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Record W4416321678 · doi:10.1007/s41030-025-00333-y

Double-Blind Randomized Placebo-Controlled Trial of a Lactobacillus Probiotic Blend in Chronic Obstructive Pulmonary Disease

2025· article· en· W4416321678 on OpenAlexaff
Teodora Nicola, Nancy M. Wenger, Michael Evans, Youfeng Yang, Dongquan Chen, William J. Van Der Pol, A Walia, Elliot J. Lefkowitz, Jun Wang, Ashley LeMoire, Lois Lin, Casey D. Morrow, Namasivayam Ambalavanan, Amit Gaggar, Charitharth Vivek Lal

Bibliographic record

VenuePulmonary Therapy · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsNutrasource
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood Institute
KeywordsProbioticPulmonary diseaseRandomized controlled trialCOPDLactobacillusRespiratory disease

Abstract

fetched live from OpenAlex

Gut microbiota modulate systemic anti-inflammatory and immune responses in the lungs, suggesting a potential to support lung health through probiotic supplementation. We hypothesized that a probiotic blend (Lactobacilli) combined with herbal extracts (resB®) could improve quality of life in patients with chronic obstructive pulmonary disease (COPD). We conducted a randomized, double-blinded, placebo-controlled study (NCT05523180) evaluating the safety and impact of resB® on quality of life in volunteers with COPD. Participants took resB® or placebo (two capsules daily) for 12 weeks. The primary endpoint was change in quality of life by Saint George’s Respiratory Questionnaire (SGRQ). Secondary outcomes included safety, serum and sputum biomarkers, and microbiome analysis. resB® was well tolerated with no related adverse events. Participants receiving resB® showed significant improvement in SGRQ symptom scores (P < 0.05), while placebo recipients did not. In the resB® group, serum and sputum levels of matrix metalloproteinase 9, C-reactive protein, and interleukin 6 decreased (P < 0.05), correlating with increased stool Lactobacilli. Additionally, Veillonella abundance increased in both stool and sputum. These findings suggest that resB® improves respiratory symptoms and reduces inflammation in patients with COPD, potentially by modulating gut and lung microbiota. ClinicalTrials.gov identifier NCT05523180. Chronic obstructive pulmonary disease (COPD) is a long-term lung condition that makes breathing difficult and greatly affects quality of life. Current treatments mostly focus on reducing symptoms, but there is a need for safe supportive approaches. The gut and the lungs communicate through what is known as the “gut–lung axis.” Changes in gut bacteria can influence inflammation and immunity in the lungs. Probiotics (beneficial bacteria) and plant-based extracts may help improve this communication and support lung health. In this study, adults with COPD were randomly assigned to receive either a probiotic and herbal blend (resB®) or a placebo for 12 weeks. Participants and researchers did not know which treatment each person received until the study ended. The main outcome was change in quality of life, measured by the Saint George’s Respiratory Questionnaire (SGRQ). We also looked at safety, inflammation in the blood and sputum, and changes in gut and lung bacteria. We found that resB® was safe and well tolerated. People who took resB® showed improvement in COPD-related symptoms compared to those who received placebo. Inflammatory markers in the blood and sputum decreased, and certain beneficial bacteria became more abundant in the gut and lungs. These results suggest that resB® may support people with COPD by improving symptoms and reducing inflammation. More research in larger studies is needed to confirm these findings and better understand how probiotics and plant extracts work through the gut–lung axis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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