Context Of Precision Medicine Research Articles

Role of Lnc-RNAs in the Pathogenesis and Development of Diabetic Retinopathy.

Important advances in diabetic retinopathy (DR) research and management have occurred in the last few years. Neurodegenerative changes before the onset of microvascular alterations have been well established. So, new strategies are required for earlier and more effective treatment of DR, which still is the first cause of blindness in working age. We describe herein gene regulation through Lnc-RNAs as an interesting subject related to DR. Long non-coding RNAs (Lnc-RNAs) are non-protein-coding transcripts larger than 200 nucleotides. Lnc-RNAs regulate gene expression and protein formation at the epigenetic, transcriptional, and translational levels and can impact cell proliferation, apoptosis, immune response, and oxidative stress. These changes are known to take part in the mechanism of DR. Recent investigations pointed out that Lnc-RNAs might play a role in retinopathy development as Metastasis-Associated Lung Adenocarcinoma Transcript (Lnc-MALAT1), Maternally expressed gene 3 (Lnc-MEG3), myocardial-infarction-associated transcript (Lnc-MIAT), Lnc-RNA H19, Lnc-RNA HOTAIR, Lnc-RNA ANRIL B-Raf proto-oncogene (Lnc-RNA BANCR), small nucleolar RNA host gene 16 (Lnc-RNA SNHG16) and others. Several molecular pathways are impacted. Some of them play a role in DR pathophysiology, including the PI3K-Akt signaling axis, NAD-dependent deacetylase sirtuin-1 (Sirti1), p38 mitogen-activated protein kinase (P38/mapk), transforming growth factor beta signaling (TGF-β) and nuclear factor erythroid 2-related factor 2 (Nrf2). The way Lnc-RNAs affect diabetic retinopathy is a question of great relevance. Performing a more in-depth analysis seems to be crucial for researchers if they want to target Lnc-RNAs. New knowledge on gene regulation and biomarkers will enable investigators to develop more specialized therapies for diabetic retinopathy, particularly in the current growing context of precision medicine.

MP29-19 PRE-OPERATIVE PLASMA METABOLITES CAN PREDICT THE RISK OF BONE METASTASIS IN PROSTATE CANCER

You have accessJournal of UrologyCME1 Apr 2023MP29-19 PRE-OPERATIVE PLASMA METABOLITES CAN PREDICT THE RISK OF BONE METASTASIS IN PROSTATE CANCER Saurav Subedi, Federico La Manna, Katja Ovchinnikova, Eugenio Zoni, George N. Thalmann, and Marianna Kruithof-de Julio Saurav SubediSaurav Subedi More articles by this author , Federico La MannaFederico La Manna More articles by this author , Katja OvchinnikovaKatja Ovchinnikova More articles by this author , Eugenio ZoniEugenio Zoni More articles by this author , George N. ThalmannGeorge N. Thalmann More articles by this author , and Marianna Kruithof-de JulioMarianna Kruithof-de Julio More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003257.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Early identification of prostate cancer (PCa) patients that are most likely to develop a lethal disease is still a major hurdle. While for most patients, a radical treatment will result in the eradication of the disease, about 70% of patients with advanced PCa are diagnosed with bone metastasis, a lethal feature with limited treatment options. There is growing evidence suggesting that lipid metabolism cooperates with the genetic landscape of prostate cancer. Currently, several studies have shown that the metabolome of PCa could be used as non-invasive diagnostic and prognostic tool. In this study, we analyzed the plasma of 73 prostate cancer patients to identify metabolites with diagnostic or prognostic value. METHODS: The plasma was collected during fasting and shortly before the patient received radical prostatectomy. The patients were then followed up for up to 10 years, to monitor the development of bone metastases. Targeted metabolomics was run on the pre-operative plasma. 10 different machine learning models were tested on the available metabolomics data-set. All patients provided informed consent for participation in this study. RESULTS: By using machine learning algorithms, we identified a signature of 41 metabolites either positively or negatively associated with the development of bone metastatic PCa. Neural network and support vector machine yielded the best results for classification of metastasis based on the metabolites. Furthermore, linear support vector machine and Gaussian process generated the best regression models for determination of time-to-bone metastasis using absolute Spearman`s R correlation. Determination of both the classification of bone metastasis and time-to-metastasis were identified based on 41 metabolites in the Neural network and support vector machine algorithms. The top-four metabolites associated with bone metastatic progression were lysophosphatidylethanolamine(LPE 22:5/0:0)b, butyrylcarnitine (C4), pyruvic acid and dodecanoylcarnitine (C12), whereas monoacylglycerol (MAG 14:0/0:0), MAG (0:0/22:5), acetylornithine and MAG (0:0/14:0) were the bottom-four metabolites. CONCLUSIONS: In this study, we evaluated metabolites in the plasma of prostate cancer patients to determine if we could identify a predictive signature of metastatic progression. Our studies show that metabolic phenotypes have substantial clinical value. Given the context of precision medicine, we show the feasibility of a simple metabolomics test on the plasma of patients that could help stratify patient`s risk of lethal disease based on the presence/absence of signature metabolites. Further studies in a larger cohort will be required in order to test the signature metabolites found in our study to test the reliability for patient stratification. Source of Funding: Swiss Cancer Research Foundation (KFS-5510-02-2022-R) © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e389 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Saurav Subedi More articles by this author Federico La Manna More articles by this author Katja Ovchinnikova More articles by this author Eugenio Zoni More articles by this author George N. Thalmann More articles by this author Marianna Kruithof-de Julio More articles by this author Expand All Advertisement PDF downloadLoading ...

