Molecularly imprinted polymers (MIPs), often called "synthetic antibodies", are highly attractive as artificial receptors with tailored biomolecular recognition to construct biosensors. Electropolymerization is a fast and facile method to directly synthesize MIP sensing elements in situ on the working electrode, enabling ultra-low-cost and easy-to-manufacture electrochemical biosensors. However, due to the high dimensional design space of electropolymerized MIPs (e-MIPs), the development of e-MIPs is challenging and lengthy based on trial and error without proper guidelines. Leveraging machine learning techniques in building the quantitative relationship between synthesis parameters and corresponding sensing performance, e-MIPs' development and optimization can be facilitated. We herein demonstrate a case study on the synthesis of cortisol-imprinted polypyrrole for cortisol detection, where e-MIPs are fabricated with 72 sets of synthesis parameters with replicates. Their sensing performances are measured using a 12-channel potentiostat to construct the subsequent data-driven framework. The Gaussian process (GP) is employed as the mainstay of the integrated framework, which can account for various uncertainties in the synthesis and measurements. The Sobol index-based global sensitivity is then performed upon the GP surrogate model to elucidate the impact of e-MIPs' synthesis parameters on sensing performance and interrelations among parameters. Based on the prediction of the established GP model and local sensitivity analysis, synthesis parameters are optimized and validated by experiment, which leads to remarkable sensing performance enhancement (1.5-fold increase in sensitivity). The proposed framework is novel in biosensor development, which is expandable and also generally applicable to the development of other sensing materials.

The purpose of this study is to clarify the mechanisms of the antineoplastic effect of decitabine (DAC), a DNA methyltransferase inhibitor. We previously reported that DAC inhibited the proliferation of the 4T1 mammary cancer cell line in vitro while inhibiting MMTV env and pol expression in the same cells. Here we present findings in vivo using two murine tumor models. We inoculated 4T1 cells into BALB/c mice from which the cell line was derived. In addition, we inoculated the murine colon carcinoma cell line, MC38, into C57BL/6 mice, the mouse strain of its origin. We treated the mice with DAC when tumors became palpable. DAC inhibited tumor growth in both tumor models and inhibited metastasis of 4T1 cells into the lungs. Quantitative PCR detected elevated expression of MMTV genes in the tumor tissues of DAC-treated mice in both models. The levels of MMTV env RNA in tumor tissues were negatively correlated with tumor mass in both models, so were the levels of the MMTV Env protein in 4T1 tumors. DAC also significantly mitigated tumor-induced splenomegaly. Hepatotoxicity of DAC was observed in C57BL/6 mice. Our data suggest the possibility that decitabine inhibits tumor growth in vivo and in vitro via different mechanisms. The findings also support the current understanding of decitabine upregulating endogenous retroviruses (ERVs) which subsequently activate an interferon response. The negative correlation between tumor mass and MMTV Env protein expression suggests a possible role of ERV proteins serving as tumor-associated antigens and decitabine enhancing adaptive immunity against cancer by stimulating ERV expression on tumor cells. Further studies of the interaction between decitabine and MMTV using immunodeficient mice may help us understand the possible involvement of adaptive immunity in the pharmacodynamics of decitabine as well as the role of ERVs in tumor development. Citation Format: Jieyu Zhang, Savannah Higgins, Riley Smith, Tania Reginald, Andrew Qi, Jiayi Li, Anna King, Yingguang Liu. Inhibition of murine tumor growth by decitabine is correlated with enhanced expression of the mouse mammary tumor virus. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4899.