Biomedical Engineering Letters最新文献

{"title":"From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.","authors":"Lukas Buess, Matthias Keicher, Nassir Navab, Andreas Maier, Soroosh Tayebi Arasteh","doi":"10.1007/s13534-025-00497-1","DOIUrl":"10.1007/s13534-025-00497-1","url":null,"abstract":"<p><p>Generative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical workflows. The field has advanced rapidly, evolving from text-only large language models for tasks such as clinical documentation and decision support to multimodal AI systems capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model. The diverse landscape of these technologies, along with rising interest, highlights the need for a comprehensive review of their applications and potential. This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings. Adhering to PRISMA-ScR guidelines, we systematically queried PubMed, IEEE Xplore, and Web of Science, prioritizing recent studies published up to the end of 2024. After rigorous screening, 145 papers were included, revealing key trends and challenges in this dynamic field. Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI. However, critical challenges remain, including the integration of heterogeneous data types, improving model interpretability, addressing ethical concerns, and validating AI systems in real-world clinical settings. This review summarizes the current state of the art, identifies critical gaps, and provides insights to guide the development of scalable, trustworthy, and clinically impactful multimodal AI solutions in healthcare.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13534-025-00497-1.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"845-863"},"PeriodicalIF":2.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized multi-stage network with multi-dimensional spatiotemporal interactions for septal and apical hypertrophic cardiomyopathy classification using 12-lead ECGs.","authors":"Qi Yu, Hongxia Ning, Jinzhu Yang, Mingjun Qu, Yiqiu Qi, Peng Cao, Honghe Li, Guangyuan Li, Yonghuai Wang","doi":"10.1007/s13534-025-00492-6","DOIUrl":"10.1007/s13534-025-00492-6","url":null,"abstract":"<p><strong>Abstract: </strong>Hypertrophic cardiomyopathy (HCM) is a common hereditary heart disease and is the leading cause of sudden cardiac death in adolescents. Septal hypertrophy (SH) and apical hypertrophy (AH) are two common types. The former is characterized by abnormal septal myocardial thickening and the latter by left ventricular apical hypertrophy, both of which significantly increase the risk of heart failure, arrhythmias, and other serious complications. Identifying hypertrophic sites in HCM patients using 12-lead electrocardiography (ECG) is crucial for early diagnosis, staging, and prognosis. However, most deep learning methods rely on 1D one-dimensional ECG signal detection, or 2D two-dimensional ECG image or spectrogram recognition, which may result in the loss of spatial or temporal information, thus limiting diagnostic accuracy. Therefore, an optimized multi-stage network with multi-dimensional spatiotemporal interactions (Ms-MdST) is proposed for detecting AH and SH in HCM. The optimized Ms-MdST model combines the advantages of different dimensional convolutions to capture the spatiotemporal characteristics of ECG and consists of a 1D convolution branch for overall temporal features and a 2D convolution branch for similar spatial features across multiple leads. Moreover, a global-local interactive attention mechanism (GLIA) and a multi-loss joint optimization strategy are employed to facilitate multi-stage multi-scale feature fusion. Experimental results show that Ms-MdST achieves F1-scores of 0.9672, 0.7250, and 0.8009 in the CONTROL, SH, and AH groups, respectively, demonstrating its superiority compared to existing ECG classification methods. In addition, the proposed model is interpretable and can be further extended to clinical applications.</p><p><strong>Graphical abstract: </strong></p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"939-950"},"PeriodicalIF":2.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subsidence reduction effect of transforaminal lumbar interbody fusion (TLIF) with upper and lower open windows modified with lattice structure.","authors":"Junsu Bae, Hyeonsu Bae, Hae Won Choi, Kyeong-Joo Yoo, Hyung-Youl Park, Jun-Seok Lee, Dohyung Lim","doi":"10.1007/s13534-025-00494-4","DOIUrl":"10.1007/s13534-025-00494-4","url":null,"abstract":"<p><p>Cage subsidence is a common complication following transforaminal lumbar interbody fusion (TLIF) that can lead to poor clinical outcomes, including recurrent pain and segmental instability. Conventional TLIF cage designs often fail to distribute stress evenly, increasing the risk of endplate damage and subsequent subsidence. This