Inhibition of neutrophil respiratory burst and degranulation responses by CVT-E002, the main active ingredient in COLD-FX
Bibliographic record
Abstract
Human peripheral blood neutrophils contribute to the first line of defence in the immune system and are critical for maintaining health and immunity against opportunistic infections. Neutrophils and their granule-derived mediators are frequently found elevated in patient samples in viral infections, asthma exacerbations, and other respiratory ailments. COLD-FX has been shown to reduce the symptoms and severity of respiratory tract viral infections. Our hypothesis is that COLD-FX modulates neutrophil activity. To determine the effects of COLD-FX on neutrophils, peripheral blood neutrophils (>97% purity) were isolated from healthy human volunteers. Neutrophils were preincubated with varying doses of CVT-E002 (0.01-1 mg/ml), the active ingredient of COLD-FX, for 30, 60, and 120 min. Extracellular ROS production was measured by cytochrome c reduction from neutrophils stimulated with 50 ng/ml phorbol myristate acetate for up to 60 min. Degranulation was measured by the presence of extracellular myeloperoxidase, a marker of the azurophilic granules, in neutrophils stimulated with cytochalasin B and f-Met-Leu-Phe for 15 min. CVT-E002 (1 mg/ml) had no significant effect on viability at up to 120 min of incubation. At 60 min of incubation with CVT-E002, neutrophils showed a 30% reduction in ROS generation (p < 0.001) which was maintained for up to 120 min. Preliminary experiments also showed that incubation of neutrophils with CVT-E002 for 30 min inhibited myeloperoxidase release. These novel findings demonstrate that COLD-FX significantly reduces activation of neutrophils. The implications of this study are that COLD-FX may reduce oxidative stress and tissue-damage triggered by neutrophilic inflammation and activation. Funding source: Afexa Life Sciences, Inc.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".