Advancing precision medicine in medical education: Integrated, precise and data‐driven smart solutions

AbstractAdvances in “Precision Medicine” initiative, also known as “Personalized Medicine” is an emerging approach for disease treatment and prevention that has already led to innovative discoveries and has created “smart” applications and solutions tailored to a person's or a group of individuals' genetic profile, lifestyle, and environment interaction. Already many physicians as part of patient care routinely prescribe various molecular/genetic and other tests enabling them to select personalized treatments that improve the chances of survival and reduce exposure to adverse effects. This initiative should provide to medical and healthcare professionals with adequate resources and readily available solutions so that the target to specific treatments and care of the illnesses is achieved while at the same time protecting the privacy and safety of the individual is secured as well as the Electronic Health Records (EHRs) and whatever additional data is necessary within the context of Precision Medicine. This study, through a literature review mostly, and some case study analysis, examine whether personalized medicine is delivered to the patient in an “accurate” and “precise” way, as expected. This requires that Health and Human Services and other stakeholders and agencies collaborate to solicit the right input from patients while at the same time can identify and address any educational, practical, legal, and technical issues and providing smart solutions. The lack of proper medical education and advanced infrastructure are still major barriers to the adoption of Precision Medicine, therefore, the role of medical training in Precision Medicine is also examined and analyzed. Specific examples are discussed in an integrated, precise, and data‐driven manner to provide “smart” solutions.

'I think we just do it once and leave it …' The collection and utility of family health history in general practice in Aotearoa New Zealand: a qualitative study.

Introduction The value of family health history as a means to understanding health risk has been long known. Its value in a precision medicine context is also now becoming apparent. General practitioners (GPs) are considered to play a key role in the collection, and investigation, of family health history, but it remains widely reported as being both poorly and infrequently undertaken. Little is known about this practice in Aotearoa New Zealand (NZ). Aim This study aimed to explore current practices in relation to the ascertainment of family health history, with a view towards precision medicine. Methods Semi-structured interviews were conducted with 10 GPs recruited from one urban area of NZ. The interviews were subjected to a thematic analysis. Results Family health history information was used to varying degrees in four areas - risk ascertainment, patient engagement with a diagnosis, social context and building relationships. Patient cultural considerations were rarely mentioned. Reliability of information provided by patients, resource constraints, context driven consults and electronic health record limitations are potential indicators of current limits of family health history. Discussion Our findings present a baseline of current practice and echo larger studies from overseas. As precision medicine is not yet routine, a unique opportunity exists for consideration to be given to establishing specific roles within the NZ health system to enable equitable practice of, and subsequent health gains from, the use of family/whānau health history information as part of precision medicine.

