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3,199 results on '"Liu, Jihong"'

1. DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning

Author

Ge, Ling, Hu, Chunming, Ma, Guanghui, Liu, Jihong, and Zhang, Hong

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Multi-Source cross-lingual transfer learning deals with the transfer of task knowledge from multiple labelled source languages to an unlabeled target language under the language shift. Existing methods typically focus on weighting the predictions produced by language-specific classifiers of different sources that follow a shared encoder. However, all source languages share the same encoder, which is updated by all these languages. The extracted representations inevitably contain different source languages' information, which may disturb the learning of the language-specific classifiers. Additionally, due to the language gap, language-specific classifiers trained with source labels are unable to make accurate predictions for the target language. Both facts impair the model's performance. To address these challenges, we propose a Disentangled and Adaptive Network (DA-Net). Firstly, we devise a feedback-guided collaborative disentanglement method that seeks to purify input representations of classifiers, thereby mitigating mutual interference from multiple sources. Secondly, we propose a class-aware parallel adaptation method that aligns class-level distributions for each source-target language pair, thereby alleviating the language pairs' language gap. Experimental results on three different tasks involving 38 languages validate the effectiveness of our approach., Comment: AAAI 2024

Published
2024

4. Observational study of intermittent solar jets: p-mode modulation

Author

Cai, Qiuzhuo, Ruan, Guiping, Zheng, Chenxi, Schmieder, Brigitte, Guo, Jinhan, Chen, Yao, Su, Jiangtao, Liu, Yang, Liu, Jihong, and Cao, Wenda

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Aims. Recurring jets are observed in the solar atmosphere, which can erupt intermittently. By the observation of intermittent jets, we want to understand the causes of periodic eruption characteristics. Methods. We report intermittent jets observed by the GST. The analysis was aided by 1400 {\AA} and 2796 {\AA} data from IRIS.These observational instruments allowed us to analyze the temporal characteristics of jet events. Results. The jet continued for up to 4 hours. The time distance diagram shows that the peak of the jet has obviously periodic eruption characteristics (5 minutes) during 18:00 UT-18:50 UT. We also found periodic brightening phenomenon (5 minutes) during jets bursts in the observed bands in the Transition Region (1400 {\AA} and 2796 {\AA}), which may be a response to intermittent jets in the upper solar atmosphere.The time lag is 3 minutes. Evolutionary images in the TiO band revealed the horizontal movement of granulation at the location of jet. Compared to the quiet region of the Sun, we found that the footpoint of the jet is enhanced at the center of the H {\alpha} spectral line profile, with no significant changes in the line wings. This suggests the presence of prolonged heating at the footpoint of the jet. In the mixed-polarity magnetic field region of the jet, we observed magnetic flux emergence, cancellation, and shear indicating possible intermittent magnetic reconnection. That is confirmed by the NLFFF model reconstructed using the magneto-friction method. Conclusions. The multi-wavelength analysis indicates that the events we studied were triggered by magnetic reconnection caused by mixed-polarity magnetic fields. We suggest that the horizontal motion of the granulation in the photosphere drives the magnetic reconnection, which is modulated by p-mode oscillations.

Published
2023

5. Observations of Magnetic Helicity Proxies in Solar Photosphere: Helicity with Solar Cycles

Author

Zhang, Hongqi, Yang, Shangbin, Xu, Haiqing, Yang, Xiao, Chen, Jie, and Liu, Jihong

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Observations of magnetic helicity transportation through the solar photosphere reflect the interaction of turbulent plasma movements and magnetic fields in the solar dynamo process. In this chapter, we have reviewed the research process of magnetic helicity inferred from the observed solar magnetic fields in the photosphere and also the solar morphological configurations with solar cycles. After introducing some achievements in the study of magnetic helicity, some key points would like to be summarized. The magnetic (current) helicity in the solar surface layer presents a statistical distribution similar to that of the sunspot butterfly diagram, but its maximum value is delayed from the extreme value of the sunspot butterfly diagram and corresponds in the phase with the statistical eruption of solar flares. During the spatial transport of magnetic (current) helicity from the interior of the sun into the interplanetary space at the time-space scale of the solar cycle, it shows the statistical distribution and the fluctuation with the hemispheric sign rule. These show that the current helicity and magnetic helicity transport calculation methods are complementary to each other. We also notice that the study of the inherent relationship between magnetic helicity and the solar cycle still depends on the observed accuracy of the solar magnetic field., Comment: 48 page,17 figures

