ImmunoPET with Zirconium-89 specifically detects postoperative biofilm-associated implant infections. A preclinical study.
Bibliographic record
Abstract
BACKGROUND: Early postoperative implant infections are difficult to diagnose due to overlapping symptoms with inflammation. However, prompt surgical intervention for an implant infection can prevent the need for repeated surgeries and improve the overall success of the treatment and preserving the implant. The primary objective of this study was to assess the sensitivity and specificity of a novel immuno-PET radiotracer for detecting Staphylococcus aureus bacteria and their biofilms in a preclinical rat model. RESULTS: An antibody against wall teichoic acid a common surface component of S. aureus, was labeled with Zirconium-89- as the PET tracer. Wistar Han rats underwent surgery with a S. aureus-related biofilm-infected femoral implant on one side and a sterile femoral implant on the contralateral side. The diagnostic efficacy of this imaging modality was compared with clinically established nuclear imaging techniques for implant infections, including [99mTc]Tc-MDP SPECT/CT, [18F]FDG PET/CT, and [18F]NaF PET/CT. Furthermore, co-injection of unlabeled (“cold”) antibodies was performed to evaluate their impact on biodistribution. All animals with a biofilm-associated femoral implant infection showed significantly higher uptake of the novel ImmunoPET tracer in the infected side compared to the sterile side throughout the 13-day postoperative study duration. A dose-dependent increase in tracer accumulation was observed with co-injection of cold antibody, suggesting its potential to improve biodistribution. CONCLUSIONS: ImmunoPET with Zirconium-89-labeled antibodies specific for wall teichoic acid antigen demonstrates sensitive and specific diagnostic capabilities compared to conventional nuclear imaging modalities, offering a promising tool for early detection of postoperative chronic low-grade infections and septic implant loosening.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".