PW005 Safety and efficacy of dabigatran compared to warfarin for periprocedural anticoagulation in patients undergoing radiofrequency ablation for atrial fibrillation
Bibliographic record
Abstract
TGF-β is a cytokine with pleiotropic functions in hematopoiesis, angiogenesis, cell proliferation, differentiation, migration and apoptosis. Although its role in rheumatoid arthritis is not well defined, TGF-β activation leads to functional immunomodulatory effects according to environmental conditions. The function of TGF-β in the development of arthritis in murine models has been extensively studied with controversial results. Recent findings point to a non-relevant role for TGF-β in a mice model of collagen-induced arthritis. The study of TGF-β on T-cell responses has shown controversial results as an inhibitor or promoter of the inflammatory response. This paper presents a review of the role of TGF-β in animal models of arthritis.El TGF-β es una citocina implicada en procesos celulares como hematopoyesis, proliferación, angiogénesis, diferenciación, migración y apoptosis celular. Aunque su papel en la artritis reumatoide no está bien definido, está considerada como una citocina inmunomoduladora según las condiciones del entorno. Numerosos trabajos han tratado de definir el papel del TGF-β en el desarrollo de la artritis murina en diferentes modelos de enfermedad, con resultados discordantes. De hecho, resultados recientemente publicados indican que TGF-β no desempeña un papel relevante en el modelo murino de artritis inducida con colágeno. Su implicación en la diferenciación y la funcionalidad de las diferentes poblaciones de células T también ha mostrado resultados dispares sobre su papel como inhibidor o promotor de la respuesta inflamatoria. En este trabajo se presenta una revisión sobre el papel de TGF-β en modelos animales de artritis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".