Screening of Anticancer Materials from Myxobacteria and Evaluation of Their Bioactivity in Vivo
Bibliographic record
Abstract
Myxobacteria are a class of prokaryotes with complex multi-cellular behavior and could produce variousbioactive materials. Forty-eight strains stored in Key Lab of Microbial Diversity Research and Application ofHebei Province were screened by MTT methods with L1210, Hela and normal diploid human embryonic lungfibroblast cell lines as screening models. The results demonstrated that most of the strains could inhibit cancercells. The inhibition rates to L1210 and Hela are about 78.5% and 59%, respectively. Strain 910018 and 920036showed higher inhibition activity to cancer cells L1210 and Hela in compare with lower inhibition activity tonormal diploid human embryonic lung fibroblast cell line. Therefore, the extracts from the two strains have thepotentials as anticancer drugs and were undertaken in vivo anticancer experiments. The results showed that strain910018 and 920036 could inhibit mice transplanted liver cancer cells H22. The inhibition rate of low dosesgroup of strain 910018 is 69.37% and that of strain 920036 is 54.98%. Therefore, the two strains could be goodcandidates for producing of anticancer drugs.
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) | 0.001 | 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 | 0.000 |
| 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".