Dermytol, A Novel Compound For The Prevention Of Melanoma Skin Cancer Tested For Efficacy In Mice
Bibliographic record
Abstract
A novel proprietary compound, trade named Dermytol™ has been shown to reduce melanoma tumor cell growth in male mice. Six Male C57BL mice (3–4 weeks old) were treated by oral gavage with 6.0% Dermytol™ w/v 0.2ml Canola oil. Mice in a control group were administered Canola oil. In a second treatment group of 6 male C57BL mice, a topical application of 6.0% Dermytol™ w/v in a cream base was applied to a shaved 1cm 2 area of the right hind flank. Control group mice for this treatment received an application of a placebo cream base. Both treatments were applied for 7 consecutive days followed by subcutaneous injection in the right hind flank of B16‐F1 malignant melanoma tumor cells (2.0x10 5 ) in 100μl saline. Oral and topical Dermytol™ treatments were continued for 25 days after tumor cell injection. Results showed that orally treated mice showed an average decrease in tumor volume of 44.5%. Tumor volume was decreased by 61.2% in topically treated mice. This pronounced decrease in tumor volume for both applications indicates that Dermytol™ is effective at inhibiting the proliferation of B16‐F1 melanoma tumor cells and that a topical application of the compound produces a more potent effect. Source: KGK Synergize, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
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".