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2. Associations between Phthalate Metabolite Concentrations in Follicular Fluid and Reproductive Outcomes among Women Undergoing in Vitro Fertilization/Intracytoplasmic Sperm Injection Treatment.

Author

Wen Yao, Chong Liu, Dan-Yu Qin, Xiao-Qiong Yuan, Qing-Yun Yao, Ni-Jie Li, Yong Huang, Wen-Tao Rao, Yu-Ying Li, Yan-Ling Deng, Qiang Zeng, and Yu-Feng Li

Subjects
OVARIES, FERTILITY clinics, STATISTICS, OOCYTE retrieval, CONFIDENCE intervals, WOMEN, PLASTICIZERS, TREATMENT effectiveness, T-test (Statistics), DESCRIPTIVE statistics, CHI-squared test, RESEARCH funding, FERTILIZATION in vitro, DATA analysis, DATA analysis software, METABOLITES, POISSON distribution
Abstract

BACKGROUND: Phthalates have been reported to impair fertility in various studies. However, evidence exploring the associations between phthalate metabolites in follicular fluid (FF) and reproductive outcomes is lacking. OBJECTIVES: To investigate the associations between phthalate metabolite concentrations in FF and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) outcomes among women recruited from a fertility clinic. METHODS: We included 641 women undergoing IVF/ICSI treatment from December 2018 to January 2020. The levels of eight phthalate metabolites, including monoethyl phthalate (MEP), mono-isobutyl phthalate (MiBP), mono-푛-butyl phthalate (MBP), monobenzyl phthalate (MBzP), mono(2- ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono(2- ethyl-5-carboxypentyl) phthalate (MECPP), were quantified in FF collected on the oocyte retrieval day. Associations between quartiles of individual phthalate metabolite concentrations and nine IVF/ICSI outcomes, including oocyte yield, mature oocyte number, two distinct pronuclei (2PN) zygote number, fertilization rate, blastocyst formation rate, implantation, clinical pregnancy, miscarriage, and live birth, were estimated with generalized linear models. The effects of phthalate mixtures on IVF/ICSI outcomes were assessed using Bayesian kernel machine regression (BKMR) models. RESULTS: After adjusting for relevant confounders, elevated quartiles of MBzP, MEHHP, and MEHP in FF were inversely associated with the numbers of retrieved oocytes, mature oocytes, and 2PN zygotes (all 푝 for trends <0.10). In comparison with the lowest quartile, the highest quartile of molar sum of di(2-ethylhexyl) phthalate metabolites (RDEHP) was associated with a reduction of 9.1% [95% confidence interval (CI): –17:1%, –0:37%] and 10.3% (95% CI: –18:8%, –0:94%) in yielded oocyte and mature oocyte numbers, respectively. Furthermore, the BKMR models revealed inverse associations between phthalate mixtures and the numbers of retrieved oocytes and mature oocytes. We generally found null results for implantation, clinical pregnancy, miscarriage, and live birth. DISCUSSION: Certain phthalate metabolites in FF are inversely associated with the numbers of retrieved oocytes, mature oocytes, and 2PN zygotes among women undergoing IVF/ICSI treatment. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Simultaneous Identification of Process Structure, Parameters and Time-Delay Based on Non-negative Garrote

Author

Wen-Tao Rao, Jian-Guo Wang, Tiao Shen, Shiwei Ma, Qian-Ping Xiao, and Yong-Jie Zhang

Subjects
0209 industrial biotechnology, Relation (database), Applied Mathematics, MIMO, Process (computing), System identification, Structure (category theory), Feature selection, 02 engineering and technology, Computer Science Applications, Identification (information), 020901 industrial engineering & automation, Control and Systems Engineering, Control theory, Modeling and Simulation, Ordinary least squares, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Algorithm, Mathematics
Abstract

In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously. However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application. In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented. The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated. The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms. The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.

Published
2016

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