The Association of uPA, uPAR, and suPAR System with Inflammation and Joint Damage in Rheumatoid Arthritis: suPAR as a Biomarker in the Light of a Personalized Medicine Perspective.

In recent years, the involvement of the soluble urokinase Plasminogen Activator Receptor (suPAR) in the pathophysiological modulation of Rheumatoid Arthritis (RA) has been documented, resulting in the activation of several intracellular inflammatory pathways. We investigated the correlation of urokinase Plasminogen Activator (uPA)/urokinase Plasminogen Activator Receptor (uPAR) expression and suPAR with inflammation and joint damage in RA, evaluating their potential role in a precision medicine context. Currently, suPAR has been shown to be a potential biomarker for the monitoring of Systemic Chronic Inflammation (SCI) and COVID-19. However, the effects due to suPAR interaction in immune cells are also involved in both RA onset and progression. To date, the literature data on suPAR in RA endorse its potential application as a biomarker of inflammation and subsequent joint damage. Available evidence about suPAR utility in the RA field is promising, and future research should further investigate its use in clinical practice, resulting in a big step forward for precision medicine. As it is elevated in different types of inflammation, suPAR could potentially work as an adjunctive tool for the screening of RA patients. In addition, a suPAR system has been shown to be involved in RA pathogenesis, so new data about the therapeutic response to Jak inhibitors can represent a possible way to develop further studies.

Liquid Biopsy in Cancer: Focus on Lymphoproliferative Disorders.

Within the context of precision medicine, the scientific community is giving particular attention to early diagnosis and intervention, guided by non-invasive methodologies. Liquid biopsy (LBx) is a recent laboratory approach consisting of a non-invasive blood draw, which allows the detection of information about potential prognostic factors, or markers to be used for diagnostic purposes; it might also allow the clinician to establish a treatment regimen and predict a patient's response. Since the discovery of circulating tumor cells (CTCs) in the nineteenth century, the possibility of integrating LBx into clinical practice has been explored, primarily because of its safeness and easy execution: indeed, compared to solid biopsy, sampling-related risks are less of a concern, and the quickness and repeatability of the process could help confirm a prompt diagnosis or to further corroborate the existence of a metastatic spreading of the disease. LBx's usefulness has been consolidated in a narrow range of oncological settings, first of all, non-small cell lung carcinoma (NSCLC), and it is now gradually being assessed also in lymphoproliferative diseases, such as acute lymphocytic leukemia (ALL), B-cell lymphomas, and multiple myeloma. The present review aims to summarize LBx's overall characteristics (such as its advantages and flaws, collection and analysis methodologies, indications, and targets of the test), and to highlight the applications of this technique within the specific field of B-cell malignancies. The perspectives on how such a simple and convenient technique could improve hemato-oncological clinical practice are broadly encouraging, yet far from a complete integration in routine clinical settings.

The Voices of Stakeholders Involved in Precision Medicine: The Co-Design and Evaluation of Qualitative Indicators of Intervention Acceptability, Fidelity and Context in PRecISion Medicine for Children With Cancer in Australia.

We report a novel approach of amalgamating implementation outcomes of acceptability and fidelity alongside context as a new way of qualitatively evaluating implementation outcomes and context of a precision medicine intervention. A rapid qualitative online proforma was co-designed with stakeholders and sent to a purposive sample of healthcare professionals involved in an early-phase clinical trial intervention. Data were analysed using Framework Analysis. A total of 24 out of 68 proformas were returned. Although some participants raised concerns about drug medication access issues, the main intervention was well accepted and understood across professional groups. Comprehension was enhanced through exposure to specialist multidisciplinary meeting arrangements. In conclusion, a rapid data collection tool and framework are now available to assess readily measurable, qualitative indicators of acceptability, fidelity of receipt and contextual fit within the dynamic precision medicine context.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.

Background: Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes. Methods: Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context. Results: Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response. Conclusion: Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Computational estimation of quality and clinical relevance of cancer cell lines.

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.

