Browsing by Author "Li, Pengfei"

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    Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching
    (The Royal Society of Chemistry, 2019-08-15) Pan, Xiaoliang; Li, Pengfei; Ho, Junming; Pu, Jingzhi; Mei, Ye; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    An efficient and accurate reference potential simulation protocol is proposed for producing ab initio quantum mechanical molecular mechanical (AI-QM/MM) quality free energy profiles for chemical reactions in a solvent or macromolecular environment. This protocol involves three stages: (a) using force matching to recalibrate a semi-empirical quantum mechanical (SE-QM) Hamiltonian for the specific reaction under study; (b) employing the recalibrated SE-QM Hamiltonian (in combination with molecular mechanical force fields) as the reference potential to drive umbrella samplings along the reaction pathway; and (c) computing AI-QM/MM energy values for collected configurations from the sampling and performing weighted thermodynamic perturbation to acquire AI-QM/MM corrected reaction free energy profile. For three model reactions (identity SN2 reaction, Menshutkin reaction, and glycine proton transfer reaction) in aqueous solution and one enzyme reaction (Claisen arrangement in chorismate mutase), our simulations using recalibrated PM3 SE-QM Hamiltonians well reproduced QM/MM free energy profiles at the B3LYP/6–31G* level of theory all within 1 kcal/mol with a 20 to 45 fold reduction in the computer time.
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    The novel ZIP4 regulation and its role in ovarian cancer
    (Impact Journals, 2017-09-30) Fan, Qipeng; Cai, Qingchun; Li, Pengfei; Wang, Wenyan; Wang, Jing; Gerry, Emily; Wang, Tian-Li; Shih, Ie-Ming; Nephew, Kenneth P.; Xu, Yan; Obstetrics and Gynecology, School of Medicine
    Our RNAseq analyses revealed that ZIP4 is a top gene up-regulated in more aggressive ovarian cancer cells. ZIP4's role in cancer stem cells has not been reported in any type of cancer. In addition, the role and regulation of ZIP4, a zinc transporter, have been studied in the context of extracellular zinc transporting. Factors other than zinc with ZIP4 regulatory effects are essentially unknown. ZIP4 expression and its regulation in epithelial ovarian cancer cells was assessed by immunoblotting, quantitative PCR, or immunohistochemistry staining in human ovarian tissues. Cancer stem cell-related activities were examined to evaluate the role of ZIP4 in human high-grade serous ovarian cancer cells in vitro and in vivo. RNAi and CRISPR techniques were used to knockdown or knockout ZIP4 and related genes. Ovarian cancer tissues overexpressed ZIP4 when compared with normal and benign tissues. ZIP4 knockout significantly reduced several cancer stem cell-related activities in EOC cells, including proliferation, anoikis-resistance, colony-formation, spheroid-formation, drug-resistance, and side-population in vitro. ZIP4-expressing side-population highly expressed known CSC markers ALDH1 and OCT4. ZIP4 knockout dramatically reduced tumorigenesis and ZIP4 overexpression increased tumorigenesis in vivo. In addition, the ZIP4-expressing side-population had the tumor initiating activity. Moreover, the oncolipid lysophosphatic acid effectively up-regulated ZIP4 expression via the nuclear receptor peroxisome proliferator-activated receptor gamma and lysophosphatic acid 's promoting effects in cancer stem cell-related activities in HGSOC cells was at least partially mediated by ZIP4 in an extracellular zinc-independent manner. Our critical data imply that ZIP4 is a new and important cancer stem cell regulator in ovarian cancer. Our data also provide an innovative interpretation for the apparent disconnection between low levels of zinc and up-regulation of ZIP4 in ovarian cancer tissues.
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    Self-sovereign identity empowered non-fungible patient tokenization for health information exchange using blockchain technology
    (Elsevier, 2023-05) Zhuang, Yan; Shyu, Chi-Ren; Hong, Shenda; Li, Pengfei; Zhang, Luxia; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Background: Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information in health information exchange (HIE). Most patient tokenization is the centralized approach that is unable to address data security concerns fundamentally. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets on the blockchain, have the potential to provide secure, decentralized, and trustworthy patient tokenization. Self-Sovereign Identity (SSI) is a user-centric approach to verify the ownership of NFTs in a decentralized manner. Methods We have developed a blockchain architecture that contains four modules: (1) Creation module for NFTs creation, (2) Linkage module to link the local patients' accounts to their NFTs, (3) Authentication module that allows patients to permit healthcare providers to access their token, and (4) Exchange module, which involves the HIE process and the validation of the legitimacy of the token through SSI. Results A case study has been conducted on the proposed architecture. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 s on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 s. Conclusions This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case studies have demonstrated the feasibility and potential of NFT with SSI to establish a secure, transparent, and patient-centric identity management and HIE.
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    The global prevalence and impact of steatotic liver disease and viral infections: A systematic review and meta-analysis
    (Wolters Kluwer, 2025-04-14) Li, Jiajing; Zhou, Jiahua; Li, Pengfei; Wang, Yining; Ridderhof, Nathalie; Al-Tawfiq, Jaffar A.; Brouwer, Willem Pieter; Chen, Kan; de Knegt, Robert J.; Peppelenbosch, Maikel P.; Hansen, Bettina E.; Engel, Maarten F. M.; Zheng, Ming-Hua; Memish, Ziad A.; Eslam, Mohammed; Janssen, Harry L. A.; Pan, Qiuwei; Ayada, Ibrahim; Medicine, School of Medicine
    Background: Steatotic liver disease (SLD) affects ~30% of adults worldwide. The global population is continuously threatened by epidemic and endemic viral diseases. This study aims to thoroughly examine the interaction between SLD and major viral diseases. Methods: We systematically searched databases from inception to April 2, 2024, for observational studies recording viral-infected adult patients with eligible data on the presence of hepatic steatosis. Results: Six hundred thirty-six eligible studies were included in the analysis of SLD prevalence. Among patients with monoinfections, the highest SLD prevalence was observed in those infected with HCV at 49% (95% CI: 47%-51%), followed by SARS-CoV-2 (39%, 95% CI [34%-44%]), HIV (39%, 95% CI [33%-44%]), and HBV (36%, 95% CI [32%-40%]). Additionally, co-infections, such as HCV-HIV and HBV-HCV, exhibit even higher SLD prevalence. The prevalence of steatohepatitis is particularly high in HIV-infected (24%, 95% CI: 17%-30%) and HCV-infected (18%, 95% CI: 13%-24%) populations. The co-existence of SLD with viral infections was associated not only with the progression of liver disease but also with more severe outcomes of the infections and poorer responses to antiviral treatment. The combination of cardiometabolic risk factors and viral-associated and host factors contributes to the higher risk of SLD in viral-infected populations. Conclusions: SLD is highly prevalent in viral-infected populations, and the reciprocal interactions between SLD and viral diseases exacerbate both conditions, leading to poorer patient outcomes in general.
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    Using a monotone single‐index model to stabilize the propensity score in missing data problems and causal inference
    (Wiley, 2019-04) Qin, Jing; Yu, Tao; Li, Pengfei; Liu, Hao; Chen, Baojiang; Biostatistics, School of Public Health
    The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log‐log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single‐index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real‐data example is used to illustrate the proposed methods.