Imaging host–pathogen interactions using epithelial and bacterial cell infection models
Bibliographic record
Abstract
The age-old saying, seeing is believing, could not be truer when we think about the value of imaging interactions between epithelial cells and bacterial pathogens. Imaging and culturing techniques have vastly improved over the years, and the breadth and depth of these methods is ever increasing. These technical advances have benefited researchers greatly; however, due to the large number of potential model systems and microscopy techniques to choose from, it can be overwhelming to select the most appropriate tools for your research question. This Review discusses a variety of available epithelial culturing methods and quality control experiments that can be performed, and outlines various options commonly used to fluorescently label bacterial and mammalian cell components. Both light- and electron-microscopy techniques are reviewed, with descriptions of both technical aspects and common applications. Several examples of imaging bacterial pathogens and their interactions with epithelial cells are discussed to provide researchers with an idea of the types of biological questions that can be successfully answered by using microscopy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".