Fetal spina bifida associates with dysregulation in nutrient‐sensitive placental gene networks: Findings from a matched case–control study
Bibliographic record
Abstract
To improve outcomes in fetuses with spina bifida (SB), better understanding is needed of the molecular drivers of SB and its comorbidities. Pregnant people carrying a fetus with isolated SB (cases; n = 12) or a fetus with no congenital anomalies (controls; n = 21) were recruited at Mount Sinai Hospital, Toronto, Ontario, Canada. Clinical data and placental samples were collected. Placental transcriptome was sequenced (Clariom D microarray) and a nutrient-focused gene expression analysis pipeline was applied to determine whether fetal SB associates with placental dysfunction. Of the 391 differentially expressed genes (DEGs) in cases, 11% (n = 42) had at least one nutrient cofactor, including B vitamins (n = 7 genes), iron/heme (n = 6), and zinc (n = 11). Cases had dysregulation in genes not previously known to associate with SB, and in placental genes that have known links to SB but have not been previously identified in the placenta. Cases also had downregulated nutrient transport and upregulated branching angiogenesis and immune/inflammatory processes. Five nutrient-dependent transcription regulators, collectively predicted to target 46% of DEGs in cases, were identified and were most commonly dependent on B vitamins (n = 3) and zinc (n = 2). Placental gene expression changes were most acute in cases with poor growth. Placentae from fetuses with SB have dysregulation in several gene networks, including those that are sensitive to multiple micronutrients beyond the well-known folic acid. An improved understanding of placental phenotype in fetuses with SB may help identify novel mechanisms associated with comorbidities in fetuses with SB, and reveal new targets to improve fetal outcomes in this population.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".