Aliado - A design concept of AI for decision support in oncological liver surgery.

BACKGROUND: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest that there are many machine learning algorithms in surgery, however, only a minority of the studies integrate AI applications in clinical workflows. Our objective was to design and evaluate a concept to use different kinds of AI for decision support in oncological liver surgery along the treatment path. METHODS: In an exploratory co-creation between design experts, surgeons, and data scientists, pain points along the treatment path were identified. Potential designs for AI-assisted solutions were developed and iteratively refined. Finally, an evaluation of the design concept was performed with n = 20 surgeons to get feedback on the different functionalities and evaluate the usability with the System Usability Scale (SUS). Participating surgeons had a mean of 14.0 ± 5.0 years of experience after graduation. RESULTS: The design concept was named "Aliado". Five different scenarios were identified where AI could support surgeons. Mean score of SUS was 68.2 ( ± 13.6 SD). The highest valued functionalities were "individualized prediction of survival, short-term mortality and morbidity", and "individualized recommendation of surgical strategy". CONCLUSION: Aliado is a design prototype that shows how AI could be integrated into the clinical workflow. Even without a fleshed out user interface, the SUS already yielded borderline good results. Expert surgeons rated the functionalities favorably, and most of them expressed their willingness to work with a similar application in the future. Thus, Aliado can serve as a surgical vision of how an ideal AI-based assistance could look like.

Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.

AIM: Interactions between patients and healthcare professionals (HCP) during hospital admissions are complex and difficult to interrogate using traditional analysis of electronic patient record (EPR) data. The objective of this study was to determine the feasibility of applying temporal network analytics to EPR data, focusing on HCP-patient interactions over time. METHOD: Network (graph) analysis was applied to routinely collected structured data from an EPR for HCP interactions with individual patients during admissions for patients undergoing renal transplantation between May 2019 and June 2023. Networks were constructed per day of admission within a session, defined by whether the patient was in the intensive care unit (ICU) or standard hospital ward. Connections between HCP were defined using a 60 min period. Reports were generated visualising daily interaction network structures, across individual admissions. RESULTS: 2300 individual networks were constructed from 127 hospital admissions for renal transplantation. The number of nodes or HCP per network varied from 2 to 45, and network metrics provided detail regarding variation in the density and transitivity, changes in structure with different diameters and radii, and variations in centralisation. Each network analysis metric has a contribution to play in describing the dynamics of a daily HCP network and the composite findings provide insights that cannot be determined with standard approaches. CONCLUSIONS: Network analysis provides a novel approach to investigate and visualise patterns of HCP-patient interactions which allow for a deeper understanding of the complex nature of hospital patient care and could have numerous practical operational applications.

Biobanking, digital health and privacy: the choices of 1410 volunteers and neurological patients regarding limitations on use of data and biological samples, return of results and sharing.

BACKGROUND: The growing diffusion of artificial intelligence, data science and digital health has highlighted the role of collection of data and biological samples, thus raising legal and ethical concerns regarding its use and dissemination. Further, the expansion of biobanking, from the basic collection of frozen specimens to the virtual biobanks of specimens and associated data that exist today, has given a revolutionary potential on healthcare systems, particularly in the field of neurological diseases, due to the inaccessibility of central nervous system and the need of non-invasive investigation approaches. Informed Consent (IC) is considered mandatory in all research studies and specimen collections, and must specifically take into account the ethical respect to the individuals to whom the used biological material and data belong. METHODS: We evaluated the attitudes of patients with neurological diseases (NP) and healthy volunteers (HV) towards the donation of biological samples to a biobank for future research studies on neurological diseases, and limitations on the use of data, related to the requirements set by the General Data Protection Regulation (GDPR). The study involved a total of 1454 subjects, including 502 HVs and 952 NPs, recruited at Santa Lucia Foundation IRCCS, Rome, from 2020 to 2024. RESULTS: We found that (i) almost all subjects agreed with the participation in biobanking (ii) and authorization to genetic studies (HV = 99.1%; NP = 98.3%); Regarding the return of results, (iii) we found a statistically significant difference between NP and HV, the latter preferring not to be informed of potential results (HV = 43%; NP = 11.3%; p < 0.0001); (iv) a small number limited the sharing inside European Union (EU) (HV = 4.6%; NP = 6.6%), whereas patients were more likely to refuse transfer outside EU (HV = 7.4%; NP = 10.7% p = 0.05); (v) nearly all patients agreed with the use of additional health data from EMR for research purposes (98.9%). CONCLUSIONS: Consent for the donation of material for research purposes is crucial for biobanking and biomedical research studies that use biological material of human origin. Here, we have shown that choices regarding participation in a neurological biobank can be different between HVs and NPs, even if the benefit for research and scientific progress is recognized. NP have a strong interest in being informed of possible results but limit sharing of samples, highlighting a perception of greater individual or relative benefit, while HV prefer a wide dissemination and sharing of data but not to have the return of the results, favoring a possible benefit for society and knowledge. The results underline the need to carefully manage biological material and data collected in biobanks, in compliance with the GDPR and the specific requests of donors.

