Multiscale modeling approach to assess the impact of antibiotic treatment for COVID-19 on MRSA transmission and alternative immunotherapy treatment options
Bibliographic record
Abstract
Methicillin-Resistant Staphylococcus Aureus (MRSA) infection can occur alongside or following COVID-19, which is a concern in healthcare settings. The effectiveness of antiviral treatments for COVID-19 depends on a functioning immune response, but antibiotics used for bacterial infections like MRSA can disrupt the immune response and reduce the effectiveness of antiviral treatments. The emergence of MRSA due to excessive antibiotic usage has led to the widespread use of vancomycin as an alternative treatment. Immunomodulatory antibiotics like azithromycin may also be considered. To study the dynamics of these coinfections, a multiscale model was developed. Parameter estimation and sensitivity analysis were performed, revealing influential parameters affecting the reproduction number. Numerical simulations showed that methicillin may increase the population of co-infected cells, while azithromycin can improve the host immune response but has limited impact on MRSA proliferation. Increased efficacy of vancomycin can lead to MRSA eradication. Combination of immunomodulatory antibiotics and vancomycin has minimal effect on co-infected cell population, but increased vancomycin efficacy can reduce coinfection severity. This study emphasizes the importance of continuous research, surveillance, and the development of effective strategies to combat the complexities of COVID-19 and MRSA coinfection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".