Analyzing and predicting of the effect of Lactobacillus peptidoglycan on gene expression in immune cells of BALB/c mice immune cells using GO database
Bibliographic record
Abstract
Objective:To explore the effect of Lactobacillus peptidoglycan (PGN) on gene expressing profile of murine immune cells.Methods:BALB/c mice were administrated (i.p.) with Lactobacillus peptidoglycan once or three times.Total RNA was extracted from pooled peritoneal macrophages and splenic lymphocytes.Affymetrix MOE430A genechip was used to analyze gene expression.Expression data was further analyzed using tools based on GO database (GoSufer,DAVID cluster analysis tool and GenMAPP).Significantly changed genes for their expressing amount was termed as PGN responsive genes.Results:1 dose of WPG administration triggered a rapid and widespread response in the expressing prifile.When treated thrice,a slow but more specific response was induced which was focused on immune response.PGN responsive genes were analyzed using GenMAPP,the results showd that PGN-representative biological process GO terms were related to macrophage phagocytic activity,lymphocyte activation functionation and positive regulation of inflammatory response.The GO terms related to molecule function were related to metabolism and signal transduction.The GO terms of cellular components were related to immunological synapse and T cell receptor complex.Conclusion:WPG mainly induceds the activations of innate immune response and enhanced antigen presentation.
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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 | 0.001 | 0.001 |
| 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".