Background Fatigue, impaired sleep quality and daytime sleepiness, common in neurodegenerative and immune-mediated diseases, are debilitating and have serious societal and economic implications. Currently, measurement of these symptoms largely relies on self-reported questionnaires, which are burdensome for patients and lack sensitivity, granularity and reliability. Methods Building on a preceding feasibility study and qualification advice of the European Medicines Agency, the Clinical Observational Study of the European project Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities of daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases (IDEA-FAST) investigates the relationship between digital and clinical parameters of the target concepts of fatigue, reduced sleep quality and daytime sleepiness. Results Between 2022 and 2025, 2000 people are being recruited at 24 European sites – 500 with Parkinson's disease, 500 with inflammatory bowel disease, 200 with each of the following diseases: Huntington's disease, rheumatoid arthritis, systemic lupus erythematosus, primary Sjögren's syndrome and 200 healthy volunteers. Participants are followed over a 24-week period with four visits, each including a 1-week assessment phase at home using CE-certified digital health (including active and passive) technologies. The latter collect information on physical activity, physiology, cognition as well as social interaction and behaviour as core dimensions of the target concepts. Conclusion This study will help to develop reliable, valid and efficient digital endpoints of fatigue, impaired sleep quality and daytime sleepiness for use in future clinical studies and trials.

1 Abstract 1.1 Background Many longitudinal omics studies contain only a small number of repeated measurements collected before, during, or after an intervention. Existing approaches, including mixed-effects models and generalized additive models, estimate temporal effects but do not generally provide a discrete representation of trajectory topology that can be queried directly across experimental groups. 1.2 Methods We developed LongOmicsTraj, an open-source R package for topology-based representation and querying of short longitudinal omics trajectories. The framework encodes the direction of change between adjacent visits as up , down , or flat , with the ordered sequence defining an Ordinal Trajectory State (OTS). LongOmicsTraj operates downstream of trajectory estimation and can therefore be applied to empirical summaries or model-derived visit-level estimates, including those from linear mixed-effects models, generalized additive models, and polynomial regression, following a maSigPro-style time-course formulation [1]. OTS labels provide a common representation for topology-based querying, cross-group comparison, and evaluation of higher-level representations such as trajectory clusters. We evaluated the framework using controlled simulations and bronchial biopsy transcriptomic data from the GLUCOLD corticosteroid intervention study (GEO accession GSE36221 ), measured at baseline, 6 months, and 30 months. The biological analysis compared continued inhaled corticosteroid (ICS) treatment, ICS withdrawal after 6 months, and placebo. 1.3 Results In simulations, LongOmicsTraj recovered predefined stable, monotonic, transient, rebound, and oscillatory trajectories with high accuracy when longitudinal signal was sufficiently clear, with performance declining under high-noise conditions and depending partly on the upstream estimator. In GLUCOLD, comparator-aware topology queries reduced 20,358 measured transcripts to 168 genes showing a corticosteroid response that was maintained during continued treatment, reversed following withdrawal, and was not reproduced under placebo. The selected genes included established corticosteroid-response genes and were enriched for immune-cell migration, chemotaxis, cell adhesion, and extracellular-matrix organisation. Topology-aware evaluation of FlexMix trajectory clusters additionally revealed substantial within-cluster temporal heterogeneity, with topology purities of approximately 46–60%. 1.4 Conclusions LongOmicsTraj provides a compact, directly queryable representation of temporal direction and order in short longitudinal omics studies. It complements existing longitudinal estimation and clustering methods by making trajectory structure explicit, enabling structured cross-group queries and quantification of temporal heterogeneity within trajectory clusters.

