Lactococcus garvieae.

The present work describes the molecular characterization of five circular plasmids found in the human clinical strain Lactococcus garvieae 21881. The plasmids were designated pGL1-pGL5, with molecular sizes of 4,536 bp, 4,572 bp, 12,948 bp, 14,006 bp and 68,798 bp, respectively. Based on detailed sequence analysis, some of these plasmids appear to be mosaics composed of DNA obtained by modular exchange between different species of lactic acid bacteria. Based on sequence data and the derived presence of certain genes and proteins, the plasmid pGL2 appears to replicate via a rolling-circle mechanism, while the other four plasmids appear to belong to the group of lactococcal theta-type replicons. The plasmids pGL1, pGL2 and pGL5 encode putative proteins related with bacteriocin synthesis and bacteriocin secretion and immunity. The plasmid pGL5 harbors genes (txn, orf5 and orf25) encoding proteins that could be considered putative virulence factors. The gene txn encodes a protein with an enzymatic domain corresponding to the family actin-ADP-ribosyltransferases toxins, which are known to play a key role in pathogenesis of a variety of bacterial pathogens. The genes orf5 and orf25 encode two putative surface proteins containing the cell wall-sorting motif LPXTG, with mucin-binding and collagen-binding protein domains, respectively. These proteins could be involved in the adherence of L. garvieae to mucus from the intestine, facilitating further interaction with intestinal epithelial cells and to collagenous tissues such as the collagen-rich heart valves. To our knowledge, this is the first report on the characterization of plasmids in a human clinical strain of this pathogen.

Genome sequence of Lactococcus garvieae 8831, isolated from rainbow trout lactococcosis outbreaks in Spain.

Lactococcus garvieae is the etiological agent of lactococcosis, one of the most important disease threats to the sustainability of the rainbow trout farming industry. Here, we present the draft genome sequence of Lactococcus garvieae strain 8831, isolated from diseased rainbow trout, which is composed of 2,087,276 bp with a G+C content of 38%.

Genome sequence of Lactococcus garvieae 21881, isolated in a case of human septicemia.

Lactococcus garvieae is a Gram-positive bacterium considered an important opportunistic emerging human pathogen and also a well-recognized fish pathogen. Here, we present the draft genome sequence of Lactococcus garvieae strain 21881 (2,164,557 bp, with a G+C content of 37.9%), which represents the first report of a genome sequence on Lactococcus garvieae.

Temporal profiling of the coding and noncoding murine cytomegalovirus transcriptomes.

The global transcriptional program of murine cytomegalovirus (MCMV), involving coding, noncoding, and antisense transcription, remains unknown. Here we report an oligonucleotide custom microarray platform capable of measuring both coding and noncoding transcription on a genome-wide scale. By profiling MCMV wild-type and immediate-early mutant strains in fibroblasts, we found rapid activation of the transcriptome by 6.5 h postinfection, with absolute dependency on ie3, but not ie1 or ie2, for genomic programming of viral gene expression. Evidence is also presented to show, for the first time, genome-wide noncoding and bidirectional transcription at late stages of MCMV infection.

Analysis of the genome content of Lactococcus garvieae by genomic interspecies microarray hybridization.

BACKGROUND: Lactococcus garvieae is a bacterial pathogen that affects different animal species in addition to humans. Despite the widespread distribution and emerging clinical significance of L. garvieae in both veterinary and human medicine, there is almost a complete lack of knowledge about the genetic content of this microorganism. In the present study, the genomic content of L. garvieae CECT 4531 was analysed using bioinformatics tools and microarray-based comparative genomic hybridization (CGH) experiments. Lactococcus lactis subsp. lactis IL1403 and Streptococcus pneumoniae TIGR4 were used as reference microorganisms. RESULTS: The combination and integration of in silico analyses and in vitro CGH experiments, performed in comparison with the reference microorganisms, allowed establishment of an inter-species hybridization framework with a detection threshold based on a sequence similarity of >or= 70%. With this threshold value, 267 genes were identified as having an analogue in L. garvieae, most of which (n = 258) have been documented for the first time in this pathogen. Most of the genes are related to ribosomal, sugar metabolism or energy conversion systems. Some of the identified genes, such as als and mycA, could be involved in the pathogenesis of L. garvieae infections. CONCLUSIONS: In this study, we identified 267 genes that were potentially present in L. garvieae CECT 4531. Some of the identified genes could be involved in the pathogenesis of L. garvieae infections. These results provide the first insight into the genome content of L. garvieae.