Interfacial nanomechanical heterogeneity of the <i>E. coli</i> biofilm matrix
Bibliographic record
Abstract
The interface between bacterial biofilms and their environment plays a vital role in the recalcitrance of biofilms to biological, chemical, and mechanical threats. Nonetheless, we know little about the physical parameters that dictate the interfacial morphology and nanomechanics of biofilms. Here, we present a robust, reproducible, and quantitative platform based on atomic force microscopy (AFM) that allows for correlated high-resolution imaging of the morphology and nanomechanical properties of an intact E. coli biofilm-under physiological conditions. We developed analysis algorithms based on linearized Hertzian contact mechanics to discriminate, at the nanoscale, the elasticity of the extracellular polymeric substances (EPS) from bacteria within the biofilm. We were able to identify two distinct EPS populations with approximately 10-fold difference in their elastic properties. A correlation between EPS' elasticity and morphology points to different functions of the EPS populations within a mature E. coli biofilm. Thus, beyond high-resolution nanomechanical maps of a complex biological sample, we provide direct evidence of nanoscale heterogeneities at the biofilm interface. As interactions between biofilms and various antimicrobial agents occur at the nanoscale, understanding the physico-mechanical properties at the interface-with nanometer resolution-is imperative in devising targeted strategies against bacterial biofilms. We anticipate that in conjunction with other existing approaches, our quantitative imaging platform will provide mechanistic insights into the action and effectiveness of antimicrobials and antibiofilm agents.
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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 | 0.001 | 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".