Informatics in Medicine Unlocked最新文献

{"title":"Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework","authors":"Khadija Pervez ,&nbsp;Syed Irfan Sohail ,&nbsp;Faiza Parwez ,&nbsp;Muhammad Abdullah Zia","doi":"10.1016/j.imu.2025.101618","DOIUrl":"10.1016/j.imu.2025.101618","url":null,"abstract":"<div><div>Accurate detection and classification of microscopic cells from acute lymphoblastic leukemia remain challenging due to the difficulty of differentiating between cancerous and healthy cells. This paper proposes a novel approach to identify and categorize acute lymphoblastic leukemia that uses explainable artificial intelligence and federated learning to train models across multiple institutions while keeping patient information decentralized and encrypted. The framework trains EfficientNetB3 for the classification of leukemia cells and incorporates explainability techniques to make decisions of the underlying model transparent and interpretable. The framework employs a hierarchical federated learning approach that allows distributed learning across clinical centers, ensuring that sensitive patient data remain localized. Explainability techniques such as saliency maps, occlusion sensitivity, and randomized input sampling for explanation with relevant evaluation scores are integrated in the framework to provide visual and textual explanations of model’s predictions to enhance interpretability. The experiments were carried out on a publicly available dataset consisting of 15,135 microscopic images. The performance of the proposed model was benchmarked against traditional centralized models and classical federated learning techniques. The proposed model demonstrated a 2.5% improvement in accuracy (96.5%) and a 5.4% increase in F1-score (94.4%) compared to baseline models. Hierarchical federated learning reduced communication costs by 15% while maintaining data privacy. The integration of explainable artificial intelligence improved the transparency of model decisions, with a high area under the ROC curve (AUC) of 0.98 for the classification of leukemia cells. These results suggest that the proposed framework offers a robust solution for intelligent systems for medical diagnostics and can also be extended to other medical imaging tasks.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101618"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability and accessibility in mHealth stroke apps: An empirical assessment","authors":"Pontus Svensson ,&nbsp;Shuanglan Lin ,&nbsp;Leonardo Horn Iwaya","doi":"10.1016/j.imu.2025.101616","DOIUrl":"10.1016/j.imu.2025.101616","url":null,"abstract":"<div><h3>Background</h3><div>Cerebrovascular accidents or strokes continue to be among the leading causes of death and disability worldwide. This has stressed the need to design digital health solutions that can be effectively used by patients, caregivers, and medical professionals, helping to alleviate the global disease burden. In this context, mobile health (mHealth) apps are shown to be valuable solutions for bridging healthcare gaps.</div></div><div><h3>Objective</h3><div>In this study, we aim to evaluate the quality aspects of usability and accessibility of stroke-related mHealth apps for Android. We seek to identify prevalent issues and discuss recommendations to enhance user experience and app quality.</div></div><div><h3>Methods</h3><div>We selected 16 mHealth stroke apps, accounting for more than 219k downloads. The apps were assessed through different methods, including accessibility testing with the Google Accessibility Scanner, overall quality assessment with the Mobile Application Rating Scale (MARS), and usability testing using heuristic evaluations.</div></div><div><h3>Results</h3><div>Our findings show significant issues with the apps’ touch target sizes and text contrast, which are particularly important for stroke app users with impaired vision and motor skills. MARS evaluations revealed that some apps, such as the Constant Therapy app, excelled in engagement and functionality. In contrast, many apps scored lower due to limited functionality and unclear/confusing interfaces, such as Stroke Recovery Predictor and Conversation Therapy Lite. Heuristic evaluations also highlighted several usability violations, such as a lack of “Visibility of System Status” and “Insufficient Error Messaging.”</div></div><div><h3>Conclusion</h3><div>Overall, most apps presented deficiencies in several aspects of usability and accessibility. As recommendations, developers can increase touch target sizes, improve text contrast, increase functional variety, optimise navigation, and enhance user engagement strategies. Addressing such issues can help improve the stroke apps’ usability and accessibility, aiming for better health outcomes for stroke patients.