Correction: Low Birthweight (LBW) and Neonatal Hyperbilirubinemia (NNH) in an Indian Cohort: Association of Homocysteine, Its Metabolic Pathway Genes and Micronutrients as Risk Factors
Bibliographic record
Abstract
Background & Aims: Indian subcontinent has the highest child mortality rates along with a very high frequency of low birthweight (LBW).Folate and vitamin B12 (Vit-B12) are necessary during foetal development and their deficiency prevalence in Indians is very high.The objective of the present paper is to assess whether foetal homocysteine (Hcy)/folate metabolic pathway genes, their cofactors and homocysteine level independently (or collectively) predispose children to Low birth weight.Methods: Cord blood was collected for the study.Frequency of 5 SNPs in 4-Hcy-pathway genes, and levels of Hcy, Vit-B12 and folate were evaluated.Results: Of the 421 newborns recruited for the study, 38% showed low birth weight (<2.5kg) and 16% were preterm babies.101 neonates developed neonatal hyperbilirubinemia (NNH).High prevalence of Vit-B12 (65%) and folate (27%) deficiency was observed in newborns along with hyperhomocystinemia (hypHcy-25%).Preterm delivery, micronutrient deficiency, hypHcy and MTHFR 677T SNP are associated as risk factor while G allele of TCN2 C776G is protective against LBW.MTHFR 677T allele and folate deficiency are also independent risk factors for NNH. Conclusion:We record the highest incidence of Vit-B12, folate deficiency and elevated Hcy levels, of all the studies so far reported on neonates.These together with MTHFR 677T are potential risk factors for LBW.Association of impaired folate/Hcy metabolism with NNH is reported for the first time and the possible way of interaction is discussed.It appears that proper nutritional management during pregnancy would reduce the risk of complex clinical outcomes.
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.007 |
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".