8. Evaluation of Snow Fungus Polysaccharides on benzo[a]pyrene-induced DNA Damage
Bibliographic record
Abstract
Snow fungus, Tremella fuciformis, has been demonstrated to have numerous health benefits including purported chemopreventive properties due to free radical-scavenging ability. Protective effects derived from snow fungus polysaccharides are evaluated on Chinese hamster lung fibroblasts (CCL-39) exposed to carcinogen benzo[a]pyrene known to cause free radical formation and oxidative stress to cells. In this experiment, it was hypothesized that the naturally occurring polysaccharides in snow fungus are able to protect against or reduce oxidative stress-induced DNA damage. Polysaccharides were isolated through an alkaline extraction and in-vitro digestion. DNA damage was measured using the single-cell gel electrophoresis comet assay after exposure to benzo[a]pyrene and polysaccharide extract to lung fibroblasts. Results were calculated using the mean and standard deviation data of tail length and area, respectively. Each damaged cell was measured and analyzed through ImageJ Editing Software. The results indicate a promising trend which depict snow fungus polysaccharides yielding lower levels of DNA damage compared to cells exposed to benzo[a]pyrene and compared to the negative control (phosphate buffered saline and Dulbecco’s cell medium). This study suggests polysaccharides from Tremella fuciformis could truly prevent cellular DNA damage by protecting against oxidative stress.
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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) | 0.002 | 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".