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101616"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient2Trial: From patient to participant in clinical trials using large language models","authors":"Surabhi Datta ,&nbsp;Kyeryoung Lee ,&nbsp;Liang-Chin Huang ,&nbsp;Hunki Paek ,&nbsp;Roger Gildersleeve ,&nbsp;Jonathan Gold ,&nbsp;Deepak Pillai ,&nbsp;Jingqi Wang ,&nbsp;Mitchell K. Higashi ,&nbsp;Lizheng Shi ,&nbsp;Percio S. Gulko ,&nbsp;Hua Xu ,&nbsp;Chunhua Weng ,&nbsp;Xiaoyan Wang","doi":"10.1016/j.imu.2025.101615","DOIUrl":"10.1016/j.imu.2025.101615","url":null,"abstract":"<div><h3>Purpose</h3><div>Large language models (LLMs) exhibit promising language understanding and generation capabilities and have been adopted for various clinical use cases. Investigating the feasibility of leveraging LLMs in building a clinical trial retrieval system for patients is crucial as it can greatly enhance the patient enrollment process by prioritizing the most suitable trials pertaining to a patient. In this work, we develop an LLM-assisted system focused on a patient-initiated approach, allowing patients with specific conditions to directly find eligible trials by completing disorder-specific questionnaires.</div></div><div><h3>Methods</h3><div>We obtained clinical trial eligibility criteria (from ClinicalTrials.gov) and simulated patient questionnaires (or topics) from the Text REtrieval Conference (TREC) 2023 Clinical Trials Track conducted by the National Institute of Standards and Technology (NIST), in which we also participated. These topics cover eight disorders across diverse domains, namely glaucoma, anxiety, chronic obstructive pulmonary disease, breast cancer, Covid-19, rheumatoid arthritis, sickle cell anemia, and type 2 diabetes. A Generative Pre-trained Transformer model (GPT-4) was employed for system development. We conducted both quantitative and qualitative evaluation using 37 patient topics.</div></div><div><h3>Results</h3><div>The system achieved an overall Precision@10 (proportion of relevant trials) of 0.7351 and NDCG@10 (considers ranking order of relevant trials) of 0.8109, indicating its effectiveness in retrieving ranked lists of suitable trials for patients. Notably, for eight out of 37 patient topics, all the top 10 retrieved trials were relevant. The system scored the highest on breast cancer (NDCG@10 = 0.9347, Precision@10 = 0.84) and the lowest on type 2 diabetes (NDCG@10 = 0.61, Precision@10 = 0.475). One probable reason could be that the information in breast cancer topics is relatively straightforward to match. Qualitative error analysis classified errors into four categories (e.g., difficulty in correctly matching inclusion criteria) and further highlighted strengths (e.g., ability to make clinical inference).</div></div><div><h3>Conclusion</h3><div>We demonstrated the feasibility of integrating LLMs in identifying and ranking suitable trials for patients across multiple disorders. Further work is required to assess the system's generalizability on other disorders and patient information sources. This system has the potential to expedite the patient-trial matching process by suggesting a ranked list of applicable trials to patients and clinicians.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101615"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning unbiased risk prediction based algorithms in healthcare: A case study with primary care patients","authors":"Vibhuti Gupta ,&nbsp;Julian Broughton ,&nbsp;Ange Rukundo ,&nbsp;Lubna J. Pinky","doi":"10.1016/j.imu.2025.101627","DOIUrl":"10.1016/j.imu.2025.101627","url":null,"abstract":"<div><div>The proliferation of Artificial Intelligence (AI) has revolutionized the healthcare domain with technological advancements in conventional diagnosis and treatment methods. These advancements lead to faster disease detection, and management and provide personalized healthcare solutions. However, most of the clinical AI methods developed and deployed in hospitals have algorithmic and data-driven biases due to insufficient representation of specific race, gender, and age group which leads to misdiagnosis, disparities, and unfair outcomes. Thus, it is crucial to thoroughly examine these biases and develop computational methods that can mitigate biases effectively. This paper critically analyzes this problem by exploring different types of data and algorithmic biases during both pre-processing and post-processing phases to uncover additional, previously unexplored biases in a widely used real-world healthcare dataset of primary care patients. Additionally, effective strategies are proposed to address gender, race, and age biases, ensuring that risk prediction outcomes are equitable and impartial. Through experiments with various machine learning algorithms leveraging the Fairlearn tool, we have identified biases in the dataset, compared the impact of these biases on the prediction performance, and proposed effective strategies to mitigate these biases. Our results demonstrate clear evidence of racial, gender-based, and age-related biases in the healthcare dataset used to guide resource allocation for patients and have profound impact on the prediction performance which leads to unfair outcomes. Thus, it is crucial to implement mechanisms to detect and address unintended biases to ensure a safe, reliable, and trustworthy AI system in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101627"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using implementation science to develop and deploy an oncology electronic health record","authors":"Carla Taramasco ,&nbsp;Rene Noel ,&nbsp;Gastón Márquez ,&nbsp;Diego Robles","doi":"10.1016/j.imu.2025.101625","DOIUrl":"10.1016/j.imu.2025.101625","url":null,"abstract":"<div><div>The management of oncology clinical processes involves the efficient management of data using electronic clinical records to effectively monitor and treat oncology patients. As