Networks as tools for defining emergent properties of microbiomes and their stability.

The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Exploring Kleefstra syndrome cohort phenotype characteristics: Prevalence insights from caregiver-reported outcomes.

Kleefstra syndrome (KLEFS1) is a rare genetic neurodevelopmental disorder affecting multiple body systems. It continues to be under-researched, and its prevalence remains unknown. This paper builds on the international KLEFS1 cohort of 172 individuals based on the caregiver-reported outcomes collected within the online data collection platform GenIDA and reports the occurrence, frequency and severity of symptoms in KLEFS1. The study clearly shows the importance of caregiver-reported outcomes collections in the rare disease domain. Moreover, the study emphasizes the need for more specific and enhanced data collection methods, suggesting recommendations to optimize caregiver-reported registries and foster an even more profound understanding of rare diseases.

GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials.

This study introduces a novel artificial intelligence (AI) force field, namely a graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic force, and stress with mean absolute error (MAE) values of 32 meV/atom, 71 meV/Å, and 0.365 GPa, respectively. The dataset used to train the model includes 37.8 million single-point energies, 11.7 billion force pairs, and 340.2 million stresses. We also demonstrated that the GPTFF can be universally used to simulate various physical systems, such as crystal structure optimization, phase transition simulations, and mass transport. The model is publicly released with this paper, enabling anyone to use it immediately without needing to train it.

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD.

Background and Aims: Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. Methods: Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. Results: Inter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. Conclusions: LLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.

Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery.

Introduction: This study aimed to identify research areas that demand attention in multimodal data-driven surgery for improving data management in minimally invasive surgery. Background: New surgical procedures, high-tech equipment, and digital tools are increasingly being introduced, potentially benefiting patients and surgical teams. These innovations have resulted in operating rooms evolving into data-rich environments, which, in turn, requires a thorough understanding of the data pipeline for improved and more intelligent real-time data usage. As this new domain is vast, it is necessary to identify where efforts should be focused on developing seamless and practical data usage. Methods: A modified electronic Delphi approach was used; 53 investigators were divided into the following groups: a research group (n=9) for problem identification and a narrative literature review, a medical and technical expert group (n=14) for validation, and an invited panel (n=30) for two electronic survey rounds. Round 1 focused on a consensus regarding bottlenecks in surgical data science areas and research gaps, while round 2 prioritized the statements from round 1, and a roadmap was created based on the identified essential and very important research gaps. Results: Consensus panelists have identified key research areas, including digitizing operating room (OR) activities, improving data streaming through advanced technologies, uniform protocols for handling multimodal data, and integrating AI for efficiency and safety. The roadmap prioritizes standardizing OR data formats, integrating OR data with patient information, ensuring regulatory compliance, standardizing surgical AI models, and securing data transfers in the next generation of wireless networks. Conclusions: This work is an international expert consensus regarding the current issues and key research targets in the promising field of data-driven surgery, highlighting the research needs of many operating room stakeholders with the aim of facilitating the implementation of novel patient care strategies in minimally invasive surgery.

A data science-based analysis of socioeconomic determinants impacting pediatric diagnostic radiology utilization during the COVID-19 pandemic.

