Protein kinase inhibitors in the treatment of inflammatory and autoimmune diseases
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
Protein kinases mediate protein phosphorylation, which is a fundamental component of cell signalling, with crucial roles in most signal transduction cascades: from controlling cell growth and proliferation to the initiation and regulation of immunological responses. Aberrant kinase activity is implicated in an increasing number of diseases, with more than 400 human diseases now linked either directly or indirectly to protein kinases. Protein kinases are therefore regarded as highly important drug targets, and are the subject of intensive research activity. The success of small molecule kinase inhibitors in the treatment of cancer, coupled with a greater understanding of inflammatory signalling cascades, has led to kinase inhibitors taking centre stage in the pursuit for new anti-inflammatory agents for the treatment of immune-mediated diseases. Herein we discuss the main classes of kinase inhibitors; namely Janus kinase (JAK), mitogen-activated protein kinase (MAPK) and spleen tyrosine kinase (Syk) inhibitors. We provide a mechanistic insight into how these inhibitors interfere with kinase signalling pathways and discuss the clinical successes and failures in the implementation of kinase-directed therapeutics in the context of inflammatory and autoimmune disorders.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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