the process of treating and monitoring cancer patients involves multiple stakeholders with differing perspectives, the implementation and deployment of oncology clinical registries represent a significant challenge. In this study, we address this complexity by employing a technique that helps translate implementation strategies into requirement identification methods, which are subsequently disseminated throughout the implementation and deployment phases of health information systems. We applied this technique to develop an electronic health record for the national cancer plan in Chile. The findings indicate that six implementation strategies are essential to addressing stakeholder needs, as well as three requirement identification techniques to describe the underlying problem. Furthermore, a study conducted with 27 stakeholders revealed that the perception of the oncology electronic clinical record has considerable acceptance in three critical functionalities related to the clinical process of oncology patient management. The use of implementation science strategies provides an alternative approach to understanding the underlying problem that stakeholders face when they require healthcare technologies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101625"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The love-hate state of mobile device management in healthcare: An international survey","authors":"George A. Gellert,&nbsp;Gabriel L. Gellert,&nbsp;Rachel Pickering,&nbsp;Sean P. Kelly","doi":"10.1016/j.imu.2024.101603","DOIUrl":"10.1016/j.imu.2024.101603","url":null,"abstract":"<div><h3>Objective</h3><div>To gather insights regarding mobile device fleet deployment, management and security in healthcare delivery organizations (HDOs), including unmet needs and gaps in capabilities, across four nations.</div></div><div><h3>Methods</h3><div>An exploratory online survey of health information technology leaders working in HDOs to gather information about respondents’ organizational deployment of mobile devices as well as existing and needed mobile management capabilities.</div></div><div><h3>Results</h3><div>HDO mobile device losses were high, with 42% reporting average annual loss rates of 11–30%. Reported organizational effectiveness in protecting confidential information on lost mobile devices was low, with 50% of respondents ranking at six or below on a 10-point scale. Perception of end user satisfaction accessing applications/data on mobile devices was low, with 56–60% ranking satisfaction at six or below on a 10-point scale. Less than half of HDOs reported seven core mobile device management capabilities. Reported costs of mobile device information security breach across nations were between $100,000 and $1 million (USD). Respondents estimated aggregate weekly downtime exceeds 500h among 28% in Australia, 49% in Germany, 45% in the UK, and 47% in the US.</div></div><div><h3>Conclusions</h3><div>HDOs reported substantial perceived gaps and challenges in effectively managing mobility. System leaders desire what mobile device workflows add to care delivery, but effectively and efficiently managing a mobile device fleet remains a significant challenge. Mobility management tools are needed to facilitate rapid mobile device authentication, and efficiency of information access, while reducing clinician friction. Existing shared mobile device management solutions can help HDOs reduce costs and improve access security, user experience and workflow flexibility.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101603"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings","authors":"Tariq Sha’ban,&nbsp;Ahmad M. Mustafa,&nbsp;Mostafa Z. Ali","doi":"10.1016/j.imu.2024.101604","DOIUrl":"10.1016/j.imu.2024.101604","url":null,"abstract":"<div><div>Drug Target Interaction (DTI) prediction is one of the main challenges in the pharmaceutical and drug discovery domains due to its high costs, time-consuming nature, and complexity of manual experiments required to evaluate interactions between large numbers of drugs and targets. In addition, a single drug can bind to multiple targets. In contrast, a single target can also bind to a number of drugs; this complicates the DTI task. Existing silico models often struggle with these challenges, particularly in managing diverse datasets. To address these issues, we introduce the Weighted Average Ensemble Drug–Target Interaction (WAE-DTI) model. Our approach integrates several descriptors and fingerprint representations to enhance prediction accuracy and generalization, namely atom pair fingerprint, Avalon, MACCS, MH, Morgan, RDKit, SEC, topological torsion, and LDP for drug representation, and ESM-2 for target representation. WAE-DTI employs a weighted average ensemble technique to handle diverse datasets effectively. The model demonstrates significant improvements over state-of-the-art methods, achieving an average mean squared error of 0.190 ±0.001 on the Davis dataset, 0.127 ±0.001 on Kiba, 0.143 ±0.001 on DTC, 0.284 ±0.004 on Metz, 0.308 ±0.001 on ToxCast, and 0.934 ±0.004 on STITCH. As for the classification task, WAE-DTI outperforms existing models with an AUPRC of 0.943 ±0.001 on BioSNAP, 0.474 ±0.011 on Davis, and 0.707 ±0.005 on BindingDB. Our code is publicly available at <span><span>¹</span></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101604"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do transformers generalise better than bespoke tools for anonymisation?","authors":"Roman Klapaukh ,&nbsp;Carol El-Hayek ,&nbsp;Douglas IR Boyle","doi":"10.1016/j.imu.2024.101607","DOIUrl":"10.1016/j.imu.2024.101607","url":null,"abstract":"<div><div>Free-text fields in clinical records contain information that may not show up in the structured health record. Automated anonymisation tools can lower the bar to using this data at scale. However, existing anonymisation tools do not always perform as well as expected when used outside of their country and domain of origin. We ran three US tertiary care targeting transformer models on 300 Australian general practice notes, and showed that they generalise better than purpose built tools.