Bibliographic record
Abstract
Apoptosis, or programmed cell death, and the elimination of apoptotic cells are crucial factors in the maintenance of liver health Apoptosis allows hepatocytes to die without provoking a potentially harmful inflammatory response In contrast to necrosis, apoptosis is tightly controlled and regulated via several mechanisms, including Fas/Fas ligand interactions, the effects of cytokines such as tumor necrosis factor alpha (TNF-alpha) and transforming growth factor beta (TGF-beta), and the influence of pro- and antiapoptotic mitochondria-associated proteins of the B-cell lymphoma-2 (Bcl-2) family. Efficient elimination of apoptotic cells in the liver relies on Kupffer cells and endothelial cells and is thought to be regulated by the expression of certain cell surface receptors. Liver disease is often associated with enhanced hepatocyte apoptosis, which is the case in viral and autoimmune hepatitis, cholestatic diseases, and metabolic disorders. Disruption of apoptosis is responsible for other diseases, for example, hepatocellular carcinoma. Use and abuse of certain drugs, especially alcohol, chemotherapeutic agents, and acetaminophen, have been associated with increased apoptosis and liver damage. Apoptosis also plays a role in transplantation-associated liver damage, both in ischemia/reperfusion injury and graft rejection. The role of apoptosis in various liver diseases and the mechanisms by which apoptosis occurs in the liver may provide insight into these diseases and suggest possible treatments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".