Targeting the TLR4/MD-2 complex for imaging inflammation by SPECT/CT
Bibliographic record
Abstract
1515 Objectives Toll-like receptors (TLR) have a role in the induction and continuation of inflammation. We aim to develop a radio-tracer utilizing bacterial lipopolysaccharide (LPS) and targeting the TLR4/ MD-2 protein complex to facilitate specific detection of inflammation in vivo. Methods HEK293-TLR4/MD-2 cells were used to investigate targeting agents. The lipid A portion of LPS is the inflammatory activator that binds TLR4/MD-2 and initiates an immune response. Two antagonists, Salmonella minnesota monophosphoryl lipid A (MPLA) and Rhodobacter sphaeroides LPS, were radiolabeled and were compared in subsequent assays with radiolabeled S. minnesota LPS, an agonist. Each agent was labeled with up to 10mCi/mg 111In in pH6.5 sodium acetate for one hour. Time course binding assays were performed. Specificity was determined by pre-incubation with unlabeled Rh. sphaeroides LPS. Immune response was monitored by Western blot, ELISA, and immunocytochemistry. Results S. minnesota LPS and Rh. sphaeroides LPS were successfully radiolabeled, S. minnesota MPLA was not. S. minnesota 111In-LPS was labeled at 97.8%, Rh. sphaeroides at 40.6%. Each tracer specifically binds TLR4/MD-2 within 30 minutes. S. minnesota 111In-LPS binding caused the activation of NFΚB as seen with Western blot and ELISA. Rh. sphaeroides 111In-LPS did not induce NFΚB. The immunocytochemistry time course showed NFΚB nuclear translocation within 30 minutes of exposure to S. minnesota 111In-LPS, not seen with Rh. sphaeroides 111In-LPS. Conclusions LPS was successfully labeled with 111In and was shown to specifically bind TLR4/MD-2. Rh. sphaeroides 111In-LPS had reduced NFΚB activation compared to S. minnesota 111In-LPS, indicating it is still acting as an antagonist. Further studies will determine the functional characteristics of these tracers for imaging inflammation in vivo
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".