Evaluation of Biopolymer Materials and Synthesis Techniques to Develop a Rod-Shaped Biopolymer Surrogate for Legionella pneumophila
Bibliographic record
Abstract
Biopolymer microparticles have been developed for applications that require biocompatibility and biodegradability, such as drug delivery. In this study, we assessed the production of microparticles using carnauba wax, κ-carrageenan, alginate, and poly (lactic-co-glycolic acid) (PLGA) with the aim of developing a novel, DNA-tracer-loaded, biopolymer surrogate with a size, shape, surface charge, and relative hydrophobicity similar to stationary-phase Legionella pneumophila to mimic the bacteria’s mobility and persistence in engineered water systems. We found that the type and concentration of biopolymer, reaction conditions, and synthesis methods affected the morphology, surface charge, relative hydrophobicity, and DNA tracer loading efficiency of the biopolymer microparticles produced. Carnauba wax, κ-carrageenan, and alginate (Protanal®, and low and medium viscosity) produced highly polydisperse microspheres. In contrast, PLGA and alginate-CaCO3 produced uniform microspheres and rod-shaped microparticles, respectively, with high DNA tracer loading efficiencies (PLGA 70% and alginate-CaCO3 95.2 ± 5.7%) and high reproducibilities. Their synthesis reproducibility was relatively high. The relative hydrophobicity of PLGA microspheres closely matched the cell surface hydrophobicity of L. pneumophila but not the bacterial morphology, whereas the polyelectrolyte layer-by-layer assembly was required to enhance the relative hydrophobicity of alginate-CaCO3 microparticles. Following this surface modification, alginate-CaCO3 microparticles represented the best match to L. pneumophila in size, morphology, surface charge, and relative hydrophobicity. This new biopolymer surrogate has the potential to be used as a mimic to study the mobility and persistence of L. pneumophila in water systems where the use of the pathogen is impractical and unsafe.
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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 | 0.001 | 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".