BACKGROUND: Research on healthcare disparities in pediatric radiology is limited, leading to the persistence of missed care opportunities (MCO). We hypothesize that the COVID-19 pandemic exacerbated existing health disparities in access to pediatric radiology services. OBJECTIVE: Evaluate the social determinants of health and sociodemographic factors related to pediatric radiology MCO before, during, and after the COVID-19 pandemic. MATERIALS AND METHODS: The study examined all outpatient pediatric radiology exams at a pediatric medical center and its affiliate centers from 03/08/19 to 06/07/21 to identify missed care opportunities. Logistic regression with the least absolute shrinkage and selection operator (LASSO) method and classification and regression tree (CART) analysis were used to explore factors and visualize relationships between social determinants and missed care opportunities. RESULTS: A total of 62,009 orders were analyzed: 30,567 pre-pandemic, 3,205 pandemic, and 28,237 initial recovery phase. Median age was 11.34 years (IQR 5.24-15.02), with 50.8% females (31,513/62,009). MCO increased during the pandemic (1,075/3,205; 33.5%) compared to pre-pandemic (5,235/30,567; 17.1%) and initial recovery phase (4,664/28,237; 16.5%). The CART analysis identified changing predictors of missed care opportunities across different periods. Pre-pandemic, these were driven by exam-specific factors and patient age. During the pandemic, social determinants like income, distance, and ethnicity became key. In the initial recovery phase, the focus returned to exam-specific factors and age, but ethnicity continued to influence missed care, particularly in neurological exams for Hispanic patients. Logistic regression revealed similar results: during the pandemic, increased distance from the examination site (OR 1.1), residing outside the state (OR 1.57), Hispanic (OR 1.45), lower household income ($25,000-50,000 (OR 3.660) and $50,000-75,000 (OR 1.866)), orders for infants (OR 1.43), and fluoroscopy (OR 2.3) had higher odds. In the initial recovery phase, factors such as living outside the state (OR 1.19), orders for children (OR 0.79), and being Hispanic (OR 1.15) correlate with higher odds of MCO. CONCLUSION: The application of basic data science techniques is a valuable tool in uncovering complex relationships between sociodemographic factors and disparities in pediatric radiology, offering crucial insights into addressing inequalities in care.

Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.

Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients' data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years ('older adults'), males, current smokers, and BMI classified as 'overweight' and 'obese' were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models' interpretability were from the 'sociodemographic characteristics', 'pre-hospital comorbidities', and 'medications' categories. However, 'pre-hospital comorbidities' played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients' conditions when urgent treatment plans are needed during the surge of patients during the pandemic.

The Concept of a Versatile Computing Tool Chain for Utilizing the Core Data Set of the Medical Informatics Initiative in the INTERPOLAR Project.

INTRODUCTION: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. METHODS: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM2, finally implemented and published in a github repository. RESULTS: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. DISCUSSION: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned.

Assessing the Capacity of High-resolution Commercial Satellite Imagery for Grapevine Downy Mildew Detection and Surveillance in New York state.

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. High resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, NY, USA from June to August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July-early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

Uncovering individualised treatment effects for educational trials.

Large-scale Randomised Controlled Trials (RCTs) are widely regarded as "the gold standard" for testing the causal effects of school-based interventions. RCTs typically present the statistical significance of the average treatment effect (ATE), which captures the effect an intervention has had on average for a given population. However, key decisions in child health and education are often about individuals who may be very different from those averages. One way to identify heterogeneous treatment effects across different individuals, not captured by the ATE, is to conduct subgroup analyses. For example, free school meal (FSM) pupils as required for projects funded by the Education Endowment Foundation (EEF) in England. These subgroup analyses, as we demonstrate in 48 EEF-funded RCTs involving over 200,000 students, are usually not standardised across studies and offer flexible degrees of freedom to researchers, potentially leading to mixed, if not misleading, results. Here, we develop and deploy an alternative to ATE and subgroup analysis, a machine-learning and regression-based framework to predict individualised treatment effects (ITEs). ITEs could show where an intervention worked, for which individuals, and to what extent. Our findings have implications for decision-makers in fields like education, healthcare, law, and clinical practices concerning children and adolescents.

Impact of Health Informatics Analyst Education on Job Role, Career Transition, and Skill Development: Survey Study.

Background: Professionals with expertise in health informatics play a crucial role in the digital health sector. Despite efforts to train experts in this field, the specific impact of such training, especially for individuals from diverse academic backgrounds, remains undetermined. Objective: This study therefore aims to evaluate the effectiveness of an intensive health informatics training program on graduates with respect to their job roles, transitions, and competencies and to provide insights for curriculum design and future research. Methods: A survey was conducted among 206 students who completed the Advanced Health Informatics Analyst program between 2018 and 2022. The questionnaire comprised four categories: (1) general information about the respondent, (2) changes before and after program completion, (3) the impact of the program on professional practice, and (4) continuing education requirements. Results: The study received 161 (78.2%) responses from the 206 students. Graduates of the program had diverse academic backgrounds and consequently undertook various informatics tasks after their training. Most graduates (117/161, 72.7%) are now involved in tasks such as data preprocessing, visualizing results for better understanding, and report writing for data processing and analysis. Program participation significantly improved job performance (P=.03), especially for those with a master's degree or higher (odds ratio 2.74, 95% CI 1.08-6.95) and those from regions other than Seoul or Gyeonggi-do (odds ratio 10.95, 95% CI 1.08-6.95). A substantial number of respondents indicated that the training had a substantial influence on their career transitions, primarily by providing a better understanding of job roles and generating intrinsic interest in the field. Conclusions: The integrated practical education program was effective in addressing the diverse needs of trainees from various fields, enhancing their capabilities, and preparing them for the evolving industry demands. This study emphasizes the value of providing specialized training in health informatics for graduates regardless of their discipline.

