Search

Your search keyword '"Hu, Jinxiu"' showing total 32 results
Start Over Author "Hu, Jinxiu"
32 results on '"Hu, Jinxiu"'

13. Effects of umbilical cord mesenchymal stem cells on expression of CYR61, FSH, and AMH in mice with premature ovarian failure

Author

He, Jie, Wang, Jinxiang, Li, Ye, Zhao, Jing, Zhu X, Iangqing, Hu, Jinxiu, Li, Zian, and Pan, Xinghua

Subjects
Anti-Mullerian Hormone, Estradiol, Caspase 3, Mesenchymal Stem Cells, General Medicine, Luteinizing Hormone, Primary Ovarian Insufficiency, Umbilical Cord, Mice, Transforming Growth Factor beta, Animals, Humans, Female, Follicle Stimulating Hormone, Cysteine-Rich Protein 61
Abstract

The aim of this study was to objective to investigate the effects of umbilical cord mesenchymal stem cells on the expression of CYR61, FSH and AMH in mice with premature ovarian failure. For this purpose, thirty SPF female SD mice were selected as the research object, 10 of which were control group, namely group α, and 20 mice with premature ovarian failure model were established by cyclophosphamide. The mice were divided into model group, namely β group and the umbilical cord mesenchymal stem cell transplantation group (γ group), with 10 mice in each group. ELSA method was used to determine the levels of follicle-stimulating hormone (FSH), Luteinizing hormone (LH), estradiol (Estradiol) in serum. The changes of E2, Antimullerian hormone (AMH) and cysteine-rich protein 61 in ovarian tissues were determined by the protein imprinting method. Connective tissue growth factor (CTGF) and caspase-3 protein expression. Results showed that in fertility rate, γ group > α group > β group, the difference was statistically significant (P γ group > β group, the difference was statistically significant (P γ group > β group, and the levels of serum FSH and LH in β group > γ group > α group were statistically significant (P γ group > β group, and the atresia follicles were β group > γ group > α group, and there was a statistical difference among all groups (P0.05). In terms of CYR61 and CTGF protein expression, α group > γ group > β group, and in terms of caspase-3, β group > γ group > α group had statistical significance (P

Published
2022

14. A Fast Reduced-Order Model for Radial Integration Boundary Element Method Based on Proper Orthogonal Decomposition in the Non-Uniform Coupled Thermoelastic Problems.

Author

Tang, Zhenbo, Hu, Jinxiu, and Li, Zejun

Subjects
*BOUNDARY element methods, *PROPER orthogonal decomposition, *REDUCED-order models, *FUNCTIONALLY gradient materials, *THERMAL shock, *IMPACT loads, *DIFFERENTIAL equations
Abstract

To efficiently address the challenge of thermoelastic coupling in functionally graded materials, we propose an approach that combines the radial integral boundary element method (RIBEM) with proper orthogonal decomposition (POD). This integration establishes a swift reduced-order model to transform the high-dimensional system of equations into a more manageable, low-dimensional counterpart. The implementation of this reduced-order model offers the potential for rapid numerical simulations of functionally graded materials (FGMs) under thermal shock loading. Initially, the RIBEM is utilized to resolve the thermal coupling issue within the FGMs. From these solutions, a snapshot matrix is constructed, capturing the solved temperature and displacement fields. Subsequently, the POD modes are established and a POD reduced-order model is constructed for the boundary element format of the thermally coupled problem. Finally, a system of low-order discrete differential equations is solved. Numerical experiments demonstrate that the results obtained from the reduced-order model closely align with those of the full-order model, even when considering variations in structural parameters or impact loads. Thus, the introduction of the reduced-order model not only guarantees solution accuracy but also significantly enhances computational efficiency. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

27. The significance of immuno-stain with HLC3-AB in cerebral metastatic tumor

Author

Zhang Fulin, Liu Shanglian, Hu Jinxiu, Zhang Jun, and Wang Shangxiang

Subjects
Cancer Research, Pathology, medicine.medical_specialty, Lung, business.industry, Metastatic adenocarcinoma, Brain tumor, medicine.disease, Metastatic tumor, Stain, Human lung, medicine.anatomical_structure, Oncology, medicine, Carcinoma, Immunohistochemistry, business
Abstract

