Human Equilibrative Nucleoside Transporter 1 and Human Concentrative Nucleoside Transporter 3 Predict Survival after Adjuvant Gemcitabine Therapy in Resected Pancreatic Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: Gemcitabine is a promising adjuvant treatment for patients with resected pancreatic adenocarcinoma and its use in combination with radiotherapy is under exploration. Human equilibrative nucleoside transporter 1 (hENT1) and human concentrative nucleoside transporter (hCNT) 1 and 3 are the major transporters responsible for 2',2'-difluoro-2-deoxycytidine (gemcitabine) uptake into cells. The aim of this study was to determine patients' outcome according to the expression of hENT1 and hCNT3 in tumoral cells after postoperative gemcitabine-based chemoradiation regimen. EXPERIMENTAL DESIGN: We studied tumor blocks from 45 pancreatic adenocarcinoma patients treated with gemcitabine-based chemoradiation after curative resection and assessed hENT1 and hCNT3 expression using immunohistochemistry. RESULTS: When adjusted for the effects of lymph node ratio and tumor diameter, patients with high hENT1 expression had significantly longer disease-free survival and overall survival (OS) than patients with low expression, whereas high hCNT3 expression was only associated with longer OS. In a combined analysis, patients with two favorable prognostic factors (hENT1(high)/hCNT3(high) expression) had a longer survival (median OS, 94.8 months) than those having one (median OS, 18.7 months) or no (median OS, 12.2 months) favorable prognostic factor. CONCLUSIONS: Pancreatic adenocarcinoma patients with a high expression of hENT1 and hCNT3 immunostaining have a significantly longer survival after adjuvant gemcitabine-based chemoradiation. These biomarkers deserve prospective evaluation in patients receiving gemcitabine-based adjuvant therapy.
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 | 0.000 | 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".