AFM-Based Correlative Microscopy Illuminates Human Pathogens
Bibliographic record
Abstract
Microbes have an arsenal of virulence factors that contribute to their pathogenicity. A number of challenges remain to fully understand disease transmission, fitness landscape, antimicrobial resistance and host heterogeneity. A variety of tools have been used to address diverse aspects of pathogenicity, from molecular host-pathogen interactions to the mechanisms of disease acquisition and transmission. Current gaps in our knowledge include a more direct understanding of host-pathogen interactions, including signaling at interfaces, and direct phenotypic confirmation of pathogenicity. Correlative microscopy has been gaining traction to address the many challenges currently faced in biomedicine, in particular the combination of optical and atomic force microscopy (AFM). AFM, generates high-resolution surface topographical images, and quantifies mechanical properties at the pN scale under physiologically relevant conditions. When combined with optical microscopy, AFM probes pathogen surfaces and their physical and molecular interaction with host cells, while the various modes of optical microscopy view internal cellular responses of the pathogen and host. Here we review the most recent advances in our understanding of pathogens, recent applications of AFM to the field, how correlative AFM-optical microspectroscopy and microscopy have been used to illuminate pathogenicity and how these methods can reach their full potential for studying host-pathogen interactions.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".