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101607"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain tumor classification using a hybrid ensemble of Xception and parallel deep CNN models","authors":"Seoyoung Yoon","doi":"10.1016/j.imu.2025.101629","DOIUrl":"10.1016/j.imu.2025.101629","url":null,"abstract":"<div><h3>Objective</h3><div>Accurate classification of brain tumors is essential for effective diagnosis and treatment planning. The purpose of this study is to develop and evaluate a hybrid ensemble brain tumor classification method to leverage the strengths of two different architectures for improving the accuracy, robustness, and reliability of brain tumor classification.</div></div><div><h3>Methodology</h3><div>This study introduces a novel and innovative classifier that concatenates the Xception convolutional neural network (CNN) with kernel size of (3,3) and a parallel deep CNN (PDCNN) with kernel size of (5,5) and (12,12) to classify brain tumor images from the Kaggle dataset into four categories: meningioma, glioma, pituitary, and no tumor.</div></div><div><h3>Results</h3><div>The Xception model alone achieved a classification accuracy of 98.26 %, while the PDCNN model achieved 94.85 % on the same dataset. By concatenating these two models, the proposed hybrid ensemble approach enhanced overall classification accuracy to 99.09 %. In comparison with state-of-the-art models, VGG19 achieved an accuracy of 94.69 %, while ResNet152V2 achieved 96.27 % on the same dataset. The proposed hybrid ensemble model with Xception and PDCNN consistently outperformed both VGG19 and ResNet152V2.</div></div><div><h3>Conclusion</h3><div>This synergy of concatenating the Xception and PDCNN architectures demonstrates the innovativeness and effectiveness of leveraging complementary strengths in feature extraction and classification, leading to enhanced performance in brain tumor detection. The results highlight the potential of ensemble deep learning models in advancing automated medical image analysis and improving clinical outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101629"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural modification of Naproxen; physicochemical, spectral, medicinal, and pharmacological evaluation","authors":"Md Omor Farque ,&nbsp;Rahat Moinul Islam ,&nbsp;Md Ferdous Rahman Joni ,&nbsp;Mimona Akter ,&nbsp;Shilpy Akter ,&nbsp;Mohammad Didarul Islam ,&nbsp;MD Jubaer Bin Salim ,&nbsp;Ahamed Abdul Aziz ,&nbsp;Emranul Kabir ,&nbsp;Monir Uzzaman","doi":"10.1016/j.imu.2025.101617","DOIUrl":"10.1016/j.imu.2025.101617","url":null,"abstract":"<div><div>Naproxen (Nap), a widely used nonsteroidal anti-inflammatory drug (NSAID), effectively reduces inflammation, pain, and fever by inhibiting cyclooxygenase enzymes (i.e., COX-1 and COX-2). However, its therapeutic use is often limited by significant adverse effects, including gastrointestinal hemorrhage, nephrotoxicity, hepatotoxicity, hematuria, and aphthous ulcers. In this study, we aimed to enhance both the efficacy and safety profile of Nap by making targeted structural modifications to the parent drug. Specifically, selected functional groups (i.e., CH_3, OCH₃, CF₃, OCF₃, NH₂, CH₂NH₂, NHCONH₂ and NHCOCH₃) were introduced into the naphthalene nucleus. The geometry of the modified compounds was optimized via DFT with the B3LYP functional and 6-31+G (d, p) basis set, facilitating physicochemical and spectral analysis. Molecular docking studies were conducted against the human Prostaglandin G/H synthase 2 (5F19) and <em>Mus musculus</em> Prostaglandin-endoperoxide synthase 2 (3NT1), and these candidates were subjected to MD simulation. ADMET and PASS analyses were performed to evaluate the medicinal efficacy and toxicological profiles of the derivatives. Our findings identified several promising candidates with enhanced therapeutic benefits and reduced toxicity compared with the parent Nap. Docking analysis revealed that analogs exhibited stronger binding affinities compared to Nap and selectivity towards COX-2. These candidates demonstrated stability over a 100 ns MD simulation, exhibiting significant hydrogen bonding. Compared with the parent drug, most of these analogs displayed reduced hepatotoxicity, nephrotoxicity, carcinogenicity, and gastrointestinal hemorrhage activity, as supported by pharmacokinetic calculations. This study demonstrated that improved chemical and medicinal properties lead to a reduction in side effects.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101617"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}