[The relationship between tumor suppressor genes p14ARF and p53 expression and biological behavior of pancreatic carcinoma].
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between tumor suppressor genes p14(ARF) and p53 expression and the biological behavior of pancreatic carcinoma. METHODS: Modified Envision method of immunohistochemistry was used on 42 specimens of pancreatic adenocarcinoma and 10 normal specimens of normal pancreas resected during operation to examine the expression of the gene p14(ARF). and ABC immunohistochemical technique was used to examine the expression of p53. RESULTS: The positive rates of p14(ARF) in of normal pancreatic tissues and pancreatic carcinoma were 90% and 35.7% respectively (P < 0.01). The positive rates of p53 in tissues of normal pancreatic and pancreatic carcinoma were 0 and 42.5% (P < 0.05) respectively. Significant correlation was detected between the expression of p14(AR) and p53 and the diameter of tumor, the rate of lymph node metastasis, pathological grade, and clinic stage in pancreatic carcinoma. No correlation was found between the expression of p14(ARF) and p53 and the tumor infiltration in pancreatic carcinoma. CONCLUSION: The tumor suppressor gene p14(ARF) and p53 are closely related to the occurrence and development of pancreatic carcinoma. It is possible to treat pancreatic carcinoma by gene intervention.
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 | 0.001 |
| 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 | 0.000 |
| 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".