Published
2023

6. Senaparib as first-line maintenance therapy in advanced ovarian cancer: a randomized phase 3 trial

Author

Wu, Xiaohua, Liu, Jihong, Wang, Jing, Wang, Li, Lin, Zhongqiu, Wang, Xiaobin, Zhu, Jianqing, Kong, Beihua, Fei, Junwei, Tang, Ying, Xia, Bairong, Liang, Zhiqing, Wang, Ke, Huang, Yi, Zheng, Hong, Lin, An, Jiang, Kui, Wang, Wei, Wang, Xin, Lou, Ge, Pan, Hongming, Yao, Shuzhong, Li, Guiling, Hao, Min, Cai, Yunlang, Chen, Xuejun, Yang, Zhijun, Chen, Youguo, Wen, Hongwu, Qu, Pengpeng, Xu, Cong, and Hsieh, Chih-Yi

Published
2024
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12. Exploratory biomarker analysis in the phase III L-MOCA study of olaparib maintenance therapy in patients with platinum-sensitive relapsed ovarian cancer

Author

Li, Huayi, Peng, Zikun, Zhu, Jianqing, Zhao, Weidong, Huang, Yi, An, Ruifang, Zheng, Hong, Qu, Pengpeng, Wang, Li, Zhou, Qi, Wang, Danbo, Lou, Ge, Wang, Jing, Wang, Ke, Kong, Beihua, Xie, Xing, Yin, Rutie, Low, John, Rozita, Abdul Malik, Sen, Lim Chun, Meng, Yong Chee, Kiong, Kho Swee, Liu, Jihong, Liang, Zhiqing, Lv, Weiguo, Zhu, Yaping, Hu, Weiguo, Sun, Wei, Su, Jingya, Wang, Qiqi, Zang, Rongyu, Ma, Ding, and Gao, Qinglei

Published
2024
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16. Sufu mutation promotes malignant progression of lung adenocarcinoma by regulating Gli1

Author

LIU Jihong and YI Xinglin

Subjects
lung adenocarcinoma, hedgehog signaling pathway, sufu, gli1, Medicine (General), R5-920
Abstract

Objective To explore the effect of Sufu on lung adenocarcinoma by observing its mutation and the changes in Gli1 transcription after its mutation. Methods cBioPortal for Cancer Genomics was used to obtain the mutation status of Sufu in lung cancer. Four Sufu single base mutation vectors were constructed and transfected into lung adenocarcinoma cell lines A549 and H1975 to establish cells with Sufu overexpression and knockout. Wounding healing assay was employed to determine the effect of Sufu on cell migration, and RT-qPCR, immunofluorescence assay and dual luciferase reporter gene assay were utilized to explore the changes in Gli1 transcription after Sufu mutation. Results cBioPortal data showed that the mutation rate of Sufu in lung cancer was 11.76% in squamous cell carcinoma (2/17), 0.96% in adenocarcinoma (6/625), 1.01% in small cell lung cancer (4/396), and 0.69% in non-small cell lung cancer (77/11 227). The median survival of lung cancer patients was significantly decreased in those with Sufu mutation than those without (38.00 vs 54.34 months, HR=1.943, P < 0.05). A549 cells were sensitive to Sufu knockout, and it resulted in significant increase of the transcription level of Gli1 (P < 0.05). Overexpression of Sufu inhibited cell migration ability in both A549 and H1975 cells (P < 0.05). Sufu mutation had no significant influence on the location of Gli1 in the cells. The Sufu-T411M mutation up-regulated the the transcription level of Gli1 (P < 0.05). Conclusion Sufu has an inhibitory effect on lung adenocarcinoma metastasis, and can be regarded as a potential target for lung adenocarcinoma metastasis.

