Pharmacogenomic factors affect the susceptibility to drug-drug interactions (DDI). We identified drug interaction perpetrators among the drugs prescribed to a cohort of 290 older adults and analysed the prevalence of gene polymorphisms that can increase their interacting potential. We also pinpointed clinical decision support systems (CDSSs) that incorporate pharmacogenomic factors in DDI risk evaluation. Perpetrator drugs were identified using the Drug Interactions Flockhart Table, the DRUGBANK website, and the Mayo Clinic Pharmacogenomics Association Table. Allelic variants affecting their activity were identified with the PharmVar, PharmGKB, dbSNP, ensembl and 1000 genome databases. Amiodarone, amlodipine, atorvastatin, digoxin, esomperazole, omeprazole, pantoprazole, simvastatin and rosuvastatin were perpetrator drugs prescribed to >5% of our patients. Few allelic variants affecting their perpetrator activity showed a prevalence >2% in the European population: CYP3A4/5*22, *1G, *3, CYP2C9*2 and *3, CYP2C19*17 and *2, CYP2D6*4, *41, *5, *10 and *9 and SLC1B1*15 and *5. Few commercial CDSS include pharmacogenomic factors in DDI-risk evaluation and none of them was designed for use in older adults. We provided a list of the allelic variants influencing the activity of drug perpetrators in older adults which should be included in pharmacogenomics-oriented CDSSs to be used in geriatric medicine.

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 Research and Innovation project. Background Appropriate nutrition and physical activity (PA) are essential for the management of a large number of non-communicable diseases. Nonetheless, our lifestyle is characterized by an irregular and often poorly balanced dietary pattern and insufficient PA. Purpose The digital health application PROTEIN, which was developed within the framework of H2020 Research and Innovation project PROTEIN, aims to engage people in a healthy, nutritionally sound and active life. To obtain information on the acceptability, usability, and feasibility as well as preliminary effectiveness, pilot trials are being conducted in various countries. Results from these pilots aim to guide the further development of personalized nutrition applications. Methods A prospective pilot study was implemented between May-October 2022. Participant attitudes and beliefs about their diet and PA were assessed by means of the readiness to change questionnaire. Their habits, including smoking, diet (MEDAS), alcohol consumption, (AUDIT) and PA (IPAQ) were evaluated at baseline and follow-up. The usability and feasibility were evaluated by the System Usability (SUS) and User Experience Questionnaire (UEQ). A general profile of the participants was assessed, as well as their attitudes and beliefs around their lifestyle. Results Fifty participants were included (women, n=30), 27 of them being overweight or diagnosed with obesity and 23 patients with CVD. On average, age was 49.9 years (19-76) and BMI was 32.3 (16.7–45.3). 40.4% of the participants believed they were PA enough, 42.9% thought they were not. 74% were motivated to become more active. Almost half of the patients (47.6%) were convinced already enjoying a healthy diet, 33.3% were not sure and 19.1% reported they were not. The majority (81%) were motivated to eat healthier. Results from MEDAS show an increase in the intake of vegetables, fruit, legumes and nuts. A decrease was observed in the consumption of meat, butter and candy. (Table 1) 70% reported being highly active at baseline, 21% and 9% reported being medium and low active respectively. Of the patients filling in all the questionnaires (n=25), 20% reported an increase in PA, 70% maintained their PA level and 8% reported a decrease. Results from the SUS showed that most of the participants found the app orderly (64%), attractive (63%) and congenial (76%). However, participants reported to find the app unnecessarily complex (60%), counterintuitive (48%) or cumbersome (68%). Conclusion Participants were motivated to change their lifestyle towards a healthier one. Despite this, a drop-out rate of 41.8% was observed. Users that completed the intervention experienced a positive effect on the maintenance or progress of their PA or diet-behaviour. Currently, it seems that the PROTEIN application is too complex to be user-friendly. There is need to adjust the functionality of the app according to the feedback from users to ensure an optimal impact on their lifestyles.

Federated learning initiatives in healthcare are being developed to collaboratively train predictive models without the need to centralize sensitive personal data. GenoMed4All is one such project, with the goal of connecting European clinical and –omics data repositories on rare diseases through a federated learning platform. Currently, the consortium faces the challenge of a lack of well-established international datasets and interoperability standards for federated learning applications on rare diseases. This paper presents our practical approach to select and implement a Common Data Model (CDM) suitable for the federated training of predictive models applied to the medical domain, during the initial design phase of our federated learning platform. We describe our selection process, composed of identifying the consortium’s needs, reviewing our functional and technical architecture specifications, and extracting a list of business requirements. We review the state of the art and evaluate three widely-used approaches (FHIR, OMOP and Phenopackets) based on a checklist of requirements and specifications. We discuss the pros and cons of each approach considering the use cases specific to our consortium as well as the generic issues of implementing a European federated learning healthcare platform. A list of lessons learned from the experience in our consortium is discussed, from the importance of establishing the proper communication channels for all stakeholders to technical aspects related to –omics data. For federated learning projects focused on secondary use of health data for predictive modeling, encompassing multiple data modalities, a phase of data model convergence is sorely needed to gather different data representations developed in the context of medical research, interoperability of clinical care software, imaging, and –omics analysis into a coherent, unified data model. Our work identifies this need and presents our experience and a list of actionable lessons learned for future work in this direction.

