[Inhibition of bFGF gene expression and tumor angiogenesis of orthotopic implantation of human gastric carcinoma by N-desulfated heparin].
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of N-desulfated heparin on tumor metastasis, tumor angiogenesis and basic fibroblast growth factor(bFGF) gene expression of orthotopically implanted human gastric carcinoma in NOD-SCID mice. METHODS: Human gastric cancer SGC-7901 tissues were orthotopically implanted into the stomach of the NOD-SCID mice. Twenty mice were randomly divided into two groups which received either intravenous injection of 0.9% NaCl solution(0.9%NaCl solution group) or 10 mg/kg N-desulfated heparin (N-desulfated heparin group) twice a week for three weeks. Mice were sacrificed six weeks after tumor implantation. Tissues from stomach and other organs were obtained for histopathological evaluation. The intratumoral microvessel density (MVD) in tumor was evaluated immunohistochemically. Real time PCR was used to detect bFGF mRNA expression. RESULTS: The tumor metastasis rates were 9/10 in 0.9% NaCl solution group and 2/10 in N-desulfated heparin group(P<0.05).MVD was 9.1+/-3.4 in 0.9% NaCl solution group and 4.7+/-1.8 in N-desulfated heparin group (t=3.617,P<0.05). bFGF mRNA expression was lower in N-desulfated heparin group(2.60+/-0.56%)than that in 0.9% NaCl solution group(30.65+/-6.84%). CONCLUSION: N-desulfated heparin can inhibit the metastasis of gastric cancer through inhibiting tumor bFGF gene expression and tumor angiogenesis with no obvious anticoagulant activity.
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 | 0.000 | 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) | 0.001 | 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".