Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Communications Medicine
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. communications medicine
  3. articles
  4. article
Divergent inflammatory and neurology-related protein levels in long COVID following primary and breakthrough SARS-CoV-2 infections
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 13 April 2026

Divergent inflammatory and neurology-related protein levels in long COVID following primary and breakthrough SARS-CoV-2 infections

  • Amit Bansal  ORCID: orcid.org/0000-0002-0681-932X1,2 na2,
  • Sam W. Z. Olechnowicz  ORCID: orcid.org/0000-0001-7879-77083,4 na2,
  • Nicholas Kiernan-Walker4,5,
  • Jacob Cumming  ORCID: orcid.org/0009-0009-1815-91605,6,7,
  • Imadh Abdul Azeez4,5,
  • Ramin Mazhari4,5,
  • COVID PROFILE consortium,
  • Rebecca J. Cox  ORCID: orcid.org/0000-0002-8341-40781,8,
  • Ivo Mueller4,5,
  • Rory Bowden  ORCID: orcid.org/0000-0001-8596-03663,4,5 na3 &
  • …
  • Emily M. Eriksson  ORCID: orcid.org/0000-0002-7851-973X4,5 na3 

Communications Medicine , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Chronic inflammation
  • Viral infection

Abstract

Background:

Long COVID is a complex condition where symptoms persist for more than 3 months after SARS-CoV-2 infection and affects an estimated 5-30% of individuals. While persistent inflammation has emerged as an important feature of this condition, it is unclear if immune responses from COVID-19 vaccination or SARS-CoV-2 re-infection exacerbate or mirror the initial inflammatory responses.

Methods:

We quantified 182 inflammatory and neurology-related proteins in plasma using multiplexed affinity proteomics. Plasma samples from the COVID PROFILE cohort conducted in Victoria, Australia, were collected 6-9 months after first infection, but before COVID-19 vaccination from individuals who had recovered from COVID-19 (n = 21) or from individuals with long COVID (n = 12). To establish baseline plasma profiles, protein levels were benchmarked against unvaccinated, SARS-CoV-2 naive individuals (n = 24). In addition, we performed longitudinal analysis in a subset of individuals (n = 34), where paired samples collected 2-4 weeks after a third COVID-19 vaccine dose and after SARS-CoV-2 breakthrough infection were available to assess inflammatory and neurology protein plasma levels after antigen exposure in these contexts.

Results:

In this cohort Boruta feature selection and lasso regression models identified IL-20, HAGH, NAAA, CLEC10A, LXN, and MCP-1, TRAIL, G-CSF, NBL1, and CCL23 as best discriminating proteins when comparing the long COVID group to groups of either healthy or COVID-19 recovered. Notably, longitudinal analysis indicated differences in the levels of a subset of plasma proteins following primary infection compared to after COVID-19 booster vaccination and breakthrough infection within the groups.

Conclusions:

These findings suggest that there is an altered immune response outcome primarily observed in individuals with long COVID upon re-exposure.

Plain language summary

Long COVID is a condition in which people continue to have symptoms for more than three months after SARS-CoV-2 infection. Ongoing inflammation is thought to contribute to long COVID, but it is unclear whether COVID-19 vaccination or re-infection with SARS-CoV-2 lead to similar, worse or different inflammatory responses compared with the initial infection in people with this condition. We examined blood proteins linked to inflammation and the nervous system to better understand these responses in people with long COVID, individuals that had completely recovered from the first infection and healthy controls. We found that in both long COVID individuals and completely recovered people there were different changes in the level of some immune-related proteins after vaccination or re-infection compared with the response after the original infection suggesting a different immune response from the initial infection upon re-exposure.

Similar content being viewed by others

Identification of soluble biomarkers that associate with distinct manifestations of long COVID

Article Open access 30 April 2025

Persistent serum protein signatures define an inflammatory subcategory of long COVID

Article Open access 09 June 2023

Aggregation potency and proinflammatory effects of SARS-CoV-2 proteins

Article Open access 04 August 2025

Data availability

To protect patient privacy, this study uses anonymised data from the COVID PROFILE study. Data have been deposited into Zenodo https://doi.org/10.5281/zenodo.15237087 and are available as Supplementary Data 2 and 3. The source data for Figs. 2, 3 and 4 is in Supplementary Data 2. The source data for Figs. 4 and 5 is in Supplementary Data 3.

