Dataset Information

RHD Genotypes in a Chinese Cohort of Pregnant Women.

ABSTRACT: RHD variants in D¯ Chinese pregnant women arose difficulties in management during pregnancy. Therefore, this study aims to precisely manage D¯ pregnant women by evaluating the spectrum of RHD mutations in D¯ pregnant women and getting insight into the possible rare alleles of RHD. A total of 76 D¯ pregnant women were analyzed by performing polymerase chain reactions with sequence-specific primers (PCR-SSP), the 10 RHD exons Sanger sequencing, RHD zygosity detection, and mRNA sequencing (mRNA-seq). About 40% of alleles are variations of RHD, including RHD 1227A homozygous, RHD-CE(2-9)-D, et al. Therefore, we developed a molecular diagnostic strategy for Chinese women, and most D¯ pregnant women can be diagnosed with this simple decision tree. After RHD genotyping for D¯ pregnancy women, we eliminated at least 15% unnecessary ante- and postpartum injections of Rh immunoglobulin (RhIG). As the first pedigree study and the first functional analysis under physiological conditions, mRNA-seq revealed that c.336-1G>A mutation mainly led to the inclusion of the intron 2, which indirectly explained the D¯ phenotype in this family. We also developed a robust protocol for determining fetal RhD status from maternal plasma. All 31 fetuses were predicted as RhD positive and confirmed the RhD status after birth.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8712876 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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RHD Genotypes in a Chinese Cohort of Pregnant Women.

Zhang Jianjun J Zeng Yan Y Wang Yuefeng Y Fan Jiaming J Chen Haijiang H Yang Dan D Shi Xiaoliang X Xu Hualin H Fu Zimu Z Sheng Fang F Xuan Jie J Pan Xiaoxi X Zhang Zhiming Z Ai Liping L Zhang Yue Y Pan Jingjing J Zhao Jing J Wang Mingming M

Frontiers in genetics 20211214

RHD variants in D¯ Chinese pregnant women arose difficulties in management during pregnancy. Therefore, this study aims to precisely manage D¯ pregnant women by evaluating the spectrum of RHD mutations in D¯ pregnant women and getting insight into the possible rare alleles of RHD. A total of 76 D¯ pregnant women were analyzed by performing polymerase chain reactions with sequence-specific primers (PCR-SSP), the 10 RHD exons Sanger sequencing, RHD zygosity detec ...[more]

PMID: 34970297

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Project description:Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628-0.648)) and specificity (0.683 (0.669-0.698)) and lowest error rate (0.362 (0.352-0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663-0.670)) and specificity (0.725 (0.721-0.729)) and decreased the error rate (0.333 (0.330-0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

Dataset Information

RHD Genotypes in a Chinese Cohort of Pregnant Women.

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<i>RHD</i> Genotypes in a Chinese Cohort of Pregnant Women.

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