Case study of diet digestibility and gut microbiome changes in a dog undergoing CHOP chemotherapy for lymphoma
Bibliographic record
Abstract
Nutritional strategies in the management of canine cancer patients undergoing CHOP chemotherapy are still in its infancy. Studies on a novel diet containing cooked navy beans and rice bran have demonstrated to have cancer chemo-preventive properties and have a strong potential to support a healthy gut microbiome in both humans and animals. In this case study we evaluated how a diet rich in rice bran and navy bean affects a dog’s gut microbiome and its function during CHOP chemotherapy. Stool samples were collected from an adult 12-year-old male castrated Labrador which is diagnosed with multicentric B-cell lymphoma with ocular involvement. The dog was undergoing a 15 week CHOP chemotherapy protocol combined with a 6 week dietary trial containing 25% cooked navy bean powder and heat stabilized rice bran. Nine breed matched control dogs undergoing the diet intervention without CHOP chemotherapy were used to evaluate diet digestibility. Four of the breed matched control dogs were used to evaluate microbial alterations. \nThe 16s rRNA gene was pyrosequenced, a digestibility analysis was performed and fatty acids were extracted and analyzed utilizing Gas Chromatography-Mass Spectrometry (GC-MS). We found that canine fecal samples collected after the diet intervention displayed notable increases in Bifidobacterium adolescentis and Faecalibacterium prausnitzii compared to fecal samples prior to the diet intervention. In addition a notable increase in Palmitic and Stearic acid and a decrease in Oleic acid and Linoleic acid was found. In this case study, the consumption of a diet containing rice bran and navy beans during chemotherapy was associated with an enhanced modulation of the canine gut microbial composition and function. Therefore a diet rich in rice bran and navy beans could potentially be combined with chemotherapy to support the gut microbiome and improve quality of life during chemotherapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".