Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aging is a major risk factor for many diseases including metabolic syndrome, cancer, inflammation, and neurodegeneration. Identifying mechanistic common denominators underlying the impact of aging is essential for our fundamental understanding of age-related diseases and the possibility to propose new ways to fight them. One can define aging biochemically as prolonged metabolic stress, the innate cellular and molecular programs responding to it, and the new stable or unstable state of equilibrium between the two. A candidate to play a role in the process is protein kinase R (PKR), first identified as a cellular protector against viral infection and today known as a major regulator of central cellular processes including mRNA translation, transcriptional control, regulation of apoptosis, and cell proliferation. Prolonged imbalance in PKR activation is both affected by biochemical and metabolic parameters and affects them in turn to create a feedforward loop. Here, we portray the central role of PKR in transferring metabolic information and regulating cellular function with a focus on cancer, inflammation, and brain function. Later, we integrate information from open data sources and discuss current knowledge and gaps in the literature about the signaling cascades upstream and downstream of PKR in different cell types and function. Finally, we summarize current major points and biological means to manipulate PKR expression and/or activation and propose PKR as a therapeutic target to shift age/metabolic-dependent undesired steady states.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it