Welcome to the 43rd Emerge Australia Research Digest, where you will find summaries of some of the latest research and information about ME/CFS, with links to the complete articles.
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One-stop resource for COVID-19
We know the COVID-19 situation is overwhelming and anxiety-provoking for everyone. There is a lot of information to digest with plenty more to come, and it’s hard to keep up.
To help take some of the stress off the Australian ME/CFS community, we have compiled an easy-read resource as a “one-stop shop”, with the latest information, advisories and resources from government and health authorities.
We will update this page on our website as more details are released about health measures, recommendations, and the availability of services including pop-up health clinics and financial supports.
Link: http://emerge.org.au/blog/corona-virus-and-me
World-wide research call-out
ME Research UK has announced a call for applications to fund biomedical research into ME/CFS including a supported PhD studentship. The call has no geographical restriction and Australian ME/CFS researchers are encouraged to apply.
Full details are available at:-
Research grants
http://www.meresearch.org.uk/our-research/research-grants/
PhD studentships
Assessing diagnostic value of microRNAs from peripheral blood mononuclear cells and extracellular vesicles in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome
Authors: Almenar-Pérez, E., Sarría, L., Nathanson, L., Oltra, E.
Link: http://www.ncbi.nlm.nih.gov/pubmed/32034172
MicroRNAs are short non-coding RNA molecules that are known to regulate gene expression. Abnormalities in microRNA expression have been strongly implicated in the development of various human diseases ranging from cancers, heart conditions, neurological diseases, and autoimmune disorders. MicroRNAs are released from cells and as a result can be found in almost all bodily fluids, so they offer themselves as an attractive biomarker candidate for the diagnosis of many pathologies, including ME/CFS.
The authors examined microRNA expression in peripheral blood mononuclear cells (PBMCs) and extracellular vesicles (EVs) from 15 severely ill ME/CFS female patients (Canadian Consensus Criteria and Fukuda criteria) and 15 healthy controls. Unlike PBMC’s, EV’s containing microRNA can originate from any cell in the body – which is particularly useful given the fact that ME/CFS usually presents with multisystemic symptomology. This study had two aims: Firstly, whether there was a difference between the microRNA profiles (miRNomes) of EV versus PBMC, and secondly whether analysis of EV miRNomes could lead to the identification of a biomarker for ME/CFS.
The authors found that there were a small number miRNA’s in PBMCs and EVs were differentially expressed between ME/CFS patients and healthy controls. There were also differences between ME/CFS patients and healthy controls in the creatine kinase (CK) blood values, the number and size of plasma EVs, and EV zeta-potential. These differences present themselves as a unique set of parameters that, together with other methods, could assist in the diagnosis of ME/CFS.
A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control
Authors: Provenzano, D., Washington, S.D., Baraniuk, J.N.
Link: http://www.ncbi.nlm.nih.gov/pubmed/32063839
In this study, researchers used machine learning algorithms to examine Functional Magnetic Resonance Imaging (fMRI) to try to differentiate ME/CFS patients from sedentary controls with promising results.
The sample included 69 participants, 38 ME/CFS patients (Fukuda criteria) and 31 controls. Participants underwent a pre-exercise fMRI scan (Day 1) and a subsequent fMRI scan after undergoing physical and mental exercises (Day 2). The model identified 29 regions of interest (ROI) which differentiated ME/CFS patients from controls in the Day 1 (pre-exercise) fMRI and 28 ROIs in the Day two (post exercise) fMRI. There were 10 regions which differentiated ME/CFS patients and controls on both Day 1 and Day 2, and these may reflect underlying pathologies in ME/CFS.
The machine learning model was able to identify ME/CFS patients from controls with an 80% accuracy on Day 1 and 76% accuracy on day Day 2. This study provides a good step towards developing fMRI biomarkers for the diagnosis of ME/CFS, utilising the cognitive and post-exertional impairments characteristic of the condition.
Systematic review and meta-analysis of the prevalence of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME)
Authors: Lim, E.J., Ahn, Y.C., Jang, E.S., Lee, S.W., Lee, S.H., Son, C.G.
Link: http://www.ncbi.nlm.nih.gov/pubmed/32093722
In this study, researchers conducted a systemic review of the prevalence of ME/CFS from studies published between 1980 to December 2018. A total of 45 articles met the criteria for inclusion in the review.
The total average prevalence across studies was 1.40 ± 1.57%. The authors noted that prevalence varied according to gender, setting (community versus primary care), age and diagnostic criteria. The prevalence estimate using the Fukuda criteria was 0.89% (95% confidence interval: 0.60-1.33), and women were approximately 1.5 to 2 times more represented than men in all categories.
The variability of estimates highlights the need for a more precise and objective diagnostic tool for the concise assessment of the prevalence of ME/CFS.
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