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Targeting human mitochondrial NAD(P)+-dependent malic enzyme (ME2) impairs energy metabolism and redox state and exhibits antileukemic activity in acute myeloid leukemia

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Cellular Oncology 2023年第5期46卷 1301-1316页

作者： Chen, Kun-Chi Hsiao, I-Hsin Huang, Yu-Nan Chou, Yu-Tung Lin, Yi-Chun Hsieh, Ju-Yi Chang, Yung-Lung Wu, Kang-Hsi Liu, Guang-Yaw Hung, Hui-Chih Department of Life Sciences National Chung Hsing University Taichung Taiwan Ph.D. Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung Taiwan Department of Biochemistry National Defense Medical Center Taipei Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department Pediatrics Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan EGG & Animal Biotechnology Center National Chung Hsing University Taichung Taiwan

Acute myeloid leukemia (AML) is a fast-growing and highly fatal blood cancer, and recent research has shown that targeting metabolism may be a promising therapeutic approach for treating AML. One promising target is the human mitochondrial NAD(P)+-dependent malic enzyme (ME2), which is involved in the production of pyruvate and NAD(P)H and the regulation of the NAD+/NADH redox balance. Inhibition of ME2 via silencing ME2 or utilizing its allosteric inhibitor disodium embonate (Na2EA) causes a decrease in pyruvate and NADH, leading to a decrease in producing ATP via cellular respiration and oxidative phosphorylation. ME2 inhibition also decreases NADPH levels, resulting in an increase in reactive oxygen species (ROS) and oxidative stress, which ultimately leads to cellular apoptosis. Additionally, ME2 inhibition reduces pyruvate metabolism and the biosynthetic pathway. ME2 silencing inhibits the growth of xenotransplanted human AML cells, and the allosteric ME2 inhibitor Na2EA demonstrates antileukemic activity against immune-deficient mice with disseminated AML. Both of these effects are a result of impaired energy metabolism in mitochondria. These findings suggest that the targeting ME2 may be an effective strategy for treating AML. Overall, ME2 plays an essential role in energy metabolism of AML cells, and its inhibition may offer a promising approach for AML treatment.

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Derivation of totipotent-like stem cells with blastocyst-like structure forming potential

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细胞研究（英文版） 2022年第6期32卷 513-529页

作者： Yaxing Xu Jingru Zhao Yixuan Ren Xuyang Wang Yulin Lyu Bingqing Xie Yiming Sun Xiandun Yuan Haiyin Liu Weifeng Yang Yenan Fu Yu Yu Yinan Liu Rong Mu Cheng Li Jun Xu Hongkui Deng MOE Engineering Research Center of Regenerative Medicine School of Basic Medical SciencesState Key Laboratory of Natural and Biomimetic DrugsPeking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and DifferentiationCollege of Life SciencesPeking-Tsinghua Center for Life SciencesPeking UniversityBeijingChina School of Life Sciences Center for BioinformaticsCenter for Statistical SciencePeking UniversityBeijingChina Department of Rheumatology and Immunology Peking University Third HospitalBeijingChina Beijing Vitalstar Biotechnology Co. LtdBeijingChina Program for Cancer and Cell Biology Department of Human AnatomyHistology and EmbryologySchool of Basic Medical SciencesPKU International Cancer Institute MOE Key Laboratory of Carcinogenesis and Translational Research and State Key Laboratory of Natural and Biomimetic Drugs Peking University Health Science CenterBeijingChina Department of Cell Biology School of Basic Medical SciencesPeking University Stem Cell Research CenterPeking University Health Science CenterPeking UniversityBeijingChina

It is challenging to derive totipotent stem cells in vitro that functionally and molecularly resemble cells from totipotent ***,we report that a chemical cocktail enables the derivation of totipotent-like stem cells,designated as totipotent potential stem(TPS)cells,from 2-cell mouse embryos and extended pluripotent stem cells,and that these TPS cells can be stably maintained long term in *** cells shared features with 2-cell mouse embryos in terms of totipotency markers,transcriptome,chromatin accessibility and DNA methylation *** vivo chimera formation assays show that these cells have embryonic and extraembryonic developmental potentials at the single-cell ***,TPS cells can be induced into blastocyst-like structures resembling preimplantation mouse ***,inhibition of HDAC1/2 and DOT1L activity and activation of RARγ signaling are important for inducing and maintaining totipotent features of TPS *** study opens up a new path toward fully capturing totipotent stem cells in vitro.

