Digital Interventions and Their Unexpected Outcomes - Time for Digitalovigilance?

The application of digital interventions in healthcare beyond research has been translated in the development of software as a medical device. Along with corresponding regulations for medical devices, there is a need for assessing adverse events to conduct post-market surveillance and to appropriately label digital health interventions to ensure proper use and patient safety. To date unexpected consequences of digital health interventions are neglected or ignored, or at least remain undescribed in literature. This paper is intended to raise awareness across the research community about these upcoming challenges. We recommend that - together with developing a new research field of digitalovigilance - a systematic assessment and monitoring of adverse events and unexpected interactions be included in clinical trials, along with the reporting of such events and the conduct of meta-analyses on critical aspects.

A Text Mining Approach to Explore IFNε Literature and Biological Mechanisms.

Interferons (IFN) constitute a primary line of protection against mucosal infection, with IFN research spanning over 60 years and encompassing a vast ever-expanding amount of literature. Most of what is currently understood has been derived from extensive research defining the roles of "classical" type I IFNs, IFNα and IFNß. However, little is known regarding responses elicited by less well-characterized IFN subtypes such as IFNε. In this paper, we combined a deductive text mining analysis of IFNε literature characterizing literature-derived knowledge with a comparative analysis of other type I and type III IFNs. Utilizing these approaches, three clusters of terms were extracted from the literature covering different aspects of IFNε research and a set of 47 genes uniquely cited in the context of IFNε. The use of these "in silico" approaches support the expansion of current understanding and the creation of new knowledge surrounding IFNε.

Distribution and diversity of type VI secretion system clusters in Enterobacter bugandensis and Enterobacter cloacae.

Gram-negative bacteria use type VI secretion systems (T6SSs) to antagonize neighbouring cells. Although primarily involved in bacterial competition, the T6SS is also implicated in pathogenesis, biofilm formation and ion scavenging. Enterobacter species belong to the ESKAPE pathogens, and while their antibiotic resistance has been well studied, less is known about their pathogenesis. Here, we investigated the distribution and diversity of T6SS components in isolates of two clinically relevant Enterobacter species, E. cloacae and E. bugandensis. T6SS clusters are grouped into four types (T6SSi-T6SSiv), of which type i can be further divided into six subtypes (i1, i2, i3, i4a, i4b, i5). Analysis of a curated dataset of 31 strains demonstrated that most of them encode T6SS clusters belonging to the T6SSi type. All T6SS-positive strains possessed a conserved i3 cluster, and many harboured one or two additional i2 clusters. These clusters were less conserved, and some strains displayed evidence of deletion. We focused on a pathogenic E. bugandensis clinical isolate for comprehensive in silico effector prediction, with comparative analyses across the 31 isolates. Several new effector candidates were identified, including an evolved VgrG with a metallopeptidase domain and a Tse6-like protein. Additional effectors included an anti-eukaryotic catalase (KatN), M23 peptidase, PAAR and VgrG proteins. Our findings highlight the diversity of Enterobacter T6SSs and reveal new putative effectors that may be important for the interaction of these species with neighbouring cells and their environment.

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.

Transcriptomics reveals shared immunosuppressive landscapes in ventilator-associated lower respiratory tract infections (VA-LRTI) patients.

BACKGROUND: Ventilator-associated pneumonia (VAP) represents a transitory status of immunoparalysis, and we hypothesized that ventilator-associated tracheobronchitis (VAT) could share also some degree of immune response to a respiratory infection. RESEARCH DESIGN AND METHODS: A prospective observational study in five medical ICUs to evaluate immunological alterations of patients with VA-LRTI. Immunological gene expression profiles in the blood using whole transcriptome microarrays in the first 24 hours following diagnosis. The area under the receiver operating characteristic curve (AUROC) was used to assess the accuracy of mRNA levels to differentiate VA-LRTI and lack of infection. A principal component analysis (PCA) was employed for analyzing the impact of each genetic expression footprint variable in explaining the variance of the cohort. RESULTS: There was overlapping between the three classes of patients encompassing gene expression levels of 8 genes (i.e. HLA, IL2RA, CD40LG, ICOS, CCR7, CD1C, CD3E). HLA-DRA was equally low among VAT and VAP patients characterizing immune depression, and significantly lower than the control group. CONCLUSIONS: Our findings suggest that VAP and VAT are not so different regarding gene expression levels suggesting a degree of immunosuppression. Our results indicate a state of immunoparalysis in respiratory infections in critically ill patients.

