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Record W2041017189 · doi:10.1155/2009/680363

Probiotic<i>Lactobacillus rhamnosus</i>GR-1 and<i>Lactobacillus reuteri</i>RC-14 May Help Downregulate TNF-Alpha, IL-6, IL-8, IL-10 and IL-12 (p70) in the Neurogenic Bladder of Spinal Cord Injured Patient with Urinary Tract Infections: A Two-Case Study

2009· article· en· W2041017189 on OpenAlexafffund
Kingsley C. Anukam, Keith C. Hayes, Kelly L. Summers, Gregor Reid

Bibliographic record

VenueAdvances in Urology · 2009
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsLawson Health Research InstituteWestern University
FundersOntario Neurotrauma Foundation
KeywordsLactobacillus rhamnosusLactobacillus reuteriProbioticMicrobiologyMedicineLactobacillusTumor necrosis factor alphaBiologyFood scienceImmunologyBacteria

Abstract

fetched live from OpenAlex

The management of urinary tract infection (UTI) in individuals with spinal cord injury (SCI) continues to be of concern, due to complications that can occur. An emerging concept that is a common underlying pathophysiological process is involved, wherein pathogens causing UTI have a role in inflammatory progression. We hypothesized that members of the commensal flora, such as lactobacilli, may counter this reaction through anti-inflammatory mediation. This was assessed in a pilot two-patient study in which probiotic Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri were administered to one patient and placebo to another, both along with antibiotics to treat acute UTI. Urinary TNF-alpha was significantly downregulated (P = .015) in the patient who received the probiotic and who used intermittent catheterization compared with patient on placebo and using an indwelling catheter. The extent to which this alteration resulted in improved well-being in spinal cord injured patients remains to be determined in a larger study.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.317
Teacher spread0.299 · 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.

Study designObservational
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

Citations46
Published2009
Admission routes2
Has abstractyes

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