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Record W2595155564 · doi:10.4172/2329-8901.1000144

Inhibitory Effect of Lactic Acid Bacteria on Uropathogenic Escherichia coli-Induced Urinary Tract Infections

2016· article· en· W2595155564 on OpenAlexfundno aff
Yu Hsuan Liu, Cheng Ying Ho

Bibliographic record

VenueJournal of Probiotics & Health · 2016
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
FundersUniversité Paris-Est Créteil Val-de-MarneBeef Cattle Research Council
KeywordsEscherichia coliMicrobiologyBacteriaLactic acidUrinary systemBiologyEnterobacteriaceaeMedicineBiochemistryGenetics

Abstract

fetched live from OpenAlex

The aim of this study evaluated the in vitro and in vivo antimicrobial activity of selected lactic acid bacteria (LAB) against UPEC for prevention and amelioration of UTIs. We screened LAB strains with antimicrobial effects on UPEC using a well-diffusion assay, bacterial adherence to the uroepithelium cell line SV-HUC-1 (BCRC 60358), and a co-culture inhibition assay. The results showed that the 7 LAB strains (Lactobacillus paracasei, L. salivarius, two Pediococcus pentosaceus strains, two L. plantarum strains, and L. crispatus) and the fermented probiotic products produced by these multi-LAB strains exhibited potent zones of inhibition against UPEC. Moreover, the LAB strains and probiotic products adhered strongly to the uroepithelium SV-HUC-1 cell line. The growth of UPEC strains was also markedly inhibited after co-culture with the LAB strains and probiotic products in human urine. In addition, the enhanced levels of IL-6, IL-8 and lactic acid dehydrogenase were significantly decreased by treatments with the LAB strains and probiotic products in UPEC-induced SV-HUC-1 cells. Furthermore, oral administration of probiotic products reduced the number of viable UPEC in the urine of UPEC-challenged BALB/c mice. Taken together, this study demonstrates that probiotic supplementation may be useful as an adjuvant therapy for the treatment of bacterial-induced urinary tract infections.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations13
Published2016
Admission routes1
Has abstractyes

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