Effectiveness and Safety of a Supplement Containing a Pharmacologically Active Basidiomycete Mushroom for Chronic Fatigue and Post–COVID-19 Fatigue Syndrome: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Chronic fatigue syndrome, or myalgic encephalomyelitis, is characterized by persistent, unexplained exhaustion unalleviated by rest, with a pathophysiology distinct from underlying medical conditions. It is diagnostically complex due to symptoms that overlap with other disorders and the absence of definitive biomarkers, contributing to limited therapeutic options in current medicine. Lingzhi, a pharmacologically active basidiomycete mushroom, has been empirically used in traditional Chinese medicine for two millennia. This study has a dual focus: (1) systematically evaluating the efficacy and safety of lingzhi in managing chronic fatigue and post-COVID-19 fatigue syndrome and (2) elucidating its clinical associations with inflammatory, immune, and oxidative stress biomarkers to uncover potential therapeutic mechanisms. OBJECTIVE: The primary objective is to investigate whether a 6-week intake of CP003, a lingzhi-containing supplement, can reduce physical and mental fatigue in patients with chronic fatigue syndrome (ie, myalgic encephalomyelitis) or post-COVID-19 fatigue. Secondary objectives include assessing its effects on sleep quality, anxiety, depression, and general health status and exploring associations with inflammatory, immune, and oxidative stress biomarkers. METHODS: This randomized, waitlist-controlled trial will enroll 130 participants in Hong Kong, equally allocated (1:1) to either the CP003 intervention group or a waitlist control group. The intervention period spans 6 weeks, followed by a 6-week follow-up phase to assess the sustained effects. The trial data will be managed using REDCap (Research Electronic Data Capture) and analyzed via an intention-to-treat approach with inverse probability weighting for missing data, assessed through generalized linear regression (adjusted for covariates and interaction terms) at 6 and 12 weeks and supplemented by subgroup and sensitivity analyses in R, with results reported as mean differences (with 95% CIs) and P<.05 considered significant. RESULTS: This study was funded in March 2024 (ITF/PRP/029/24FX). As of December 2025, all participants had been enrolled, but data entry had not yet commenced. Data analysis will commence after the completion of the 12-week follow-up for all participants. Results are expected to be submitted for publication in mid-2026. Safety outcomes will also be assessed and reported. CONCLUSIONS: This trial will evaluate the efficacy and safety of CP003 for chronic fatigue and post-COVID-19 fatigue syndrome and explore potential underlying mechanisms of action. The findings will provide experimental data to inform future clinical applications and research on traditional Chinese medicine-based interventions for fatigue-related disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT06739720; https://clinicaltrials.gov/study/NCT06739720. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/82633.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".