<title>Sensitivity of detection of bacteria with fluorescent and luminescent phenotypes using different instruments</title>
Bibliographic record
Abstract
The problem of bacterial enumeration in different samples is of great importance in many fields of research. Construction of recombinant fluorescent and luminescent bacteria that can be easily detected by nondestructive instrumental methods proves us with an opportunity to monitor bacteria in a wide variety of clinical, environmental and food samples in real time. Three different labels were employed: Green Fluorescent Protein (GFP), Bacterial luciferase (BL) and Firefly Luciferase (FFL). Both plasmid and chromosomal transformants of different strains of E. coli, P. putida and S. enteritidis were used. For the detection of the in vivo GFP the Shimadzu RF 540 spectrofluorimeter, Labsystems FL- 500 plate fluorimeter and Night Owl LB 98 CCD-camera from EG and G Berthold supplied with excitation light source and proper spectral filters both in macroscopic and microscopic mode were used. For the detection of in vivo luminescence of BL and FFL, tube luminometer BG-P from GEM Biomedical Inc., luminometric plate reader from BioOrbit, BIQ Bioview CCD camera from Cambridge Imaging Ltd and Night Owl LB 98 CCD camera both in macroscopic and microscopic mode were used. The expression levels of the labels, their stability, stability of the signal and detection limits of tagged bacteria were investigated. The detection limits for GFP tagged bacteria were 5 X 10<SUP>4</SUP> - 6 X 10<SUP>6</SUP>, for BL tagged bacteria 5 X 10<SUP>2</SUP> - 2 X 10<SUP>5</SUP>, and for FFL tagged bacteria - 4 X 10<SUP>3</SUP> - 10<SUP>6</SUP> CFU/ml, depending on the instrument used. Single bacteria could be detected with the help of the Night Owl in the microscopic mode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".