C-reactive protein: a target for therapy to reduce inflammation
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
C-reactive protein (CRP) is well-recognized as a sensitive biomarker of inflammation. Association of elevations in plasma/serum CRP level with disease state has received considerable attention, even though CRP is not a specific indicator of a single disease state. Circulating CRP levels have been monitored with a varying degree of success to gauge disease severity or to predict disease progression and outcome. Elevations in CRP level have been implicated as a useful marker to identify patients at risk for cardiovascular disease and certain cancers, and to guide therapy in a context-dependent manner. Since even strong associations do not establish causality, the pathogenic role of CRP has often been over-interpreted. CRP functions as an important modulator of host defense against bacterial infection, tissue injury and autoimmunity. CRP exists in conformationally distinct forms, which exhibit distinct functional properties and help explaining the diverse, often contradictory effects attributed to CRP. In particular, dissociation of native pentameric CRP into its subunits, monomeric CRP, unmasks "hidden" pro-inflammatory activities in pentameric CRP. Here, we review recent advances in CRP targeting strategies, therapeutic lowering of circulating CRP level and development of CRP antagonists, and a conformation change inhibitor in particular. We will also discuss their therapeutic potential in mitigating the deleterious actions attributed to CRP under various pathologies, including cardiovascular, pulmonary and autoimmune diseases and cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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