352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer

OBJECTIVES/GOALS: Our goal is to develop a cost-effective approach for precision medicine treatment by providing computational predictions for new uses of currently available FDA approved, and experimental drugs for NSCLC. METHODS/STUDY POPULATION: Cell Lines: A549 (ATCC- CCL-185) Human epithelial Lung Carcinoma cells, H1792 (ATCC-CRL-5895) Human Lung Carcinoma cells. In Vitro Cytotoxicity Assay: A Vybrant® MTT Cell Proliferation Assay was used. Colony Formation Assay: NCI-H1792, A549 cells were seeded at a density of 500 cells/ dish, then treated with ARS-1620, Osimertinib. The Computational Analysis of Novel Drug Opportunities (CANDO):  Herein, we employed the bioanalytic docking (BANDOCK) protocol within CANDO to calculate the compound-protein interaction scores for a library of 13,218 compounds from DrugBank against a library of 5,317 protein structures from the Protein Data Bank, resulting in a proteomic interaction signature for each compound, and identified Osimertinib as the most likely EGFR/ErbB inhibitor to synergize with ARS-1620. RESULTS/ANTICIPATED RESULTS: ARS-1620 and Osimertinib in combination displays potent anti-tumor activity as evident by a decrease in cell viability with cytotoxicity assays, as well as reduced number of colonies in the colony formation assay for both A549 and H1792 cells. By using CANDO, and cross-referencing the obtained rankings with known experimental information, we have obtained drug predictions within the context of precision medicine. Our preliminary data indicates that EGFR inhibitor Osimertinib may be most structurally similar to KRAS G12C inhibitors overall, compared to other ErbB/ EGFR inhibitors. Validations with human cancer cell lines A549 and H1792 have confirmed that Osimertinib in combination with KRAS G12C inhibitor ARS-1620 may exhibit a synergistic effect in decreasing cellular proliferation and colony formation. DISCUSSION/SIGNIFICANCE: This suggests that this innovative drug combination therapy may help improve treatment outcomes for KRAS G12C(H1792) and KRASG12S(A549) mutant cancers. Cell migration and cell invasion studies in response to treatment with Osimertinib and ARS-1620 are currently ongoing.

Re-Imagining the Data Collection and Analysis Research Process by Proposing a Rapid Qualitative Data Collection and Analytic Roadmap Applied to the Dynamic Context of Precision Medicine

Our implementation science study focuses on implementing a new way of practice and offers methodological specificity about how to rapidly investigate an individually tailored precision medicine intervention. A qualitative study advancing a new methodology for speedily identifying barriers and enablers to implementation in the context of childhood cancer. Data were collected through rapid ethnography, coded using the Consolidated Framework for Implementation Research, and analysed by Sentiment Analysis. Thirty-eight data collection events occurred during 14 multidisciplinary tumour board meetings, 14 curation meetings, and 10 informal conversations. Sentiment Analysis distilled Consolidated Framework for Implementation Research codes to reveal key barriers and enablers to implementation. A traffic light labelling system has been used to present levels of positivity and negativity (green for strong enablers and red for strong barriers), highlighting levels of concern regarding implementation. Within the intervention design characteristics, “Adaptability” was the strongest enabler and “Design quality and safety” the strongest barrier. Among the contextual factors: “Networks and communication” were the strongest enabler, and “Available resources” were the strongest barrier. Overall, there was a higher percentage of negative sentiment towards intervention design characteristics and contextual factors than positive sentiment, while more concerns were raised about intervention design factors than contextual factors. This study offers a rapid qualitative data collection and analytic methodological roadmap for establishing barriers and enablers to a paediatric precision medicine intervention.

Fully-Coupled FSI Computational Analyses in the Ascending Thoracic Aorta Using Patient-Specific Conditions and Anisotropic Material Properties.

