Signaling pathways and targeted therapies for psoriasis
Bibliographic record
Abstract
Psoriasis is a common, chronic, and inflammatory skin disease with a high burden on individuals, health systems, and society worldwide. With the immunological pathologies and pathogenesis of psoriasis becoming gradually revealed, the therapeutic approaches for this disease have gained revolutionary progress. Nevertheless, the mechanisms of less common forms of psoriasis remain elusive. Furthermore, severe adverse effects and the recurrence of disease upon treatment cessation should be noted and addressed during the treatment, which, however, has been rarely explored with the integration of preliminary findings. Therefore, it is crucial to have a comprehensive understanding of the mechanisms behind psoriasis pathogenesis, which might offer new insights for research and lead to more substantive progress in therapeutic approaches and expand clinical options for psoriasis treatment. In this review, we looked to briefly introduce the epidemiology, clinical subtypes, pathophysiology, and comorbidities of psoriasis and systematically discuss the signaling pathways involving extracellular cytokines and intracellular transmission, as well as the cross-talk between them. In the discussion, we also paid more attention to the potential metabolic and epigenetic mechanisms of psoriasis and the molecular mechanistic cascades related to its comorbidities. This review also outlined current treatment for psoriasis, especially targeted therapies and novel therapeutic strategies, as well as the potential mechanism of disease recurrence.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.004 | 0.002 |
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".