Cellular Automata modeling of shallow landslides
Bibliographic record
Abstract
Patients with cystic fibrosis (CF) often suffer from skeletal muscle atrophy, most often attributed to physical inactivity and nutritional factors. CF is also characterized by abnormally elevated systemic inflammation. However, it is unknown whether the lack of a functional CF transmembrane conductance regulator (CFTR) gene predisposes to exaggerated inflammation-induced muscle proteolysis. CF mice (<i>CFTR<sup>-/-</sup></i>) and their wild-type (WT = <i>CFTR<sup>+/+</sup></i>) littermate controls were systemically injected with <i>Pseudomonas</i>-derived lipopolysaccharide (LPS). After 24 h, the diaphragm and limb muscles (fast-twitch tibialis anterior, and slow-twitch soleus) were assessed for induction of inflammatory cytokines (TNFα, IL1β, and IL6), oxidative stress, canonical muscle proteolysis pathways (Calpain, Ubiquitin-Proteasome, Autophagy), muscle fiber histology, and diaphragm contractile function. At baseline, CF and WT muscles did not differ with respect to indices of inflammation, proteolysis, or contractile function. After LPS exposure, there was significantly greater induction of all proteolysis pathways (calpain activity; ubiquitin-proteasome: MuRF1 and Atrogin1; autophagy: LC3B, Gabarapl-1, and BNIP3) in CF mice for the diaphragm and tibialis anterior, but not the soleus. Proteolysis pathway upregulation and correlations with inflammatory cytokine induction were most prominent in the tibialis anterior. Diaphragm force normalized to muscle cross-sectional area was reduced by LPS to an equivalent degree in CF and WT mice. CF skeletal muscles containing a high proportion of fast-twitch fibers (diaphragm, tibialis anterior) exhibit abnormally exaggerated upregulation of multiple muscle wasting pathways after exposure to an acute inflammatory stimulus, but not under basal conditions.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".