Solubilization and Bio-conjugation of Quantum Dots and Bacterial Toxicity Assays by Growth Curve and Plate Count
Bibliographic record
Abstract
Quantum dots (QDs) are fluorescent semiconductor nanoparticles with size-dependent emission spectra that can be excited by a broad choice of wavelengths. QDs have attracted a lot of interest for imaging, diagnostics, and therapy due to their bright, stable fluorescence. QDs can be conjugated to a variety of bio-active molecules for binding to bacteria and mammalian cells. QDs are also being widely investigated as cytotoxic agents for targeted killing of bacteria. The emergence of multiply-resistant bacterial strains is rapidly becoming a public health crisis, particularly in the case of Gram negative pathogens. Because of the well-known antimicrobial effect of certain nanomaterials, especially Ag, there are hundreds of studies examining the toxicity of nanoparticles to bacteria. Bacterial studies have been performed with other types of semiconductor nanoparticles as well, especially TiO(2), but also ZnO and others including CuO. Some comparisons of bacterial strains have been performed in these studies, usually comparing a Gram negative strain with a Gram positive. With all of these particles, mechanisms of toxicity are attributed to oxidation: either the photogeneration of reactive oxygen species (ROS) by the particles or the direct release of metal ions that can cause oxidative toxicity. Even with these materials, results of different studies vary greatly. In some studies the Gram positive test strain is reportedly more sensitive than the Gram negative; in others it is the opposite. These studies have been well reviewed. In all nanoparticle studies, particle composition, size, surface chemistry, sample aging/breakdown, and wavelength, power, and duration of light exposure can all dramatically affect the results. In addition, synthesis byproducts and solvents must be considered. High-throughput screening techniques are needed to be able to develop effective new nanomedicine agents. CdTe QDs have anti-microbial effects alone or in combination with antibiotics. In a previous study, we showed that coupling of antibiotics to CdTe can increase toxicity to bacteria but decrease toxicity to mammalian cells, due to decreased production of reactive oxygen species from the conjugates. Although it is unlikely that cadmium-containing compounds will be approved for use in humans, such preparations could be used for disinfection of surfaces or sterilization of water. In this protocol, we give a straightforward approach to solubilizing CdTe QDs with mercaptopropionic acid (MPA). The QDs are ready to use within an hour. We then demonstrate coupling to an antimicrobial agent. The second part of the protocol demonstrates a 96-well bacterial inhibition assay using the conjugated and unconjugated QDs. The optical density is read over many hours, permitting the effects of QD addition and light exposure to be evaluated immediately as well as after a recovery period. We also illustrate a colony count for quantifying bacterial survival.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".