Effect of a Novel Food Rich in Miraculin on the Oral Microbiome of Malnourished Oncologic Patients with Dysgeusia
Bibliographic record
Abstract
Background/Objectives: Dysgeusia contributes to the derangement of nutritional status in patients with cancer as well as worsening the quality of life. There has been a lack of effective treatments for taste disorders provided by the pharmaceutical industry. Methods: This was a pilot randomized, parallel, triple-blind, and placebo-controlled intervention clinical trial in which 31 malnourished patients with cancer and dysgeusia receiving antineoplastic treatment were randomized into three arms [standard dose of DMB (150 mg DMB/tablet), high dose of DMB (300 mg DMB/tablet) or placebo (300 mg freeze-dried strawberry)] for three months. Patients consumed a DMB or placebo tablet before each main meal. Using the nanopore methodology, we analyzed the oral microbiome of patients with cancer using saliva samples. Results: All patients with cancer and dysgeusia had dysbiosis in terms of lower bacterial diversity and richness. DMB consumption was associated with changes in oral microbiome composition. Neither selected bacteria nor taste perception, type of diet, and cytokine levels were associated with mucositis. Likewise, alcohol and tobacco consumption as well as general and digestive toxicity due to systemic therapy were not associated with specific changes of the oral microbiome, according to logistic binary regression. The standard dose of DMB resulted in a lower abundance of Veillonella compared with the high DMB dose and placebo at 3 months after intervention with DMB. In particular, some species such as Streptococcus parasanguinis, Veillonella parvula, and Streptococcus mutans were less abundant in the DMB standard-dose group. Additionally, the consumption of a standard dose of DMB revealed a negative association between the concentrations of TNF-α and the abundance of species such as Streptococcus thermophilus, Streptococcus pneumoniae, Streptococcus dysgalactiae and Streptococcus agalactiae. Conclusions: Accordingly, regular DMB consumption could modify the oral microbiome in patients with cancer and dysgeusia, which may contribute to maintaining an appropriate immune response. However, as the present pilot study involved a small number of participants, further studies are necessary to draw robust conclusions from the data.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".