Modest and Severe Maternal Iron Deficiency in Pregnancy are Associated with Fetal Anaemia and Organ-Specific Hypoxia in Rats
Bibliographic record
Abstract
Prenatal iron-deficiency (ID) is known to alter fetal developmental trajectories, which predisposes the offspring to chronic disease in later life, although the underlying mechanisms remain unclear. Here, we sought to determine whether varying degrees of maternal anaemia could induce organ-specific patterns of hypoxia in the fetuses. Pregnant female Sprague Dawley rats were fed iron-restricted or iron-replete diets to induce a state of moderate (M-ID) or severe ID (S-ID) alongside respective controls. Ultrasound biomicroscopy was performed on gestational day (GD)20 to assess uterine and umbilical artery blood flow patterns. On GD21, tissues were collected and assessed for hypoxia using pimonidazole staining. Compared to controls, maternal haemoglobin (Hb) in M- and S-ID were reduced 17% (P < 0.01) and 48% (P < 0.001), corresponding to 39% (P < 0.001) and 65% (P < 0.001) decreases in fetal Hb. Prenatal ID caused asymmetric fetal growth restriction, which was most pronounced in S-ID. In both severities of ID, umbilical artery resistive index was increased (P < 0.01), while pulsatility index only increased in S-ID (P < 0.05). In both M-and S-ID, fetal kidneys and livers showed evidence of hypoxia (P < 0.01 vs. controls), whereas fetal brains and placentae remained normoxic. These findings indicate prenatal ID causes organ-specific fetal hypoxia, even in the absence of severe maternal anaemia.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".