Digitally Assessed Long COVID Symptomatology Is Associated With Lymphocyte Mitochondrial Dysfunction and Altered Immune Potential
Bibliographic record
Abstract
Background: Postacute sequelae of SARS-CoV-2 infection, also known as long COVID (LC), is a complex and heterogenous condition affecting millions worldwide with a poorly understood underlying pathology. Although metabolic dysregulations have been described in LC, it remains unclear whether circulating immune cells exhibit immunometabolic alterations. Methods: We conducted a detailed clinical, immunologic, and mitochondrial analysis on 27 patients with LC and 27 who recovered from COVID-19 and were healthy. Symptom burden and severity were assessed and quantified via a digital platform with the modified COVID-19 Yorkshire Rehabilitation Scale. Mitochondrial function of circulating immune cell populations (lymphocytes and monocytes) was analyzed by measuring mitochondrial mass and mitochondrial membrane potential. Production of 11 cytokines after whole blood stimulation with bacterial and viral agonists was measured by multiplex immunoassay. Relationships between mitochondrial and immune parameters with LC symptoms were investigated. Results: natural killer cells, particularly in patients experiencing dizziness, whereas reduced mitochondrial membrane potential in CD4+ lymphocytes was found in patients with worsening breathlessness. Upon LPS stimulation, patients with LC demonstrated significantly lower IFN-γ production. In response to viral ligand R848, impaired IFN-β and IL-10 responses were associated with worsening cough and executive functions. Conclusions: Symptom severity in LC is associated with immune cell mitochondrial dysfunction and altered cytokine responses, highlighting potential disease biomarkers and targets for future therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".