Gene expression of immunomodulatory cytokines induced by <i>Narcissus tazetta</i> lectin in the mouse
Bibliographic record
Abstract
The immunomodulation of Narcissus tazetta lectin (NTL) on the induction of gene expression of cytokines in the mouse was studied using specific cytokine primers, total RNA isolated from mouse splenocytes and macrophages, and reverse transcription-polymerase chain reaction (RT-PCR). For comparison, a fungal antimitogenic lectin from Agaricus bisporus (ABL) was used to test and compare the acute (kinetic) induction of cytokine gene expression. NTL was able to induce the expression of IL-1beta, TNF-alpha, and immunoreactive nitric oxide synthase (NOS) in both splenocytes and macrophages in vivo after 10-day consecutive peritoneal injections of 5 mg NTL x kg(-1) x day(-1) in the mouse. Nevertheless, the expression levels of IFN-gamma and TGF-beta were markedly increased in macrophages, and the levels of IL-2 and IL-4 were up-regulated only in splenocytes. From the kinetic pattern of cytokine induction and gene expression, ABL appeared to induce the up-regulation of IL-1beta and TNF-alpha in splenocytes up to 24 h, whereas NTL showed a more sustained effect on the expression of these cytokines in macrophages. While NTL manifested TGF-beta expression at the onset of 12 and 24 h in macrophages and splenocytes, respectively, ABL induced TGF-beta in neither splenocytes nor macrophages. After injection of NTL, stem-cell factor was clearly down-regulated in macrophages at 24 and 48 h but up-regulated in splenocytes at the end of 24 h. The immunopotentiating effect of NTL is quite similar to that of LZ-8, a fungal immunomodulatory lectin isolated from the Chinese premier medicinal mushroom Ganoderma lucidium. However, the mechanism of immunomodulation of NTL still awaits to be elucidated.
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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) | 0.001 | 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".