Chemically immobilized T4-bacteriophage for specific Escherichia coli detection using surface plasmon resonance
Bibliographic record
Abstract
A bioassay platform using T4 bacteriophage (T4) as the specific receptor and surface plasmon resonance (SPR) as the transduction technique has been developed for the detection of Escherichia coli K12 bacteria. The T4 phages have been covalently immobilized onto gold surfaces using a self-assembled monolayer of dithiobis(succinimidyl propionate) (DTSP). Substrates of BSA/EA-T4/DTSP/Au prepared using different T4 phage concentrations have been characterized using scanning electron microscopy (SEM). The studies reveal that the use of DTSP results in a uniform binding of T4 phages onto the surface. The SPR analysis demonstrates that these BSA/EA-T4/DTSP/Au interfaces can detect the E. coli K12 with high specificity against non-host E. coli NP10 and NP30. Results of SEM and SPR studies indicate that the maximum host bacterial capture is obtained when 1.5 × 10(11) pfu ml(-1) concentration of T4 phages was used for immobilization. The surface of these chemically anchored phage substrates can be regenerated for repeated detection of E. coli K12 and can be used for detection in 7 × 10(2) to 7 × 10(8) cfu ml(-1) range. The results of these studies have implications for the development of online bioassays for the detection of various food and water borne pathogens using the inherent selectivity of bacteriophage recognition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".