关键词： Self-renewal Totipotent stem cells

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In vivo programming of adult pericytes aids axon regeneration by providing cellular bridges for SCI repair

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Molecular Therapy 2025年

作者： Sun, Wenjing Dion, Elliot Laredo, Fabio Okonak, Allyson Sepeda, Jesse A. Haykal, Esraa Zhou, Min El-Hodiri, Heithem M. Fischer, Andy J. Silver, Jerry Peng, Juan Sas, Andrew Tedeschi, Andrea Department of Neuroscience Wexner Medical Center The Ohio State University Columbus 43210 OH United States University of Nottingham Nottingham NG7 2QL United Kingdom Department of Neurology Wexner Medical Center The Ohio State University Columbus 43210 OH United States Department of Neurosciences Case Western Reserve University Cleveland 44106 OH United States Center for Biostatistics and Bioinformatics The Ohio State University Columbus 43210 OH United States Chronic Brain Injury Program The Ohio State University Columbus 43210 OH United States

Pericytes are contractile cells of the microcirculation that participate in wound healing after spinal cord injury (SCI). Thus far, the extent to which pericytes cause or contribute to axon growth and regeneration failure after SCI remains controversial. Here, we found that SCI leads to profound changes in vasculature architecture and pericyte coverage. We demonstrated that pericytes constrain sensory axons on their surface, causing detrimental structural and functional changes in adult dorsal root ganglion neurons that contribute to axon regeneration failure after SCI. Perhaps more excitingly, we discovered that in vivo programming of adult pericytes via local administration of platelet-derived growth factor BB (PDGF-BB) effectively promotes axon regeneration and recovery of hindlimb function by contributing to the formation of cellular bridges that span the lesion. Ultrastructural analysis showed that PDGF-BB induced fibronectin fibril alignment and extension, effectively converting adult pericytes into a permissive substrate for axon growth. In addition, PDGF-BB localized delivery positively affects the physical and chemical nature of the lesion environment, thereby creating more favorable conditions for SCI repair. Thus, therapeutic manipulation rather than wholesale ablation of pericytes can be exploited to prime axon regeneration and SCI repair. © 2025 The Author(s)

关键词： axon regeneration DRG neurons extracellular matrix functional recovery PDGF-BB pericytes spinal cord injury vasculature remodeling

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Swarm intelligence optimization for feature selection of biomolecules 14

Swarm intelligence optimization for feature selection of bio...

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14th International Conference on Computer Engineering and Systems, ICCES 2019

作者： Alkady, Walaa Gad, Walaa Bahnasy, Khaled Program of Bioinformatics Faculty of Computer and Information Sciences Ain Shams University Department of Information System Cairo Egypt Dean of Obour Institutes Cairo Egypt

ISBN: (纸本)9781728152608

The biological activity of molecules is usually measured in assays to establish the level of inhibition of signal transduction or metabolic pathways. Drug discovery involves the use of Quantitative Structure Activity Relationship (QSAR) to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity. QSAR has very complicated 3D structure. Therefore, the flower-based optimization model (FBOM) for molecules is proposed to solve the curse of dimensionality problems. Four performance measures: accuracy, precision, sensitivity and specificity are used to evaluate the proposed model. Molecules activity is predicted using support vector machine (SVM), Naive Bayesian (NB), K-Nearest Neighbor (KNN), Decision Tree (DT) and Neural Network (NN) Classifiers. The results of the proposed model are promising. The proposed model reduces the number of features to 8 features out of 1666 features. Moreover, the average classification accuracy reaches to 95%. © 2019 IEEE.

