Application of atomic force microscopy in bacterial research
Bibliographic record
Abstract
The atomic force microscope (AFM) has evolved from an imaging device into a multifunctional and powerful toolkit for probing the nanostructures and surface components on the exterior of bacterial cells. Currently, the area of application spans a broad range of interesting fields from materials sciences, in which AFM has been used to deposit patterns of thiol-functionalized molecules onto gold substrates, to biological sciences, in which AFM has been employed to study the undesirable bacterial adhesion to implants and catheters or the essential bacterial adhesion to contaminated soil or aquifers. The unique attribute of AFM is the ability to image bacterial surface features, to measure interaction forces of functionalized probes with these features, and to manipulate these features, for example, by measuring elongation forces under physiological conditions and at high lateral resolution (<1 A). The first imaging studies showed the morphology of various biomolecules followed by rapid progress in visualizing whole bacterial cells. The AFM technique gradually developed into a lab-on-a-tip allowing more quantitative analysis of bacterial samples in aqueous liquids and non-contact modes. Recently, force spectroscopy modes, such as chemical force microscopy, single-cell force spectroscopy, and single-molecule force spectroscopy, have been used to map the spatial arrangement of chemical groups and electrical charges on bacterial surfaces, to measure cell-cell interactions, and to stretch biomolecules. In this review, we present the fascinating options offered by the rapid advances in AFM with emphasizes on bacterial research and provide a background for the exciting research articles to follow.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".