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How to select a probiotic? A review and update of methods and criteria
Abstract
International competition within the dairy market and increasing public awareness about the importance of functional food consumption are providing new challenges for innovation in the probiotic sector. In this context, countless references are currently dedicated to the selection and characterization of new species and more specific strains of probiotic bacteria. In general, these studies adopt basic selection criteria established by the World Health Organization (WHO), including host-associated stress resistance, epithelium adhesion ability, and antimicrobial activity. These aspects are applied to ensure that the candidate probiotic could withstand the stressful conditions of the human digestive system and exert functional proprieties. However, it cannot be assumed that these novel microbial strains are capable of offering several biological benefits attributed to probiotics. Additionally, safety-associated selection criteria, such as plasmid-associated antibiotic resistance spreading and enterotoxin production, are often neglected. This article reviews the recent developments in the processes, strategies, and methods, such as anticarcinogenic, antidepression, antianxiety, antiobesity, antidiabetic, immunostimulatory, and cholesterol-lowering assessments, to select probiotic strains with the ultimate objective of assisting future probiotic microbe evaluation studies.
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Metadata
- Authors
- de Melo Pereira, G. V., de Oliveira Coelho, B., Magalhães Júnior, A. I., Thomaz-Soccol, V., & Soccol, C. R.
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 36
- Issue
- 8
- Pages
- 2060-2076
- ISSN
- 0734-9750 (ISSN-L)
- OpenAlex
- W2892926776
- Cited by
- 523
- References
- 256
- FWCI
- 31.91
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.