Treating infections with ionizing radiation: a historical perspective and emerging techniques
Bibliographic record
Abstract
BACKGROUND: Widespread use and misuse of antibiotics have led to a dramatic increase in the emergence of antibiotic resistant bacteria, while the discovery and development of new antibiotics is declining. This has made certain implant-associated infections such as periprosthetic joint infections, where a biofilm is formed, very difficult to treat. Alternative treatment modalities are needed to treat these types of infections in the future. One candidate that has been used extensively in the past, is the use of ionizing radiation. This review aims to provide a historical overview and future perspective of radiation therapy in infectious diseases with a focus on orthopedic infections. METHODS: A systematic search strategy was designed to select studies that used radiation as treatment for bacterial or fungal infections. A total of 216 potentially relevant full-text publications were independently reviewed, of which 182 focused on external radiation and 34 on internal radiation. Due to the large number of studies, several topics were chosen. The main advantages, disadvantages, limitations, and implications of radiation treatment for infections were discussed. RESULTS: In the pre-antibiotic era, high mortality rates were seen in different infections such as pneumonia, gas gangrene and otitis media. In some cases, external radiation therapy decreased the mortality significantly but long-term follow-up of the patients was often not performed so long term radiation effects, as well as potential increased risk of malignancies could not be investigated. Internal radiation using alpha and beta emitting radionuclides show great promise in treating fungal and bacterial infections when combined with selective targeting through antibodies, thus minimizing possible collateral damage to healthy tissue. CONCLUSION: The novel prospects of radiation treatment strategies against planktonic and biofilm-related microbial infections seem feasible and are worth investigating further. However, potential risks involving radiation treatment must be considered in each individual patient.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".