Electromagnetic Fields Literature Analysis for Precision Medicine.

During the last century technological advances have increased the number of anthropogenic electromagnetic fields (EMFs) and therefore human exposures. In this work we have mined from more than 30,000 EMF-related publications the genes, diseases and molecular mechanisms associated with the exposure to six different subsets of EMFs. Results show 3653 unique disease MeSH terms and 9966 unique genes identified of which only 4340 genes are human. Overall, our approach highlights the molecular aspects of the increasing exposure to EMFs.

Mapping Exposome Derived Phenotypes into SNOMED Codes.

Human phenotypes define the healthy or diseased status of an individual and they arise from the complex interactions between environmental and genetic factors. The whole set of human exposures constitute the human exposome. These exposures have multiple sources including physical and socioeconomic factors. In this manuscript we have used text mining techniques to retrieve 1295 and 1903 Human Phenotype Ontology terms associated with these exposome factors and we have subsequently mapped 83% and 90% of the HPO terms respectively) into SNOMED as a clinically actionable code. We have developed a proof-of-concept approach to facilitate the integration of exposomic and clinical data.

Defining and Scoping Participatory Health Informatics: An eDelphi Study.

BACKGROUND: Health care has evolved to support the involvement of individuals in decision making by, for example, using mobile apps and wearables that may help empower people to actively participate in their treatment and health monitoring. While the term "participatory health informatics" (PHI) has emerged in literature to describe these activities, along with the use of social media for health purposes, the scope of the research field of PHI is not yet well defined. OBJECTIVE: This article proposes a preliminary definition of PHI and defines the scope of the field. METHODS: We used an adapted Delphi study design to gain consensus from participants on a definition developed from a previous review of literature. From the literature we derived a set of attributes describing PHI as comprising 18 characteristics, 14 aims, and 4 relations. We invited researchers, health professionals, and health informaticians to score these characteristics and aims of PHI and their relations to other fields over three survey rounds. In the first round participants were able to offer additional attributes for voting. RESULTS: The first round had 44 participants, with 28 participants participating in all three rounds. These 28 participants were gender-balanced and comprised participants from industry, academia, and health sectors from all continents. Consensus was reached on 16 characteristics, 9 aims, and 6 related fields. DISCUSSION: The consensus reached on attributes of PHI describe PHI as a multidisciplinary field that uses information technology and delivers tools with a focus on individual-centered care. It studies various effects of the use of such tools and technology. Its aims address the individuals in the role of patients, but also the health of a society as a whole. There are relationships to the fields of health informatics, digital health, medical informatics, and consumer health informatics. CONCLUSION: We have proposed a preliminary definition, aims, and relationships of PHI based on literature and expert consensus. These can begin to be used to support development of research priorities and outcomes measurements.

Modelling the Gastrointestinal Carriage of Klebsiella pneumoniae Infections.

Klebsiella pneumoniae is a leading cause of nosocomial and community acquired infections, making K. pneumoniae the pathogen that is associated with the second largest number of deaths attributed to any antibiotic resistant infection. K. pneumoniae colonizes the nasopharynx and the gastrointestinal tract in an asymptomatic manner without dissemination to other tissues. Importantly, gastrointestinal colonization is a requisite for infection. Our understanding of K. pneumoniae colonization is still based on interrogating mouse models in which animals are pretreated with antibiotics to disturb the colonization resistance imposed by the gut microbiome. In these models, infections disseminate to other tissues. Here, we report a murine model to allow for the study of the gastrointestinal colonization of K. pneumoniae without tissue dissemination. Hypervirulent and antibiotic resistant strains stably colonize the gastrointestinal tract of in an inbred mouse population without antibiotic treatment. The small intestine is the primary site of colonization and is followed by a transition to the colon over time, without dissemination to other tissues. Our model recapitulates the disease dynamics of the metastatic K. pneumoniae strains that are able to disseminate from the gastrointestinal tract to other sterile sites. Colonization is associated with mild to moderate histopathology, no significant inflammation, and no effect on the richness of the microbiome. Our model sums up the clinical scenario in which antibiotic treatment disturbs the colonization of K. pneumoniae and results in dissemination to other tissues. Finally, we establish that the capsule polysaccharide is necessary for the colonization of the large intestine, whereas the type VI secretion system contributes to colonization across the gastrointestinal tract. IMPORTANCE Klebsiella pneumoniae is one of the pathogens that is sweeping the world in the antibiotic resistance pandemic. Klebsiella colonizes the nasopharynx and the gut of healthy subjects in an asymptomatic manner, making gut colonization a requisite for infection. This makes it essential to understand the gastrointestinal carriage in preventing Klebsiella infections. Current research models rely on the perturbation of the gut microbiome by antibiotics, resulting in an invasive infection. Here, we report a new model of K. pneumoniae gut colonization that recapitulates key features of the asymptomatic human gastrointestinal tract colonization. In our model, there is no need to disturb the microbiota to achieve stable colonization, and there is no dissemination to other tissues. Our model sums up the clinical scenario in which antibiotic treatment triggers invasive infection. We envision that our model will be an excellent platform upon which to investigate factors enhancing colonization and invasive infections and to test therapeutics to eliminate Klebsiella asymptomatic colonization.

