Mutational Screening for Mitochondrial tRNA Genes in 100 Women with Pre-Eclampsia
Bibliographic record
Abstract
INTRODUCTION: Impairment of mitochondrial function caused by pathogenic mitochondrial DNA (mtDNA) mutations has been found to be associated with pre-eclampsia (PE). However, the underlying mechanism of PE remains poorly undetermined. The aim of this study is to evaluate the relationship between mitochondrial tRNA (mt-tRNA) variants and PE. METHODS: The mt-tRNA variants in a cohort of 100 pregnant women with PE and 100 healthy subjects were examined by PCR-Sanger sequencing. Moreover, the phylogenetic conservation analysis, mitochondrial haplogroup analysis, and pathogenicity scoring system were used to assess the potential pathogenicity of these tRNA variants. RESULTS: We identified five possible pathogenic mt-tRNA variants: tRNAPhe A608G, tRNAIle A4263G, tRNAAla T5587C, tRNALeu(CUN) G12294C, and tRNAPro G15995A. We noticed that these variants were not detected in control subjects and occurred at the positions which were extremely conserved. Alternations in tRNA structure caused by these variants may lead to the failures in tRNA metabolism, which may subsequently lead to the impairment of mitochondrial translation as well as the respiratory chain functions. Thus, mt-tRNA variants may be involved in the pathogenesis of PE. CONCLUSION: Taken together, our data indicated that variants in mt-tRNA genes were the important contributors to PE; screening for mt-tRNA variants was recommended for early detection and prevention of PE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".