Published
2024
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17. ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition

Author

Ge, Ling, Hu, Chunming, Ma, Guanghui, Zhang, Hong, and Liu, Jihong

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

For named entity recognition (NER) in zero-resource languages, utilizing knowledge distillation methods to transfer language-independent knowledge from the rich-resource source languages to zero-resource languages is an effective means. Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language. Despite the impressive performance achieved by these methods, we argue that they have two limitations. Firstly, the teacher network fails to effectively learn language-independent knowledge shared across languages due to the differences in the feature distribution between the source and target languages. Secondly, the student network acquires all of its knowledge from the teacher network and ignores the learning of target language-specific knowledge. Undesirably, these limitations would hinder the model's performance in the target language. This paper proposes an unsupervised prototype knowledge distillation network (ProKD) to address these issues. Specifically, ProKD presents a contrastive learning-based prototype alignment method to achieve class feature alignment by adjusting the distance among prototypes in the source and target languages, boosting the teacher network's capacity to acquire language-independent knowledge. In addition, ProKD introduces a prototypical self-training method to learn the intrinsic structure of the language by retraining the student network on the target data using samples' distance information from prototypes, thereby enhancing the student network's ability to acquire language-specific knowledge. Extensive experiments on three benchmark cross-lingual NER datasets demonstrate the effectiveness of our approach., Comment: AAAI 2023

Published
2023

18. Temporal Events Detector for Pregnancy Care (TED-PC): A Rule-based Algorithm to Infer Gestational Age and Delivery Date from Electronic Health Records of Pregnant Women with and without COVID-19

Author

Lyu, Tianchu, Liang, Chen, Liu, Jihong, Campbell, Berry, Hung, Peiyin, Shih, Yi-Wen, Ghumman, Nadia, and Li, Xiaoming

Subjects
Statistics - Applications
Abstract

Objective: To develop a rule-based algorithm that detects temporal information of clinical events during pregnancy for women with COVID-19 by inferring gestational weeks and delivery dates from Electronic Health Records (EHR) from the National COVID Cohort Collaborate (N3C). Materials and Methods: The EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Clinical Data Model (CDM). EHR phenotyping resulted in 270,897 pregnant women (2018-06-01 to 2021-05-31). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity of the algorithm; and extreme value analysis for individuals with <150 or >300 days of gestation. Results: The algorithm identified 296,194 pregnancies (16,659 COVID-19 174 and 744 without COVID-19 peri-pandemic) in 270,897 pregnant women. For inferring gestational age, 95% cases (n=40) have moderate-high accuracy (Cohen Kappa = 0.62); 100% cases (n=40) have moderate-high granularity of temporal information (Cohen Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen Kappa = 1). Accuracy of gestational age detection for extreme length of gestation is 93.3% (Cohen Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (COPD) (0.2% vs. 0.1%), respiratory distress syndrome (ARDS) (1.8% vs. 0.2%). Discussion: We explored the characteristics of pregnant women by different timing of COVID-19 with our algorithm: the first to infer temporal information from complete antenatal care and detect the timing of SARS-CoV-2 infection for pregnant women using N3C. Conclusion: The algorithm shows excellent validity in inferring gestational age and delivery dates, which supports national EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.

Published
2022
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19. Enhanced Myogenesis by Silencing Myostatin with Nonviral Delivery of a dCas9 Ribonucleoprotein Complex

Author

Chen, Yinwei, Banie, Lia, Breyer, Benjamin N, Tan, Yan, Wang, Zhao, Zhou, Feng, Wang, Guifang, Lin, Guiting, Liu, Jihong, Qi, Lei S, and Lue, Tom F

Subjects
Medical Physiology, Biomedical and Clinical Sciences, Regenerative Medicine, Urologic Diseases, Animals, CRISPR-Cas Systems, Female, Gene Editing, Glycogen Synthase Kinase 3 beta, Humans, Muscle Development, Myostatin, Rats, Ribonucleoproteins, Urinary Incontinence, Stress
Abstract

