Flow cytometry: a versatile technology for specific quantification and viability assessment of micro-organisms in multistrain probiotic products
Bibliographic record
Abstract
AIMS: Classical microbiology techniques are the gold standard for probiotic enumeration. However, these techniques are limited by parameters of time, specificity and incapacity to detect viable but nonculturable (VBNC) micro-organisms and nonviable cells. The aim of the study was to evaluate flow cytometry as a novel method for the specific quantification of viable and nonviable probiotics in multistrain products. METHODS AND RESULTS: 24 and propidium iodide was applied to quantify these strains in three commercial products. Analyses were conducted on two flow cytometry instruments by two operators and compared with classical microbiology using selective media. Results indicated that flow cytometry provides higher cell counts than classical microbiology (P < 0·05) in 73% of cases highlighting the possible presence of VBNC. Equivalent performances (repeatability and reproducibility) were obtained for both methods. CONCLUSIONS: This study showed that flow cytometry methods can be applied to probiotic enumeration and viability assessment. Combination with polyclonal antibodies can achieve sufficient specificity to differentiate closely related strains. SIGNIFICANCE AND IMPACT OF THE STUDY: Flow cytometry provides absolute and specific quantification of viable and nonviable probiotic strains in a very short time (<2 h) compared with classical techniques (>48 h), bringing efficient tools for research and development and quality control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".