Immunohistochemical analysis of kallikrein-related peptidases in the normal kidney and renal tumors: potential clinical implications
Bibliographic record
Abstract
Kallikrein-related peptidases (KLKs) have been shown to be differentially expressed in various malignancies and shown to be useful tumor markers. Previous immunohistochemistry (IHC) analysis demonstrated that KLKs 5, 6, 10, and 11 have a potential prognostic significance in renal cell carcinoma (RCC). To further explore the significance of KLKs, we examined KLKs 1, 6, 7, and 15 in different subtypes of renal tumors. KLK1 has stronger expression in high grade compared to low grade clear cell RCC. However, KLK6 and KLK7 show strong expression in low grade in contrast to high grade clear cell RCC. Furthermore, the expression of KLK7 can distinguish between oncocytoma and chromophobe RCC. Oncocytoma showed diffuse, strong granular cytoplasmic staining, but chromophobe RCC showed focal weak homogeneous cytoplasmic stain. The pattern of staining of different KLKs can also be helpful in differentiating some of the subtypes of renal tumors. Our results show the potential ability of KLKs to serve as diagnostic markers and expand previous data about the prognostic significance of KLKs in kidney cancer. In addition, our study is the first to show the ability of KLK staining to distinguish various types of kidney cancers when morphology is similar.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".