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Record W4415948461 · doi:10.3389/fneur.2025.1523814

Does C1 esterase inhibitor play a role in post COVID-19 neurological symptoms? A randomized, double-blind, placebo-controlled, crossover, proof-of-concept study

2025· article· en· W4415948461 on OpenAlexaboutno aff
Isaac Melamed, Mary Ellen Bayko, John Williams, Noga Or‐Geva

Bibliographic record

VenueFrontiers in Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsnot available
Fundersnot available
KeywordsEsterasePathogenesisClinical neurology

Abstract

fetched live from OpenAlex

Background: Many patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection experience neurologic changes post-infection, which has been hypothesized to be due to dysregulation in the infectious-immune axis that leads to a neuro-immune response. This immune dysfunction has been termed "Alzheimer's of the Immune System" or AIS and there are several immune factors that may play a key role. These include, among others, complement activation due to low levels of C1-esterase inhibitor (C1-INH) and function, and a decrease in signaling of Toll-like receptor (TLR)-3. We propose that C1-INH replacement may upregulate the immune dysfunction, thereby improving neurological symptoms. Methods: In this randomized, double-blind, placebo-controlled, crossover, proof-of-concept study, adults experiencing SARS-CoV-2 post-viral fatigue syndrome for >4 weeks post-recovery from coronavirus disease 2019 (COVID-19) infection were randomized 1:1 to two arms: Arm 1 (C1-INH for 8 weeks, then placebo for 8 weeks) or to Arm 2 (placebo for 8 weeks, then C1-INH for 8 weeks). Patients were assessed for adult executive function, abnormal cognitive decline, depression [Beck Depression Inventory-II (BDI-II)], migraine, fatigue [Fatigue Severity Scale (FSS)] and pain (Short-form McGill Pain Questionnaire). Percent change in TLR signaling in response to zymosan was compared with controls at baseline, Week 8 and Week 16. Safety was assessed throughout. Results: At this interim analysis, 36 patients with SARS-CoV-2 post-viral fatigue syndrome had completed the two 8-week treatment periods. In Arm 1, trends toward improvements from baseline at Week 8 of C1-INH therapy were observed in BDI-II score (-8.7 points), mean FSS score (0.6 points), and mean McGill Pain Questionnaire score (-0.4 points). These improvements were either sustained or worsened at Week 16, following crossover to placebo. The outcomes in Arm 2 were compatible with those in Arm 1. Patients with SARS-CoV-2 post-viral fatigue syndrome had low levels of TLR-related signaling biomarkers compared with healthy controls. Conclusion: This proof-of-concept study demonstrates sustained dysregulation of the immune system after COVID-19 infection. Improvements in depression, fatigue, and pain were observed with C1-INH treatment in patients with SARS-CoV-2 post-viral fatigue syndrome, indicating C1-INH may be a potential therapeutic target. Clinical trial registration: The study was registered on September 21, 2024, with the identifier number NCT04705831.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.288
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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