Skin of the Catfish (Arius Bilineatus, Val.) Contains Lipid Compounds that Regulate NET Formation and NET‐Mediated Inflammation
Bibliographic record
Abstract
Neutrophil Extracellular Traps (NETs) are networks of DNA fibers that are coated with cytotoxic components, released by neutrophils. NETs are beneficial for killing microbial pathogens, extracellularly, or by ensnaring toxins and cytokines. In different disease context, NETs can either be pro‐inflammatory or anti‐inflammatory. Therefore, identifying compounds that could regulate NET formation is important. Two major pathways of NETosis have been identified: NADPH oxidase‐dependent and calcium‐mediated NADPH oxidase‐independent NETosis. We have recently shown that NETosis is stimulated by a natural lipid eicosanoid, hepoxilin A3, which has pro‐inflammatory properties. Depending on the concentration, hepoxilin A3 induces either one or both types of NETosis. Preparations from the skin of the catfish contain lipids and proteins which have several important biological properties, such as anti‐thrombotic, anti‐inflammatory and wound healing activities. Our goal is to isolate and characterize products present in the lipid fractions of the preparations and study their biological activities. The lipids were separated from the proteins and fractionated by column chromatography. The neutral lipid fraction was separated into several sub‐fractions by thin layer chromatography (TLC). These sub‐fractions were tested in a battery of bioassays including the NETosis assays. We discovered that various isolated TLC fractions regulated NETosis, and importantly suppressed NETosis induced by the calcium ionophore A23187 to various degrees. This discovery suggests a novel mechanism for the anti‐inflammatory actions of the preparations. Support or Funding Information The study was Supported by: A grant from the Kuwait Foundation For Advancement of Sciences (KFAS 2013‐120701 A‐C), Kuwait University (KU SL03/14) and the Hospital for Sick Children and University of Toronto.
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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".