Glucan rich nutrition does not increase gut translocation of beta‐glucan
Bibliographic record
Abstract
Abstract Background (1‐3)‐b‐D‐glucan (BDG) is a fungal cell wall component and, in the absence of invasive fungal infection, a novel biomarker for microbial translocation of endogenous fungal products from the gastrointestinal tract into systemic circulation. However, its value as a marker of fungal translocation is limited by a concern that plant BDG‐rich food influences blood BDG levels. Methods We conducted a pilot clinical trial to evaluate the impact of a standardised oral BDG challenge on blood BDG levels in participants with and without elevated microbial translocation. We enrolled 14 participants including 8 with HIV infection, 2 with advanced liver cirrhosis, and 4 healthy controls. After obtaining a baseline blood sample, participants received a standardised milkshake containing high levels of BDG followed by serial blood samples up to 8 hours after intake. Results The standardised oral BDG challenge approach did not change the blood BDG levels over time in all participants. We found consistently elevated blood BDG levels in one participant with advanced liver cirrhosis and a single person with HIV with a low CD4 count of 201 cells/mm3. Conclusion Our findings indicate that BDG blood levels were not influenced by plant origin BDG‐rich nutrition in PWH, people with advanced liver cirrhosis, or healthy controls. Future studies are needed to analyse gut mycobiota populations in individuals with elevated blood BDG levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".