Surface-immobilization of chromatographically purified bacteriophages for the optimized capture of bacteria
Bibliographic record
Abstract
Bacteriophages offer interesting alternatives to antibodies for the specific capture and detection of pathogenic bacteria onto biosensing surfaces. Procedures for the optimal chemical immobilization of lytic bacteriophages onto surfaces are presented. More specifically, the removal of lysate contaminants from bacteriophage suspensions by size exclusion chromatography significantly increases the resultant planar surface density of immobilized bacteriophages. E. coli T4 and Salmonella enterica serovar Typhimurium P22 phage systems seem to undergo highly heterogeneous adsorption to the surface, possibly explaining the observed phage clustering at higher surface densities. The T4 phage and its E. coli host were initially employed as a model system where we discovered an optimal planar surface density of phages for best bacterial capture: 18.9 ± 0.8 phages/μm(2) capturing 18.0 ± 0.3 bacteria/100 μm(2). Phage surface clustering ultimately limits the T4 phage-immobilized surface's ability to specifically capture its host bacteria. Nevertheless, this is to our knowledge the largest surface capture density of E. coli reported using intact T4 bacteriophages. Two additional purified bacteriophage systems (P22 and Campylobacter jejuni phage NCTC 12673) were then similarly studied for their ability to capture their corresponding host bacteria (Salmonella enterica serovar Typhimurium and Campylobacter jejuni respectively) on a surface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".