Effects of Environmental and Clinical Interferents on the Host Capture Efficiency of Immobilized Bacteriophages
Bibliographic record
Abstract
Bacteriophage-functionalized surfaces are a new class of advanced functional material and have been demonstrated to be applicable for use as antimicrobial surfaces in medical applications (e.g., indwelling medical devices or wound dressings) or as biosensors for bacterial capture and detection. However, the complex composition of many real life samples (e.g., blood, natural waters, etc.) can potentially interfere with the interaction of phage and its bacterial host, leading to a decline in the efficiency of the phage-functionalized surface. In this study, the bacterial capture efficiency of two model phage-functionalized surfaces was assessed in the presence of potential environmental and biomedical interferents. The two phage-bacteria systems used in this study are PRD1 with Salmonella Typhimurium and T4 with Escherichia coli. The potential interferents tested included humic and fulvic acids, natural groundwater, colloidal latex microspheres, host extracellular polymeric substances (EPS), albumin, fibrinogen, and human serum. EPS and human serum decreased the host capture efficiency for immobilized PRD1 and T4, and also impaired the infectivity of the nonimmobilized (planktonic) phage. Interestingly, humic and fulvic acids reduced the capture efficiency of T4-functionalized surfaces, even though they did not lead to inactivation of the suspended virions. Neither humic nor fulvic acids affected the capture efficiency of PRD1. These findings demonstrate the inadequacy of traditional phage selection methods (i.e., infectivity of suspended phage toward its host in clean buffer) for designing advanced functional materials and further highlight the importance of taking into account the environmental conditions in which the immobilized phage is expected to function.
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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 | 0.001 | 0.001 |
| 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 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".