Clinical research consortia increasingly rely on asynchronous data contributions from multiple organizations, which makes it difficult to maintain consistency, synchronization, and clear lines of responsibility. Traditional stage-based pipelines struggle to accommodate continuous updates while preserving reliable provenance. This work presents a practical framework developed within the IDEA-FAST project to support active data governance under evolving multi-organization workflows. The system separates raw measurements from contextual metadata, enabling targeted updates, controlled corrections, and reproducible reconstruction without disrupting downstream analyses. A companion Analytical Environment provides a unified, permission-controlled interface for accessing each versioned release, ensuring that analysts work from consistent, well-governed datasets without distributing raw data. When deployed as a centralized platform, the framework demonstrates how continuously updated datasets can remain coherent, traceable, and manageable across large academic collaborations with minimal operational overhead.

The peer-review process, which serves as the quality-control mechanism of scientific knowledge production, has been criticized for its bias, unreliability, and inefficiency. Academic conferences and journals typically rely on a centralized mechanism for reviewer assignment and paper assessment. We argue that this centralization is a major factor contributing to the unreliability of the review process, leading to deficiencies in the current knowledge-assessment systems. To address this, we propose a novel decentralized model that democratizes peer review by shifting decision-making rights from centralized authorities to all scholars participating in a scholarly community. Our model includes a dual-rewarding incentive mechanism that motivates scholars to actively participate in peer review by recognizing both their effort and scientific contributions. This model transforms peer review from passive judgment to active collaboration. We simulated the model in conference settings and demonstrated its potential to revolutionize knowledge production and dissemination.

AbstractBackgroundClustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi‐omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development.ObjectivesTo determine whether multi‐omics integration of sputum leads to improved granularity of the molecular classification of severe asthma.MethodsWe analyzed six ‐omics data blocks–microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing–from induced sputum samples of 57 severe asthma patients, 15 mild‐moderate asthma patients, and 13 healthy volunteers in the U‐BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi‐omics datasets of the 72 asthmatics.ResultsFive stable omics‐associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL‐22 cytokine activation, with the prediction of therapeutic response to anti‐IL22 antibody therapy.ConclusionMulti‐omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high‐dimensional interactions will contribute to a more comprehensive understanding of complex endotypes.Key Points Unsupervised clustering on sputum multi‐omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL‐22.

Recent studies have demonstrated promising performance of ChatGPT and GPT-4 on several medical domain tasks. However, none have assessed its performance using a large-scale real-world electronic health record database, nor have evaluated its utility in providing clinical diagnostic assistance for patients across a full range of disease presentation. We performed two analyses using ChatGPT and GPT-4, one to identify patients with specific medical diagnoses using a real-world large electronic health record database and the other, in providing diagnostic assistance to healthcare workers in the prospective evaluation of hypothetical patients. Our results show that GPT-4 across disease classification tasks with chain of thought and few-shot prompting can achieve performance as high as 96% F1 scores. For patient assessment, GPT-4 can accurately diagnose three out of four times. However, there were mentions of factually incorrect statements, overlooking crucial medical findings, recommendations for unnecessary investigations and overtreatment. These issues coupled with privacy concerns, make these models currently inadequate for real world clinical use. However, limited data and time needed for prompt engineering in comparison to configuration of conventional machine learning workflows highlight their potential for scalability across healthcare applications.

A multi-omics approach revealed the underlying biological pathways in the microbiome-driven severe asthma phenotypes. This may help to elucidate new leads for treatment development, particularly for the therapeutically challenging neutrophilic asthma.https://bit.ly/3FWuJqS

