Auswertung von Häufigkeitsverteilungen und Korrelationen der Proteine Hdm2, P53, P63, P14ARF und P16INK4a im invasiven Harnblasenkarzinom an digitalisierten Tissue Mikroarrays
Bibliographic record
Abstract
In this thesis the protein expression profiles of P53, P63, Hdm2, P14ARF and P16INK4a on a tissue microarray (TMA) from invasive bladder cancer were digitalized and their phenotypic expression-pattern was investigated. P53, P14ARF and Hdm2 are part of the regulatory mechanism which plays a vital role in cell cycle arrest in healthy cells. However, mutation often occurs in tumour cells leading to immortalization and progression [133, 224, 222]. Karni-Schmidt et al. [114] discovered that ΔNP63 positive bladder carcinomas hold an especially poor prognosis. Additionally, Choi et al. [34] were able to demonstrate the important role of epithelial-mesenchymal transition in the development of invasive bladder cancer. Common alterations seen in invasive bladder carcinomas are mutations of the INK4a/ARF gene locus, which codes for P14ARF and P16INK4a [136]. Thus, the present thesis implemented discussions on the correlation of the regulation of these two proteins. The fast development of new technical devices to digitalize and share scanned histologic slides had a strong impact of the integration of telemedicine in routine use as a tool for histopathologic evaluation. With that patient care could be improved in countries covering an extensive surface and countries suffering from a shortage of medical personnel as positive results in Canada and Egypt are demonstrating [7, 8, 243]. To investigate, if these measures are also suitable to be implemented in addition to the established techniques for the preparation and analyses as part of the investigation of marker profiles of tumour samples, was an additional part of the provided work.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".