Differentiating bacteria by their unique surface interactions
Bibliographic record
Abstract
New, rapid, and accessible approaches to bacterial detection are necessary to help curb the rising impacts of antimicrobial resistance. In this study, we introduce a technique that distinguishes bacteria through their unique surface interactions. By measuring and combining the interaction strengths of a bacterium across a set of chemically defined surfaces, we produced a novel bacterial identifier termed the surface interaction profile (SIP). The interaction strengths of twelve test bacteria across three discrete polyelectrolyte multilayer films (PEMs) were measured, facilitated by introducing each bacterial suspension to individual PEMs in microfluidic channels over a 10-minute interaction period and rinsing to remove bulk and loosely bound bacteria. The remaining surface-bound cells were counted via microscopy and plotted against suspension concentrations to build attachment curves whose slopes were measured as the strength of interaction for a given bacteria-PEM combination. These slopes were collected, per bacterial type, to produce each SIP. SIPs were capable of distinguishing between our pathogenic strains (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterococcus faecalis, methicillin-resistant Staphylococcus aureus, and vancomycin-intermediate Staphylococcus aureus) by Gram stain and individual species, and each blind test pathogen was successfully identified through SIP comparison. Furthermore, SIPs were also successful at differentiating between select Staphylococcus aureus walKR mutants impacting cell wall metabolism and high-risk antibiotic resistance mutants (MRSA and VISA), highlighting the utility and future diagnostic potential of this technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".