Stress urinary incontinence (SUI) and pelvic floor disorder (PFD) are common conditions with limited treatment options in women worldwide. Regenerative therapy to restore urethral striated and pelvic floor muscles represents a valuable therapeutic approach. We aim to determine the CRISPR interference-mediated gene silencing effect of the nonviral delivery of nuclease-deactivated dCas9 ribonucleoprotein (RNP) complex on muscle regeneration at the cellular and molecular level. We designed four myostatin (MSTN)-targeting sgRNAs and transfected them into rat myoblast L6 cells together with the dCas9 protein. Myogenesis assay and immunofluorescence staining were performed to evaluate muscle differentiation, while CCK8 assay, cell cycle assay, and 5-ethynyl-2'-deoxyuridine staining were used to measure muscle proliferation. Reverse transcription-polymerase chain reaction and Western blotting were also performed to examine cellular signaling. Myogenic factors (including myosin heavy chain, MSTN, myocardin, and serum response factor) increased significantly after day 5 during myogenesis. MSTN was efficiently silenced after transfecting the dCas9 RNP complex, which significantly promoted more myotube formation and a higher fusion index for L6 cells. In cellular signaling, MSTN repression enhanced the expression of MyoG and MyoD, phosphorylation of Smad2, and the activity of Wnt1/GSK-3β/β-catenin pathway. Moreover, MSTN repression accelerated L6 cell growth with a higher cell proliferation index as well as a higher expression of cyclin D1 and cyclin E. Nonviral delivery of the dCas9 RNP complex significantly promoted myoblast differentiation and proliferation, providing a promising approach to improve muscle regeneration for SUI and PFD. Further characterization and validation of this approach in vivo are needed.

Published
2022

20. The Critical Value of Maternal and Child Health (MCH) to Graduate Training in Public Health: A Framework to Guide Education, Research and Practice

Author

Deardorff, Julianna, Tissue, Michelle Menser, Elliott, Patricia, Handler, Arden, Vamos, Cheryl, Bonilla, Zobeida, Turchi, Renee, Obeng, Cecilia Sem, Liu, Jihong, and Grason, Holly

Subjects
Health Services and Systems, Public Health, Health Sciences, Child, Child Health, Education, Public Health Professional, Humans, Leadership, Maternal-Child Health Centers, Maternal and child health, Training, Education, Life course, Research, Practice, CEPH, Medical and Health Sciences, Studies in Human Society, Biomedical and clinical sciences, Health sciences, Human society
Abstract

IntroductionIn light of persistent health inequities, this commentary describes the critical role of maternal and child health (MCH) graduate training in schools and programs of public health (SPPH) and illustrates linkages between key components of MCH pedagogy and practice to 2021 CEPH competencies.MethodsIn 2018, a small working group of faculty from the HRSA/MCHB-funded Centers of Excellence (COEs) was convened to define the unique contributions of MCH to SPPH and to develop a framework using an iterative and consensus-driven process. The working group met 5 times and feedback was integrated from the broader faculty across the 13 COEs. The framework was further revised based on input from the MCHB/HRSA-funded MCH Public Health Catalyst Programs and was presented to senior MCHB leaders in October 2019.ResultsWe developed a framework that underscores the critical value of MCH to graduate training in public health and the alignment of core MCH training components with CEPH competencies, which are required of all SPPH for accreditation. This framework illustrates MCH contributions in education, research and evaluation, and practice, and underscores their collective foundation in the life course approach.ConclusionsThis new framework aims to enhance training for the next generation of public health leaders. It is intended to guide new, emerging, and expanding SPPH that may currently offer little or no MCH content. The framework invites further iteration, adaptation and customization to the range of diverse and emerging public health programs across the nation.

