Liver disease is frequently asymptomatic, challenging early identification in the primary care setting. The fibrosis 4 (FIB4) index is a liver fibrosis biomarker that is a potential alternative to liver biopsy for diagnosing and managing liver disease. This study aimed to calculate the FIB4 index for screening individuals at high risk of liver disease at the community level. This was a retrospective real-world study analyzing blood and serum test results from a central laboratory. The primary outcome was the number of individuals within each risk category for hepatic fibrosis: high risk (FIB4 ≥ 3.25) and low risk (FIB4 < 1.3). The analysis included samples from 31,753 patients, of which 18,102 were aged 40 to 75 years. In these patients, the FIB4 index had been explicitly requested in 1852 (10.2%) cases and estimated ad hoc in the rest. Of the 263 (1.5%) cases with FIB4 ≥ 3.25, the FIB4 index was requested in 46 (17.5%), and 52 (19.8%) showed evidence of liver fibrosis in their medical records, while the rest did not report any data regarding liver fibrosis. FIB4 is a simple score that can play a role as a “red flag” for early identification of patients at high risk of advanced liver fibrosis and their referral to specialized care.

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross‐model reproducibility and translation to human biology, while sustaining biological interpretability.DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross‐species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi‐omics analysis with AI offers exciting future possibilities in drug discovery.

Body mass results from a complex interplay between genetics and environment. Previous studies of the genetic contribution to body mass have excluded repetitive regions due to the technical limitations of platforms used for population scale studies. Here we apply genome-wide approaches, identifying an association between adult body mass and the copy number (CN) of 47S-ribosomal DNA (rDNA). rDNA codes for the 18 S, 5.8 S and 28 S ribosomal RNA (rRNA) components of the ribosome. In mammals, there are hundreds of copies of these genes. Inter-individual variation in the rDNA CN has not previously been associated with a mammalian phenotype. Here, we show that rDNA CN variation associates with post-pubertal growth rate in rats and body mass index in adult humans. rDNA CN is not associated with rRNA transcription rates in adult tissues, suggesting the mechanistic link occurs earlier in development. This aligns with the observation that the association emerges by early adulthood.

Hepatitis B virus (HBV) causes a complex and persistent infection with a major impact on patients health. Viral-genome sequencing can provide valuable information for characterizing virus genotype,...

Although genome‐scale analyses have provided insights into the connection between genetic variability and complex human phenotypes, much trait variation is still not fully understood. Genetic variation within repetitive elements, such as the multi‐copy, multi‐locus ribosomal DNA (rDNA), has emerged as a potential contributor to trait variation. Whereas rDNA was long believed to be largely uniform within a species, recent studies have revealed substantial variability in the locus, both within and across individuals. This variation, which takes the form of copy number, structural arrangement, and sequence differences, has been found to be associated with human phenotypes. This review summarizes what is currently known about human rDNA variation, its causes, and its association with phenotypic outcomes, highlighting the technical challenges the field faces and the solutions proposed to address them. Finally, we suggest experimental approaches that can help clarify the elusive mechanisms underlying the phenotypic consequences of rDNA variation.

We address the problem of confounding in the design of music classification experiments, that is, the inability to distinguish the effects of multiple potential influencing variables in the measurements. Confounding affects the validity of conclusions at many levels, and so must be properly accounted for. We propose a procedure for characterising effects of confounding in the results of music classification experiments by creating regulated test conditions through interventions in the experimental pipeline, including a novel resampling strategy. We demonstrate this procedure on the GTZAN genre collection, which is known to give rise to confounding effects.