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TP53 mutations andMDM2 gene amplification in squamous-cell carcinomas of the esophagus in South Thailand

2000· article· en· W2022106386 on OpenAlexaff
Philippe Tanière, Ghyslaine Martel‐Planche, Puttisak Puttawibul, Alan G. Casson, Ruggero Montesano, A Chanvitan, Pierre Hainaut

Bibliographic record

VenueInternational Journal of Cancer · 2000
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMutationEsophagusIncidence (geometry)BiologyGeneGeneticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

Squamous-cell carcinoma of the esophagus (SCCE) shows geographic variations in incidence that are thought to reflect the etiological involvement of environmental or dietary risk factors. Mutations of TP53 are frequent in SCCE, and there is evidence that both the frequency and type of these mutations may differ from one geographic area to the other. Although SCCE is relatively rare in most parts of Thailand, the province of Songkhla (south Thailand) has been described as a high-risk area for SCCE. We have analyzed 56 SCCE cases from this area for TP53 mutations by denaturing gradient gel electrophoresis (DGGE, exons 5-8) and direct DNA sequencing. The same tumors were also analyzed for MDM2 gene amplification by differential PCR. TP53 mutations were detected in 23 cases (41%). In contrast, clear amplification of MDM2 was detected in only 2 cases (4%), both of which contained wild-type TP53. Comparison with published results from other geographic areas of high SCCE incidence revealed that the spectrum of TP53 mutations in south Thailand is similar to that observed in central China (Henan Province) but clearly differs from that of SCCE from western Europe (Normandy, France; northern Italy), with more G:T transversions and fewer mutations affecting A and T base pairs. These results suggest that SCCE from south Thailand and from central China may involve similar risk factors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.279
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2000
Admission routes1
Has abstractyes

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