Abnormalities of cell structures in tumors: apoptosis in tumors
Bibliographic record
Abstract
A conceptual shift has occurred in recent years from considering cancer as simply a disease of deregulated cell proliferation to a view that incorporates the aberrant control of apoptosis into the equation. Apoptosis is an organized, genetically programmed cell death process by which multicellular organisms specifically destroy, dismantle and dispose of cells. In cancer cells, this tightly controlled process is suppressed by genetic lesions, allowing cancer cells to survive beyond their normal life span even in hostile environments that are prone to hypoxia and lack many trophic factor supports. In the last two decades, cancer researchers have made great strides in our understanding of the underlying molecular mechanism of apoptosis in chemoresistance generation and tumorigenesis. This tremendous increase in our knowledge of apoptosis in tumors has greatly impacted our perspective on carcinogenesis. Key regulators of apoptosis such as members of the Inhibitors of Apoptosis family and Bcl-2 family have been shown to play a pivotal role in allowing most cancer cells to escape apoptosis. The identification of specific targets involved in the suppression of apoptosis in cancer cells has facilitated the design and development of therapeutic strategies based on rational molecular approaches that aim to modulate apoptotic pathways. Many promising apoptosis-dependent strategies have been translated into clinical trials in the continued assessment of regimens that can effectively eradicate cancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".