PurposeA broader challenge of co-creating digital solutions with patients addresses the question how to apply an open-access digital platform with trusted digital health information as a measure to transform the way patients access and understand health information. It further addresses use this for adherence to treatment, risk minimization and quality of life throughout the integrated patient journey. The aim of this paper is to demonstrate the early steps in towards progress to co-creating the digital solution.Design/methodology/approachTo coordinate the co-creation process, the authors established a multiphased plan to deep-dive into user needs and behaviors across patient journeys, to identify nuances and highlight important patterns in stakeholder and end-user segment at various stages in the patient's journey.FindingsA set of tools was designed to serve as a human-centered compass throughout the lifecycle of the project. Those tools include shared objects; personas, user journeys, a set of performance indicators with related requirements – all those tools being consistently refined in ongoing co-creation workshops with members of the cross-functional stakeholder groups.Originality/valueIn this study, a multidisciplinary, public-private partnership looked at integrated digital tool to improve access, understanding and adherence to treatment for diverse groups of patients across all stages of their health journeys in a number of countries including European Union (EU) and United States of America (USA). As a result of this work, the authors attempt to increase the possibility that the improved availability and understanding of health information from trusted sources translates to higher levels of adherence to treatment, safer use of medication (pharmacovigilance), better health outcomes and quality of life integrated in the patient's journey.

Purpose: The use of Artificial Intelligence (AI) for personalized medicine has recently guided dramatic improvements in the diagnostic pathway of several diseases. The European Project GENOMED4ALL, aims at using European level data of patients affected by Multiple Myeloma, Myelodysplastic Syndromes and Sickle Cell Disease (SCD) to find correlation between genomics – and other omics data – with phenotypic manifestations and seize the opportunity of improving diagnostics through AI. Silent Cerebral Infarcts (SCIs) are a significant determinant of morbidity since childhood in SCD. One of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics – quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI through the analysis of cerebral MRI, secondly, to correlate imaging data with other types of omics data in order to predict risk of recurrence. Materials and methods: The MRI protocol included 3D-T1, FLAIR and DWI sequences. Neuroradiological reports were cross checked for consistency. A stepwise segmentation (identification of lesion volume), pre-processing and extraction of MRIs was performed utilizing different open-source software: Lesion Segmentation Tools and UNet for segmentation, Freesurfer for brain extraction. To optimize SCI identification, we have selected the best software for segmentation; freesurfer was substituted with a faster approach. Results: Six European SCD centers participated in this first phase of the Radiomics project with 501 MRIs: 225 were classified as abnormal by the local neuroradiologists due to presence of SCI, 275 were normal; 70% were pediatric MRIs. Different instruments were used in the 6 centers: Philips 1.5 T (n.2), Siemens 1.5 T and 3 T (n.2), GE 3T (n.2). A stepwise procedure allowed the optimization of SCI identification, with distinction between SCI and background non clinically significant noise (i.e periventricular areas) or other white matter hyperintensities (i.e transient glial maturation). As shown in Figure 1, to segment SCI in FLAIR MRI, we have used a pre-trained UNet [Li H, 2018]. Firstly, we have extracted the brain registering the MNI152 on the T1 image. Then we have applied the brain mask and a threshold taking only the largest component. The UNet performs the segmentation of hyperintensities. We have removed the regions surrounded by less than the 90% of White Matter or near the brain ventricles to remove non-SCI region. In the end, we have enlarged the remaining ones. To date, segmentation on 303 exams from different centers showed very few false positive and false negatives highlighting the need to take into account different technical characteristics due to the various equipment used in the different centers. Conclusion: SCD is a systemic disorder with extreme phenotypic variability. Radiomics and AI offer the opportunity to seize the potential of big datasets analysis to optimize diagnostic in SCD. SCI can be detected automatically from different datasets. Four more European Centers will add their MRI in the second phase of the project, to increase variability and allow correlation of in detailed Radiomics data (lesion volume, lesion site, etc) with clinical-hematological characteristics and other omics data.Figure 1. Stepwise Segmentation (identification and selection of lesion volume) workflow for automatic selection of all SCI. From left to right: Axial slices of the input scan, Slice after skull stripping to highlight only the brain parenchyma, Segmentatio The authors do not declare any conflict of interest

The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1–H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1–H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R2) was found within the range of 0.224–0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.

Abstract Background: Cotton is a major cash crop in the global and, in particular, the Indian markets, playing an important economic role in the textile and oil industries. The cotton plant is one of the highly bred plants that is highly sensitive to salt stress. As cotton is a non-food crop, the availability of non-saline terrain and water for the cultivation of cotton plants is only next to other food crops, thereby posing a need to better understand the salt tolerance of this plant. Gossypium hirsutum L. cultivars MCU 5, LRA 5166, and SVPR 2 were selected based on exomorphic traits like staple length and cropping season so that the genotypic responses to salt stress and salt shock can be compared for interpreting the effects of salinity on in vitro germination. Thus, this study aims to establish genotypic dependence on salinity tolerance. Results: The results affirmed genotypic variation in salinity tolerance, with MCU 5 tolerating salt stress better than LRA 5166 and SVPR 2 in all the observed stages of growth of the plant and the parameters measured. Further salt-tolerant cotton varieties were observed to be long-staple length varieties; staple length is the fiber character of the cotton lint. Moreover, salt tolerance in the vegetative growth stage of cotton plants is not independent of the germination stage of the plant.Conclusion: Nevertheless, the correlation of genotypic dependence to morphological characteristics, in particular, staple length (and cropping season), is of agronomic and commercial significance. Further research by screening and investigating a greater number of cultivars using biochemical and molecular techniques will provide a better understanding of this observed phenotypical relationship to the genotypes of cotton cultivars under salt stress.

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