Abstract Background Exacerbation‐prone asthma is a feature of severe disease. However, the basis for its persistency remains unclear. Objectives To determine the clinical and transcriptomic features of frequent exacerbators (FEs) and persistent FEs (PFEs) in the U‐BIOPRED cohort. Methods We compared features of FE (≥2 exacerbations in past year) to infrequent exacerbators (IE, <2 exacerbations) and of PFE with repeat ≥2 exacerbations during the following year to persistent IE (PIE). Transcriptomic data in blood, bronchial and nasal epithelial brushings, bronchial biopsies and sputum cells were analysed by gene set variation analysis for 103 gene signatures. Results Of 317 patients, 62.4% had FE, of whom 63.6% had PFE, while 37.6% had IE, of whom 61.3% had PIE. Using multivariate analysis, FE was associated with short‐acting beta‐agonist use, sinusitis and daily oral corticosteroid use, while PFE was associated with eczema, short‐acting beta‐agonist use and asthma control index. CEA cell adhesion molecule 5 (CEACAM5) was the only differentially expressed transcript in bronchial biopsies between PE and IE. There were no differentially expressed genes in the other four compartments. There were higher expression scores for type 2, T‐helper type‐17 and type 1 pathway signatures together with those associated with viral infections in bronchial biopsies from FE compared to IE, while there were higher expression scores of type 2, type 1 and steroid insensitivity pathway signatures in bronchial biopsies of PFE compared to PIE. Conclusion The FE group and its PFE subgroup are associated with poor asthma control while expressing higher type 1 and type 2 activation pathways compared to IE and PIE, respectively.

Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary. Such DApps open a marketplace in which services are delivered to end-users by contributors which are then incentivised by cryptocurrencies in an automated, peer-to-peer, and trustless fashion. However, blockchain, consolidated by smart contracts, only ensures on-chain data security, autonomy and integrity of the business logic execution defined in smart contracts. It cannot guarantee the quality of service of DApps, which entirely depends on the services’ performance. Thus, there is a critical need for a trust system to reduce the risk of dealing with fraudulent counterparts in a blockchain network. These reasons motivate us to develop a fully decentralised trust framework deployed on top of a blockchain platform, operating along with DApps in the marketplace to demoralise deceptive entities while encouraging trustworthy ones. The trust system works as an underlying decentralised service providing a feedback mechanism for end-users and maintaining trust relationships among them in the ecosystem accordingly. We believe this research fortifies the DApps ecosystem by introducing an universal trust middleware for DApps as well as shedding light on the implementation of a decentralised trust system.

Background. Patients with severe asthma may have a greater risk of dying from COVID-19 disease. Angiotensin converting enzyme-2 (ACE2) and the enzyme proteases, transmembrane protease serine 2 (TMPRSS2) and FURIN, are needed for viral attachment and invasion into host cells. Methods. We examined microarray mRNA expression of ACE2, TMPRSS2 and FURIN in sputum, bronchial brushing and bronchial biopsies of the European U-BIOPRED cohort. Clinical parameters and molecular phenotypes, including asthma severity, sputum inflammatory cells, lung functions, oral corticosteroid (OCS) use, and transcriptomic-associated clusters, were examined in relation to gene expression levels. Results. ACE2 levels were significantly increased in sputum of severe asthma compared to mild-moderate asthma. In multivariate analyses, sputum ACE2 levels were positively associated with OCS use and male gender. Sputum FURIN levels were significantly related to neutrophils (%) and the presence of severe asthma. In bronchial brushing samples, TMPRSS2 levels were positively associated with male gender and body mass index, whereas FURIN levels with male gender and blood neutrophils. In bronchial biopsies, TMPRSS2 levels were positively related to blood neutrophils. The neutrophilic molecular phenotype characterised by high inflammasome activation expressed significantly higher FURIN levels in sputum than the eosinophilic Type 2-high or the pauci-granulocytic oxidative phosphorylation phenotypes. Conclusion. Levels of ACE2 and FURIN may differ by clinical or molecular phenotypes of asthma. Sputum FURIN expression levels were strongly associated with neutrophilic inflammation and with inflammasome activation. This might indicate the potential for a greater morbidity and mortality outcome from SARS-CoV-2 infection in neutrophilic severe asthma.