Code availability

R code has been deposited into Zenodo https://doi.org/10.5281/zenodo.15237087. This paper does not report original code. This project leverages open-source R code. We have documented the specific R packages and functions used in relevant sections.

References

  1. WHO. Post COVID-19 condition (long COVID). https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition (2022)

  2. Davis, H. E., McCorkell, L., Vogel, J. M. & Topol, E. J. Long COVID: major findings, mechanisms and recommendations. Nat. Rev. Microbiol. 21, 133–146 (2023).

    Google Scholar 

  3. Ceban, F. et al. Fatigue and cognitive impairment in Post-COVID-19 Syndrome: a systematic review and meta-analysis. Brain Behav. Immun. 101, 93–135 (2022).

    Google Scholar 

  4. Al-Aly, Z., Bowe, B. & Xie, Y. Long COVID after breakthrough SARS-CoV-2 infection. Nat. Med. 28, 1461–1467 (2022).

    Google Scholar 

  5. Ayoubkhani, D. et al. Risk of long COVID in people infected with severe acute respiratory syndrome coronavirus 2 after 2 doses of a coronavirus disease 2019 vaccine: community-based, matched cohort study. Open Forum Infect. Dis. 9, ofac464 (2022).

  6. Cervia-Hasler, C. et al. Persistent complement dysregulation with signs of thromboinflammation in active long Covid. Science 383, eadg7942 (2024).

    Google Scholar 

  7. Greene, C. et al. Blood-brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment. Nat. Neurosci. 27, 421–432 (2024).

    Google Scholar 

  8. Hanson, A. L. et al. Iron dysregulation and inflammatory stress erythropoiesis associates with long-term outcome of COVID-19. Nat. Immunol. 25, 471–482 (2024).

    Google Scholar 

  9. Klein, J. et al. Distinguishing features of long COVID identified through immune profiling. Nature 623, 139–148 (2023).

    Google Scholar 

  10. Phetsouphanh, C. et al. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection. Nat. Immunol. 23, 210–216 (2022).

    Google Scholar 

  11. Eaton-Fitch, N. et al. Immune exhaustion in ME/CFS and long COVID. JCI Insight 9, e183810 (2024).

  12. Yin, K. et al. Long COVID manifests with T cell dysregulation, inflammation and an uncoordinated adaptive immune response to SARS-CoV-2. Nat. Immunol. 25, 218–225 (2024).

    Google Scholar 

  13. Group, P.-C. C. Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study. Lancet Respir. Med. 10, 761–775 (2022).

    Google Scholar 

  14. Peluso, M. J. et al. Plasma markers of neurologic injury and inflammation in people with self-reported neurologic postacute sequelae of SARS-CoV-2 infection. Neurol. Neuroimmunol. Neuroinflamm. 9, e200003 (2022).

  15. Gu, X. et al. Probing long COVID through a proteomic lens: a comprehensive two-year longitudinal cohort study of hospitalised survivors. EBioMedicine 98, 104851 (2023).

    Google Scholar 

  16. Etter, M. M. et al. Severe neuro-COVID is associated with peripheral immune signatures, autoimmunity and neurodegeneration: a prospective cross-sectional study. Nat. Commun. 13, 6777 (2022).

    Google Scholar 

  17. Tandon, P. et al. Unraveling links between chronic inflammation and long COVID: workshop report. J. Immunol. 212, 505–512 (2024).

    Google Scholar 

  18. Phetsouphanh, C. et al. Improvement of immune dysregulation in individuals with long COVID at 24-months following SARS-CoV-2 infection. Nat. Commun. 15, 3315 (2024).

    Google Scholar 

  19. Eriksson, E. M. et al. Cohort profile: a longitudinal Victorian COVID-19 cohort (COVID PROFILE). Preprint at medRxiv https://doi.org/10.1101/2023.04.27.23289157 (2023).

  20. Ernster, V. L. Nested case-control studies. Prev. Med. 23, 587–590 (1994).

    Google Scholar 

  21. Mazhari, R. et al. SARS-CoV-2 multi-antigen serology assay. Methods Protoc. 4, 72 (2021).

  22. Assarsson, E. et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9, e95192 (2014).

    Google Scholar 

  23. Nevola, K. et al. OlinkAnalyze: facilitate analysis of proteomic data from Olink. R package version 3.6.2 (2024).

  24. Olink. Introduction to bridging Olink® NPX datasets. https://cran.r-project.org/web/packages/OlinkAnalyze/vignettes/bridging_introduction.html (2025).

