Tag: methodology

A Novel Fluorogenic Probe Reveals Lipid Droplet Dynamics in ME/CFS Fibroblasts

Abstract:

Lipid droplets (LDs) are dynamic cellular organelles that play an essential role in lipid metabolism and storage. LD dysregulation has been implicated in various diseases. However, investigations into the cellular LD dynamics under disease conditions have been rarely reported, possibly due to the absence of high performing LD imaging agents.

Here a novel fluorogenic probe, AM-QTPA, is reported for specific LD imaging. AM-QTPA demonstrates viscosity sensitivity and aggregation-induced emission enhancement characteristics. It is live cell permeable and can specifically light up LDs in cells, with low background noise and superior signals that can be quantified.

After validation in cell model with LD accumulation induced by oleic acid treatment, AM-QTPA is applied in a small proof-of-concept number of human fibroblast samples derived from people diagnosed with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a complex and debilitating disease with unknown cause.

The results indicate the presence of larger but fewer LDs in ME/CFS fibroblasts compared to the healthy counterparts, accompanying with frequent LD-mitochondria contacts, suggesting potential upregulation of lipolysis in ME/CFS connective tissue like fibroblasts.

Overall, AM-QTPA provides new understanding of the anomalous LD dynamics in disease status, which, potentially, will facilitate in-depth investigation of the pathogenesis of ME/CFS.

Source: Ding, S., Sanislav, O., Missailidis, D., Allan, C.Y., Owyong, T.C., Wu, M.-Y., Chen, S., Fisher, P.R., Annesley, S.J. and Hong, Y. (2024), A Novel Fluorogenic Probe Reveals Lipid Droplet Dynamics in ME/CFS Fibroblasts. Adv. Sensor Res. 2300178. https://doi.org/10.1002/adsr.202300178 https://onlinelibrary.wiley.com/doi/full/10.1002/adsr.202300178 (Full text)

Mixed methods system for the assessment of post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome: an exploratory study

Abstract:

Background A central feature of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is post-exertional malaise (PEM), which is an acute worsening of symptoms after a physical, emotional and/or mental exertion. Dynamic measures of PEM have historically included scaled questionnaires, which have not been validated in ME/CFS. To enhance our understanding of PEM and how best to measure it, we conducted semistructured qualitative interviews (QIs) at the same intervals as visual analogue scale (VAS) measures after a cardiopulmonary exercise test (CPET).

Methods Ten ME/CFS and nine healthy volunteers participated in a CPET. For each volunteer, PEM symptom VAS (12 symptoms) and semistructured QIs were administered at six timepoints over 72 hours before and after a single CPET. QI data were used to plot the severity of PEM at each time point and identify the self-described most bothersome symptom for each ME/CFS volunteer. Performance of QI and VAS data was compared with each other using Spearman correlations.

Results Each ME/CFS volunteer had a unique PEM experience, with differences noted in the onset, severity, trajectory over time and most bothersome symptom. No healthy volunteers experienced PEM. QI and VAS fatigue data corresponded well an hour prior to exercise (pre-CPET, r=0.7) but poorly at peak PEM (r=0.28) and with the change from pre-CPET to peak (r=0.20). When the most bothersome symptom identified from QIs was used, these correlations improved (r=0.0.77, 0.42. and 0.54, respectively) and reduced the observed VAS scale ceiling effects.

Conclusion In this exploratory study, QIs were able to capture changes in PEM severity and symptom quality over time, even when VAS scales failed to do so. Measurement of PEM can be improved by using a quantitative–qualitative mixed model approach.

Source: Stussman B, Calco B, Norato G, et al. Mixed methods system for the assessment of post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome: an exploratory study. BMJ Neurology Open 2024;6:e000529. doi: 10.1136/bmjno-2023-000529  https://neurologyopen.bmj.com/content/6/1/e000529 (Full text)

Investigating the Human Intestinal DNA Virome and Predicting Disease-Associated Virus-Host Interactions in Severe Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract:

Understanding how the human virome, and which of its constituents, contributes to health or disease states is reliant on obtaining comprehensive virome profiles. By combining DNA viromes from isolated virus-like particles (VLPs) and whole metagenomes from the same faecal sample of a small cohort of healthy individuals and patients with severe myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), we have obtained a more inclusive profile of the human intestinal DNA virome.

Key features are the identification of a core virome comprising tailed phages of the class Caudoviricetes, and a greater diversity of DNA viruses including extracellular phages and integrated prophages. Using an in silico approach, we predicted interactions between members of the Anaerotruncus genus and unique viruses present in ME/CFS microbiomes. This study therefore provides a framework and rationale for studies of larger cohorts of patients to further investigate disease-associated interactions between the intestinal virome and the bacteriome.

