Sarcodia suieae acetyl-xylogalactan regulate RAW 264.7 macrophage NF-kappa B activation and IL-1 beta cytokine production in macrophage polarization
Bibliographic record
Abstract
In this study, the effects of acetyl-xylogalactan extracted from Sarcodia suieae on RAW 264.7 macrophage polarisation were evaluated. This extracted acetyl-xylogalactan had a monosaccharide composition of 91% galactose and 9% xylose, with polysaccharide and acetyl contents of 80.6% and 19.3%, respectively. MALDI-TOF mass spectrometry and NMR spectroscopy revealed the molecular weight of the acetyl-xylogalactan to be 88.5 kDa. After acetyl-xylogalactan treatment, RAW 264.7 macrophage polarisation was noted, along with enhanced phagocytic ability. Furthermore, the Cell Counting Kit-8 (CCK-8) assay was performed and the results demonstrated non-significant alteration in lactate dehydrogenase levels in the treated cells. Next, interleukin (IL) 1β, TNF, and Malt-1 expression in RAW 264.7 macrophages treated with the S. suieae acetyl-xylogalactan was investigated through real-time quantitative polymerase chain reaction, and the results demonstrated that S. suieae acetyl-xylogalactan induced IL-1β and Malt-1 expression. RNA sequencing analysis results indicated the S. suieae acetyl-xylogalactan positively regulated cytokine production and secretion, protein secretion, and response to IL-1 activation, based on the observed GO terms. The predicted target genes in the GO enrichment analysis were found to upregulate NF-κB signalling and M0 to M1 macrophage conversion through the observed cytokine production. Thus, acetyl-xylogalactan can positively regulate RAW 264.7 macrophage polarisation.
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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".