Bibliographic record
Abstract
Since the beginning of this work in 1998, it is estimated that 1780 men died of prostate cancer in Quebec. Molecular analysis of prostate cancer will eventually lead to the discovery of key genes involved in its onset and progression. The present project was to compare gene expression profiles in human non-tumorigenic versus tumorigenic prostate cell lines generated in our laboratory. A putative tumor suppressor gene present on 12q13 would be responsible for the non-tumorigenic phenotype of one cell line as discovered earlier by our team. In order to compare gene expression patterns, expression arrays from Clontech, bearing 588 genes known to be involved in human cancers, were hybridized with cDNA derived from two related cell lines available in our laboratory. This one experiment provided interesting hints on differentially expressed genes that could be involved in human prostate cancer. Interesting clones were confirmed by Northern blots. When commercial antibody was available, analysis was extended at the protein level. A combination of these analyses revealed no striking difference in the level of expression for the genes previously identified by the arrays hybridization. Simultaneously, differential display PCR techniques, allowing the discovery of unknown differentially expressed molecules and thus complementing the previous approach, were applied to compare related cell lines and unique hybrids. Cloning and sequencing of differential fragments brought us to what could be a new cDNA expressed in many human cell lines. Prostate cancer is not well characterized enough to allow accurate diagnosis or appropriate therapy strategies. Differentially expressed molecules analyzed in this project as well as the putative new cDNA might fulfil part of this lack in the understanding of this disease.
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 | 0.001 |
| 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) | 0.002 | 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".