Bibliographic record
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation and progressive joint destruction. Anti-TNF biologics have been developed to treat RA and although effective in the majority of patients, they require repeated administration and systemically inhibit TNF. This systemic inhibition leads to systemic immune suppression and can result in side-effects including opportunistic infections, serious infections and malignancy. To address these limitations, I developed a novel, local-acting biologic known as TNF sticky trap. This biologic was shown capable of inhibiting TNF while sticking or localizing to the extracellular matrix (ECM) where it is produced or administered. Next, cell lines inducibly expressing this local-acting biologic were generated, characterized and evaluated for their therapeutic efficacy in an animal model of RA. A single injection of cells expressing TNF sticky trap was sufficient to reduce arthritis and this local-acting biologic was undetectable in the serum of treated animals. Lastly, to help advance the translation of RA cell therapies into the clinic, a cell safety mechanism was developed and characterized. The development of a safe cell therapy inducibly expressing local-acting biologics could avoid the limitations associated with systemic therapies and improve the current treatment of RA.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".