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Record W4386271250 · doi:10.1089/jmf.2023.0062

Controlling Intestinal Infections and Digestive Disorders Using Probiotics

2023· review· en· W4386271250 on OpenAlexaff
Sanjeev Kumar, Md Faruque Ahmad, Priyakshi Nath, Rubina Roy, Rudrarup Bhattacharjee, Eman Shama, Indira Gahatraj, Manisha Sehrawat, Vaishali Dasriya, Harmeet Singh Dhillon, Monica Puniya, Mrinal Samtiya, Tejpal Dhewa, Rotimi E. Aluko, Gulab Khedkar, António Raposo, Anil Kumar Puniya

Bibliographic record

VenueJournal of Medicinal Food · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsProbioticDiseaseMechanism (biology)MedicineInflammatory bowel diseaseCompetitive exclusionBiologyInternal medicineBacteria

Abstract

fetched live from OpenAlex

After consumption, probiotics provide health benefits to the host. Probiotics and their metabolites have therapeutic and nutritional properties that help to alleviate gastrointestinal, neurological, and cardiovascular problems. Probiotics strengthen host immunity through various mechanisms, including improved gut barrier function, receptor site blocking, competitive exclusion of pathogens, and the production of bioactive molecules. Emerging evidence suggests that intestinal bowel diseases can be fatal, but regular probiotic consumption can alleviate disease symptoms. The use and detailed description of the health benefits of probiotics to consumers in terms of reducing intestinal infection, inflammation, and digestive disorders are discussed in this review. The well-designed and controlled studies that examined the use of probiotics to reduce life-threatening activities caused by intestinal bowel diseases are also covered. This review discussed the active principles and potency of probiotics as evidenced by the known effects on host health, in addition to providing information on the mechanism of action.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.368
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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