NEW P53 GENE MUTATION IN NON-CANCEROUS MUSTARD GAS EXPOSED LUNG
Bibliographic record
Abstract
Objective Mustard gas (MG) is a poisoning chemical, mutagenic and carcinogenic alkylating agent. It is used during World War I and also Iran-Iraq conflict. The p53 tumor suppressor gene is involved in the pathogenesis of malignant disease. The aim of this study is to determine possible mutation in p53 gene of lung sample from mustard gas exposed patients. Material and Methods Twelve lung biopsy samples from 12 Mustard Gas exposed soldiers cases along with control cell line were studied for the presence of mutations in exons 4-9 of the p53 gene by PCR and direct sequencing. Results Among examined biopsies most of the samples demonstrated normal polymorphism with no significant defected mutations but in one sample one type of p53 gene alteration at codon 278 (CCT→CCA) on transcribed strand was detected. This Mutation has not been observed in another studies related to mustard gas exposure and p53 mutation databases. Conclusion In this study we have reported for the first time new p53 mutation in the lung sample of MG exposed patients. It is concluded that only one silent mutation were scanned with no signs of any type of cancer. This type of mutation was not in IARC p53 gene mutation database. Moreover, surrounding sequences of the mutated p53 gene codons have more 5'-GT and 5-GC sequences which have been found both by our study and only one another study on Japanese exposed to MG.
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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 | 0.001 |
| 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 | 0.001 | 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".