  25. Olink. Calculating LOD from Olink® Explore data, <https://cran.r-project.org/web//packages/OlinkAnalyze/vignettes/LOD.html#introduction> (2025).

  26. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Google Scholar 

  27. Tay, J. K., Narasimhan, B. & Hastie, T. Elastic net regularization paths for all generalized linear models. J. Stat. Softw. 106, 1 (2023).

  28. Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).

    Google Scholar 

  29. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & F, L. _e1071: misc functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien_. R. Package Vers. 1, 7–14 (2023).

    Google Scholar 

  30. Gianfagna, F. et al. Anti-SARS-CoV-2 antibody levels and kinetics of vaccine response: potential role for unresolved inflammation following recovery from SARS-CoV-2 infection. Sci. Rep. 12, 385 (2022).

    Google Scholar 

  31. Koutsakos, M. et al. The magnitude and timing of recalled immunity after breakthrough infection is shaped by SARS-CoV-2 variants. Immunity 55, 1316–1326 e1314 (2022).

    Google Scholar 

  32. Zhang, Z. et al. Humoral and cellular immune memory to four COVID-19 vaccines. Cell 185, 2434–2451 e2417 (2022).

    Google Scholar 

  33. Grady, C. B. et al. Impact of COVID-19 vaccination on symptoms and immune phenotypes in vaccine-naive individuals with Long COVID. Commun. Med. 5, 163 (2025).

  34. Hamzaraj, K. et al. Impact of circulating anti-spike protein antibody levels on multi-organ long COVID symptoms. Vaccines 12, 610 (2024).

  35. Joung, S. et al. Serological response to vaccination in post-acute sequelae of COVID. BMC Infect. Dis. 23, 97 (2023).

    Google Scholar 

  36. Madenbayeva, A. M. et al. Impact of QazVac vaccination on clinical manifestations and immune responses in post-COVID syndrome: a cross-sectional study. Front. Med. 12, 1556623 (2025).

    Google Scholar 

  37. Tsuchida, T. et al. Relationship between changes in symptoms and antibody titers after a single vaccination in patients with Long COVID. J. Med. Virol. 94, 3416–3420 (2022).

    Google Scholar 

  38. Iosef, C. et al. Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function. J. Transl. Med. 21, 377 (2023).

    Google Scholar 

  39. Liew, F. et al. Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease. Nat. Immunol. 25, 607–621 (2024).

    Google Scholar 

  40. Patel, M. A. et al. Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning. Mol. Med. 29, 26 (2023).

    Google Scholar 

  41. Zhao, J. et al. Plasma biomarkers for systemic inflammation in COVID-19 survivors. Proteom. Clin. Appl. 16, e2200031 (2022).

    Google Scholar 

  42. Rajamanickam, A. et al. Characterization of IL-10 family of cytokines in acute and convalescent COVID-19 individuals. J. Interferon Cytokine Res. 43, 469–477 (2023).

    Google Scholar 

  43. Chen, J., Caspi, R. R. & Chong, W. P. IL-20 receptor cytokines in autoimmune diseases. J. Leukoc. Biol. 104, 953–959 (2018).

    Google Scholar 

  44. Kragstrup, T. W. et al. The interleukin-20 receptor axis in early rheumatoid arthritis: novel links between disease-associated autoantibodies and radiographic progression. Arthritis Res. Ther. 18, 61 (2016).

    Google Scholar 

  45. Chen, Y. et al. New-onset autoimmune phenomena post-COVID-19 vaccination. Immunology 165, 386–401 (2022).

    Google Scholar 

  46. Wang, E. Y. et al. Diverse functional autoantibodies in patients with COVID-19. Nature 595, 283–288 (2021).

    Google Scholar 

  47. Hsu, Y. H. et al. Anti-IL-20 monoclonal antibody inhibited inflammation and protected against cartilage destruction in murine models of osteoarthritis. PLoS ONE 12, e0175802 (2017).

    Google Scholar 

  48. Kragstrup, T. W. et al. Increased interleukin (IL)-20 and IL-24 target osteoblasts and synovial monocytes in spondyloarthritis. Clin. Exp. Immunol. 189, 342–351 (2017).

    Google Scholar 

  49. Llaurador-Coll, M. et al. Plasma levels of neurology-related proteins are associated with cognitive performance in an older population with overweight/obesity and metabolic syndrome. Geroscience 45, 2457–2470 (2023).

