Manipulating TLR4 surface expression to investigate its influence on UV-induced cell death 3666
Bibliographic record
Abstract
Abstract Description Activation of TLR4 signaling by UV has been shown to cause cell death by apoptosis promoting UV-induced carcinogenesis. Without TLR4, necroptosis, an inflammatory cell death, may occur instead and lead to an immune-driven decrease in UV-carcinogenesis. This study investigates the use of lipopolysaccharide (LPS) to induce decreased TLR4 surface expression and how this effects cell death mechanism following UV exposure. If cell death can be skewed toward necroptosis with addition of LPS to skin cells before UV exposure, the incidence of UV-induced carcinogenesis may decrease. To investigate this hypothesis, thioglycolate-induced mouse peritoneal macrophages cells were stimulated with 0 mJ/cm2, 25 mJ/cm2, and 50 mJ/cm2 of UVB to measure for cell death. The UVB 50 mJ/cm2 dose reliably results in visible cell death via DNA laddering assays and cleavage of caspase-3 as measured by Western blot. Cells were treated with 0.1 ng/mL, 1 ng/mL, or 10 ng/mL LPS for 24 hours and decreased surface expression of TLR4 is confirmed by immunohistochemistry. Cells are then exposed to 50 mJ/cm2 of UVB, 24 hours later evidence of cell death including DNA laddering, cleavage of caspase-3, phosphorylation of necroptotic protein MLKL, and concentration of thymine dimers is investigated. Understanding how innate immune ligands impact epithelial cell fate may provide proof of principal for use of prophylactic low dose LPS application before UV exposure. Funding Sources FCSM Undergraduate Research Grant Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".