检索结果-内蒙古大学图书馆

development of anti-aflatoxin B1 nanobodies from a novel mutagenesis-derived synthetic library for traditional Chinese medicine and foods safety testing

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Journal of biological engineering 2023年第1期17卷 30页

作者： Yu-Ching Lee Gar-Hwa Lai Tsai-Yu Lin Tien-Sheng Tseng Tsung-Hsun Tsai Wang-Chuan Chen Cheng-Chung Lee Keng-Chang Tsai TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan. Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Ph.D. Program in Drug Discovery and Development Industry College of Pharmacy Taipei Medical University Taipei Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Orthopedics Kaohsiung Veterans General Hospital Kaohsiung Taiwan. Institute of Molecular Biology National Chung Hsing University Taichung Taiwan. Department of Psychiatry Kaohsiung Armed Forces General Hospital Kaohsiung Taiwan. The School of Chinese Medicine for Post Baccalaureate I-Shou University Kaohsiung Taiwan. Department of Chinese Medicine E-Da Hospital Kaohsiung Taiwan. The Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. tkc@nricm.edu.tw. National Research Institute of Chinese Medicine Ministry of Health and Welfare No. 155-1 Sec. 2 Linong St. Beitou District Taipei 11221 Taiwan. tkc@nricm.edu.tw.

BACKGROUNd:The main commercially available methods for detecting small molecules of mycotoxins in traditional Chinese medicine (TCM) and functional foods are enzyme-linked immunosorbent assay and mass spectrometry. Regarding the development of diagnostic antibody reagents, effective methods for the rapid preparation of specific monoclonal antibodies are inadequate. METHOdS:In this study, a novel synthetic phage-displayed nanobody Golden Glove (SynaGG) library with a glove-like cavity configuration was established using phage display technology in synthetic biology. We applied this unique SynaGG library on the small molecule aflatoxin B1 (AFB1), which has strong hepatotoxicity, to isolate specific nanobodies with high affinity for AFB1. RESULT:These nanobodies exhibit no cross-reactivity with the hapten methotrexate, which is recognized by the original antibody template. By binding to AFB1, two nanobodies can neutralize AFB1-induced hepatocyte growth inhibition. Using molecular docking, we found that the unique non-hypervariable complementarity-determining region 4 (CdR4) loop region of the nanobody was involved in the interaction with AFB1. Specifically, the CdR4's positively charged amino acid arginine directed the binding interaction between the nanobody and AFB1. We then rationally optimized the interaction between AFB1 and the nanobody by mutating serine at position 2 into valine. The binding affinity of the nanobody to AFB1 was effectively improved, and this result supported the use of molecular structure simulation for antibody optimization. CONCLUSION:In summary, this study revealed that the novel SynaGG library, which was constructed through computer-aided design, can be used to isolate nanobodies that specifically bind to small molecules. The results of this study could facilitate the development of nanobody materials to detect small molecules for the rapid screening of TCM materials and foods in the future.

关键词： Aflatoxin B1 Glove-like cavity Nanobody Nonhypervariable complementarity-determining region 4 loop phage display technology

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Interleukin-1 receptor 1 deficiency worsens hepatocellular carcinoma, while gemcitabine treatment alleviates the hepatocellular carcinoma-induced increase in intra-hepatic immune cells

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Journal of gastroenterology and hepatology 2024年第10期39卷 2208-2218页

作者： Chia-Sheng Chu Hsiao-Ping Chen Pin-Hung Lin Chi-Chen Cheng Ho-Yu Kuo Pei-Han Fan Wei-Hao Peng Li-Ling Wu Ph.D. Program of Interdisciplinary Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Ever Health Clinic Taichung Taiwan. Health Innovation Center National Yang Ming Chiao Tung University Taipei Taiwan. Microbiota Research Center National Yang Ming Chiao Tung University Taipei Taiwan. Department of Microbiology National Taiwan University College of Medicine Taipei Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Department and Institute of Physiology National Yang Ming Chiao Tung University Taipei Taiwan. School of Medicine Department of Medical Science National Tsing Hua University Hsinchu Taiwan. Institute of Biotechnology National Tsing Hua University Hsinchu Taiwan.