Source: Hsieh SY, Savva GM, Telatin A, Tiwari SK, Tariq MA, Newberry F, Seton KA, Booth C, Bansal AS, Wileman T, Adriaenssens EM, Carding SR. Investigating the Human Intestinal DNA Virome and Predicting Disease-Associated Virus-Host Interactions in Severe Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Int J Mol Sci. 2023 Dec 8;24(24):17267. doi: 10.3390/ijms242417267. PMID: 38139096. https://www.mdpi.com/1422-0067/24/24/17267 (Full text)

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Abstract:

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power.

We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels.

Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients.

Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Source: Brydges C, Che X, Lipkin WI, Fiehn O. Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites. 2023 Aug 31;13(9):984. doi: 10.3390/metabo13090984. PMID: 37755264; PMCID: PMC10535181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/ (Full text)

Developing and validating a brief screening scale for ME/CFS

Abstract:

Objective: The purpose of the current study was to develop and evaluate a brief screening instrument for ME/CFS. The current study identified 4 symptom items that identify those positive for the IOM ME/CFS case definition.

Study Design: A data set of over 2,000 patients with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and over 350 controls were assessed for the 4-item DePaul Symptom Questionnaire-Brief (DSQ-Brief). All respondents also completed the longer 54-item DePaul Symptom Questionnaire (DSQ-1) as well as the 14-item DePaul Symptom Questionnaire-Short Form (DSQ-SF). These data sets were collected from multiple countries.

We also examined the DSQ-Brief, DSQ-1, and DSQ-SF with other chronic illness groups [Multiple Sclerosis (MS) and Post-Polio Syndrome (PPS)] and those with Long COVID. Random Forest comparisons were employed in these analyses.

Results: When contrasting ME/CFS from controls, high levels of accuracy occurred using the DSQ-1, DSQ-SF, and DSQ-Brief. High accuracy again occurred for differentiating those with ME/CFS from MS, PPS, and Long COVID using the DSQ-1 and DSQ-SF, but accuracy was less for the DSQ-Brief.

Conclusions: The DSQ-Brief had high sensitivity, meaning it could identify those with ME/CFS versus controls, whereas accuracy dropped with other chronic illnesses. However, it was possible to achieve better accuracy and identify those cases where misidentification occurred by administering the DSQ-SF or DSQ-1 following the DSQ-Brief. It is now possible to screen individuals for ME/CFS using the DSQ-Brief and in so doing, identify those who are most likely to have ME/CFS.

Source: Leonard A. Jason, Sage Benner, Jacob Furst & Paul Cathey (2023) Developing and validating a brief screening scale for ME/CFS, Fatigue: Biomedicine, Health & Behavior, 11:2-4, 176-187, DOI: 10.1080/21641846.2023.2252613 https://www.tandfonline.com/doi/abs/10.1080/21641846.2023.2252613

Myalgic Encephalomyelitis-Chronic Fatigue Syndrome Common Data Element item content analysis

Abstract:

Introduction: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multisystem chronic disease estimated to affect 836,000-2.5 million individuals in the United States. Persons with ME/CFS have a substantial reduction in their ability to engage in pre-illness levels of activity. Multiple symptoms include profound fatigue, post-exertional malaise, unrefreshing sleep, cognitive impairment, orthostatic intolerance, pain, and other symptoms persisting for more than 6 months. Diagnosis is challenging due to fluctuating and complex symptoms. ME/CFS Common Data Elements (CDEs) were identified in the National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) Common Data Element Repository. This study reviewed ME/CFS CDEs item content.

Methods: Inclusion criteria for CDEs (measures recommended for ME/CFS) analysis: 1) assesses symptoms; 2) developed for adults; 3) appropriate for patient reported outcome measure (PROM); 4) does not use visual or pictographic responses. Team members independently reviewed CDEs item content using the World Health Organization International Classification of Functioning, Disability and Health (ICF) framework to link meaningful concepts.

Results: 119 ME/CFS CDEs (measures) were reviewed and 38 met inclusion criteria, yielding 944 items linked to 1503 ICF meaningful concepts. Most concepts linked to ICF Body Functions component (b-codes; n = 1107, 73.65%) as follows: Fatiguability (n = 220, 14.64%), Energy Level (n = 166, 11.04%), Sleep Functions (n = 137, 9.12%), Emotional Functions (n = 131, 8.72%) and Pain (n = 120, 7.98%). Activities and Participation concepts (d codes) accounted for a smaller percentage of codes (n = 385, 25.62%). Most d codes were linked to the Mobility category (n = 69, 4.59%) and few items linked to Environmental Factors (e codes; n = 11, 0.73%).

