Vitamin B6 status and its relationship with polyunsaturated fatty acid concentrations during pregnancy
Bibliographic record
Abstract
Rationale Suboptimal vitamin B6 status was shown to alter polyunsaturated fatty acid (PUFA) profiles, decreasing plasma (n‐3) and (n‐6) PUFA concentrations, in healthy men and women. Aim To assess the relationships between plasma pyridoxal 5'‐phosphate (PLP) and docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AA) concentrations at different stages of pregnancy. Methods Prospective cohort study in 213 healthy pregnant women (aged 20‐40 y) followed across trimesters at 5‐13, 20‐26 and 30‐36 wk. The study was conducted in Rio de Janeiro, Brazil. Fasting plasma PLP (nmol/L) and serum PUFA (µg/mL) were measured by HPLC and GLC, respectively. Results Plasma PLP concentrations [median (IQR)] dropped from 35.8 (28.4‐43.6) in the 1 st to 21.2 (16.2‐27.1) in the 2 nd trimester ( P <0.0001) and further decreased to 16.9 (12.8‐21.6) in the 3 rd trimester ( P <0.0001). Median (IQR) PUFA concentrations were 55.8 (44.0‐65.5), 70.7 (60.9‐84.7), 74.5 (63.6‐89.6) for DHA; 8.4 (6.6‐11.7), 8.4 (6.5‐12.0), 7.4 (5.2‐10.9) for EPA; and 206 (179‐245), 236 (205‐270), 237 (207‐279) for AA in the 1 st , 2 nd , and 3 rd trimester, respectively. Plasma PLP and DHA were correlated ( P <0.05) in the 1 st ( ρ =0.14) and 2 nd trimester ( ρ =0.20); plasma PLP and EPA ( ρ =0.24) and AA ( ρ =0.18) were correlated in the 2 nd trimester ( P <0.05); no significant correlations were observed between PLP and any PUFA in the 3 rd trimester. Conclusions Plasma PLP concentrations were correlated with DHA, EPA and AA in early pregnancy. Funding: CNPq, FAPERJ, VRF‐FNH‐UBC
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".