BACKGROUNd ANd AIM:Primary liver cancer, particularly hepatocellular carcinoma (HCC), represents a substantial global health challenge. Although immune checkpoint inhibitors are effective in HCC treatment, several patients still experience disease progression. Interleukin-1 (IL-1) regulates immunity and inflammation. We investigate the role of IL-1 in HCC development and progression and determine the potential therapeutic impact of gemcitabine in treating HCC. METHOdS:Hydrodynamics-based transfection, employing the sleeping beauty transposase system, delivered surrogate tumor antigens, NRAS (NRAS proto-oncogene, GTPase), ShP53, and SB100 to C57BL/6 mice. A basic HCC mouse model was established. Pathogen-free animals were tested for serum and hepatotoxicity. The HCC prognosis was monitored using alanine aminotransferase and aspartate aminotransferase levels. Liver histology immunohistochemistry and mouse splenocyte/intra-hepatic immune cell flow cytometry were conducted. IL-1β levels in human and mouse serum were assessed. RESULTS:Interleukin-1β levels were elevated in patients with HCC compared with those in non-HCC controls. Hepatic IL-1β levels were higher in HCC mouse models than those in non-HCC mice, suggesting localized hepatic inflammation. IL-1 receptor type 1 (IL-1R1) knockout (IL-1R1) mice exhibited less severe HCC progression than that in wild-type mice, despite the high intra-hepatic IL-1β concentration. IL-1R1 mice exhibited increased hepatic levels of myeloid-derived suppressor cells and regulatory T cells, which may exacerbate HCC. Gemcitabine significantly reduced the HCC tumor burden, improved liver conditions, and increased survival rates in HCC mouse models. Gemcitabine reduced the hepatic levels of myeloid-derived suppressor cells and regulatory T cells, potentially alleviating immune suppression in the liver. CONCLUSIONS:Targeting IL-1 or combining gemcitabine with immunotherapy is a promising approach for treating advanced-stage HCC.

关键词： IL‐1R1 T regulatory cells gemcitabine hepatocellular carcinoma myeloid‐derived suppressor cells

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVId-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, di Zhang, Shuo Mura, Cameron Bourne, philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOd) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Exploration of dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVId-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOd) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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#41 : The Effectiveness of GnRH Agonist Administration Prior to Frozen-Thawed Embryo Transfer with Artificial Cycle: A Systematic Review and Meta-Analysis of different Protocols and Infertile Populations

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Fertility & Reproduction 2023年第4期5卷 337-337页

