Broad-spectrum treatment of bacterial biofilms using magneto-responsive liquid metal particles
Bibliographic record
Abstract
The formation and proliferation of bacterial biofilms on surfaces, particularly those on biomedical devices, is a significant issue that results in substantial economic losses, presenting severe health risks to patients. Furthermore, heterogeneous biofilms consisting of different bacterial species can induce the increase in pathogenicity, and the resistance to antimicrobial agents due to the synergistic interactions between the different species. Heterogeneous bacterial biofilms are notoriously difficult to treat due to the presence of extracellular polymeric substances (EPS) and, in conjunction with the rapid rise of multi-drug resistant pathogens, this means that new solutions for anti-biofilm treatment are required. In this study, we investigate the application of magneto-responsive gallium-based liquid metal (GLM-Fe) nanomaterials against a broad range of Gram-positive and Gram-negative bacterial mono-species and multi-species biofilms. The GLM-Fe particles exhibit a magneto-responsive characteristic, causing spherical particles to undergo a shape transformation to high-aspect-ratio nanoparticles with sharp asperities in the presence of a rotating magnetic field. These shape-transformed particles are capable of physically removing bacterial biofilms and rupturing individual cells. Following treatment, both mono-species and multi-species biofilms demonstrated significant reductions in their biomass and overall cell viability, demonstrating the broad-spectrum application of this antibacterial technology. Furthermore, the loss of integrity of the bacterial cell wall and membranes was visualized using a range of microscopy techniques, and the leakage of intracellular components (such as nucleic acids and protein) was observed. Insights gained from this study will impact the design of future liquid metal-based biofilm treatments, particularly those that rely on magneto-responsive properties.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".