关键词： Molecules

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Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry

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JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency Data Registry (CEDR) to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health Data Sciences and Informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

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Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text

arXiv

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arXiv 2023年

作者： Rehana, Hasin Çam, Nur Bengisu Basmaci, Mert Zheng, Jie Jemiyo, Christianah He, Yongqun Özgür, Arzucan Hur, Junguk Computer Science Graduate Program University of North Dakota Grand ForksND58202 United States Department of Computer Engineering Bogazici University Istanbul34342 Turkey Unit for Laboratory Animal Medicine Department of Microbiology and Immunology University of Michigan Ann ArborMI48109 United States Department of Biomedical Sciences University of North Dakota School of Medicine and Health Sciences Grand ForksND58202 United States Center for Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedical literature, there is a growing need for automated and accurate extraction of PPIs to facilitate scientific knowledge discovery. Pre-trained language models, such as generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT), have shown promising results in natural language processing (NLP) tasks. We evaluated the performance of PPI identification of multiple GPT and BERT models using three manually curated gold-standard corpora: Learning Language in Logic (LLL) with 164 PPIs in 77 sentences, Human Protein Reference Database with 163 PPIs in 145 sentences, and Interaction Extraction Performance Assessment with 335 PPIs in 486 sentences. BERT-based models achieved the best overall performance, with BioBERT achieving the highest recall (91.95%) and F1-score (86.84%) and PubMedBERT achieving the highest precision (85.25%). Interestingly, despite not being explicitly trained for biomedical texts, GPT-4 achieved commendable performance, comparable to the top-performing BERT models. It achieved a precision of 88.37%, a recall of 85.14%, and an F1-score of 86.49% on the LLL dataset. These results suggest that GPT models can effectively detect PPIs from text data, offering promising avenues for application in biomedical literature mining. Further research could explore how these models might be fine-tuned for even more specialized tasks within the biomedical domain. © 2023, CC BY.

关键词： Extraction

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Disparities in Psychological Distress and Coping Behaviors Amongst Patients with Indolent Hematologic Malignancies

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Blood 2024年 144卷 3739-3739页

作者： Tammarah Sklarz Jill S Hasler Carolyn Y. Fang Zachary AK Frosch Department of Hematology/Oncology Fox Chase Cancer Center Philadelphia PA Biostatistics and Bioinformatics Facility Fox Chase Cancer Center Philadelphia PA Cancer Prevention and Control Research Program Fox Chase Cancer Center Temple University Health System Philadelphia PA Department of Hematology/Oncology； Cancer Prevention and Control Research Program Fox Chase Cancer Center Philadelphia PA

Introduction : Patients with indolent blood cancers often do not have an indication for treatment at diagnosis and are recommended to undergo observation as the initial management strategy. This strategy may lead to psychological distress which is associated with decreased cancer-specific survival. In the US, there are significant racial disparities in survival for patients diagnosed with blood cancers, yet less is known about the basis for these differences. One potential contributor to disparities in survival is the differential impact of mental health comorbidities given that racial minorities living in the US disproportionately experience more psychosocial stress. The aims of this study are to evaluate for racial disparities in the level of cancer-related stress experienced by patients undergoing active observation for indolent blood cancers and to contrast coping strategy use by race. Methods : This study employs use of existing data from a prospective observational research study where participants with indolent blood cancers managed by observation were asked to complete measures regarding sociodemographic background, health behaviors, cancer-specific stress, and psychological adjustment to cancer. Participants were enrolled from April 2016 to November 2020 and measures were obtained at study entry. Patients with chronic lymphocytic leukemia, follicular lymphoma, smoldering myeloma, and a B-cell lymphoma not otherwise specified were included. Race/ethnicity was defined into several categories: African American/Black, Asian, White, more than one race/other or Hispanic. For study comparison, patients were grouped into racially minoritized (Asian, Black or Hispanic/Latinx) and non-minoritized (White non-Hispanic) arms. Cancer-specific stress was assessed using the Revised Impact of Events Scale (RIES). Psychological adjustment to cancer was assessed using the Mental Adjustment to Cancer (MAC) scale which measures several types of coping responses. Patient health

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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction

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Scientific Reports 2025年第1期15卷 1-11页

作者： Huang, Kuo-Yang Hsu, Ying-Lin Chung, Che-Liang Chen, Huang-Chi Horng, Ming-Hwarng Lin, Ching-Hsiung Liu, Ching-Sen Xu, Jia-Lang Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Ph.D. Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Yuanlin Christian Hospital Changhua Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. The system was deployed on edge devices to enable on-site inference with minimal latency. Among the evaluated models, Random Forest and XGBoost, the latter demonstrated superior predictive performance under both holdout and tenfold cross-validation schemes. Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. This paper details the model evaluation and demonstrates the feasibility and efficiency of edge intelligence in ventilator weaning decision support. © The Author(s) 2025.