Computational hemodynamics has become increasingly important within the context of precision medicine, providing major insight in cardiovascular pathologies. However, finding appropriate compromise between speed and accuracy remains challenging in computational hemodynamics for an extensive use in decision making. For example, in the ascending thoracic aorta, interactions between the blood and the aortic wall must be taken into account for the sake of accuracy, but these fluid structure interactions (FSI) induce significant computational costs, especially when the tissue exhibits a hyperelastic and anisotropic response. The objective of the current study is to use the Small On Large (SOL) theory to linearize the anisotropic hyperelastic behavior in order to propose a reduced-order model for FSI simulations of the aorta. The SOL method is tested for fully-coupled FSI simulations in a patient-specific aortic geometry presenting an Ascending Thoracic Aortic Aneurysm (aTAA). The same model is also simulated with a fully-coupled FSI with non-linear material behavior, without SOL linearization. Eventually, the results and computational times with and without the SOL are compared. The SOL approach is demonstrated to provide a significant reduction of computational costs for FSI analysis in the aTAA, and the results in terms of stress state distribution are comparable. The method is implemented in ANSYS and will be further evaluated for clinical applications.

Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider.

Learning healthcare systems have recently emerged as a strategy to continuously use experiences and outcomes of clinical care for research purposes in precision medicine. Although it is known that learning healthcare transitions in general raise important ethical challenges, the ethical ramifications of such transitions in the specific context of precision medicine have not extensively been discussed. Here, we describe three levers that institutions can pull to advance learning healthcare systems in precision medicine: (1) changing testing of individual variability (such as genes); (2) changing prescription of treatments on the basis of (genomic) test results; and/or (3) changing the handling of data that link variability and treatment to clinical outcomes. Subsequently, we evaluate how patients can be affected if one of these levers are pulled: (1) patients are tested for different or more factors than before the transformation, (2) patients receive different treatments than before the transformation and/or (3) patients’ data obtained through clinical care are used, or used more extensively, for research purposes. Based on an analysis of the aforementioned mechanisms and how these potentially affect patients, we analyze why learning healthcare systems in precision medicine need a different ethical approach and discuss crucial points to consider regarding this approach.

A Conceptual Model-Based Approach to Improve the Representation and Management of Omics Data in Precision Medicine

Precision medicine has emerged as a disrupting medical model to transform a historically reactive medicine into a proactive one that focuses on delivering individualized treatment. A relevant challenge of precision medicine is to integrate the large amount of omics data that exists. This data has a high degree of heterogeneity, dispersion, and isolation. In addition, there is a lack of a solid ontological commitment regarding domain concepts and definitions, and a unified guideline of how to transform data into knowledge is missing. In this work, we report our experience applying conceptual modeling to deal with these challenges in a specific genomics dimension, i.e., Precision Medicine. To do so, we have applied conceptual modeling techniques. The use of these techniques allows us to create representations of the world (i.e., conceptual schemes) that can be used for the purposes of understanding, communicating, and problem-solving. They also help to establish a common ontological framework to facilitate both communication and knowledge evolution in complex domains. We identify a set of limitations that emerged after working in a precision medicine context, and we describe how we have solved them using conceptual modeling. Thus, the main contribution of this work is to present the subsequent Conceptual Schema that allowed us to overcome these limitations, which provides a better representation of proteomics data and eases its integration.

Regeneration during Obesity: An Impaired Homeostasis.

Simple SummaryRegeneration represents the biological processes that allow cells and tissues to renew and develop. During obesity, a variety of changes and reactions are seen. This includes inflammation and metabolic disorders. These obesity-induced changes do impact the regeneration processes. Such impacts that obesity has on regeneration would affect tissues and organs development and would also have consequences on the outcomes of therapies that depend on cells regeneration (such as burns, radiotherapy and leukemia) given to patients suffering from obesity. Therefore, a particular attention should be given to patients suffering from obesity in biological, therapeutic and clinical contexts that depend on regeneration ability.Obesity is a health problem that, in addition to the known morbidities, induces the generation of a biological environment with negative impacts on regeneration. Indeed, factors like DNA damages, oxidative stress and inflammation would impair the stem cell functions, in addition to some metabolic and development patterns. At the cellular and tissulaire levels, this has consequences on growth, renewal and restoration which results into an impaired regeneration. This impaired homeostasis concerns also key metabolic tissues including muscles and liver which would worsen the energy balance outcome towards further development of obesity. Such impacts of obesity on regeneration shows the need of a specific care given to obese patients recovering from diseases or conditions requiring regeneration such as burns, radiotherapy and leukemia. On the other hand, since stem cells are suggested to manage obesity, this impaired regeneration homeostasis needs to be considered towards more optimized stem cells-based obesity therapies within the context of precision medicine.

