Impact of intravenous laser irradiation of blood on cognitive function and molecular pathways in long COVID patients: a pilot study
Bibliographic record
Abstract
BACKGROUND: Long COVID presents persistent neurological symptoms, including brain fog, with limited therapeutic options. Intravenous laser irradiation of blood (ILIB) has been proposed as a potential intervention. This pilot study explores the efficacy of ILIB in alleviating brain fog symptoms and examines the underlying molecular mechanisms. AIM: To evaluate the effectiveness of ILIB in improving cognitive function in long COVID patients with brain fog and to investigate the molecular pathways involved. DESIGN: A prospective, single-center pilot study involving six long COVID patients with brain fog who underwent ILIB therapy. METHODS: Patients received 30 ILIB sessions over eight weeks. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE) and Athens Insomnia Scale (AIS) at baseline, post-treatment and one-month follow-up. RNA sequencing and pathway enrichment analyses (KEGG, Gene Ontology) identified differentially expressed genes and molecular pathways influenced by ILIB. RESULTS: MoCA and AIS scores significantly improved post-ILIB, suggesting enhanced cognitive function and sleep quality. RNA sequencing revealed 141 upregulated and 130 downregulated genes. Upregulated pathways were associated with mitochondrial electron transport and oxidative phosphorylation, while immune response and inflammatory pathways were downregulated. Notably, the glutathione metabolism pathway was significantly altered, suggesting reduced oxidative stress. CONCLUSIONS: ILIB shows potential in alleviating brain fog symptoms in long COVID patients, possibly through modulation of oxidative stress, mitochondrial function and inflammation. However, larger randomized controlled trials are needed to confirm these findings and establish ILIB as a viable therapeutic option.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".