Histamine Produced by Gram-Negative Bacteria Impairs Neutrophil's Antimicrobial Response by Engaging the Histamine 2 Receptor.

We found that histamine (10-9 M) did not have any effect on the in vitro capture of Escherichia coli by neutrophils but accelerated its intracellular killing. In contrast, histamine (10-6 M) delayed the capture of Escherichia coli by neutrophils and reduced the amounts of pHrodo zymosan particles inside acidic mature phagosomes. Histamine acted through the H4R and the H2R, which are coupled to the Src family tyrosine kinases or the cAMP/protein kinase A pathway, respectively. The protein kinase A inhibitor H-89 abrogated the delay in bacterial capture induced by histamine (10-6 M) and the Src family tyrosine kinase inhibitor PP2 blocked histamine (10-9 M) induced acceleration of bacterial intracellular killing and tyrosine phosphorylation of proteins. To investigate the role of histamine in pathogenicity, we designed an Acinetobacter baumannii strain deficient in histamine production (hdc::TOPO). Galleria mellonella larvae inoculated with the wild-type Acinetobacter baumannii ATCC 17978 strain (1.1 × 105 CFU) died rapidly (100% death within 40 h) but not when inoculated with the Acinetobacter baumannii hdc::TOPO mutant (10% mortality). The concentration of histamine rose in the larval haemolymph upon inoculation of the wild type but not the Acinetobacter baumannii hdc::TOPO mutant, such concentration of histamine blocks the ability of hemocytes from Galleria mellonella to capture Candida albicans in vitro. Thus, bacteria-producing histamine, by maintaining high levels of histamine, may impair neutrophil phagocytosis by hijacking the H2R.

Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes.

Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple omics data, it is possible to develop predictive models that are able to prognosticate the course and development of the disease. The interpretability of these models, and the study of the variables used, allows the identification of biological aspects of great importance in the development of the disease. In this work we generated a metagenomic signature with predictive capacity to identify IBD from fecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the identified signature was validated in two external cohorts. More precisely a cohort containing samples from patients suffering Ulcerative Colitis and another from patients suffering Crohn's Disease, the two major subtypes of IBD. The results obtained in this validation (AUC 0.74 and AUC = 0.76, respectively) show that our signature presents a generalization capacity in both subtypes. The study of the variables within the model, and a correlation study based on text mining, identified different genera that play an important and common role in the development of these two subtypes.

A Comparative Analysis of Phenotypes Derived from Genes or Biomedical Literature in COVID-19.

Since the emergence of SARS-CoV-2 in November 2019, there has been an exponential production of literature due to worldwide efforts to understand the interactions between the virus and the human body. Using an "in-house" developed script we retrieved gene annotations and identified phenotype enrichments. Human Phenotype Ontology terms were retrieved from the literature using the Onassis R package. This produced both disease-gene and disease-phenotype data as well as data for gene-phenotype interactions. Overall, we retrieved 181 human phenotypes that were identified by both approaches. Further in-depth analysis of these relationships could provide further insights in the molecular mechanisms related with the observed phenotypes, answers and hypotheses for key concepts within COVID-19 research.

Exploring Molecular Mechanisms Within Biomedical Literature.