作者： Nguyen Tuong Ho dang Khanh Ngan Ho Xuan Hong Tomai Yu-Ming Hu Yen-Ping Ho Shyr-Yeu Lin Ruey-Sheng Wang Yi-Xuan Lee Shun-Jen Tan Shu-Huei Kao Chii-Ruey Tzeng International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Taipei Fertility Center Taipei Taiwan School of Nutrition and Health Sciences College of Nutrition Taipei Medical University Taipei Taiwan Office of International Relations Ho Chi Minh City University of Medicine and Pharmacy Ho Chi Minh City Vietnam School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background and Aims: We investigated the use of GnRHa pretreatment with different protocols and durations of treatment on pregnancy outcomes in women undergoing FET with artificial cycles (AC-FET). Method: A systematic search of PubMed, EMBASE, Google Scholar, and Cochrane library was performed to include only eligible peer-reviewed studies in English published before September 01, 2022. This review was registered on PROSPERO (CRd42022299259) Results: A total of 41 studies (43021 individuals) were included with low to moderate risk of bias. Overall, GnRHa pretreatment improved clinical pregnancy rate (CPR) (OR=1.27, 95%CI:1.12-1.44), implantation rate (IR) (OR=1.24, 95%CI:1.07-1.45), live birth rate (LBR) (OR=1.31, 95%CI:1.07-1.60) and endometrial thickness (Md=0.16, 95%CI:0.03-0.30). Subgroup analyses of randomized and non-randomized studies both demonstrated the benefits of GnRHa pretreatment in CPR, IR, and LBR. Protocols using depot GnRHa (OR=1.19, 95%CI:1.04-1.36) showed a better LBR than daily GnRHa (OR=1.04, 95%CI:0.93-1.16). Women administered GnRHa for more than one cycle had better CPR (OR=2.01, 95%CI:1.29-3.13) and IR (OR=2.14, 95%CI:0.98-4.67) than those treated with GnRHa within one cycle before FET ([OR=1.18, 95%CI:1.04-1.33] and [OR=1.16, 95%CI:1.01-1.32], respectively). In women with PCOS, pituitary suppression with GnRHa significantly improved CPR (OR=1.24, 95%CI:0.98-4.67), LBR (OR=1.22, 95%CI:1.05-1.42) and reduced the miscarriage rate (OR=0.75, 95%CI:0.59-0.95). In contrast, women with regular menstruation had a higher endometrial thickness (Md=0.16, 95%CI: 0.04-0.28) but no significant improvements in pregnancy outcomes. GnRHa pretreatment considerably improved IR (OR=2.21, 95%CI:1.60-3.07) in women with adenomyosis and CPR (OR=1.74, 95%CI:1.12-2.71), and LBR (OR=1.67, 95%CI:1.06-2.63) in women with endometriosis. Conclusion: GnRHa pretreatment, especially with longer suppression duration and the use of depot GnRHa, demonstrates a beneficial rol

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Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study

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Cancer science 2023年第10期114卷 4063-4072页

作者： Quynh Thi Nhu Nguyen phung-Anh Nguyen Chun-Jung Wang phan Thanh phuc Ruo-Kai Lin Chin-Sheng Hung Nei-Hui Kuo Yu-Wen Cheng Shwu-Jiuan Lin Zong-You Hsieh Chi-Tsun Cheng Min-Huei Hsu Jason C Hsu School of Pharmacy College of Pharmacy Taipei Medical University Taipei City Taiwan. Clinical Data Center Office of Data Science Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei City Taiwan. Research Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei City Taiwan. Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan. Oncology Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Data Science College of Management Taipei Medical University Taipei City Taiwan. International Ph.D. Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei City Taiwan.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and december 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

关键词： TMUCRd breast cancer survival machine learning prediction models real-world data

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Haploidentical and matched unrelated donor allogeneic hematopoietic stem cell transplantation offer similar survival outcomes for acute leukemia

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Cancer Reports 2024年第4期7卷 e2060页

作者： Wang, Yin-Che Lai, Cheng-Lun Chen, Tsung-Chih Gau, Jyh-Pyng Teng, Chieh-Lin Jerry Division of Hematology/Medical Oncology Department of Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Division of Hematology and Oncology Department of Medicine Taipei Medical University Hospital Taiwan Ph.D. Program in Translational Medicine National Chung Hsing University Taichung Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Life Science Tunghai University Taichung Taiwan

