Tag: research methods

Assessment of symptoms in myalgic encephalomyelitis/chronic fatigue syndrome: a comparative study of existing scales

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a multifaceted disorder characterized by persistent fatigue, post-exertional malaise (PEM), cognitive dysfunction, sleep disturbance, pain, psychological distress, orthostatic intolerance, and impaired multidimensional health status and functioning. In the absence of reliable biomarkers, standardized symptom assessment is essential for accurate diagnosis and comparability across studies.

This narrative literature review synthesized studies identified through PubMed and Web of Science up to June 2024, covering assessment instruments across major ME/CFS symptom domains. Tools were evaluated for their psychometric validity, clinical applicability, and key limitations.

Overall, existing scales demonstrate acceptable reliability but vary in sensitivity and disease specificity. Harmonized, multidimensional, and digitally or objectively validated measures are needed to improve diagnostic precision, longitudinal monitoring, and clinical translation in ME/CFS.

Source: Lu J, Sun W, Li S, Qu Y, Liu T, Guo S, Feng C, Yang T. Assessment of symptoms in myalgic encephalomyelitis/chronic fatigue syndrome: a comparative study of existing scales. Front Neurol. 2025 Nov 18;16:1618272. doi: 10.3389/fneur.2025.1618272. PMID: 41341517; PMCID: PMC12668935. https://pmc.ncbi.nlm.nih.gov/articles/PMC12668935/ (Full text)

KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome

Abstract:

The microbes present in the human gastrointestinal tract are regularly linked to human health and disease outcomes. Thanks to technological and methodological advances in recent years, metagenomic sequencing data, and computational methods designed to analyze metagenomic data, have contributed to improved understanding of the link between the human gut microbiome and disease. However, while numerous methods have been recently developed to extract quantitative and qualitative results from host-associated microbiome data, improved computational tools are still needed to track microbiome dynamics with short-read sequencing data.

Previously we have proposed KOMB as a de novo tool for identifying copy number variations in metagenomes for characterizing microbial genome dynamics in response to perturbations. In this work, we present KombOver (KO), which includes four key contributions with respect to our previous work: (i) it scales to large microbiome study cohorts, (ii) it includes both k-core and K-truss based analysis, (iii) we provide the foundation of a theoretical understanding of the relation between various graph-based metagenome representations, and (iv) we provide an improved user experience with easier-to-run code and more descriptive outputs/results.

To highlight the aforementioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring less than 10 minutes and 10 GB RAM per sample to process these data. Furthermore, we highlight how graph-based approaches such as k-core and K-truss can be informative for pinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at: https://github.com/treangenlab/komb.

Source: Sapoval N, Tanevski M, Treangen TJ. KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome. Pac Symp Biocomput. 2024;29:506-520. PMID: 38160303; PMCID: PMC10764071. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764071/ (Full text)