Serum pregnancy-associated plasma protein A (PAPPA) as a predictor of eosinophilic Type-2 high asthmaTo the Editor,Pregnancy-associated plasma protein A (PAPPA), a metalloproteinase that cleaves insulin-like growth factor (IGF)-binding proteins (IGFBPs) to increase IGF availability, is expressed systemically in pregnant women but also in other tissues (1). Higher serum PAPPA levels are reported in patients with newly-diagnosed asthma (1) and allergic rhinitis compared to healthy controls and are decreased following omalizumab treatment (2). We determined whether PAPPA could represent a novel biomarker for Type-2 (T2) asthma by exploring the relationship between asthma severity and phenotypes of severe asthma and PAPPA gene and protein expression (3).We recruited 288 severe non-smoking asthma (NSA), 102 smokers and ex-smokers with severe asthma (SSA), 86 mild/moderate non-smoking asthmatics (MMA) and 95 healthy non-smoking controls (HC) from the U-BIOPRED cohort (NCT01976767) (4) (Table S1 ). Transcriptomic and proteomic profiling of blood and sputum samples and specific serum periostin ELISA were performed (3). Gene set variation analysis (GSVA) was used to calculate the enrichment score (ES) of 34 genes that were upregulated following in vitro stimulation of primary human bronchial epithelial cells with IL-13 (T2_IL-13_IVS) (3). Eosinophilic inflammation was defined by sputum eosinophilia >1.49% (3). Local Ethics Committees of the recruiting centres approved the study and all participants gave written informed consent.Sputum cell PAPPA mRNA was elevated in NSA compared to SSA, MMA and HC subjects particularly in granulocytic asthmatics and in the transcriptomic-associated cluster (TAC)1; an eosinophilic cluster (5) (Figure 1A-C ). This was more pronounced with sputum PAPPA protein analysis according to asthma severity, in eosinophilic and mixed granulocytic asthmatics and in T2-high asthmatics identified by the T2_IL-13_IVS signature (Figure 1D-F ).PAPPA mRNA expression in blood cells was similar across asthma severities, blood granulocytes and molecular phenotypes (Supplementary Figure 1A-C ). However, serum PAPPA protein levels supported the discrimination seen in sputum with significant elevation seen in SA compared to HC, in eosinophilic and mixed granulocytic asthma and in T2-high asthma (SupplementaryFigure 1D-F ).Sputum eosinophil percentages were significantly correlated with sputum (r=0.88, p=10-6) and serum (r=0.41, p=10-6) PAPPA protein levels. Overall, sputum PAPPA protein gave a greater distinction between asthma severity, granulocyte composition and T2-high asthma than with serum although fewer samples were available.These results were validated in sputum from the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study (6) (Supplementary Figure S2 ). Elevated PAPPA protein in the serum and sputum of severe asthmatics and in eosinophilic compared to non- eosinophilic subjects was seen (SupplementaryFigure S2A-D ). In addition, sputum PAPPA mRNA levels were also elevated in eosinophilic versus non-eosinophilic asthma in the ADEPT cohort (Supplementary Figure S2E ).The ES score of the T2_IL-13_IVS gene signature in bronchial brushings was significantly, but weakly, correlated with blood eosinophil counts (r=0.329, p=10-6), serum PAPPA (r=0.356, p=10-6), but not with serum periostin levels (r=0.07, p-value=0.48). In contrast, the T2 IL-13 IVS ES score was strongly correlated with sputum PAPPA levels (r=0.72, p=10-3). Sputum PAPPA protein levels also significantly correlated with markers of remodelling such as MMP10 (r=0.646, p<10-6) and MET (r=0.429, p<10-6).Receiver-operating characteristics (ROC) curve analysis was performed for sputum eosinophilia (Supplementary Table S2 ). The area under the ROC curve (AUC) for serum indicated that there was no good predictor although blood eosinophilia was the best (0.79) being marginally better than serum PAPPA and exhaled NO (Figure 2A ). In contrast, sputum PAPPA was an excellent predictor of sputum eosinophilia (0.98), better than blood eosinophilia and exhaled nitric oxide levels (Figure 2B ).Therefore, sputum PAPPA is an excellent biomarker for sputum eosinophilia and for T2-high asthma whilst serum PAPPA is as effective as blood eosinophilia in predicting high sputum eosinophil levels and with T2-high asthma.