study aims to evaluate the effect of a modified TLIF cage with upper and lower open windows (lattice structure) in reducing cage subsidence in patients with lumbar degenerative disc disease (LDDD). A finite element (FE) model of the lumbar spine was developed and validated. Three TLIF cage designs (Open, Lattice, Closed) were simulated under various loading conditions (flexion-extension, lateral bending, axial rotation), and von Mises stresses were analyzed within the TLIFs, endplates, and cancellous bone. The FE model demonstrated ROMs consistent with cadaveric studies. Elevated stresses were found in all cages, especially Open and Closed designs. The Lattice TLIF showed improved stress distribution, reducing peak stress on endplates. However, increased contact area had a limited effect on reducing subsidence under physiological loads. While contact area alone does not significantly mitigate subsidence risk, incorporating lattice structures may enhance resistance to physiological stress. These findings suggest that optimized TLIF designs integrating lattice structures can improve stability and reduce the likelihood of subsidence, leading to better clinical outcomes (e.g., reduced pain, improved fusion success, long-term stability) in LDDD patients.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"951-962"},"PeriodicalIF":2.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal theta- and gamma-band cortical activities during visuospatial attention in idiopathic REM sleep behavior disorder patients.","authors":"Hyun Kim, Jung-Ick Byun, Ki-Young Jung, Kyung Hwan Kim","doi":"10.1007/s13534-025-00493-5","DOIUrl":"10.1007/s13534-025-00493-5","url":null,"abstract":"<p><p>Purpose: Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a sleep disorder considered to be a prodromal stage of neurodegeneration disease and is often accompanied by cognitive impairments. The purpose of this study was to investigate spatiotemporal characteristics of abnormal oscillatory cortical activity associated with dysfunction of visuospatial attention in iRBD based on an explainable machine learning approach. Methods: EEGs were recorded from 49 iRBD patients and 49 normal controls while they were performing Posner's cueing task and transformed to cortical current density time-series. Spectral cortical activities for four frequency bands (theta, alpha, beta, and gamma) were estimated, and then converted to three-dimensional (3D) spatiotemporal data. A pattern classifier based on 3D convolutional neural network was devised to discriminate the cortical activities of iRBD patients and those of normal controls. Results: The location, time, and frequency which characterize the difference between the patients and normal controls, thereby deemed to be associated with cognitive impairment due to the iRBD, were identified by finding the input nodes which were most critical to the classifier's decision. Conclusion: Our results suggest that theta- and gamma-band activities in parietal and occipital regions, which may underlie efficient visuospatial processing and attentional reallocation, are impaired in iRBD patients, resulting in poor visuospatial attention performance.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13534-025-00493-5.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"929-937"},"PeriodicalIF":2.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning model for predicting the probability of hypothermia in trauma patients: a multi-center retrospective cohort study.","authors":"Guang Zhang, YiJing Fu, Jing Yuan, Qingyan Xie, GuanJun Liu, JiaMeng Xu, Wei Chen","doi":"10.1007/s13534-025-00485-5","DOIUrl":"10.1007/s13534-025-00485-5","url":null,"abstract":"<p><p>Hypothermia, a component of the \"lethal triad,\" commonly complicates the condition of critically injured trauma patients, thereby substantially elevating the risk of mortality. This study develop and evaluate a dynamic warning system based on non-invasive features, aimed at predicting the likelihood of hypothermia occurring in trauma patients within the next hour. 462 patients from the eICU database were selected on the basis of meeting the inclusion criteria, and 19 non-invasive and 17 invasive features were extracted. Five classic machine learning methods were employed to develop dynamic early warning model for hypothermia based on various observation windows, with multi-center data used for model validation. The shapley additive explanations (SHAP) algorithm was utilized to analyze the interpretability of the model, and ablation experiments were conducted to further evaluate the contribution of significant feature to the prediction performance. The AUC values of the optimal models based on non-invasive features in the same test set are 0.838. When using cross-hospital data as the validation set, the highest AUC values for the same models based on non-invasive features decrease by only 0.015. In addition, ablation experiments reveal that the model's AUC exhibited a 0.010 improvement when the three most influential invasive features were incorporated into the non-invasive feature set. The results show that machine learning models have shown significant potential in predicting hypothermia through the utilization of solely non-invasive features.