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Inducible Nitric Oxide Synthase (iNOS): More Than an Inducible Enzyme? Rethinking the Classification of NOS Isoforms

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Pharmacological Research 2025年 107781页

作者： Aryan Farahani Arman Farahani Khosrow Kashfi Asghar Ghasemi Endocrine Physiology Research Center Research Institute for Endocrine Molecular Biology Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Department of Molecular Cellular and Biomedical Sciences Sophie Davis School of Biomedical Education City University of New York School of Medicine New York NY USA Graduate Program in Biology City University of New York Graduate Center New York 10091 USA

Nitric oxide (NO) is a critical signaling molecule synthesized from L -arginine by nitric oxide synthase (NOS). The three NOS isoforms—neuronal NOS (nNOS; NOS1), inducible NOS (iNOS; NOS2), and endothelial NOS (eNOS; NOS3)—have traditionally been classified as either constitutive (nNOS and eNOS) or inducible (iNOS). However, this binary classification oversimplifies their functions, particularly by neglecting the physiological roles of iNOS and misrepresenting its involvement in pathological processes. Increasing evidence demonstrates that all three isoforms can exhibit both constitutive and inducible expression. Notably, iNOS is constitutively expressed at low levels in several tissues, including blood, heart, bone marrow, lung, brain, spinal cord, retina, colonic mucosa, liver, ileum, skeletal muscle, epidermis, adipose tissue, endometrium, ovary, and kidney under normal physiological conditions, a form we refer to as constitutive iNOS (ciNOS). This basal expression contributes to essential functions such as heart rate regulation, respiratory exchange, and microbiome balance in the gut. Moreover, in certain pathological contexts, iNOS may exert protective rather than harmful effects, challenging the prevailing view that it is solely a pro-inflammatory mediator. Current drug development strategies targeting NOS are largely based on the outdated dichotomy of constitutive “physiologic” versus inducible “pathologic” isoforms, focusing primarily on iNOS inhibition. The failure of iNOS inhibitors in most clinical trials highlights the limitations of this approach. To address these gaps, we propose a revised nomenclature that incorporates both gene expression mode (constitutive vs. inducible) and discovery order, offering a more nuanced framework for understanding NOS isoforms in both health and disease.

关键词： Nitric oxide constitutive inducible nitric oxide synthase constitutivity inducibility

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Characterization of non-coding variants associated with transcription-factor binding through ATAC-seq-defined footprint QTLs in liver

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American Journal of Human Genetics 2025年

作者： Dudek, Max F. Wenz, Brandon M. Brown, Christopher D. Voight, Benjamin F. Almasy, Laura Grant, Struan F.A. Center for Spatial and Functional Genomics Children's Hospital of Philadelphia Philadelphia 19104 PA United States Graduate Group in Genomics and Computational Biology Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Cell and Molecular Biology Graduate Group Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia 19104 PA United States Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Lifespan Brain Institute Children's Hospital of Philadelphia and Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Biomedical and Health Informatics Children's Hospital of Philadelphia Philadelphia PA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Division of Human Genetics Children's Hospital of Philadelphia Philadelphia 19104 PA United States Division of Endocrinology and Diabetes Children's Hospital of Philadelphia Philadelphia 19104 PA United States

Non-coding variants discovered by genome-wide association studies (GWASs) are enriched in regulatory elements harboring transcription factor (TF) binding motifs, strongly suggesting a connection between disease association and the disruption of cis-regulatory sequences. Occupancy of a TF inside a region of open chromatin can be detected in ATAC-seq where bound TFs block the transposase Tn5, leaving a pattern of relatively depleted Tn5 insertions known as a “footprint.” Here, we sought to identify variants associated with TF binding, or “footprint quantitative trait loci” (fpQTLs), in ATAC-seq data generated from 170 human liver samples. We used computational tools to scan the ATAC-seq reads to quantify TF binding likelihood as “footprint scores” at variants derived from whole-genome sequencing generated in the same samples. We tested for association between genotype and footprint score and observed 809 fpQTLs associated with footprint-inferred TF binding (FDR < 5%). Given that Tn5 insertion sites are measured with base-pair resolution, we show that fpQTLs can aid GWAS and QTL fine-mapping by precisely pinpointing TF activity within broad trait-associated loci where the underlying causal variant is unknown. Liver fpQTLs were strongly enriched across ChIP-seq peaks, liver expression QTLs (eQTLs), and liver-related GWAS loci, and their inferred effect on TF binding was concordant with their effect on underlying sequence motifs in 78% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding-site disruption in complex traits, and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases. © 2025 American Society of Human Genetics

