Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS
Bibliographic record
Abstract
Long COVID (LC) manifests with sex-specific differences, particularly in those with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Our study reveals that female LC patients (LCF) with ME/CFS show a shift toward myelopoiesis, reduced lymphocytes, increased neutrophils/monocytes, and depleted regulatory T cells—suggesting persistent immune activation. Elevated CD71 + erythroid cells and disrupted erythropoiesis contribute to fatigue and tissue damage in LCF. Cytokine profiling indicates a stronger pro-inflammatory response in LCF compared to males (LCM), along with markers of gut barrier dysfunction. Hormonal analysis shows reduced testosterone in LCF and estradiol in LCM. Transcriptomic data reveal neuroinflammatory signatures in LCF, potentially explaining cognitive symptoms. We also identify biomarkers that distinguish LCF from LCM and correlate with sex-specific clinical symptoms. Overall, LC with ME/CFS is characterized by sex-specific immune, hormonal, and transcriptional alterations, with females exhibiting more severe inflammation. These insights underscore the need for sex-tailored interventions, including consideration of hormone replacement therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".