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"877-890"},"PeriodicalIF":2.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey on sampling conditioned brain images and imaging measures with generative models.","authors":"Sehyoung Cheong, Hoseok Lee, Won Hwa Kim","doi":"10.1007/s13534-025-00487-3","DOIUrl":"10.1007/s13534-025-00487-3","url":null,"abstract":"<p><p>Generative models have become innovative tools across various domains, including neuroscience, where they enable the synthesis of realistic brain imaging data that captures complex anatomical and functional patterns. These models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, leverage deep learning to generate high-quality brain images while maintaining biological and clinical relevance. These models address critical challenges in brain imaging, e.g., the high cost and time required for data acquisition and the frequent imbalance in datasets, particularly for rare diseases or specific population groups. By conditioning the generative process on variables such as age, sex, clinical phenotypes, or genetic factors, these models enhance dataset diversity and provide opportunities to study underrepresented scenarios, model disease progression, and perform controlled experiments that are otherwise infeasible. Additionally, synthetic data generated by these models offer a potential solution to data privacy concerns, as they provide realistic non-identifiable data. As generative models continue to develop, they hold significant potential to substantially advance neuroscience by augmenting datasets, improving diagnostic accuracy, and accelerating the development of personalized treatments. This paper provides a comprehensive overview of the advancements in generative modeling techniques and their applications in brain imaging, with a particular emphasis on conditional generative methods. By categorizing existing approaches, addressing key challenges, and highlighting future directions, this paper aims to advance the integration of conditional generative models into neuroscience research and clinical workflows.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"831-843"},"PeriodicalIF":2.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lung nodule synthesis guided by customized multi-confidence masks.","authors":"Huashan Chen, Yongxu Liu, Chen Liu, Qiuli Wang, Rongping Wang","doi":"10.1007/s13534-025-00490-8","DOIUrl":"10.1007/s13534-025-00490-8","url":null,"abstract":"<p><p>The generated lung nodule data plays an indispensable role in the development of intelligent assisted diagnosis of lung cancer. Existing generative models, primarily based on Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM), have demonstrated effectiveness but also come with certain limitations: GANs often produce artifacts and unnatural boundaries, and due to dataset limitations, they struggle with irregular nodules. While DDPMs are capable of generating a diverse range of nodules, their inherent randomness and lack of control limit their applicability in tasks such as segmentation. To synthesize controllable shapes and details of lung nodules, in this study, we propose a unified model that combines GAN and DDPM. Guided by multi-confidence masks, our method can synthesize customized lung nodule images by adding spikes or dents to the input mask, allowing control over shape, size, and other medical image features. The model consists of two parts: (1) a Rough Lung Nodule Generator, based on GAN, which synthesizes rough lung nodules of specified sizes and shapes using a multi-confidence mask, and (2) a Lung Nodule Optimizer, based on DDPM, which refines the rough results from the first part to produce more authentic boundaries. We validate our method using the LIDC-IDRI dataset. Experimental results demonstrate that our unified model achieves the best FID score, and the synthetic lung nodules it generates can serve as a valuable supplement to training datasets for segmentation tasks. Our study presents a unified model that effectively combines GAN and DDPM to generate high-quality and customized lung nodule images. This approach addresses the limitations of existing models by leveraging the strengths of both techniques. Our code is available at https://github.com/UtaUtaUtaha/CMCMGN.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"917-927"},"PeriodicalIF":2.