Single-Cell Identification and Characterization of Viable but Nonculturable <i>Campylobacter jejuni</i> Using Raman Optical Tweezers and Machine Learning
Bibliographic record
Abstract
Campylobacter jejuni is a leading foodborne pathogen that may enter a viable but nonculturable (VBNC) state to survive under environmental stresses, posing a significant health concern. VBNC cells can evade conventional culture-based detection methods, while viability-based assays are usually hindered by low sensitivity, insufficient specificity, or technical challenges. There are limited studies analyzing VBNC cells at the single-cell level for accurate detection and an understanding of their unique behavior. Here, we present a culture-independent approach to identify and characterize VBNC C. jejuni using single-cell Raman spectra collected by optical tweezers and machine learning. C. jejuni strains were induced into the VBNC state under osmotic pressure (7% w/v NaCl solution) and aerobic stress (atmospheric condition). Using single-cell Raman spectra and a convolutional neural network (CNN), VBNC C. jejuni cells were distinguished from their culturable counterparts with an accuracy of ∼92%. There were no significant spectral differences between the VBNC cells formed under different stressors or induction periods. Furthermore, we utilized gradient-weighted class activation mapping to highlight the spectral regions that contribute most to the CNN-based classification between culturable and VBNC cells. These regions align with previously identified changes in proteins, nucleic acids, lipids, and peptidoglycan in VBNC cells, providing insights into the molecular characterization of the VBNC state of C. jejuni .
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".