Radioimmunotherapy combating biofilm-associated infection in vitro
Bibliographic record
Abstract
Background Addressing prosthetic joint infections poses a significant challenge within orthopedic surgery, marked by elevated morbidity and mortality rates. The presence of biofilms and infections attributed to Staphylococcus aureus (S. aureus) further complicates the scenario. Objective To investigate the potential of radioimmunotherapy as an innovative intervention to tackle biofilm-associated infections. Methods Our methodology involved employing specific monoclonal antibodies 4497-IgG1, designed for targeting wall teichoic acids found on S. aureus and its biofilm. These antibodies were linked with radionuclides actinium-225 (225Ac) and lutetium-177 (177Lu) using DOTA as a chelator. Following this, we evaluated the susceptibility of S. aureus and its biofilm to radioimmunotherapy in vitro, assessing bacterial viability and metabolic activity via colony-forming unit enumeration and xylenol tetrazolium assays. Results Both [225Ac]4497-IgG1 and [177Lu]4497-IgG1 exhibited a noteworthy dose-dependent reduction in S. aureus in planktonic cultures and biofilms over a 96-h exposure period, compared to non-specific antibody control groups. Specifically, doses of 7.4 kBq and 7.4 MBq of [225Ac]4497-IgG1 and [177Lu]4497-IgG1 resulted in a four-log reduction in planktonic bacterial counts. Within biofilms, 14.8 kBq of [225Ac]4497-IgG1 and 14.8 Mbq [177Lu]4497-IgG1 led to reductions of two and four logs, respectively. Conclusion Our findings underscore the effectiveness of [225Ac]4497-IgG1 and [177Lu]4497-IgG1 antibodies in exerting dose-dependent bactericidal effects against planktonic S. aureus and biofilms in vitro. This suggests that radioimmunotherapy might serve as a promising targeted treatment approach for combating S. aureus and its biofilm.
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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 | 0.000 | 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 | 0.000 |
| 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".