Immunohistochemical Expression of Vascular Endothelial Growth Factor and Vascular Endothelial Growth Factor Receptor Associated with Tumor Cell Proliferation in Canine Cutaneous Squamous Cell Carcinomas and Trichoepitheliomas
Bibliographic record
Abstract
The expression of 5 markers associated with angiogenesis was studied in canine squamous cell carcinomas (SCCs) (n = 19) and canine trichoepitheliomas (TCPs) (n = 24). SCCs were assigned histologic grades, and tissue sections from both tumor types were immunohistochemially stained for the expression of vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor-2 (VEGFR-2), as well as intratumoral microvessel density (iMVD), tumor proliferation index (PI), and tumor apoptotic index (AI), using antibodies against VEGF, VEGFR-2, von Willebrand's factor, Ki-67 antigen, and the terminal deoxynucleotidyl transferase-mediated 2'-deoxyuridine 5'-triphosphate end-labeling method (TUNEL), respectively. VEGF and VEGFR-2 were detected in 17/19 (89.4%) and 19/19 (100%) SCCs and in 17/24 (70.8%) and 20/24 (83.3%) TCPs, respectively. In SCCs, there was substantial correlation between histologic grade and PI (r = 0.51); and moderate correlation between VEGF and histologic grade (r = 0.43), VEGFR-2 and histologic grade (r = 0.47), VEGF and PI (r = 0.47), and VEGFR-2 and PI (r = 0.47) (Spearman rank correlation coefficient). In TCPs, there was substantial correlation between VEGF and PI (r = 0.51) and a moderate correlation between VEGFR-2 and iMVD (r = 0.36). The median iMVD of SCCs (15.5) was significantly higher than the median iMVD of TCPs (9.05) (P value < .05). It was concluded that VEGF and VEGFR-2 may promote tumor cell proliferation in TCPs and SCCs. An autocrine pathway for VEGF probably operates in canine SCCs and TCPs, as VEGF and VEGFR-2 expression was found in most tumors and was associated with evidence for tumor cell proliferation.
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 | 0.000 |
| 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) | 0.001 | 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".