Construction of tissue micro array from prostate needle biopsies using the vertical clustering re‐arrangement technique
Bibliographic record
Abstract
BACKGROUND: Tissue microarray (TMA) allows for simultaneous rapid expression analysis of multiple molecular targets in many tissue specimens. TMA's are specifically in demand for the screening for diagnostic and prognostic markers in prostate cancer (PC). Consequently, TMAs from prostate needle biopsy (PNB) material taken at diagnosis before any treatment commenced are in demand. However, since PNB contain only limited amount of tumor arranged within a very thin tissue core, TMA construction from PNB is problematic. METHODS: Archival PNB from 30 PC patients with variable Gleason scores (6-10) and % of cores involvement (30-90%) were used. Following selection of representative cores, the paraffin blocks were melted. Each core was sectioned into equal parts of 3-4 mm in length. For each case, a group of fragments was then re-embedded in a vertical orientation. Using Manual TMA Apparatus, 2 mm cores from each of the vertically rearranged fragments were harvested. Sections (4 µm) were stained with H&E and with high-molecular weight cytokeratin (HMWCK), PIN-cocktail (p63 + p504S), and PSA immunohistochemical stains. RESULTS: A TMA from PNB with a capacity of 80 serial 4 µm sections was constructed. In all cases, identical tumor and neighboring tissue morphology (atrophic changes and high-grade prostatic intra-epithelial neoplasia) with no loss of tissue was evident. CONCLUSIONS: The vertical clustering re-arrangement (VCR) technique is suitable for large scale construction of TMA blocks from PNB maintaining the morphological and immunohistochemical characteristics of the original samples. This method is promising both in terms of archival tissue preservation and biomarkers research.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".