Massive detection of cryptic recessive genetic defects in dairy cattle mining millions of life histories.

BACKGROUND: Dairy cattle breeds are populations of limited effective size, subject to recurrent outbreaks of recessive defects that are commonly studied using positional cloning. However, this strategy, based on the observation of animals with characteristic features, may overlook a number of conditions, such as immune or metabolic genetic disorders, which may be confused with pathologies of environmental etiology. RESULTS: We present a data mining framework specifically designed to detect recessive defects in livestock that have been previously missed due to a lack of specific signs, incomplete penetrance, or incomplete linkage disequilibrium. This approach leverages the massive data generated by genomic selection. Its basic principle is to compare the observed and expected numbers of homozygotes for sliding haplotypes in animals with different life histories. Within three cattle breeds, we report 33 new loci responsible for increased risk of juvenile mortality and present a series of validations based on large-scale genotyping, clinical examination, and functional studies for candidate variants affecting the NOA1, RFC5, and ITGB7 genes. In particular, we describe disorders associated with NOA1 and RFC5 mutations for the first time in vertebrates. CONCLUSIONS: The discovery of these many new defects will help to characterize the genetic basis of inbreeding depression, while their management will improve animal welfare and reduce losses to the industry.

A Digital Intervention for Capturing the Real-Time Health Data Needed for Epilepsy Seizure Forecasting: Protocol for a Formative Co-Design and Usability Study (The ATMOSPHERE Study).

BACKGROUND: Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies. OBJECTIVE: The ATMOSPHERE study aimed to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualized seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modeling. The first aim was to collaboratively design the prototype (work completed). The second aim is to conduct an "in-the-wild" study to assess usability and refine the prototype (planned research). METHODS: This study uses a person-based approach to design and test the usability of a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and health care professionals. Sessions explored users' requirements for the prototype, followed by iterative design of low-fidelity, static prototypes. Phase 2 (planned research) will be an "in-the-wild" usability study involving the deployment of a mid-fidelity, functional prototype for 4 weeks, with the collection of mixed methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in 3 rounds of deployment and data collection, aiming to recruit 5 participants per round, with prototype refinement between rounds. RESULTS: The phase-1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, functional prototype based on identified requirements, including the tracking of evidence-based and personalized seizure precipitants. The upcoming phase-2 usability study is expected to provide insights into the prototype's real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of phase 2 is the last quarter of 2024. CONCLUSIONS: The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centered, noninvasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalized machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life, through increased predictability and seizure management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60129.

Harnessing digital footprint data for population health: a discussion on collaboration, challenges and opportunities in the UK.

Digital footprint data are inspiring a new era in population health and well-being research. Linking these novel data with other datasets is critical for future research wishing to use these data for the public good. In order to succeed, successful collaboration among industry, academics and policy-makers is vital. Therefore, we discuss the benefits and obstacles for these stakeholder groups in using digital footprint data for research in the UK. We advocate for policy-makers' inclusion in research efforts, stress the exceptional potential of digital footprint research to impact policy-making and explore the role of industry as data providers, with a focus on shared value, commercial sensitivity, resource requirements and streamlined processes. We underscore the importance of multidisciplinary approaches, consumer trust and ethical considerations in navigating methodological challenges and further call for increased public engagement to enhance societal acceptability. Finally, we discuss how to overcome methodological challenges, such as reproducibility and sharing of learnings, in future collaborations. By adopting a multiperspective approach to outlining the challenges of working with digital footprint data, our contribution helps to ensure that future research can navigate these challenges effectively while remaining reproducible, ethical and impactful.

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence.

Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

Harnessing population diversity: in search of tools of the trade.

Big neuroscience datasets are not big small datasets when it comes to quantitative data analysis. Neuroscience has now witnessed the advent of many population cohort studies that deep-profile participants, yielding hundreds of measures, capturing dimensions of each individual's position in the broader society. Indeed, there is a rebalancing from small, strictly selected, and thus homogenized cohorts toward always larger, more representative, and thus diverse cohorts. This shift in cohort composition is prompting the revision of incumbent modeling practices. Major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically studying. In our opinion, as we sample individuals from always wider diversity backgrounds, we will require a new stack of quantitative tools to realize diversity-aware modeling. We here take inventory of candidate analytical frameworks. Better incorporating driving factors behind population structure will allow refining our understanding of how brain-behavior relationships depend on human subgroups.