The human microbiota is a diverse microbial ecosystem associated with many beneficial physiological functions as well as numerous disease etiologies. Dominated by bacteria, the microbiota also includes commensal populations of fungi, viruses, archaea, and protists. Unlike bacterial microbiota, which was extensively studied in the past two decades, these non-bacterial microorganisms, their functional roles, and their interaction with one another or with host immune system have not been as widely explored. This review covers the recent findings on the non-bacterial communities of the human gastrointestinal microbiota and their involvement in health and disease, with particular focus on the pathophysiology of inflammatory bowel disease.

The current paradigm of urinary tract infection (UTI) pathogenesis takes into account the contamination of the periurethral space by specific uropathogens residing in the gut, which is followed by urethral colonization and pathogen ascension to the urinary bladder. Consequently, studying the relationship between gut microbiota and the subsequent development of bacteriuria and UTI represents an important field of research. However, the well-established diagnostic and therapeutic paradigm for urinary tract infections (UTIs) has come into question with the discovery of a multifaceted, symbiotic microbiome in the healthy urogenital tract. More specifically, emerging data suggest that vaginal dysbiosis may result in Escherichia coli colonization and prompt recurrent UTIs, while urinary microbiome perturbations may precede the development of UTIs and other pathologic conditions of the urinary system. The question is whether these findings can be exploited for risk reduction and treatment purposes. This review aimed to appraise the three aforementioned specific microbiomes regarding their potential influence on UTI development by focusing on the recent studies in the field and assessing the potential linkages between these different niches, as well as evaluating the state of translational research for novel therapeutic and preventative approaches.

Molecular biomarkers are valuable tools to predict the disease and determine its course. Several markers have been associated with inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS); however, none is sufficiently reliable to enable accurate diagnosis. We characterized a broad panel of serum proteins to assess disease-specific biomarker profiles and associate these findings with faecal microbiota composition in newly diagnosed IBD and IBS patients and healthy individuals. The study cohort consisted of 49 newly diagnosed treatment-naïve adult patients (13 Crohn’s disease (CD), 13 ulcerative colitis (UC), and 23 IBS) and 12 healthy individuals. Inflammatory and metabolism-related serum proteins were assessed using PEA multiplex panels, while gut microbiota composition was determined by 16 s rRNA gene amplicon sequencing. Serum proteins AXIN1, TNFSF14, RNASE3, EN-RAGE, OSM, ST1A1, CA13 and NADK were identified as markers with the most promising specificity/sensitivity and predictivity between healthy and disease groups, while IL-17A and TNFRSF9 enabled differentiation between IBD and IBS patients. Increased abundance of Enterobacteriaceae was associated with protein markers significantly elevated in IBD/IBS. In contrast, depletion of beneficial taxa like Ruminococcaceae and Verucomicrobiaceae (i.e. Akkermansia muciniphila) was associated with decrease of a set of markers in diseased groups. Differences in the abundance of Turicibacteriaceae were more predictive to discern CD from UC than any of the serum proteins investigated. By using a broad panel of inflammation and metabolism-related proteins, we determined serum markers with significantly different levels in treatment-naïve IBD and IBS patients compared to healthy individuals, as well as between IBD and IBS. Key messages: Significant changes in the levels of several serum proteins and abundances of faecal bacterial taxa between study groups were found. Increased levels of AXIN1, TNFSF14, RNASE3, EN-RAGE, OSM, ST1A1, CA13 and NADK characterize both IBD and IBS, while IL-17A and TNFRSF9 differentiate IBD from IBS. Increase of Enterobacteriaceae and depletion of beneficial taxa Ruminococcaceae and Verucomicrobiaceae (i.e. Akkermansia muciniphila) was found in IBD and IBS. Differences in Turicibacteriaceae were more predictive to discern CD from UC than any of the serum proteins investigated.