Background: Haploidentical hematopoietic stem cell transplantation (haplo-HSCT) has emerged as an effective approach for acute leukemia, primarily due to the inherent difficulty in finding human leukocyte antigen-matched unrelated donors (MUd). Nevertheless, it remains uncertain whether haplo-HSCT and MUd-HSCT can provide comparable outcomes in patients with acute leukemia. Aims: This study aimed to assess the overall survival (OS) and leukemia-free survival (LFS) outcomes between the MUd-HSCT and haplo-HSCT groups. Methods and results: This retrospective analysis encompassed adult patients with acute leukemia undergoing the initial allo-HSCT. Among these 85 patients, we stratified 33 patients into the MUd-HSCT group and 52 to the haplo-HSCT group. The primary outcomes were OS and LFS. The median OS was not reached in the haplo-HSCT group, while it reached 29.8 months in patients undergoing MUd-HSCT (p =.211). Likewise, the median LFS periods were 52.6 months in the haplo-HSCT group and 12.7 months in the MUd-HSCT group (p =.212). Importantly, neither the OS nor LFS showed substantial differences between the MUd-HSCT and haplo-HSCT groups. Furthermore, univariate analyses revealed that haplo-HSCT did not demonstrate a significantly higher risk of worse LFS (hazard ratio [HR], 0.69;95% confidence interval [CI], 0.38–1.25;p =.216) or OS (HR, 0.67;95% CI, 0.36–1.26;p =.214) than MUd-HSCT. Notably, a high European Group for Blood and Marrow Transplantation risk score (HR, 1.44;95% CI, 1.10–1.87;p =.007) and non-complete remission (HR, 2.48;95% CI, 1.17–5.23;p =.017) were significantly correlated with worse OS. Conclusion: Haplo-HSCT may serve as an alternative to MUd-HSCT for the treatment of acute leukemia, offering similar survival outcomes. © 2024 The Authors. Cancer Reports published by Wiley Periodicals LLC.

关键词： hematopoietic stem cell transplantation human leukocyte antigen leukemia survival analysis

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An Innovative Real-Time Cell Viability Analysis: A Cutting-Edge Flexible Bioimpedance Sensor

SSRN

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SSRN 2023年

作者： Chou, Hsin-Yu Ching, Congo Tak-Shing Huang, Hui-Xuan phan, Thien Luan Wang, Hui-Min david Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung402 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung40227 Taiwan Department of Electrical Engineering National Chi Nan University Nantou54561 Taiwan Department of Physic and Electronic Engineering Faculty of Physic and Engineering Physic Vietnam National University Ho Chi Minh City University of Science Viet Nam Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Laboratory Science and Biotechnology China Medical University Taichung City404 Taiwan

There are many compounds to be used to treat cancer but still only 20% of proposed anticancer agents have been commercialized after clinical trials due to serious side effects and unsatisfactory results. To screen potential drugs precisely and quickly, this study develops a flexible bioimpedance sensor. The sensor is positive to detect the half maximal inhibitory concentration (IC50) of drugs real-time by analyzing phase angle changes during cell mortality. The best results are achieved using a probe separation of A12B34 at logarithmic frequencies of 163 Hz and 77.87 kHz. At these two frequencies, there is a linear relationship with the phase angle at 0% and 50% of the dead cells. dividing the phase angle at the two frequencies shows a 17.98% change in the phase angle, which allows self-correction and insensitivity to the number of cells. A custom phase angle measurement device is developed for detection at 163 Hz and 77.87 kHz, respectively. This study develops a novel sensor that is precise, fast and allows high throughput analysis to detect the inhibition of cancer in real-time. This sensor is an alternative to traditional chemical detection methods because it is faster, cheaper and more accurate. © 2023, The Authors. All rights reserved.

关键词： Chemical detection

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GMI, a fungal immunomodulatory protein, ameliorates SARS-CoV-2 envelope protein-induced inflammation in macrophages via inhibition of MAPK pathway

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International Journal of Biological Macromolecules 2023年 241卷 124648页

作者： Lin, Zhi-Hu Yeh, Hsin Lo, Hung-Chih Hua, Wei-Jyun Ni, Ming-Yang Wang, Li-Kai Chang, Ting-Ting Yang, Muh-Hwa Lin, Tung-Yi Institute of Traditional Medicine National Yang Ming Chiao Tung University Taipei Taiwan Program in Molecular Medicine National Yang Ming Chiao Tung University Taipei Taiwan Biomedical Industry Ph.D. Program National Yang Ming Chiao Tung University Taipei Taiwan Hungchi Women & Children's Hospital Taoyuan Taiwan Department of Anesthesiology Chi Mei Medical Center Tainan Taiwan Department of Hospital and Health Care Administration Chia Nan University of Pharmacy and Science Tainan Taiwan Department and Institute of Pharmacology National Yang Ming Chiao Tung University Taipei Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan

