Nanodiagnostics to monitor biofilm oxygen metabolism for antibiotic susceptibility testing
Bibliographic record
Abstract
In clinical environments, many serious antibiotic-resistant infections are caused by biofilm-forming species. This presents issues when attempting to determine antimicrobial dosing as traditional antibiotic susceptibility tests (ASTs) are typically designed around planktonic bacteria and thus offer information that is not relevant to the biofilm phenotype present in the patient. Even the popular Calgary biofilm device may provide inaccurate minimum biofilm inhibitory concentrations (MBICs) and can be time- and material-intensive. In this work, we present a method utilizing oxygen-sensitive nanosensor technology to monitor the oxygen consumption dynamics of living biofilms as they are exposed to antibiotics. We incorporated our nanosensors into biofilms grown from P. aeruginosa strains of varying sensitivity to traditional classes of antibiotics. Through measuring nanosensor response under antibiotic administration we determined the concentrations able to cease biofilm metabolism. This method provides information on the MBIC as well as kinetic response information in a manner that requires fewer materials and is more reflective of biofilm behavior than a traditional AST.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".