Colonic microbiome modulation and metabolic effects of Bryndza sheep cheese in pediatric acute lymphoblastic leukemia: An in vitro study
Bibliographic record
Abstract
This study investigated the effects of lyophilized, unpasteurized Bryndza sheep cheese powder on gut microbiota and short-chain fatty acid (SCFA) production using an in vitro colonic fermentation model of pediatric acute lymphoblastic leukemia (ALL) patients. Low-dose (LD; 500 mg) and high-dose (HD; 1000 mg) treatments led to distinct microbial shifts within 48 h, affecting Firmicutes , Bacteroidota , and Proteobacteria . The α-diversity decreased in LD ( p < 0.05) but remained highest in HD ( p ≤ 0.01) after 48 h. Parasutterella and Enterococcus were enriched at 6 h, with HD promoting SCFA-producing bacteria ( Faecalibacterium ) at later stages. By 48 h, the HD increased Eubacterium hallii , Faecalibacterium , and Bacteroides , whereas LD enriched Lachnospira and Veillonella . Both treatments significantly increased acetate production ( p ≤ 0.01), with network analysis linking key microbiota families to SCFA production. Bryndza cheese modulates gut microbiota dose- and time-dependently, fostering SCFA-producing taxa and potentially improving gut health in pediatric ALL patients. • Bryndza sheep cheese significantly modulates colonic microbiota composition in vitro . • High-dose treatment enhances microbial diversity and production of short-chain fatty acids (SCFAs) in patients with cancer. • High-dose cheese enriched beneficial SCFA-producing taxa, such as Faecalibacterium and Butyricicoccus . • Study provides novel insights into gut microbiota-targeted nutritional interventions in oncology.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".