    Google Scholar 

  50. Kindrachuk, J. et al. Antiviral potential of ERK/MAPK and PI3K/AKT/mTOR signaling modulation for Middle East respiratory syndrome coronavirus infection as identified by temporal kinome analysis. Antimicrob. Agents Chemother. 59, 1088–1099 (2015).

    Google Scholar 

  51. Kong, F. et al. Sirtuins as potential therapeutic targets for hepatitis B virus infection. Front. Med. 8, 751516 (2021).

    Google Scholar 

  52. Roche, K. L. et al. An allosteric inhibitor of sirtuin 2 deacetylase activity exhibits broad-spectrum antiviral activity. J. Clin. Invest. 133, e158978 (2023).

  53. Acosta-Ampudia, Y. et al. Persistent autoimmune activation and proinflammatory state in post-coronavirus disease 2019 syndrome. J. Infect. Dis. 225, 2155–2162 (2022).

    Google Scholar 

  54. Blomberg, B. et al. Long COVID in a prospective cohort of home-isolated patients. Nat. Med. 27, 1607–1613 (2021).

    Google Scholar 

  55. Ghafari, M. et al. Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature 626, 1094–1101 (2024).

    Google Scholar 

  56. Zollner, A. et al. Postacute COVID-19 is characterized by gut viral antigen persistence in inflammatory bowel diseases. Gastroenterology 163, 495–506 e498 (2022).

    Google Scholar 

  57. Augustin, M. et al. Immunological fingerprint in coronavirus disease-19 convalescents with and without post-COVID syndrome. Front. Med. 10, 1129288 (2023).

    Google Scholar 

  58. Kervevan, J. et al. Divergent adaptive immune responses define two types of long COVID. Front. Immunol. 14, 1221961 (2023).

    Google Scholar 

  59. COVID-19 Australia: Epidemiology Report 57 https://www1.health.gov.au/internet/main/publishing.nsf/Content/C50CAE02452A48A7CA2587320081F7BF/<span>$File/covid_19_australia_epidemiology_report_57_reporting_period_ending_16_january_2022.pdf (2022).

  60. COVID-19 Australia: Epidemiology Report 60. https://www1.health.gov.au/internet/main/publishing.nsf/Content/C50CAE02452A48A7CA2587320081F7BF/<span>$File/covid_19_australia_epidemiology_report_60_reporting_period_ending_10_april_2022.pdf (2022).

  61. Wik, L. et al. Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis. Mol. Cell Proteom. 20, 100168 (2021).

    Google Scholar 

  62. Therneau T, A. B. rpart: recursive partitioning and regression trees. R package version 4.1.23. https://CRAN.R-project.org/package=rpart (2023).

  63. Williams, G. J. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Series Use R! (Springer, 2011).

  64. Lenth, R. emmeans: estimated marginal means, aka least-squares means. R package version 1.10.0. (2024).

Download references

Acknowledgements

We wish to thank Anne Hart, Maureen Ford and all members of the COVID PROFILE consortium and the study participants in the COVID PROFILE study. We also wish to thank Dr. Lauren Smith for her valuable discussion and input around data analysis. Dr. Bansal received funding from the University of Bergen, The National Graduate School in Infection Biology and Antimicrobials (or IBA) and Pasteur legatet & Thjøtta’s legat, University of Oslo, Norway [101563]. The COVID PROFILE study was supported by WHO Unity funds (2020/1085469-0), and WEHI philanthropic funds. I.M. is supported by an NHMRC Senior Research Fellowship (#1043345). This work was made possible through Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS.

Author information

Author notes
  1. These authors contributed equally: Amit Bansal, Sam W. Z. Olechnowicz.

  2. These authors jointly supervised this work Rory Bowden, Emily M. Eriksson.

Authors and Affiliations

  1. Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway

    Amit Bansal & Rebecca J. Cox

  2. Department of Infectious Diseases, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia

    Amit Bansal

  3. The Walter and Eliza Hall Institute of Medical Research, Advanced Technology and Biology Division, Melbourne, VIC, Australia

    Sam W. Z. Olechnowicz & Rory Bowden

  4. The University of Melbourne, Department of Medical Biology, Melbourne, VIC, Australia

    Sam W. Z. Olechnowicz, Nicholas Kiernan-Walker, Imadh Abdul Azeez, Ramin Mazhari, Ivo Mueller, Rory Bowden & Emily M. Eriksson