By using the immunohistochemical technique (ABC method), 186 cases of cerebral metastatic tumors labeled with McAb against human lung carcinoma are reported. They were divided into two groups: primary site determined group and undetermined group. In determined group (59 cases), 33/36 cases (91.6%) of lung carcinomas showed HLC3 positive reactions, but among 23 cases of other cancers, only two cases showed weak positive reactions. This result indicated that the HLC3 have strong specific reaction to the lung carcinomas. In undetermined group (127 cases), 68 cases of metastatic tumors reacted to HLC3 positively. In all 186 cases of cerebral metastatic tumor, there were 101 cases (53.5%) showing positive reaction to HLC3−AB. Among 125 cases of cerebral metastatic adenocarcinoma, 76 cases (61%) showed positive reactions with HLC3-AB. The result in this paper proved that the lung carcinoma was the important primary site of the cerebral metastatic tumor operated neurosurggically. The specifity of McAb HLC3- AB, the incidance of the cerebral metastatic lung carcinoma and its metastatic course were discaussed.

Published
1994

30. Functional classification of pituitary adenoma

Author

Yang Detai, Zhang Fulin, Wang Shanxiang, Zhang Yulin, and Hu Jinxiu

Subjects
endocrine system, Cancer Research, medicine.medical_specialty, endocrine system diseases, Adenoma, business.industry, medicine.disease, Endocrinology, Oncology, Pituitary adenoma, Internal medicine, Medicine public health, medicine, Immunohistochemistry, business, hormones, hormone substitutes, and hormone antagonists, Hormone
Abstract

Pituitary adenomas might be classified into different groups according to different variety of hormone produced. 225 surgical specimens were labeled with anti- sera of GH, PRL, ACTH, TSH, FSH, and LH, via immunohistochemical technique (ABC method). Data indicated that 100 out of the 225 (44.5%) were monohormonal adenomas, including 24 GH, 39 PRL, 1 FSH and 9 LH, 77 (34.2%) were multi- hormonal adenomas, including 28 positive for 2 hormones, 30 positive for 3 hormones, 19 positive for 4 or more different hormones, and the remaining 48 (21.3%) were nonfunctional adenomas. In comparison with Kovacs series, factors which might participate in the mechanism of developing monohormonal or multi- hormonal adenomas are discussed.

Published
1991

32. An age classification model based on DNA methylation biomarkers of aging in human peripheral blood using random forest and artificial neural network.

Author

Li Y, Yang Y, He J, Hu J, Zhu X, Tian C, Chen M, Zhao X, Ye L, Pan H, and Pan X

Subjects
Humans, CpG Islands, Aging genetics, Biomarkers, Genetic Markers, Neural Networks, Computer, DNA Methylation genetics, Random Forest
Abstract

Recent epigenetic studies have revealed a strong association between DNA methylation and aging and lifespan, which changes (increases or decreases) with age. Based on these, the construction of age prediction models associated with DNA methylation levels can be used to infer biological ages closer to the functional state of the organism. We downloaded methylation data from the Gene Expression Omnibus (GEO) public database for normal peripheral blood samples from people of different ages. We grouped the samples according to age (18-35 years and >50 years), screened the methylation sites that differed between the two groups, identified 44 differentially methylated sites, and subsequently obtained 11 age-related characteristic methylation sites using the random forest method. Then, we constructed an age classification model with these 11 characteristic methylation sites using an artificial neural network and evaluated its efficacy. The age classification model was constructed by an artificial neural network and its efficacy was evaluated. The model predicted an area under the curve (AUC) of 0.97 in the validation set and accurately distinguished between those aged 18-35 and >50 years. Furthermore, the levels of these 11 characteristic methylation sites also differed significantly between the two sets of samples in the validation set, including six newly identified age-related methylation sites (P<0.001). Finally, we constructed a multifactor regulatory network based on the corresponding genes of age-related methylation sites to reveal the transcriptional and post-transcriptional regulation patterns. As a result of the increasing problem of aging, the age classification model we constructed allows us to accurately distinguish different age groups at the molecular level, which will be more predictive than chronological age for assessing individual aging and future health status.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results