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MRC DiMeN Doctoral Training Partnership: Exploring the biomarkers for new-onset chronic pain in Long Covid. A mechanistic study


   MRC DiMeN Doctoral Training Partnership

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  Dr Manoj Sivan, Dr Christopher Brown, Dr Alex Casson  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Project summary:

Long COVID (LC) is a new condition that affects more than 2 million people in the UK and more than 40 million worldwide. LC-chronic pain syndrome, which includes new-onset muscle pain, joint pain or nerve pain persisting for more than 3 months since the COVID-19 illness, affects more than one-fifth of people with LC. The burden of this new pain syndrome will add to the already existing global challenge of chronic pain. There is an urgent need to study the underlying mechanisms of LC-chronic pain to identify targetable biomarkers of this new pain syndrome. The objectives of this PhD project are to a) understand the patient risk factors associated with LC-chronic pain, and b) understand the underlying mechanisms of LC-chronic pain and identify targetable biomarkers for informing treatments. Study 1 comprises a longitudinal study (n=2000) using questionnaires to assess the nature of pain, functioning, demographic variables and quality of life regression-based approaches (linear mixed models) and machine learning (random forests) to understand the natural progression of LC-chronic pain. Study 2 will be a nested longitudinal case-control study (n=100) to do in-depth analysis of underlying physiological mechanisms at two point points (baseline and 12 months) to understand role of a) CD4 and CD8 cells, CRP, IL-1/6/17, IL-1b, TNF-a, and IFN-d using blood sampling; b) Central amplification using Quantitative Sensory Testing (QST) to assess peripheral and central sensitization and Electroencephalography (EEG) to assess spectral power of different waveforms and event related potentials (ERPs) in response to electrical stimulation; c) autonomic nervous system functioning using Autonomic Profile (AP), Heart Rate Variability (HRV) and Hypothalamic Pituitary Axis (HPA) profile.

Novelty:

Analysis will reveal biomarkers of LC-chronic pain that can be targeted for timely management of this new and evolving chronic pain condition. Predictive machine learning pain models to identify individuals “at risk” will be developed that will inform preventive management strategies.

PhD training will involve:

  • Multisystem physiology (training in neurophysiology (EEG, QST), autonomic system, immunology and translational medicine).
  • Quantitative skills (data analytics and informatics, machine learning and Artificial Intelligence in developing prediction models applied to a variety of data sources)
  • Interdisciplinary skills (training in clinical medicine of understanding a new health problem and deriving solutions that can directly translate to clinical benefit) 

The PhD student will significantly benefit from expert training from eminent scientists and clinicians on combining several physiological techniques and clinical skills and using advanced programming skills for design and analysis. The project builds capacity in precision medicine and diagnostic analytics in an emerging global health condition. The work enables mobility across disciplines and sectors, and development of advanced analytical and informatics skills in medical research. The Human Pain Research Group (Leeds-Liverpool-Manchester) has an established reputation of high impact research in chronic pain research and will further its claim to undertaking cutting edge translational pain research and providing a highly supportive, stimulating, and inspiring environment for future researchers.

Dr Manoj Sivan: https://medicinehealth.leeds.ac.uk/medicine/staff/3148/dr-manoj-sivan-md-frcp-edin

Dr Christopher Brown: https://www.liverpool.ac.uk/population-health/staff/chris-brown/

Dr Alex Casson: https://www.research.manchester.ac.uk/portal/alex.casson.html

iCASE partner: http://www.elaros.com/

Benefits of being in the DiMeN DTP:

This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle, York and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.

We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.

Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: http://www.dimen.org.uk/overview/student-profiles/flexible-supplement-awards

Further information on the programme and how to apply can be found on our website:

http://www.dimen.org.uk/how-to-apply/application-overview


Funding Notes

iCASE Award: Industrial partnership project
Funded by the MRC for 4yrs, including a minimum of 3 months working within the industry partner.

Funding will cover UK tuition fees and an enhanced stipend (around £18,109). We also aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will be awarded to exceptional candidates only, due to the competitive nature of this scheme. Please read additional guidance here: http://www.dimen.org.uk/how-to-apply/eligibility-funding
Studentships commence: 1st October 2022
Good luck!

References

1. Brown CA, Almarzouki AF, Brown RJ, Jones AKP. Neural representations of aversive value encoding in pain catastrophizers. Neuroimage 2019; 184: 508-519 https://pubmed.ncbi.nlm.nih.gov/30243959/
2. Sivan M, Halpin S, Gee J, Makower S, Parkin A, Ross D, Horton D, O’Connor R. The self-report version and digital format of the COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) for Long Covid or Post-COVID syndrome assessment and monitoring. Advances in Clinical Neurosciences and Rehabilitation 2021; 20(3): 1-5
3. https://acnr.co.uk/2021/04/digital-format-long-covid-c19-yrs-tool/
4. Lopez-Diaz K, Henshaw J, Casson AJ, Brown CA, Taylor JR, Trujillo-Barreto NJ, Arendsen LJ, Jones AKP, Sivan M. Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients: a proof-of-concept controlled study. Neuroreport 2021; 32(5): 394-398 https://pubmed.ncbi.nlm.nih.gov/33661810/
5. Power A, Brown CA, Sivan M, Lenton A, Rainey T, El-Deredy W, Peter Jones AK, Watson A. Individuals with chronic pain have the same response to placebo analgesia as healthy controls in terms of magnitude and reproducibility. PAIN. 2020; 161(12): 2720-2730 https://pubmed.ncbi.nlm.nih.gov/32639369/
6. Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a limited sample size. PLoS One. 2019 https://pubmed.ncbi.nlm.nih.gov/31697686/
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