Oxidized Cholesterol Derivatives in Fraction B Prepared from Gulf Catfish (Arius bilineatus, Val.) Skin Regulate Calcium Response and Neutrophil Extracellular Traps (NETs) Formation
Bibliographic record
Abstract
In this study, we present in vitro actions of pure commercial preparations of oxidized and/or dehydrated metabolites of cholesterol (OS) identified in the lipid fraction of Fraction B (FB) prepared from a catfish skin preparation on calcium transients and on the formation of human neutrophil extracellular traps (NETs). These investigations are part of an ongoing effort to understand the important roles these compounds play as components of FB when FB is applied to accelerate the healing of wounds and the healing of highly infected non-healing diabetic foot ulcers, without the use of antibiotics. Our aim was to determine potential therapeutic interventions for various disease states. Our results reveal interesting findings, demonstrating specific actions of the individual compounds. Compounds 7α-hydroxy-cholesterol (S3), Cholestane-3,5,6-triol (S5), 5-cholesten-3β-ol-7-one (S8) and Cholesta-3,5 dien-7-one (S10) are inhibitory, while Cholesterol 5β,6β-epoxide (S4) and 5α-cholestane-3,6-dione (S11) activate the response for calcium influx in human neutrophils. A somewhat similar response is observed in dHL60 cell lines, where S3, S5, S7, S8, and cholesta-2,4-diene (S14) inhibit the calcium influx, although S4, S10, and S11 activate the response in this cell line. Furthermore, we observed a relationship between actions against NETosis and calcium transients. Interestingly, relative to the vehicle control, S3, Cholesta-3,5 diene (S9), and S14 appeared to significantly stimulate DNA release (NETosis), while S2, 7α-hydroxy-cholesterol (S6) and cholesta-3,5 dien-7-one (S10) caused lesser stimulation. We provide the IC50 activities for each compound tested in each assay. Calcium influx and NETs formation (NETosis) correlate with diseases exacerbation. These findings offer valuable insights into the potential therapeutic applications of individual OS for various diseases, highlighting their importance in future interventions.
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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 | 0.000 | 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) | 0.001 | 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".