Lactic Acid Bacteria in Pharmaceutical Formulations: Presence and Viability of “Healthy Microorganisms”
Bibliographic record
Abstract
Many probiotic formulations are available in the market and are advertised for several preventive or curative roles. The aim of this study was the identification of microorganisms composing different lactic acid bacteria based pharmaceutical formulations and the ascertainment of their ability to survive gastro-intestinal (GI) stresses, the main requisite to produce beneficial effects. For this purpose, viable bacteria were enumerated by plate counts in different media. Denaturing Gradient Gel Electrophoresis-Polymerase Chain Reaction (PCR-DGGE) analysis was applied on pure isolates and on crude formulations to confirm the composition in species. Also, crude formulations were subjected to stresses characteristic of the GI tract (GIT) to assess cell survival. Results highlighted concentrations lower than those reported in the labels in almost all the formulations. Moreover, some discrepancies were observed between reported species and those ascertained through the identification, and the use of an erroneous nomenclature was highlighted. The GI stress test revealed that bacteria are strongly injured, and this fact was evidenced by a marked reduction in viable counts after the stress. In conclusion, a widespread number of lactic acid bacteria based formulations are sold as probiotics, but their probiotic requisites are not adequately observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".