Evaluating the Targeting of a Staphylococcus-aureus-Infected Implant with a Radiolabeled Antibody In Vivo
Bibliographic record
Abstract
Implant infections caused by Staphylococcus aureus are difficult to treat due to biofilm formation, which complicates surgical and antibiotic treatment. We introduce an alternative approach using monoclonal antibodies (mAbs) targeting S. aureus and provide evidence of the specificity and biodistribution of S.-aureus-targeting antibodies in a mouse implant infection model. The monoclonal antibody 4497-IgG1 targeting wall teichoic acid in S. aureus was labeled with indium-111 using CHX-A”-DTPA as a chelator. Single Photon Emission Computed Tomography/computed tomographyscans were performed at 24, 72 and 120 h after administration of the 111In-4497 mAb in Balb/cAnNCrl mice with a subcutaneous implant that was pre-colonized with S. aureus biofilm. The biodistribution of this labelled antibody over various organs was visualized and quantified using SPECT/CT imaging, and was compared to the uptake at the target tissue with the implanted infection. Uptake of the 111In-4497 mAbs at the infected implant gradually increased from 8.34 %ID/cm3 at 24 h to 9.22 %ID/cm3 at 120 h. Uptake at the heart/blood pool decreased over time from 11.60 to 7.58 %ID/cm3, whereas the uptake in the other organs decreased from 7.26 to less than 4.66 %ID/cm3 at 120 h. The effective half-life of 111In-4497 mAbs was determined to be 59 h. In conclusion, 111In-4497 mAbs were found to specifically detect S. aureus and its biofilm with excellent and prolonged accumulation at the site of the colonized implant. Therefore, it has the potential to serve as a drug delivery system for the diagnostic and bactericidal treatment of biofilm.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".