关键词： ATAC-seq footprint quantitative trait loci footprinting fpQTLs genome-wide association studies GWAS liver transcription factor binding

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Oversized Conduits Predict Stenosis in Tissue Engineered Vascular Grafts

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JACC: Basic to Translational Science 2025年

作者： Blum, Kevin M. Turner, Mackenzie E. Schwarz, Erica L. Best, Cameron A. Kelly, John M. Yates, Andrew R. Hor, Kan N. Matsuzaki, Yuichi Drews, Joseph D. Zakko, Jason Shah, Kejal Shinoka, Toshiharu Humphrey, Jay D. Marsden, Alison L. Breuer, Christopher K. Center for Regenerative Medicine The Abigail Wexner Research Institute Nationwide Children's Hospital Columbus OH United States Department of Biomedical Engineering The Ohio State University Columbus OH United States Department of Molecular Cellular and Developmental Biology The Ohio State University Columbus OH United States Biomedical Sciences Graduate Program The Ohio State University College of Medicine Columbus OH United States Department of Pediatrics and Bioengineering Stanford University Stanford CA United States Department of Biomedical Engineering Yale University New Haven CT United States Department of Pediatric Cardiology Nationwide Children's Hospital Columbus OH United States Department of Surgery The Ohio State University Wexner Medical Center Columbus OH United States Department of Surgery Nationwide Children's Hospital Columbus OH United States

Tissue-engineered vascular grafts (TEVGs) offer promising advancements in treating congenital heart disease by enabling the creation of autologous tissue for complex cardiac repairs. Our approach involves implanting biodegradable scaffolds seeded with autologous cells that remodel into functional neovessels. To understand better the factors guiding neovessel formation, we evaluated 50 ovine thoracic TEVGs using angiography at 1 and 6 weeks postimplantation. Nondimensionalization accounted for anatomical differences between animals and identified hemodynamics and surgical sizing as potential driving factors. Regression analysis revealed that narrowing at the inflow anastomosis and graft oversizing correlated significantly with stenosis development. Computational fluid dynamics showed that these factors influenced wall shear stress and flow patterns, contributing to neovessel narrowing. Comparisons with clinical trial data from Fontan conduits supported these findings, emphasizing that matching graft size to the native inflow vessel can reduce stenosis and enhance TEVG performance. © 2025 The Authors

关键词： computational fluid dynamics oversizing stenosis tissue engineered vascular graft

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LM22A-4-loaded smart mesoporous balls enhance neuroprotection and functional recovery after ischemic stroke

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Biomedicine and Pharmacotherapy 2025年 183.0卷 117863-117863页

作者： Lee, Jae Ho Lee, Kyeong Hyeon Ryu, Ji Hyeon Kim, Min Jae Kim, Eunji Lee, Seo-Yeon Han, Sang-Cheol Choi, Byung Tae Shin, Yong-Il Shin, Hwa Kyoung Department of Korean Medical Science School of Korean Medicine Pusan National University Gyeongnam Yangsan 50612 South Korea Graduate Training Program of Korean Medical Therapeutics for Healthy-Aging Pusan National University Gyeongnam Yangsan 50612 South Korea Department of Rehabilitation Medicine School of Medicine Pusan National University Gyeongnam Yangsan 50612 South Korea Research Institute for Convergence of Biomedical Science and Technology Pusan National University Yangsan Hospital Gyeongnam Yangsan 50612 South Korea Department of Pharmacology Wonkwang University School of Medicine Jeonbuk Iksan 54538 South Korea CEN Co. Ltd. Nano-Convergence Center 761 Muan-ro Miryang 50404 South Korea

Stroke is globally recognized as the second leading cause of death, significantly impairing both motor and cognitive functions. Enhancing regeneration after stroke is crucial for restoring these functions and necessitates strategies to promote neuroregeneration to achieve better post-stroke outcomes. Brain-derived neurotrophic factor (BDNF) plays a key role in neuroregeneration by influencing motor ability, learning, memory, and rehabilitation after stroke. However, challenges such as the substantial protein size, short half-life of BDNF, and blood-brain barrier hinder its efficient delivery to the brain. In this study, LM22A-4, a BDNF mimetic, was utilized and delivered through a Smart Mesoporous Ball (SMB-3) system to target the ischemic injured brain and explore its potential therapeutic effects in a mouse ischemic stroke model. Treatment with LM22A-4-loaded SMB-3 (LM22A-4-SMB-3) markedly restored neurological, motor, and cognitive deficits following ischemic stroke compared to LM22A-4 alone. Additionally, administration of LM22A-4-SMB-3 reduced apoptotic cell death and glial activation, as evidenced by the TUNEL assay results, and decreased GFAP and Iba-1 expression levels. Furthermore, the phosphorylation of TrkB and Akt, but not that of Erk, was considerably increased in the LM22A-4-SMB-3-treated group. Treatment also enhanced the number of BrdU+/NeuN+ cells, with a marked reduction in post-stroke brain atrophy. These findings suggest that LM22A-4-SMB-3 can attenuate ischemic damage and recover neurological, motor, and cognitive functions by increasing p-TrkB and p-Akt levels and promoting neurogenesis. Therefore, SMB-3-mediated delivery of LM22A-4 presents a potentially applicable delivery system, and LM22A-4-SMB-3 use could be considered a novel therapeutic strategy to improve post-stroke outcomes. © 2025 The Authors

关键词： BDNF mimetic Drug delivery system Ischemic stroke LM22A-4 Smart mesoporous ball

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Implementing globally unique identifier architecture in date collection for a health management study in taiwan aboriginal tribe 1

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9th FTRA International Conference on Future Information Technology, FutureTech 2014

作者： Hung, Jui-Fu Rau, Hsiao-Hsien Hsu, Chien-Yeh Chen, Shih-Chang Tsai, Duu-Jian Fann, Yang Park, Joshua Eng, Joshua Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Graduate Institute of Patent National Taiwan University of Science and Technology Taipei Taiwan Intramural IT and Bioinformatics Program NINDS NIH HHS Bethesda United States

ISBN: (数字)9789401795586

ISBN: (纸本)9789401795579

In present, more and more attention has been put on the effort of protection of personal data and also new encryption technologies are derived. In this article, we introduce a personal information protection technology, global unique identifier (GUID), which is developed by National institutes of Health U.S. and used for personal health information protection. The GUID is an identifier that allows researchers to associate and share data specific to a study participant without using or exposing personally identifiable information (PII). The GUID is made up of random alphanumeric characters and is not directly generated from PII/Protected Health Information (PHI). It has been approved by the NIH/USA office General Counsel for secure sharing of anonymized database. In our study, we used GUID as key identifier for health data collection from aboriginal tribe in a health promotion project. © Springer Science+Business Media Dordrecht 2015.

关键词： Cryptography

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Toward the data-driven 'smart' and 'green' hospital-care 9

Toward the data-driven 'smart' and 'green' hospital-care

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9th ITU Kaleidoscope Academic Conference: Challenges for a Data-Driven Society, ITU K 2017

作者： Spyropoulos, B. Alexandropoulos, A. Boci, N. Chatziapostolou, E. Frappa, E. Georgiadou, E. Louts, I. Pantelakis, I. Poultsaki, M. Xenaki, M. Department of Biomedical Engineering Technological Education Institute of Athens Athens Greece Graduate Program 'Information Technologies in Medicine and Biology' Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9789261242916

The Hospital is the most complex and representative establishment of the society and nowadays, among the most costly ones. ICTs may rationalize personnel-efforts and reduce energy and material-wasting, to enable health-care coverage, of unprivileged social-groups. The aim of the paper is to present the most effective and efficient means and tools, reducing unnecessary cost, as Mobile IP-network, Service-oriented architecture, provided to other components, through a communication protocol, over a network, Ubiquitous-computing, Femtocells, small, low-power cellular base-stations, typically designed for use in a hospital-department, ward-room or unit, Wireless mesh-networks, made-up of radio-nodes, organized in a mesh-topology, supporting intra-hospital data-exchange, training Multimedia-sharing over wireless networks, for real-time or compressed data-streaming over IP/wireless-networks for communication or archiving. Green-computing in wireless-networks, are limiting people and material intrahospital 'circulation' and, thus, they are enabling the necessary environmentally friendly and smooth procedures. Standardization, policies and regulations for green communications and computing, in 'green' Hospitals are indispensable and ICTs enable procedures optimization, solving energy- and material-waste problems, reducing the overall operational-cost, in the emerging 'smart, green and networked' Hospital, in favor of the people in need. © 2017 ITU.

关键词： Standardization

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Autophagy in hepatitis C virus-host interactions:Potential roles and therapeutic targets for liver-associated diseases

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World Journal of Gastroenterology 2014年第19期20卷 5773-5793页

作者： Po-Yuan Ke Steve S-L Chen Institute of Biomedical Sciences Academia Sinica Department of Biochemistry and Molecular Biology Graduate Institute of Biomedical Sciencesand Molecular Medicine Research CenterCollege of MedicineChang Gung University Liver Research Center Chang-Gung Memorial Hospital PhD Program in Translational Medicine Kaohsiung Medical UniversityKaohsiung 80708 and Academia Sinica

Autophagy is a lysosome-associated,degradative process that catabolizes cytosolic components to recycle nutrients for further use and maintain cell *** C virus(HCV)is a major cause of chronic hepatitis,which often leads to end-stage liverassociated diseases and is a significant burden on worldwide public *** lines of evidence indicate that autophagy plays an important role in promoting the HCV life cycle in host ***,the diverse impacts of autophagy on a variety of signaling pathways in HCV-infected cells suggest that the autophagic process is required for balancing HCVhost cell interactions and involved in the pathogenesis of HCV-related liver ***,the detailed molecular mechanism underlying how HCV activates autophagy to benefit viral growth is still ***,how the autophagic response contributes to disease progression in HCV-infected cells remains largely ***,in this review,we overview the interplay between autophagy and the HCV life cycle and propose possible mechanisms by which autophagy may promote the pathogenesis of HCVassociated chronic liver ***,we outline the related studies on how autophagy interplays with HCV replication and discuss the possible implications of autophagy and viral replication in the progression of HCV-induced liver diseases,e.g.,steatosis and hepatocellular ***,we explore the potential therapeutics that target autophagy to cure HCV infection and its related liver diseases.

关键词： Autophagy Hepatitis C virus Steatosis Cirrhosis Hepatocellular carcinoma

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Tailored Mesoporous Silica Nanoparticles and the Chick Chorioallantoic Membrane: A Promising Strategy and Model for Efficient Blood-Brain Barrier Crossing

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ACS Applied Materials and Interfaces 2025年 2025 May 6页

作者： Lin, Cong-Kai Yang, Yi-Shan Liu, Tsang-Pai Lin, Jiunn-Chang Bupphathong, Sasinan Tamanoi, Fuyuhiko Chen, Yi-Ping Graduate Institute of Biomedical Materials Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan Department of Neurosurgery Taipei Medical University Hospital Taipei110 Taiwan Department of Surgery MacKay Memorial Hospital Taipei104 Taiwan MacKay Junior College of Medicine Nursing and Management New Taipei252 Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan Institute for Integrated Cell-Material Sciences Institute for Advanced Study Kyoto University Kyoto606-8501 Japan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan

Crossing the blood-brain barrier (BBB) remains a major challenge for brain-targeted drug delivery. Mesoporous silica nanoparticles (MSNs) with tunable size and surface properties are promising vehicles for crossing the BBB. In this study, we explored the potential applications of the chick chorioallantoic membrane (CAM) model in combination with nanotherapeutics. We synthesized ∼25 nm MSNs and RITC-conjugated MSNs (RMSNs) with short PEG chains and varying amounts of positively charged molecules, specifically tertiary amine (polyethylenimine, PEI) or quaternary amine (trimethylammonium, TA), to investigate the positive charge effects on BBB penetration. Strongly positively charged TA-modified RMSNs (s-RMSN@PEG/TA, where s denotes strongly positively charged) effectively crossed the chick embryo BBB, whereas PEI-modified RMSNs did not. Although the weakly positively charged formulation (w-MSN@PEG/TA, where w denotes weakly positively charged) exhibited higher Dox loading capacity and a faster release rate, s-MSN@PEG/TA demonstrated superior BBB penetration and drug permeability. Consistent with chick CAM results, RMSN@PEG/TA also penetrated the BBB in mice. Long-chain PEG-modified RMSN@PEG/TA (RMSN@PEG(L)/TA, where L denotes long-chain PEG) showed reduced BBB penetration due to steric hindrance, possibly shielding TA molecules. This study highlights the effectiveness of optimizing short PEG chain density and TA modification for MSN-based BBB crossing without additional biological ligands. Furthermore, the chick CAM model proves to be a valuable alternative to mouse models for assessing BBB crossing of nanoparticles, offering significant research opportunities. © 2025 The Authors. Published by American Chemical Society.

关键词： Controlled drug delivery

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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