Discussion: Relatively few items assess the impact of ME/CFS symptoms on Activities and Participation. Findings support development of ME/CFS-specific PROMs, including items that assess activity limitations and participation restrictions. Development of psychometrically-sound, symptom-based item banks administered as computerized adaptive tests can provide robust assessments to assist primary care providers in the diagnosis and care of patients with ME/CFS.

Source: Slavin MD, Bailey HM, Hickey EJ, Vasudevan A, Ledingham A, Tannenbaum L, Bateman L, Kaufman DL, Peterson DL, Ruhoy IS, Systrom DM, Felsenstein D, Kazis LE. Myalgic Encephalomyelitis-Chronic Fatigue Syndrome Common Data Element item content analysis. PLoS One. 2023 Sep 12;18(9):e0291364. doi: 10.1371/journal.pone.0291364. PMID: 37698999. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291364 (Full text)

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Abstract:

Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression.

The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education.

The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69-0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment.

Source: Lacasa M, Prados F, Alegre J, Casas-Roma J. A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires. Sci Rep. 2023 Aug 31;13(1):14256. doi: 10.1038/s41598-023-40364-6. PMID: 37652910; PMCID: PMC10471690. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10471690/ (Full text)

A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with a significant global prevalence of over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. A multidisciplinary approach is essential for diagnosing, treating, and managing this complex disease.

The current study aims at employing explainable artificial intelligence (XAI) and machine learning (ML) techniques to identify discriminative metabolites for ME/CFS.

Material and Methods: The present study used a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.

Random forest-based feature selection and Bayesian Approach based-hyperparameter optimization were implemented on the target data. Four different ML algorithms [Gaussian Naive Bayes (GNB), Gradient Boosting Classifier (GBC), Logistic regression (LR) and Random Forest Classifier (RFC)] were used to classify individuals as ME/CFS patients and healthy individuals. XAI approaches were applied to clinically explain the prediction decisions of the optimum model. Performance evaluation was performed using the indices of accuracy, precision, recall, F1 score, Brier score, and AUC.

Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.

The RFC learning model outperformed GNB, GBC, and LR in ME/CFS prediction using the 1000 iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC.

Conclusion: RFC model proposed in this study correctly classified and evaluated ME/CFS patients through the selected biomarker candidate metabolites. The methodology combining ML and XAI can provide a clear interpretation of risk estimation for ME/CFS, helping physicians intuitively understand the impact of key metabolomics features in the model.

Source: Yagin, F.H., Alkhateeb, A., Raza, A., Samee, N.A., Mahmoud, N.F., Colak, C., & Yagin, B. (2023). A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Preprints. https://doi.org/10.20944/preprints202307.1585.v1 https://www.preprints.org/manuscript/202307.1585/v1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706650/ (Full text of completed study)

Identification of the pathogenic relationship between Long COVID and Alzheimer’s disease by bioinformatics methods

Abstract:

Background: The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an unprecedented global health crisis. Although many Corona Virus Disease 2019 (COVID-19) patients have recovered, the long-term consequences of SARS-CoV-2 infection are unclear. Several independent epidemiological surveys and clinical studies have found that SARS-CoV-2 infection and Long COVID are closely related to Alzheimer’s disease (AD). This could lead to long-term medical challenges and social burdens following this health crisis. However, the mechanism between Long COVID and AD is unknown.

Methods: Genes associated with Long COVID were collected from the database. Two sets of AD-related clinical sample datasets were collected in the Gene Expression Omnibus (GEO) database by limiting screening conditions. After identifying the differentially expressed genes (DEGs) of AD, the significant overlapping genes of AD and Long COVID were obtained by taking the intersection. Then, four kinds of analyses were performed, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis, protein-protein interaction (PPI) analysis, identification of hub genes, hub gene verification and transcription factors (TFs) prediction.

Results: A total of 197 common genes were selected for subsequent analysis. GO and KEGG enrichment analysis showed that these genes were mainly enriched in multiple neurodegenerative disease related pathways. In addition, 20 important hub genes were identified using cytoHubba. At the same time, these hub genes were verified in another data set, where 19 hub gene expressions were significantly different in the two diseases and 6 hub genes were significantly different in AD patients of different genders. Finally, we collected 9 TFs that may regulate the expression of these hub genes in the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRUSST) database and verified them in the current data set.

Conclusion: This work reveals the common pathways and hub genes of AD and Long COVID, providing new ideas for
the pathogenic relationship between these two diseases and further mechanism research.

Source:

Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration

Abstract:

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic.

In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%.

Source: Scarpino I, Zucco C, Vallelunga R, Luzza F, Cannataro M. Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration. BioTech (Basel). 2022 Sep 3;11(3):41. doi: 10.3390/biotech11030041. PMID: 36134915; PMCID: PMC9496775. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496775/ (Full text)