关键词： Clinical decision support system Edge-computing Extubation Internet of medical things Machine learning

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Dental Disease Detection based on CNN for Panoramic Dental Radiographs

Dental Disease Detection based on CNN for Panoramic Dental R...

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IEEE International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Manar El Bagoury Mirna Al-Shetairy Omar Ahmed Samah S Mehanny Tassneem Hamdy Gihan Omar Marwan Akram Rana Mohamed Hebatullah Amr Walaa Gad Bioinformatics Program Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Oral Biology Department Faculty of Dentistry Cairo and Galala Universities Cairo Egypt Oral and Maxillofacial Radiology Department Faculty of Dentistry Cairo University Cairo Egypt Oral Radiology Department Faculty of Oral and Dental Medicine Future University in Egypt Cairo Egypt Information Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Panoramic dental radiography is one of the examination tools frequently performed in dental clinics and hospitals as it captures a wide range of the dental region and its surroundings, but due to the complex structure of the dental area and lack of time, dentists focus on tiny parts of the images. With the help of Machine Learning, dentists can detect dental defects and anomalies faster and more accurately. In this study, a data set of 733 panoramic radiographs of adult patients was collected from the faculty of Oral and Dental Medicine, Future University, Egypt. Using a combination of pre-trained CNN architecture and a finetuned network, the collected panoramic radiographs were used to determine the optimal architecture. The purpose of this research was to develop a computer-aided detection system based on a finetuned CNN algorithm, and to evaluate the accuracy and usefulness of this system for the detection of proximal and occlusal caries, interdental and inter radicular alveolar bone loss, periapical lesions, and impacted teeth in panoramic radiographs. Different object detection models were tested to detect selected dental diseases that were found in the panoramic radiographs, and after a few experiments, the YOLOv5 architecture was proved to be the most optimal after achieving 0.61 mAP@0.5 and 0.28 mAP@ [0.5-0.95] for the total six classes. This is the best-achieved results to our knowledge.

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Shotgun Sequencing of Southern Thailand Traditional Sweet Pickled Mango During the Production Process

SSRN

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SSRN 2023年

作者： Indrati, Niken Sirisarn, Wanchat Nuanpirom, Jiratchaya Yodsawat, Prasert Wanna, Warapond Prombutara, Pinidphon Phonsatta, Natthaporn Panya, Atikorn Maneerat, Suppasil Poungsombat, Patcha Khoomrung, Sakda Sumpavapol, Punnanee Food Microbiology and Safety Laboratory Food Science and Technology Program Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Center of Genomics and Bioinformatics Research Faculty of Science Prince of Songkla University Songkhla90110 Thailand Omics Sciences and Bioinformatics Center Faculty of Science Chulalongkorn University Bangkok10330 Thailand Thailand Science Park Pathumthani12120 Thailand Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Metabolomics and Systems Biology Department of Biochemistry Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Siriraj Metabolomics and Phenomics Center Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Faculty of Science Mahidol University Bangkok10400 Thailand Faculty of Science and Technology Songkhla Rajabhat University Songkhla90000 Thailand

Southern Thailand's traditional sweet pickled mango, known as Ma-Muang Bao Chae-Im (MBC), holds an important place in the local community. Several immersion processes, including brining and submersion in a hypertonic sugar solution, have been employed in the MBC production process. The occurrence of microbial succession during the production process affects its flavor due to the produced metabolites. However, microbial succession during the production process has received inadequate research. Therefore, this study aimed to (i) understand the microbiome alteration and (ii) investigate the functional profiling (metabolic pathways) during MBC production process. Through shotgun metagenomics, the microbial succession was shifting from bacteria in raw mango to fungi in MBC, with Ascomycota as the dominant phylum and Hanseniaspora as the dominant genus in MBC. The abundance of biosynthesis pathways was in line with the occurrence of microbial growth throughout the production process and prolonged storage. Polymyxin resistance was found as a significant pathway during brining 1 (B1-RM) and brining 2 (B2-B1). Mixed acid fermentation pathway and glyoxylate cycle were suggested to have a role in the aroma development of MBC by synthesizing metabolites related to the acidic and alcoholic aroma. © 2023, The Authors. All rights reserved.

关键词： Brining

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