An increase in complex carbohydrate consumption results in higher colonic folate content in African Americans (827.16)
Bibliographic record
Abstract
It has been proposed that inclusion of complex carbohydrates in the diet may increase the production of bacterially synthesized folate impacting the folate concentration of colonocytes. Folate has been extensively studied for its potential colorectal cancer risk‐lowering effects. The aim of this study was to investigate whether reciprocal dietary switch between African Americans (AA, usually with low carbohydrates intake) and Native Africans (NA, usually with high carbohydrates intake) affects the production of colonic folate. Colonic evacuants were obtained from 20 NA and 20 AA at baseline. Thereafter, NA were provided with a typical AA diet (protein 27%, fat 52%, carbohydrate 21% and fiber 12g/d) for two weeks. AA were provided with a typical NA diet (protein 14%, fat 16%, carbohydrate 70% and fiber 55 g/d) for 2 weeks. Colonic evacuants were again collected. The folate in evacuants was measured by microbial assay. There was no difference in colonic folate concentration (FC) or total folate content (TF) of NA and AA at baseline. The dietary intervention did not affect the FC (1.3±1.2 vs 1.3±1.2 µg/mL [pre vs post]) or TF (2261±2017 vs 2157±1956 µg/d) in the NA group. However, the high complex carbohydrate, low protein intervention diet consumed by AA resulted in higher FC (1.5±1.3 vs 2.3±1.3 µg/mL; p=0.0003) and TF (2197±2483 vs 3107±1811 µg/d; p=0.0037) compared to baseline. In conclusion, introduction of a high complex carbohydrate, low protein diet resulted in higher production of colonic folate. Grant Funding Source : by NSERC (#453108) and NIH (#CA135379)
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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 | 0.000 |
| 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) | 0.003 | 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".