Journal article
Encephalitis
- Abstract:
- Brain inflammation secondary to encephalitis is an urgent global emergency and presents multiple opportunities to reduce current substantial morbidity and mortality. Aetiologies can be divided into infectious and autoimmune causes. In this Seminar, we highlight pragmatic clinical approaches to recognise and distinguish the most common pathogenic viruses and emerging range of autoantibodies encountered in routine practice. These pre-test impressions are judiciously shaped by valuable, simple investigations—particularly serum and cerebrospinal fluid nucleic acid and autoantibody testing—to identify the precise causative agent. This clinically led approach ensures early recognition of encephalitis subtypes, facilitates the timely administration of antivirals and immunotherapies proven to improve patient outcomes, and minimises the frequent misdiagnosis of autoimmune encephalitis. Finally, we review emerging targeted therapeutic approaches, measurements of clinical encephalitis outcomes, and environmental and vaccine-centred strategies to improve prevention, diagnosis, and care for patients with encephalitis, cognisant of long-term patient, caregiver, and economic burdens.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
-
(Preview, Accepted manuscript, pdf, 208.3KB, Terms of use)
-
- Publisher copy:
- 10.1016/S0140-6736(26)00363-6
Authors
+ Wellcome Trust
More from this funder
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 104079/Z/14/Z
+ Medical Research Council
More from this funder
- Funder identifier:
- https://ror.org/03x94j517
- Grant:
- MR/V007173/1
- Publisher:
- Elsevier
- Journal:
- Lancet More from this journal
- Volume:
- 407
- Issue:
- 10542
- Pages:
- p1968-1983
- Publication date:
- 2026-05-16
- Acceptance date:
- 2026-02-17
- DOI:
- EISSN:
-
1474-547X
- ISSN:
-
0140-6736
- Language:
-
English
- Keywords:
- Pubs id:
-
2377230
- Local pid:
-
pubs:2377230
- Deposit date:
-
2026-02-18
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier Ltd
- Copyright date:
- 2026
- Rights statement:
- © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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