At 1 year, 45% of severe asthma change molecular phenotype as determined by sputum transcriptomic analysis. Together with concomitant shift in sputum granulocytic markers, this may indicate variability of driving mechanisms in this unstable group.https://bit.ly/35aj489

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from genome-wide data due to large number of molecular features and small number of available samples, which is also called the curse of in machine learning. To tackle this problem and pave way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework called OmiEmbed to capture a holistic and relatively precise profile of phenotype from high-dimensional omics data. The deep embedding module of OmiEmbed learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on new representation of multi-omics data, different downstream networks of OmiEmbed were trained together with multi-task strategy to predict comprehensive phenotype profile of each sample. We trained model on two publicly available omics datasets to evaluate performance of OmiEmbed. The OmiEmbed model achieved promising results for multiple downstream tasks including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. Instead of training and applying different downstream networks separately, multi-task strategy combined them together and conducted multiple tasks simultaneously and efficiently. The model achieved better performance with multi-task strategy comparing to training them individually. OmiEmbed is a powerful tool to accurately capture comprehensive phenotypic information from high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making.

Along with the blooming of AI and Machine Learning-based applications and services, data privacy and security have become a critical challenge. Conventionally, data is collected and aggregated in a data centre on which machine learning models are trained. This centralised approach has induced severe privacy risks to personal data leakage, misuse, and abuse. Furthermore, in the era of the Internet of Things and big data in which data is essentially distributed, transferring a vast amount of data to a data centre for processing seems to be a cumbersome solution. This is not only because of the difficulties in transferring and sharing data across data sources but also the challenges on complying with rigorous data protection regulations and complicated administrative procedures such as the EU General Data Protection Regulation (GDPR). In this respect, Federated learning (FL) emerges as a prospective solution that facilitates distributed collaborative learning without disclosing original training data whilst naturally complying with the GDPR. Recent research has demonstrated that retaining data and computation on-device in FL is not sufficient enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system still conceal sensitive information, which can be exploited in some privacy attacks. Therefore, FL systems shall be empowered by efficient privacy-preserving techniques to comply with the GDPR. This article is dedicated to surveying on the state-of-the-art privacy-preserving techniques which can be employed in FL in a systematic fashion, as well as how these techniques mitigate data security and privacy risks. Furthermore, we provide insights into the challenges along with prospective approaches following the GDPR regulatory guidelines that an FL system shall implement to comply with the GDPR.

BACKGROUND: Although estimates of suboptimal adherence to oral corticosteroids in asthma range from 30% to 50%, no ideal method for measurement exists; the impact of poor adherence in severe asthma is likely to be particularly high. RESEARCH QUESTIONS: What is the prevalence of suboptimal adherence detected by self-reporting and direct measures? Is suboptimal adherence associated with disease activity? STUDY DESIGN AND METHODS: Data were included from individuals with severe asthma taking part in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study and prescribed daily oral corticosteroids. Participants completed the Medication Adherence Report Scale, a five-item questionnaire used to grade adherence on a scale from 1 to 5, and provided a urine sample for analysis of prednisolone and metabolites by liquid chromatography-mass spectrometry. RESULTS: Data from 166 participants were included in this study: mean (SD) age, 54.2 (+/- 11.9) years; FEV1, 65.1% (+/- 20.5%) predicted; female, 58%; 37% completing the Medication Adherence Report Scale reported suboptimal adherence; and 43% with urinary corticosteroid data did not have detectable prednisolone or metabolites in their urine. Good adherence by both methods was detected in 49 of the 142 (35%) of participants in whom both methods were performed; adherence detection did not match between methods in 53%. Self-reported high adherers had better asthma control and quality of life, whereas directly measured high adherers had lower blood eosinophil levels. INTERPRETATION: Low adherence is a common problem in severe asthma, whether measured directly or self-reported. We report poor agreement between the two methods, suggesting some disassociation between self-assessment of medication adherence and regular oral corticosteroid use, which suggests that each approach may provide complementary information in clinical practice.

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called "the curse of dimensionality" in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.