CD30 associates with EBER-EBV but not HCV-NS3 in T-cell non-Hodgkin lymphoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction T-cell lymphoma contributes to malignancy worldwide, and the prognosis of this cancer is related to CD30 expression. Some T-cell lymphomas, including extranodal NK/T-cell lymphoma (ENKTCL), an aggressive lymphoma, have been commonly associated with Epstein-Barr virus (EBV) infection. EBV can be detected using Epstein-Barr-encoded RNA (EBER) in situ hybridization (ISH). In addition, hepatitis C virus (HCV) infection also has a role in the occurrence of non-Hodgkin Lymphoma (NHL). A nonstructural protein of the HCV, NS3, may be involved in lymphoma development. Furthermore, the Epstein-Barr virus has also long been associated with CD30. However, the relationship between CD30 and NS3 was unknown. This research aims to study the relationship between CD30, NS3, and EBER in T-cell lymphoma. Methods Data and paraffin blocks were collected from NHL T-cell patients, and 30 samples were chosen for the study after meeting the criteria. The paraffin block was stained with CD30, NS3, and EBER immunohistochemistry and read by two pathologists. Results From 30 cases, the dominant subtype expressing CD30 was Extranodal Natural Killer T-cell Lymphoma (ENKTCL) (84.62%). In total, 20 (66.7%) samples expressed CD30, 6 (20%) expressed NS3, and 18 (60%) samples expressed EBER. There is no significant relationship between NS3 and EBER. Meanwhile, CD30 expression correlated statistically with EBER ( P = 0.001). Discussion CD30 expression in T-cell non-Hodgkin lymphoma was not significantly associated with clinicopathology data in this study. CD30, NS3, and EBER were expressed in T-cell non-Hodgkin lymphoma. There exists a relationship between CD30 and EBER, but this study revealed no relationship between NS3 and EBER expression.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it