Bibliographic record
Abstract
Protein kinase C (PKC) represents a family of phospholipid-dependent serine/threonine kinase. PKC was detected in almost all types of cells and tissues in the body. The activation of PKC is involved in the signal regulation of many physiological and pathological processes. PKC has multiple isoforms (alpha, betaI, betaII, gamma delta, epsilon, eta, theta, xi, iota and micro). PKC-mediated cellular processes are tissue- and isoform-specific. Investigations on selective or isoform-specific PKC inhibitors have attracted great attention during last two decades. Recent studies demonstrated that LY333531, a PKC-beta-specific inhibitor, reduced the development of diabetic vascular complications in animal models and prevented hyperglycemia-induced impairment of endothelial-dependent vasodilation in healthy subjects. Results from phase I clinical trial suggested low risk of the inhibitor. Phase III clinical trials on the safety and the preventive effects of the PKC-beta-specific inhibitor on diabetic complications are under progress. The efficacy of ISSI-3521, a PKC-alpha antisense inhibitor, on slowing the growth and metastasis of solid tumors is currently being examined in clinical trials. Partial responses in the prevention of the progress of malignancies were found in early phases of clinical trials for UCN-01 and CGP41251, two partially isoform-specific PKC inhibitors. Recent findings suggest that isoform-specific PKC inhibitors are potentially beneficial to the prevention or treatment of some common diseases, including cancers and diabetic vascular complications. Safety and efficacy studies of the PKC inhibitors will be required through large-scale long-term clinical trials.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".