Furanoid F-Acid F6 Uniquely Induces NETosis Compared to C16 and C18 Fatty Acids in Human Neutrophils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various biomolecules induce neutrophil extracellular trap (NET) formation or NETosis. However, the effect of fatty acids on NETosis has not been clearly established. In this study, we focused on the NETosis-inducing ability of several lipid molecules. We extracted the lipid molecules present in Arabian Gulf catfish (Arius bilineatus, Val) skin gel, which has multiple therapeutic activities. Gas chromatography–mass spectrometry (GC-MS) analysis of the lipid fraction-3 from the gel with NETosis-inducing activity contained fatty acids including a furanoid F-acid (F6; 12,15-epoxy-13,14-dimethyleicosa-12,14-dienoic acid) and common long-chain fatty acids such as palmitic acid (PA; C16:0), palmitoleic acid (PO; C16:1), stearic acid (SA; C18:0), and oleic acid (OA; C18:1). Using pure molecules, we show that all of these fatty acids induce NETosis to different degrees in a dose-dependent fashion. Notably, F6 induces a unique form of NETosis that is rapid and induces reactive oxygen species (ROS) production by both NADPH oxidase (NOX) and mitochondria. F6 also induces citrullination of histone. By contrast, the common fatty acids (PA, PO, SA, and OA) only induce NOX-dependent NETosis. The activation of the kinases such as ERK (extracellular signal-regulated kinase) and JNK (c-Jun N-terminal kinase) is important for long-chain fatty acid-induced NETosis, whereas, in F-acid-induced NETosis, Akt is additionally needed. Nevertheless, NETosis induced by all of these compounds requires the final chromatin decondensation step of transcriptional firing. These findings are useful for understanding F-acid- and other fatty acid-induced NETosis and to establish the active ingredients with therapeutic potential for regulating diseases involving NET formation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it