Skin thickness measurements for optimal intradermal injections in children

BackgroundIn the context of precision medicine and in response to the highly needed capacity of rapid interventions towards new infectious diseases and pandemic outbreaks, intradermal immunization is gaining increased attention. However, the currently used Mantoux technique for ID injection is difficult to standardize and requires training, especially when used in children.To allow determining the maximum penetration depth and needle characteristics for the development of a platform of medical devices suited for intradermal injection, VAX-ID® and to ensure an accurate ID injection in children, the epidermal and dermal thickness at the proximal ventral and dorsal forearm (PVF & PDF) and at the deltoid region in children aged 8 weeks to 18 years were assessed. The lateral part of the upper leg was assessed as well in children aged 8 weeks to 2 years since it is a commonly used injection site in this population. Materials & MethodsMean thickness of the PVF, PDF, lateral part of the upper leg and deltoid were measured using high-frequency ultrasound. Association with gender, age and BMI was assessed using Mann-Whitney U Test, Spearman correlation and Wilcoxon Signed Ranks Test, respectively. ResultsResults showed an overall mean skin thickness of 0.99 mm (SD: 0.14 mm) at the PVF, 1.20 mm (SD: 0.17) at the PDF, 1.28 mm (SD: 0.16) at the lateral part of the upper leg and increasing to 1.32 mm (0.25) at the deltoid region. Age and BMI correlated significantly (p < 0.001) with skin thickness at all investigated body sites. Gender did not affect skin thickness in the investigated population. ConclusionSignificant differences in skin thickness at the PVF, PDF and deltoid region were seen according to age and BMI. An optimal needle length of 0.7 mm is advised to guarantee intradermal injection in children at all investigated injection sites. (NCT02727114).

The role of mobile health technologies in allergy care: An EAACI position paper.

Mobile health (mHealth) uses mobile communication devices such as smartphones and tablet computers to support and improve health-related services, data and information flow, patient self-management, surveillance, and disease management from the moment of first diagnosis to an optimized treatment. The European Academy of Allergy and Clinical Immunology created a task force to assess the state of the art and future potential of mHealth in allergology. The task force endorsed the "Be He@lthy, Be Mobile" WHO initiative and debated the quality, usability, efficiency, advantages, limitations, and risks of mobile solutions for allergic diseases. The results are summarized in this position paper, analyzing also the regulatory background with regard to the "General Data Protection Regulation" and Medical Directives of the European Community. The task force assessed the design, user engagement, content, potential of inducing behavioral change, credibility/accountability, and privacy policies of mHealth products. The perspectives of healthcare professionals and allergic patients are discussed, underlining the need of thorough investigation for an effective design of mHealth technologies as auxiliary tools to improve quality of care. Within the context of precision medicine, these could facilitate the change in perspective from clinician- to patient-centered care. The current and future potential of mHealth is then examined for specific areas of allergology, including allergic rhinitis, aerobiology, allergen immunotherapy, asthma, dermatological diseases, food allergies, anaphylaxis, insect venom, and drug allergy. The impact of mobile technologies and associated big data sets are outlined. Facts and recommendations for future mHealth initiatives within EAACI are listed.

The role of laboratory developed tests in the development of precision medicine and clinical experimental diagnosis

The modern laboratory developed tests (LDTs) plays a very important role in the development of precision medicine and clinical laboratory diagnosis. The perfect LDTs inspection platform not only requires strict quality control system and management standards, but also requires technical support from high-end professional teams. By combing the development process of LDTs, this paper discusses the significance of developing LDTs in the context of precision medicine, the positive role played by the development of laboratory medicine and the cultivation of laboratory talents, and how to effectively develop LDTs. Key words: Medical laboratory science; Clinical laboratory techniques; Precision medicine

Exploring the dark genome: implications for precision medicine.

The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.