Published
2022

23. Clinical research in endometrial cancer: consensus recommendations from the Gynecologic Cancer InterGroup

Author

Barretina-Ginesta, Pilar, Bennett, Katherine, Berek, Jonathan, Berger, Regina, Bjørge, Line, Boere, Ingrid, Brennan, Donal, Bruchim, Ilan, Chang, Ting-Chang, Chavez Blanco, Adriana, Chen, Xiaojun, Colombo, Nicoletta, Crosbie, Emma, Denys, Hannelore, Duska, Linda, Fruehauf, Filip, Gomez Garcia, Eva Maria, van Gorp, Toon, Grimm, Christoph, Guitmann, Gustavo, Han, Kathy, Hanker, Lars, Harano, Kenichi, Hasegawa, Kosei, Herrington, C Simon, Ip, Philip, Joly, Florence, Khaw, Pearly, Kohn, Elise, Kristeleit, Rebecca, Kroep, Judith, Leary, Alexandra, Lee, Jung-Yun, Lheureux, Stephanie, Liu, Jihong, Mackay, Helen, Mahner, Sven, Mariani, Andrea, McAlpine, Jessica, Mikami, Yoshiki, Mirza, Mansoor Raza, Mukhopadhyay, Asima, Nagao, Shoji, Ng, Joseph, Nogueira-Rodrigues, Angelica, Novák, Zoltán, O'Donnell, Jennifer, Osborne, Sherill, Perez-Fidalgo, J. Alejandro, Romeo Marin, Margarita, Roy Chowdhury, Rahul, Sadozye, Azmat, Safra, Tamar, Scott, Claire, Sehouli, Jalid, Slomovitz, Brian, Tan, David, Taylor, Alexandra, Valabrega, Giorgio, Veneziani, Ana, Verhoeven, Karen, Vetter, Marcus, Wampfler, Julian, Westin, Shannon, Wimberger, Pauline, Zola, Paolo, Creutzberg, Carien L, Kim, Jae-Weon, Eminowicz, Gemma, Allanson, Emma, Eberst, Lauriane, Kim, Se Ik, Nout, Remi A, Park, Jeong-Yeol, Lorusso, Domenica, Mileshkin, Linda, Ottevanger, Petronella B, Brand, Alison, Mezzanzanica, Delia, Oza, Amit, Gebski, Val, Pothuri, Bhavana, Batley, Tania, Gordon, Carol, Mitra, Tina, White, Helen, Howitt, Brooke, Matias-Guiu, Xavier, Ray-Coquard, Isabelle, Gaffney, David, Small, William, Jr, Miller, Austin, Concin, Nicole, Powell, Matthew A, Stuart, Gavin, and Bookman, Michael A

Published
2024
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27. Association of a Lifestyle Intervention with Blood Pressure Trajectories During Pregnancy and Postpartum in Women with Pre-pregnancy Overweight and Obesity

Author

Lane, Abbi, Wilcox, Sara, Wingard, Ellen, McLean, Marnie K., and Liu, Jihong

Subjects
Health care industry
Abstract

Objectives Healthy lifestyle, including healthy diet and physical activity, has been associated with lower blood pressure (BP). We hypothesized that overweight/obese women randomized to a lifestyle intervention beginning in early pregnancy would be less likely to have a higher BP trajectory. Methods The Health in Pregnancy and Postpartum study promoted a healthy lifestyle in pregnant and postpartum women with pre-pregnancy overweight or obesity. BP was measured at study visits at [less than or equal to] 16 and 32 weeks of pregnancy and 6 and 12 months postpartum. Latent class trajectories were constructed to identify groups of participants with similar BP patterns throughout the study period. Odds of being assigned to the higher BP trajectories between the intervention and control group were determined with logistic regression, adjusted for age, race, pre-pregnancy BMI, gestational weight gain, smoking during the study period, and household income. Results Of the 219 participants enrolled (44.3% Black participants, 55.7% White participants, mean age = 29.7 ± 0.3 years), 112 (51.1%) were randomized to the intervention. Two systolic and diastolic BP trajectory groups were identified (Low-Stable, Moderate-Increasing). Randomization to the lifestyle intervention was not associated with odds of Moderate-Increasing systolic (OR 0.83, 95%CI 0.4, 1.6) or diastolic (OR 0.67, 95%CI 0.3, 1.3) BP trajectory assignment. Those in the Moderate-Increasing BP groups had higher BMI and were more likely to have an adverse pregnancy outcome. Conclusions A lifestyle intervention initiated in early pregnancy to help prevent excessive gestational weight gain was not associated with odds of specific BP patterns through pregnancy and postpartum., Author(s): Abbi Lane [sup.1] , Sara Wilcox [sup.1] [sup.2] , Ellen Wingard [sup.2] , Marnie K. McLean [sup.1] , Jihong Liu [sup.2] [sup.3] Author Affiliations: (1) grid.254567.7, 0000 0000 9075 [...]