  5. The Walter and Eliza Hall Institute of Medical Research, Population Health and Immunity Division, Melbourne, VIC, Australia

    Nicholas Kiernan-Walker, Jacob Cumming, Imadh Abdul Azeez, Ramin Mazhari, Ivo Mueller, Rory Bowden & Emily M. Eriksson

  6. Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia

    Jacob Cumming

  7. The University of Melbourne, Department of Mathematics and Statistics, Melbourne, VIC, Australia

    Jacob Cumming

  8. Department of Microbiology, Haukeland University Hospital, Bergen, Norway

    Rebecca J. Cox

Authors
  1. Amit Bansal
    View author publications

    Search author on:PubMed Google Scholar

  2. Sam W. Z. Olechnowicz
    View author publications

    Search author on:PubMed Google Scholar

  3. Nicholas Kiernan-Walker
    View author publications

    Search author on:PubMed Google Scholar

  4. Jacob Cumming
    View author publications

    Search author on:PubMed Google Scholar

  5. Imadh Abdul Azeez
    View author publications

    Search author on:PubMed Google Scholar

  6. Ramin Mazhari
    View author publications

    Search author on:PubMed Google Scholar

  7. Rebecca J. Cox
    View author publications

    Search author on:PubMed Google Scholar

  8. Ivo Mueller
    View author publications

    Search author on:PubMed Google Scholar

  9. Rory Bowden
    View author publications

    Search author on:PubMed Google Scholar

  10. Emily M. Eriksson
    View author publications

    Search author on:PubMed Google Scholar

Consortia

COVID PROFILE consortium

  • Siavash Foroughi
  • , Jason A. Tye-Din
  • , Anna K. Coussens
  • , Vanessa L. Bryant
  • , Emily M. Eriksson
  • , Anne Hart
  • , Maureen Forde
  • , Nicholas Kiernan-Walker
  • , Ramin Mazhari
  • , Erin C. Lucas
  • , Mai Margetts
  • , Anthony Farchione
  • , Dylan Sheerin
  • , George Ashdown
  • , Rachel Evans
  • , Catherine Chen
  • , Shazia Ruybal-Pesántez
  • , Eamon Conway
  • , Marilou H. Barrios
  • , Jasper Cornish
  • , Maria Edmonds
  • , Lee M. Henneken
  • , Lisa J. Ioannidis
  • , Sam W. Z. Olechnowicz
  • , Ryan B. Munnings
  • , Joanna R. Groom
  • , Diana S. Hansen
  • , Rory Bowden
  •  & Ivo Mueller

Contributions

S.W.Z.O and E.M.E. conceptualised the study. A.B., S.W.Z.O., J.C., I.A. and E.M.E. performed the data analysis. S.W.Z.O., N.W.K. and R.M. generated the data. A.B., S.W.Z.O., N.W.K., R.B. and E.M.E. designed experimental work; A.B., S.W.Z.O. and E.M.E. wrote the first draft of the manuscript. R.B., I.M., I.A. and R.J.C. provided guidance and discussion and edited the manuscript.

Corresponding authors

Correspondence to Rory Bowden or Emily M. Eriksson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Communications Medicine thanks Ruth Drury, Gerald Mboowa, Lalit Batra, Guo-Lin Wang, Andreza Lemos Salvio and the other, anonymous, reviewer for their contribution to the peer review of this work. Peer review reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary material (download DOCX )

Supplementary Data 1 (download DOCX )

Supplementary Data 2 (download XLSX )

Supplementary Data 3 (download XLSX )

Peer Review file (download PDF )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bansal, A., Olechnowicz, S.W.Z., Kiernan-Walker, N. et al. Divergent inflammatory and neurology-related protein levels in long COVID following primary and breakthrough SARS-CoV-2 infections. Commun Med (2026). https://doi.org/10.1038/s43856-026-01541-6

Download citation

  • Received: 14 August 2024

  • Accepted: 11 March 2026

  • Published: 13 April 2026

  • DOI: https://doi.org/10.1038/s43856-026-01541-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Collections
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Journal Information
  • Open Access Fees and Funding
  • Journal Metrics
  • Editors
  • Editorial Board
  • Calls for Papers
  • Contact
  • Conferences
  • Editorial Values Statement
  • Posters
  • Editorial policies

Publish with us

  • For Authors
  • For Referees
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Communications Medicine (Commun Med)

ISSN 2730-664X (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing