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Novel PdE9 inhibitors, Kr39526 and Kr39582, attenuate cardiac hypertrophy and fibrosis induced by pressure overload

Journal of Pharmacological Sciences 2025年第4期158卷 353-362页

作者： Lee, Jeong Hyun Park, Hyunjin Lee, Seung Hyeong Kim, Su Ah Choi, Jun Young Lim, Chae Jo Shin, Ju-Young Lee, Byung Ho Oh, Kwang-Seok Data Convergence Drug Research Center Korea Research Institute of Chemical Technology Daejeon South Korea of New Drug Discovery and Development Chungnam National University Daejeon South Korea R&D Center Huons co. Ltd. Gyeonggi-do Ansan South Korea Department of Biohealth Regulatory Science Sungkyunkwan University Gyeonggi-do Suwon South Korea School of Pharmacy Sungkyunkwan University Gyeonggi-do Suwon South Korea Department of Medicinal and Pharmaceutical Chemistry University of Science and Technology Daejeon South Korea

The natriuretic peptide and cyclic guanosine monophosphate (cGMP) cascade are promising therapeutic target for heart failure. This study evaluated the pharmacological properties and cardioprotective effects of novel phosphodiesterase 9 (PdE9) inhibitors, Kr39526 and Kr39582, in vitro and in vivo. The potency and selectivity of these compounds were assessed through PdE9 inhibitory activity and subfamily selectivity assays. Functional analyses in cardiomyocytes included calcium mobilization, cellular hypertrophy, and protein expression. The cardioprotective efficacy was investigated using a mouse model of pressure-overload-induced cardiac hypertrophy. Kr39526 and Kr39582 demonstrated potent PdE9A inhibitory activities (IC₅₀: 5 ± 2 nM and 0.4 ± 0.1 nM, respectively) with high selectivity. This inhibition led to concentration-dependent calcium influx through maintaining intracellular cGMP levels and significantly suppressed phenylephrine-induced cardiomyocyte hypertrophy. Oral administration (50 mg·kg-1) of these compounds markedly attenuated cardiac hypertrophy and myocardial fibrosis induced by pressure overload. These results suggest that Kr39526 and Kr39582, as potent and selective PdE9A inhibitors, have strong potential for exerting anti-hypertrophic and anti-fibrotic effects in heart failure. These compounds can serve as valuable pharmacological tools to elucidate the roles of PdE9A signaling in heart failure pathophysiology and hold significant promise as potential therapeutic agents. © 2025

关键词： Cardiac hypertrophy Heart failure Inhibitor Phosphodiesterase 9A Transverse aortic constriction

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revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely detection

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： S. durai d. Jaganathan Prabhu V r. Harini Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Artificial Intelligence Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Thiruvallur India Artificial Intelligence & Data Science vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), random Forest (rF), decision Tree (dT), and Logistic regression (Lr), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions.

关键词： Support vector machines Logistic regression Accuracy Cardiac arrest Nearest neighbor methods Prediction algorithms Bayes methods

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dynamic Key Generationfor Secure File Sharing System Using Time Stamp

Dynamic Key Generationfor Secure File Sharing System Using T...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S H Annie Silviya C M Hilda Jerlin N Mohana Suganthi V Helen Sathiya r Jayashree N Oviya Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Panimalar Engineering College Chennai India School of Computing Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

dynamic key generation for secure file sharing system using timestamp is a novel approach to military-based secure file sharing, which adds an additional layer of security to AES-2S6 which is prominently considered as the military-grade encryption. It is designed to enhance the confidentiality and integrity of transmitted files. When a Sender uploads a file, the system prompts for a character, converts it to its ASCII value, adds the current system time value to it and utilizes this aggregated value as a unique code to secure the uploaded file. To retrieve a file, the user must possess either the file key or initiate the retrieval process during the exact time of the original upload or in the next precise clock cycle. The combination of these factors provides an extra layer of security, making unauthorized access significantly more challenging.

关键词： Hands Protocols Codes receivers real-time systems database systems Peer-to-peer computing Encryption ASCII Clocks

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3d CNN on Nodule data

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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Emerging research in Computational Science (ICErCS), International Conference on

作者： Carmel Mary Belinda M J Kannan E Alex david S Sriram Kannan ravi Kumar S. Anstey Vathani Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Data Engineering Fidelity Investments Chennai India Department of Civil Engineering Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated.

关键词： Image segmentation Three-dimensional displays Lungs Lung cancer Feature extraction Vectors Gabor filters X-ray imaging Medical diagnostic imaging Image enhancement

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Challenges and Best Practices in Corporate AI Governance: Lessons from the Biopharmaceutical Industry

SSRN

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SSrN 2022年

作者： Mökander, Jakob Sheth, Margi Gersbro-Sundler, Mimmi Blomgren, Peder Floridi, Luciano Oxford Internet Institute University of Oxford 1 St Giles’ OxfordOX1 3JS United Kingdom Center for Information Technology Policy Princeton University PrincetonNJ08544 United States R&D Data Office Data Sciences and AI BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Department of Legal Studies University of Bologna Via Zamboni 33 Bologna40126 Italy

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management. © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Strengthening Authentication: The role of Multi-Level Hashing in OSPA Protocol

Strengthening Authentication: The Role of Multi-Level Hashin...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Hariharasudhan Vineetha Varghese K Malarvizhi S.P. Santhoshkumar T. Nalini d. Vikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Computer Science al Business Systems Jct College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of computer science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Artificial Intelligence Data Science Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture.

关键词： Hash functions resistance Protocols Heuristic algorithms Authentication Passwords Universal Serial Bus Computer crime Intelligent systems Eavesdropping

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Constructing a Blockchain-Based Loyalty Program Model for Secure and Transparent Customer rewards in retail Marketing

Constructing a Blockchain-Based Loyalty Program Model for Se...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Ch. Anudeep Kathari Santosh M Sandeep Kumar Jahnavi M Sabareesh r B Kiran Bala GITAM school of business GITAM University Visakhapatnam A.P. Department of MBA CMR Institute of Technology Bengaluru India RV Institute of Management Bengaluru India Skill Development & Employability Programs Fortuna Business Solutions(R&D) Coimbatore Tamil Nadu Department of Artificial Intelligence and Data Science K. Ramakrishnan college of Engineering Trichy TN

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customer rewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions and rewards. Smart contracts, self-executing contracts with predefined rules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential for disputes and enhancing transparency. The decentralized nature of the blockchain ensures that customer data is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions per day, accompanied by an exceptional transaction transparency score of 99.5 .

关键词： Industries data privacy Communication systems Smart contracts Throughput Blockchains Fraud

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Performance Analysis for Cloud-Based Olap over Big data

Performance Analysis for Cloud-Based Olap over Big Data

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Jagan, S. Mishra, Pavan Turai, Anjaneya Krishna Joan, Nithesh derin Anuradha, Nandula Gangodkar, durgaprasad Perwej, Yusuf Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Ambalika Institute of Management & Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Symbiosis Skills and Professional University Department of Data Science Maharashtra Mumbai India St.Joseph's Institute of Technology Department of Data Science Tamil Nadu Chennai India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Department of Computer Science & Engineering Uttar Pradesh Lucknow India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

OLAP-based middleware for parallel OLAP query processing is presented in this study, which builds on previous research results to provide two new areas of authority application built on top of OLAP. This multi-faceted investigation of several business viewpoints is referred to as "online analytical processing"(OLAP).The fundamental purpose of OLAP systems is to allow users to conduct manual interpretation and analysis of corporate summary and extensive statistics. It's critical to realise that OLAP require users to know what data they are looking for OLAP merely analyses past facts, whereas "data-mining techniques"allow organisations to make predictions about future events. Clustering, identifying, and predictive analysis approaches are the three types of data-mining approaches. This research has considered secondary data collection method to gather informative and relevant "cloud based OLAP over big data"related information from different secondary sources such as journals, articles. © 2022 IEEE.

关键词： Big data

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Agent-Based Uncertainty Awareness Improves Automated radiology report Labeling with an Open-Source Large Language Model

arXiv

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arXiv 2025年

作者： Ben-Atya, Hadas Gavrielov, Naama Badash, Zvi Focht, Gili Cytter-Kuint, ruth Hagopian, Talar Turner, dan Freiman, Moti Faculty of Biomedical Engineering Technion - Israel Institute of Technology Haifa Israel Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel Juliet Keidan Institute of Pediatric Gastroenterology Hepatology and Nutrition Shaare Zedek Medical Center The Hebrew University School of Medicine Jerusalem Israel Pediatric Radiology Unit Radiology Department The Eisenberg R&D Authority Shaare Zedek Medical Center The Hebrew University of Jerusalem Jerusalem Israel

Purpose: To improve the reliability and performance of Large Language Models (LLMs) in extracting structured data from radiology reports, particularly in domains with complex and non-English texts (e.g., Hebrew), by incorporating agent-based uncertainty-awareness method to achieve trustworthy predictions in medical applications. Materials and Methods: This retrospective study analyzed 9,683 Hebrew radiology reports from Crohn’s disease patients (2010–2023) across three medical centers. A subset of 512 reports was manually annotated for six gastrointestinal organs and 15 pathological findings, while the remaining data was automatically annotated using the HSMP-BErT model. Structured data extraction used Llama 3.1 (Llama 3-8b-instruct), an open-source LLM, with Bayesian Prompt Ensembles (BayesPE) leveraging six semantically equivalent prompts for uncertainty estimation. An Agent-Based decision Model synthesized multiple prompt outputs into five confidence levels for calibrated uncertainty, compared against three entropy-based models. Performance was assessed via accuracy, F1 score, precision, recall, and Cohen’s Kappa, both before and after filtering high-uncertainty cases. results: The agent-based model outperformed the baseline across all metrics, achieving an F1 score of 0.3967, recall of 0.6437, and Cohen’s Kappa of 0.3006. After filtering high uncertainty cases (≥ 0.5), the F1 score increased to 0.4787 and Kappa to 0.4258. Uncertainty histograms showed clear separation between correct and incorrect predictions, with the agent-based model exhibiting the best-calibrated uncertainty predictions. Conclusion: Incorporating uncertainty-aware prompt ensembles and an agent-based decision model significantly enhances the performance and reliability of LLMs in structured data extraction from radiology reports, offering a calibrated and interpretable approach for high-stakes medical applications. © 2025, CC BY-NC-Nd.

关键词： radiology

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ELCCFd: An Efficient and Enhanced Credit Card Fraud detection using Enhanced deep Learning Principle

ELCCFD: An Efficient and Enhanced Credit Card Fraud Detectio...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Pandarinath Potluri Sajja Suneel B. Chandra d. Muthukumaran Makarand Upadhyaya r rajesh Kanna department of CSE Swarnandhra college of engineering and technology Sitarampuram Narsapuram West Godavari district Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Management Studies Vignan's Institute of Information Technology(A) AP India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Management & Marketing College of Business Administration University of Bahrain Department of Computer Science CHRIST (Deemed to be University)

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud detection (ELCCFd) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN’s output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFd methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems.

关键词： deep learning Accuracy Protocols Image processing Merging Credit cards Cleaning

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