Text mining of the biomedical literature enables vast quantities of information to be extracted and summarised. Here we describe an updated and improved version of previous methodology for the analysis of gene and protein biomarkers that enables the use of the newer Pubtator Central annotations, based in full text, improving the performance using a local SQLite database, that reduces the running time and resources required to perform the analyses facilitating its use in any computer, and expands its capabilities to enable the retrieval and analysis of chemical and metabolic biomarkers.

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.

An endogenously activated antiviral state restricts SARS-CoV-2 infection in differentiated primary airway epithelial cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the coronavirus disease-19 (COVID-19) pandemic, was identified in late 2019 and caused >5 million deaths by February 2022. To date, targeted antiviral interventions against COVID-19 are limited. The spectrum of SARS-CoV-2 infection ranges from asymptomatic to fatal disease. However, the reasons for varying outcomes to SARS-CoV-2 infection are yet to be elucidated. Here we show that an endogenously activated interferon lambda (IFNλ1) pathway leads to resistance against SARS-CoV-2 infection. Using a well-differentiated primary nasal epithelial cell (WD-PNEC) culture model derived from multiple adult donors, we discovered that susceptibility to SARS-CoV-2 infection, but not respiratory syncytial virus (RSV) infection, varied. One of four donors was resistant to SARS-CoV-2 infection. High baseline IFNλ1 expression levels and associated interferon stimulated genes correlated with resistance to SARS-CoV-2 infection. Inhibition of the JAK/STAT pathway in WD-PNECs with high endogenous IFNλ1 secretion resulted in higher SARS-CoV-2 titres. Conversely, prophylactic IFNλ treatment of WD-PNECs susceptible to infection resulted in reduced viral titres. An endogenously activated IFNλ response, possibly due to genetic differences, may be one explanation for the differences in susceptibility to SARS-CoV-2 infection in humans. Importantly, our work supports the continued exploration of IFNλ as a potential pharmaceutical against SARS-CoV-2 infection.

Comparison of SARS-CoV-2 Evolution in Paediatric Primary Airway Epithelial Cell Cultures Compared with Vero-Derived Cell Lines.

SARS-CoV-2 can efficiently infect both children and adults, albeit with morbidity and mortality positively associated with increasing host age and presence of co-morbidities. SARS-CoV-2 continues to adapt to the human population, resulting in several variants of concern (VOC) with novel properties, such as Alpha and Delta. However, factors driving SARS-CoV-2 fitness and evolution in paediatric cohorts remain poorly explored. Here, we provide evidence that both viral and host factors co-operate to shape SARS-CoV-2 genotypic and phenotypic change in primary airway cell cultures derived from children. Through viral whole-genome sequencing, we explored changes in genetic diversity over time of two pre-VOC clinical isolates of SARS-CoV-2 during passage in paediatric well-differentiated primary nasal epithelial cell (WD-PNEC) cultures and in parallel, in unmodified Vero-derived cell lines. We identified a consistent, rich genetic diversity arising in vitro, variants of which could rapidly rise to near fixation within two passages. Within isolates, SARS-CoV-2 evolution was dependent on host cells, with paediatric WD-PNECs showing a reduced diversity compared to Vero (E6) cells. However, mutations were not shared between strains. Furthermore, comparison of both Vero-grown isolates on WD-PNECs disclosed marked growth attenuation mapping to the loss of the polybasic cleavage site (PBCS) in Spike, while the strain with mutations in Nsp12 (T293I), Spike (P812R) and a truncation of Orf7a remained viable in WD-PNECs. Altogether, our work demonstrates that pre-VOC SARS-CoV-2 efficiently infects paediatric respiratory epithelial cells, and its evolution is restrained compared to Vero (E6) cells, similar to the case of adult cells. We highlight the significant genetic plasticity of SARS-CoV-2 while uncovering an influential role for collaboration between viral and host cell factors in shaping viral evolution and ultimately fitness in human respiratory epithelium.

Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data.

In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in the world, a classification model has been developed based on Machine Learning (ML) capable of predicting the country of origin (United Kingdom vs United States) according to metagenomic data. The data were used for the training of a glmnet algorithm and a Random Forest algorithm. Both algorithms obtained similar results (0.698 and 0.672 in AUC, respectively). Furthermore, thanks to the application of a multivariate feature selection algorithm, eleven metagenomic genres highly correlated with the country of origin were obtained. An in-depth study of the variables used in each model is shown in the present work.

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.