Published
2023
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31. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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32. Niraparib maintenance therapy using an individualised starting dose in patients with platinum-sensitive recurrent ovarian cancer (NORA): final overall survival analysis of a phase 3 randomised, placebo-controlled trial

Author

Wu, Xiaohua, Zhu, Jianqing, Yin, Rutie, Yang, Jiaxin, Liu, Jihong, Wang, Jing, Wu, Lingying, Liu, Ziling, Gao, Yunong, Wang, Danbo, Lou, Ge, Yang, Hongying, Zhou, Qi, Kong, Beihua, Huang, Yi, Chen, Lipai, Li, Guiling, An, Ruifang, Wang, Ke, Zhang, Yu, Yan, Xiaojian, Lu, Xin, Lu, Weiguo, Hao, Min, Wang, Li, Cui, Heng, Chen, Qionghua, Abulizi, Guzhalinuer, Huang, Xianghua, Tian, Xiaofei, Wen, Hao, Huang, Zhao, Dong, Juan, Zhang, Charlie, Hou, Jianmei, and Mirza, Mansoor R.

Published
2024
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40. Preoperative and intraoperative assessment of myometrial invasion in patients with FIGO stage I non-endometrioid endometrial carcinoma—a large-scale, multi-center, and retrospective study

Author

Yang, Xiaohang, Yin, Jingjing, Fu, Yu, Shen, Yuanming, Zhang, Chuyao, Yao, Shuzhong, Xu, Congjian, Xia, Min, Lou, Ge, Liu, Jihong, Lin, Bei, Wang, Jianliu, Zhao, Weidong, Zhang, Jieqing, Cheng, Wenjun, Guo, Hongyan, Guo, Ruixia, Xue, Fengxia, Wang, Xipeng, Han, Lili, Li, Xiaomao, Zhang, Ping, Zhao, Jianguo, Li, Wenting, Dou, Yingyu, Wang, Zizhuo, Liu, Jingbo, Li, Kezhen, Chen, Gang, Sun, Chaoyang, Sun, Pengming, Lu, Weiguo, and Yao, Qin

Published
2023
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48. Metabolome-Wide Associations of Gestational Weight Gain in Pregnant Women with Overweight and Obesity

Author

Dai, Jin, Boghossian, Nansi S, Sarzynski, Mark A, Luo, Feng, Sun, Xiaoqian, Li, Jian, Fiehn, Oliver, Liu, Jihong, and Chen, Liwei

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Prevention, Clinical Research, Obesity, Good Health and Well Being, biomarkers, gestational weight gain, metabolomics, pregnancy, obesity, overweight, Analytical Chemistry, Biochemistry and Cell Biology, Clinical Sciences, Biochemistry and cell biology, Medical biochemistry and metabolomics, Analytical chemistry
Abstract

Excessive gestational weight gain (GWG) is associated with adverse pregnancy outcomes. This metabolome-wide association study aimed to identify metabolomic markers for GWG. This longitudinal study included 39 Black and White pregnant women with a prepregnancy body mass index (BMI) of ≥ 25 kg/m2. Untargeted metabolomic profiling was performed using fasting plasma samples collected at baseline (mean: 12.1 weeks) and 32 weeks of gestation. The associations of metabolites at each time point and changes between the two time points with GWG were examined by linear and least absolute shrinkage and selection operator (LASSO) regression analyses. Pearson correlations between the identified metabolites and cardiometabolic biomarkers were examined. Of the 769 annotated metabolites, 88 metabolites at 32 weeks were individually associated with GWG, with four (phosphatidylcholine (PC) 34:4, triacylglycerol (TAG) 52:6, arachidonic acid, isoleucine) jointly associated with GWG (area under the receiver operating characteristic curve (AUC) for excessive GWG: 0.80, 95% CI: 0.67, 0.93). No correlations were observed between the 88 metabolites and insulin, C-peptide, and high-sensitivity C-reactive protein at 32 weeks. Twelve metabolites at baseline (AUC for excessive GWG: 0.80, 95% CI: 0.62, 0.99) and three metabolite changes (AUC for excessive GWG: 0.73, 95% CI: 0.44, 1.00) were jointly associated with GWG. We identified novel metabolites in the first and third trimesters associated with GWG, which may shed light on the pathophysiology of GWG.

Published
2022

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