Clinically, COVId-19 is often accompanied by a severe immune response (cytokine storm) which produces a large number of cytokines, such as TNF-α, IL-6 and IL-12, and consequently causes acute respiratory distress syndrome (ARdS). GMI is a type of fungal immunomodulatory protein that is cloned from Ganoderma microsporum and acts as modulating immunocyte for various inflammatory diseases. This study identifies GMI as a potential anti-inflammatory agent and determines the effects of GMI on the inhibition of SARS-CoV-2-induced cytokine secretion. Functional studies showed that SARS-CoV-2 envelope (E) protein induces inflammatory process in murine macrophages RAW264.7 and MH-S cells and in phorbol 12-myristate 13-acetate (PMA)-stimulated human THP-1 cells. GMI exhibits a strong inhibitory effect for SARS-CoV-2-E-induced pro-inflammatory mediators, including NO, TNF-α, IL-6, and IL-12 in macrophages. GMI reduces SARS-CoV-2-E-induced intracellular inflammatory molecules, such as iNOS and COX-2, and inhibits SARS-CoV-2-E-stimulated phosphorylation of ERK1/2 and P38. GMI also downregulates pro-inflammatory cytokine levels in lung tissue and serum after the mice inhale SARS-CoV-2-E protein. In conclusion, this study shows that GMI acts as an agent to alleviate SARS-CoV-2-E-induced inflammation. © 2023 Elsevier B.V.

关键词： Coronavirus

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Electrochemical biosensor based on an atomic layered composite of graphene oxide/graphene as an electrode material towards selective and sensitive detection of miRNA-21

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Microchemical Journal 2024年 199卷

作者： Huang, Min-Shin Govindasamy, Mani Chinnapaiyan, Sathishkumar Lin, Yi-Ting Lu, Shao-Yang Samukawa, Seiji Huang, Chi-Hsien Department of Materials Engineering Ming Chi University of Technology New Taipei City 243303 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 24303 Taiwan Centre for Applied Research Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai-602105 Tamil Nadu India Institute of Fluid Science Tohoku University Sendai 980-8577 Japan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Division of Rheumatology Allergy and Immunology Chang Gung Memorial Hospital Taoyuan City 33305 Taiwan College of Engineering Chang Gung University Taoyuan City 33302 Taiwan

development of an efficient technique towards the detection of miRNAs is highly significant for the timely diagnosis and prognosis of cancers. In this study, we utilized the chemical vapor deposition (CVd) technique to grow bilayer graphene (BLG) on copper foil and subsequently transferred the BLG to fluorine doped tin oxide (FTO) substrate via a facile wet transfer technique. The upper layer of the BLG was modified to graphene oxide (GO) with a low damage plasma treatment (LdPT) process to serve as a biomolecule receiving layer and the lower graphene layer (G) serves as an electron transport layer. The developed GO/G/FTO platform offers superior sensing ability towards the miRNA-21. The results demonstrated that the proposed GO/G/FTO substrate detect the miRNA-21 with a wide linear range (10 fM ∼ 1 nM), low limit of detection (3.18 fM), high sensitivity (0.6 mApM⁻¹cm⁻²), selectivity and stability via the differential pulse voltammetry method. Additionally, the sensors can efficiently discriminate the target miRNA-21 from its complementary base mismatched counterparts. The proposed approach serves as an excellent tool for the early diagnosis of diseases. © 2024 Elsevier B.V.

关键词： Atomic layer oxidation Biosensor Cancer biomarkers Electrochemical Graphene oxide Plasma treatment

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