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Biomedical subjects

Jian Hou

Publications and source records attributed to Jian Hou.

2 recordsLinked to original sources

Fetal hypoxia causes oocyte oxidative stress damage via the Sirt3/Sod2 pathway and can be alleviated by nicotinamide mononucleotide.

Environmental hypoxia exerts detrimental effects on the reproductive capabilities of both humans and animals. A fetal hypoxia model was established in which fetal mice were kept in a high-plateau hypoxic setting from embryonic day (E) 0 to 16.5. In our previous research, we found that fetal hypoxia exposure perturbs the methylation of imprinted genes in adult sperm and causes intergenerational placental impairments in male offspring. However, the specific impacts of fetal hypoxia on the female reproductive system, particularly regarding oocyte maturation, remain poorly understood. First, we found that fetal hypoxia mice exhibited a significant reduction in the average number of pups per litter. We conducted a comprehensive analysis of the transcriptome in oocytes from the hypoxic group and investigated the metabolic alterations within the follicular microenvironment. Fetal hypoxic stress contributed to cleavage and blastocyst rate reduction and induced early apoptosis and DNA damage triggered by mitochondrial dysfunction, oxidative stress aggravation and Sirt3/Sod2 downregulation. Additionally, administration of nicotinamide mononucleotide (NMN) has been shown to prevent oocytes from mitochondrial dysfunction and developmental impairment by increasing the expression of Sirt3/Sod2 and autophagy. The number of pups per litter in fetal hypoxia mice was reduced by 57.7% compared to the control group, while NMN intervention could restore it to 73.1% of the control group. These results indicate that fetal hypoxia exposure exerts multiple potential damages to adult female reproduction, while highlighting the clinical potential of NMN supplementation as a targeted intervention to alleviate such hypoxia-associated female reproductive impairment.

Animals

Quantile Tensor Regression for Integrative Genomic Analysis of Oesophageal Carcinoma.

Recent integrative genomic studies have increasingly exploited the tensor structure of multi-omics data to develop statistical methods that jointly model the relationship between clinical outcomes and multiple genomes. However, genomic measurements and clinical outcomes are frequently contaminated by outliers or heavy-tailed noise, necessitating robust tensor-based inference approaches. In this paper, we investigate the quantile tensor regression with an emphasis on the region selection problem. We introduce a novel estimator that integrates quantile regression for robustness with a nonconvex penalty to encourage sparsity in the tensor coefficient, thereby enabling the identification of localized genomic regions that significantly influence the clinical response. To solve the resulting optimization problem, we devise an effective algorithm tailored to the nonconvex objective and tensor architecture. We establish the asymptotic properties of the proposed nonconvex penalized estimator. Extensive simulations demonstrate the excellent finite-sample performance of the proposed estimator. We further illustrate the practical utility of the proposed estimator through an application to esophageal carcinoma data, providing empirical validation.

Humans