The uptake of new digital and AI based technologies in the healthcare setting, including digital diagnostics, has been comparatively slower than in other sectors. This is a consequence of the complex legislature, and the lack of routine procedures for the secondary use of sensitive medical data. Inadequate level of education and incentives for different stakeholders prevents overcoming strong barriers, such as clinical workflow integration, and need for comprehensive evaluation of utility of interventions which requires multi-disciplinary collaboration effort. The paper presents a re-hospitalization challenge, organised by the European digital innovation hub (EDIH) - AI4Health.Cro, a co-creation activity and an effective tool for overcoming the mentioned barriers for implementation of new AI technologies and overall innovation ecosystem maturation. Crowdsourcing challenges or datathons are R&D strategies where ad-hoc assembled competing teams collaborate on early-stage technology development or research problem solving. Our re-hospitalisation challenge has had multiple facets: as activity for construction of an AI predictive model with explanatory support and plausible business model for its deployment; it involved also extensive organisers’ effort: data anonymisation, provision of secure processing environment, and comprehensive assessment of solutions. The paper details the challenge structure and processes, from problem inception to execution and evaluation. As a co-creation effort in digital healthcare, the challenge provided results on multiple levels: (i) tailored solutions based on primary data, serving as prototypes for later deployment in practice; (ii) sandboxing EHDS principles to enable secondary data use; (iii) ecosystem building event yielding cross-disciplinary participative learning experience. Innovation challenges aren’t just competitions; they are a building tool that provides several direct and indirect impacts, from developing healthcare technologies, supporting startups, to streamlining secondary use of clinical data for research and innovation and eliciting regulatory needs. Raising the awareness of all relevant stakeholders about the translation of AI technologies into routine clinical setting. Author summary: Healthcare has been slower than other sectors to adopt new digital and AI-based technologies. This slow progress largely reflects the complexity of EU legislation and the lack of practical procedures that translate legal requirements into guidance for stakeholders using sensitive medical data in research and innovation. A crowdsourcing pilot to predict likelihood of early rehospitalisation using AI methodology provided opportunity for hands-on experience for experts from clinical, computational and business background on both synthetic and real-world medical data. Together we clarify legal responsibilities and data access conditions, and facilitate collaboration of relevant stakeholders, clinical, technical, public and research entities for ecosystem building. We provide the first Croatian European Health Data Space compliant secure processing environment, created for future use by researchers and innovators. Methodologically varied AI based prototypes were developed to predict early rehospitalization carefully balancing data overfitting, AI explainability and market potential of technology, in turn raising multidisciplinary skills, and awareness of conditions required for innovation to thrive in 28 participating teams. This type of competition, provided within European Digital Innovation Hub is an ideal platform to build regulatory sandboxes and pilot novel legislative platforms ahead of their full-scale implementation.

Background Head and neck cancer is the sixth most common malignancy worldwide, with the relatively low 5-year survival rate, mainly because it is diagnosed at a late stage. Infection with HPV is well known etiology, which affects nature of these cancers and patients’ survival. Besides, it is considered that the main driving force for this type of cancer could be epigenetics. In this study we aimed to find potential epigenetic biomarkers, by integrating miRNome, methylome, and transcriptome analyses. Methods From the fresh head and neck cancer tissue samples, we chose a group for miRNome, methylome and transcriptome profiling, in comparison to adequate control samples. Bioinformatics analyses are performed in R v4.2.2. Count normalization and group differential expression for mRNA and the previously obtained miRNA count data was performed with DESeq2 v1.36. Gene set enrichment analysis was performed and visualized using gProfiler2 v0.2.1 Identification of miRNA targets was performed by querying in miRTarBase using multiMiR v1.18.0. Annotation of CpG sites merging into islands was obtained from RnBeads.hg19 v1.28.0. package. For the integrative analysis we performed kmeans clustering using stats v4.2.2 package, using 8-12 clusters and nstart 100. Results We found that transcriptome analysis divides samples into cancers and controls clusters, with no relation to HPV status or cancer anatomical location. Differently expressed genes (n=2781) were predominantly associated with signaling pathways of tumour progression. We identified a cluster of genes under control of the transcription factor E2F, that are significantly underexpressed in cancer tissue, as well as T cell immunity genes and genes related to regulation of transcription. Among overexpressed genes in tumours we found those that belong to cell cycle regulation and vasculature. A small number of genes were found significantly differently expressed in HPV positive versus HPV negative tumours (for example NEFH, ZFR2, TAF7L, ZNF541, and TYMS). Conclusions In this comprehensive study on an overlapping set of samples where the integration of miRNome, methylome and transcriptome analysis were performed for head and neck cancer, we demonstrated that the majority of genes were associated exclusively with miRNome or methylome and, to a lesser extent, under control of both epigenetic mechanisms.

Genome-wide association studies (GWAS) are the key tools for the discovery of associations between single nucleotide polymorphisms (SNPs) and phenotypic traits and have been successfully applied to many diseases and disorders. However, a great challenge is to find the gene affected by the non-coding fraction of SNPs, especially if the gene is distal in terms of genomic distance. In this study, we present a novel approach, named targPred, which utilises genomic regulatory blocks (GRBs) for inference of a connection between a certain SNP/locus and the target gene located in the same GRB, in a more robust and generalisable manner. We identified that many disease traits such as cancer and psychiatric disease have a propensity for long-range regulation. Furthermore, we showcased a childhood obesity locus which is connected to the distal BDNF gene. Finally, we propose a new web-based service based on enhancer-promoter association, to facilitate finding the causal genes for a wide array of traits and conditions.