Bibliographic record
Abstract
Virus-induced apoptosis and host antiviral innate immunity are key issues in understanding virus-host interactions and viral pathogenesis. Coronavirus infection of mammalian cells induces apoptotic and host innate immune responses. Global gene expression profiles are determined in coronavirus-infected Vero cells by Affymetrix array, using avian coronavirus infectious bronchitis virus (IBV) as a model system, to reveal up-regulation of both pro-apoptotic B cell lymphoma-2 (BCL-2)-antagonist/killer 1 (Bak) and pro-survival myeloid cell leukemia-1 (Mcl-1). Apoptosis occurred earlier in IBV-infected cells silenced with short interfering RNA targeting Mcl-1 (siMcl-1), and was delayed in siBak cells. Up-regulation of RNA helicases from the RIG-I-like receptor (RLR) family, Melanoma differentiation-associated gene 5 (MDA5) and Retinoic-inducible gene I (RIG-I), was also observed in IBV-infected cells, leading to downstream Mitochondrial antiviral signalling (MAVS) adaptor protein activation for interferon induction in response to viral invasion. RIG-I, MDA5 and MAVS also play a part in modulating IBV-induced apoptosis. Bcl-2 family proteins, too, regulates MAVS cleavage during apoptosis, thus further establishing a link between MAVS and intrinsic apoptotic pathway activation in the mitochondria during host innate immunity induction.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".