[Viruses involved in allograft recipients'cutaneous carcinomas].
Bibliographic record
Abstract
BACKGROUND: Cutaneous carcinomas are frequent in renal allograft recipients. Their treatment can be difficult especially in cases of multiple carcinomas. The aim of this study was to determine whether human papillomavirus are more frequent in patients group with multiple cutaneous carcinomas and whether other viruses such as Epstein-Barr virus, cytomegalovirus, and herpes simplex might be associated in this kind of tumour. PATIENTS AND METHODS: Forty-three patients were included. Twenty-two had a single carcinoma (group 1) and 21 had multiple cutaneous carcinomas (group 2). Histologic analysis and in situ hybridization were used to search for Epstein-Barr virus, human papillomavirus, herpes simplex virus and cytomegalovirus latency genes. RESULTS: In both groups, epidermoid carcinomas were more frequent than basal cell carcinomas and delay between graft and first carcinoma was similar (5 years). In situ hybridization was more often positive in group 2 (41/50) than in group 1 (13/22). Human papillomavirus DNA was detected more frequently in the group with multiple carcinomas (26/50) than in the group with a single carcinoma (6/22). Moreover, cytomegalovirus was more frequent in group 2. CONCLUSION: This study shows a higher prevalence of human papillomavirus DNA in the carcinomas of the multiple carcinoma population. Moreover, for the first time, cytomegalovirus DNA was detected in carcinomas of renal allograft recipients with a higher frequency in the patients with multiple carcinomas.
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) | 0.005 | 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".