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of time-dependent viscoelastic behaviors of alginate-calcium chloride hydrogels for bioprinting applications.","authors":"Vesper Evereux, Sunjeet Saha, Chandrabali Bhattacharya, Seungman Park","doi":"10.1007/s13534-025-00488-2","DOIUrl":"10.1007/s13534-025-00488-2","url":null,"abstract":"<p><p>Alginate is known to readily aggregate and form a physical gel when exposed to cations, making it a promising material for bioprinting applications. Alginate and its derivatives exhibit viscoelastic behavior due to the combination of solid and fluid components, necessitating the characterization of both elastic and viscous properties. However, a comprehensive investigation into the time-dependent viscoelastic properties of alginate hydrogels specifically optimized for bioprinting is still lacking. In this study, we investigated and quantified the time-dependent viscoelastic properties (elastic modulus, shear modulus, and viscosity) of calcium chloride (CaCl₂) crosslinked-alginate hydrogels across 5 different alginate concentrations under 2 environmental conditions and 3 indentation depths using the Prony series. Moreover, we evaluated the printability of alginate solutions at different concentrations through bioprinted-filament collapse and fusion tests to assess their potential for bioprinting applications. The results demonstrated significant effects of alginate concentration, indentation depth, and environmental conditions on the viscoelastic behavior of alginate-based hydrogels. Furthermore, we identified 5% alginate as the optimal concentration for bioprinting. This study establishes a foundational workflow for characterizing various biomaterials, enabling their assessment for suitability in bioprinting and other tissue engineering applications.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"891-901"},"PeriodicalIF":2.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.","authors":"Dogeun Park, Kwangsub So, Sunil Kumar Prabhakar, Chulho Kim, Jae Jun Lee, Jong-Hee Sohn, Jong-Ho Kim, Sang-Hwa Lee, Dong-Ok Won","doi":"10.1007/s13534-025-00486-4","DOIUrl":"10.1007/s13534-025-00486-4","url":null,"abstract":"<p><p>Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"717-734"},"PeriodicalIF":3.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antibacterial and anticancer activity of multifunctional iron-based magnetic nanoparticles against urinary tract infection and cystitis-related bacterial strains and bladder cancer cells.","authors":"Ki Chang Nam, Bong Joo Park","doi":"10.1007/s13534-025-00489-1","DOIUrl":"10.1007/s13534-025-00489-1","url":null,"abstract":"<p><strong>Purpose: </strong>This study investigates the antibacterial and anticancer activity of previously reported iron oxide (Fe₃O₄)-based nanoparticles (NPs) conjugated with chlorin e6 and folic acid (FCF) in photodynamic therapy (PDT) using a human bladder cancer (BC) (T-24) cell line and three bacterial strains.</p><p><strong>Method: </strong>To investigate the potential applicability of the synthesized NPs as therapeutic agents for image-based photodynamic BC therapy, their photodynamic anticancer activity was analyzed and the mechanisms of cell death in T-24 cells treated with these NPs were assessed qualitatively and quantitatively through atomic absorption spectroscopy, fluorescence imaging, and transmission electron microscopy.</p><p><strong>Results: </strong>The effective localization of FCF NPs in T-24 cells were confirmed, validating their excellent cellular fluorescence and magnetic resonance imaging capabilities. Moreover, the FCF NPs exhibited excellent anticancer activity via distinct mechanisms of cell death; they induced apoptotic cancer cell death by strongly upregulating apoptosis-related mRNA genes, such as Bcl-2-interacting killer, growth arrest DNA damage-inducible protein 45 beta, and Caspase-3, -6, and -9. Furthermore, the FCF NPs showed significant antibacterial activity against <i>Escherichia coli</i>, <i>Staphylococcus aureus</i>, and the clinically isolated methicillin-resistant strain <i>Staphylococcus aureus</i>.</p><p><strong>Conclusion: </strong>FCF NPs effectively induce cancer cell death, show excellent photodynamic anticancer efficacy against BC cells, and exhibit potent antibacterial activity against uropathogenic bacterial strains via PDT, exhibiting high potential for application in versatile imaging-based diagnostics and therapeutics in BC treatment and urinary tract infection management. However, prior to their clinical application, in vivo studies using animal models are required to validate these biological and physiological effects.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 5","pages":"903-915"},"PeriodicalIF":2.8,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}