Pub Date : 2026-03-01Epub Date: 2026-01-13DOI: 10.1016/j.cobme.2026.100649
Maria A. Zuluaga , Ivana Išgum , Meritxell Bach Cuadra
Trustworthy AI is critical for effectively adopting AI systems in medical imaging and broader healthcare contexts. While the Trustworthy AI framework defines seven core principles —ranging from technical robustness to societal well-being— these are often addressed in isolation, lacking a coherent integration strategy. In this perspective paper, we propose a unified, layered framework that organizes these principles across three tiers of increasing trust: core operations, feedback, and explainability. Each layer aligns with the fundamental components of an AI system—input data, model, and outputs, integrating the different principles and offering a structured path toward increasing levels of trust. Central to our framework is technical robustness, positioned as a cross-cutting enabler that intertwines with the other trust principles across all layers. Through this lens, we review recent advances in trustworthy AI techniques in medical imaging and highlight persistent challenges and future research directions for building trustworthy AI systems in medical imaging.
{"title":"Trustworthy AI in medical image analysis: A unified perspective built on robustness and layers of trust","authors":"Maria A. Zuluaga , Ivana Išgum , Meritxell Bach Cuadra","doi":"10.1016/j.cobme.2026.100649","DOIUrl":"10.1016/j.cobme.2026.100649","url":null,"abstract":"<div><div>Trustworthy AI is critical for effectively adopting AI systems in medical imaging and broader healthcare contexts. While the Trustworthy AI framework defines seven core principles —ranging from technical robustness to societal well-being— these are often addressed in isolation, lacking a coherent integration strategy. In this perspective paper, we propose a unified, layered framework that organizes these principles across three tiers of increasing trust: core operations, feedback, and explainability. Each layer aligns with the fundamental components of an AI system—input data, model, and outputs, integrating the different principles and offering a structured path toward increasing levels of trust. Central to our framework is technical robustness, positioned as a cross-cutting enabler that intertwines with the other trust principles across all layers. Through this lens, we review recent advances in trustworthy AI techniques in medical imaging and highlight persistent challenges and future research directions for building trustworthy AI systems in medical imaging.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100649"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-15DOI: 10.1016/j.cobme.2025.100645
Delphine Gourdon
{"title":"Cellular and molecular aspects of mechanobiology of the extracellular matrix","authors":"Delphine Gourdon","doi":"10.1016/j.cobme.2025.100645","DOIUrl":"10.1016/j.cobme.2025.100645","url":null,"abstract":"","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100645"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-13DOI: 10.1016/j.cobme.2025.100632
Florenc Demrozi, Mina Farmanbar, Kjersti Engan
Multimodal artificial intelligence (MMAI) is reshaping the landscape of next-generation healthcare by integrating diverse data sources—ranging from medical imaging and electronic health records (EHRs) to wearable sensor data and genomic sequencing. This convergence enables more accurate diagnostics, personalized treatment strategies, and real-time patient monitoring, ultimately transforming healthcare from reactive to predictive and preventive. Additionally, MMAI can lead to improved operational efficiency by enabling automated reporting and streamlining clinical workflows, helping to reduce clinician burnout and accelerate diagnostic turnaround times. Despite significant advancements, several challenges hinder the widespread adoption of MMAI, including data fragmentation, interoperability issues, computational demands, and the need for explainable AI in clinical decision-making. This opinion paper explores four key aspects driving the future of MMAI in healthcare: (1) the evolution of multimodal data; (2) advancements in AI models and fusion strategies for extracting insights from heterogeneous data streams; (3) major challenges such as synchronization across modalities, interpretability, and regulatory constraints; and (4) emerging future directions, including the role of digital twins, automated clinical reporting, and precision medicine.
{"title":"Multimodal AI (MMAI) for next-generation healthcare: data domains, algorithms, challenges, and future perspectives","authors":"Florenc Demrozi, Mina Farmanbar, Kjersti Engan","doi":"10.1016/j.cobme.2025.100632","DOIUrl":"10.1016/j.cobme.2025.100632","url":null,"abstract":"<div><div>Multimodal artificial intelligence (MMAI) is reshaping the landscape of next-generation healthcare by integrating diverse data sources—ranging from medical imaging and electronic health records (EHRs) to wearable sensor data and genomic sequencing. This convergence enables more accurate diagnostics, personalized treatment strategies, and real-time patient monitoring, ultimately transforming healthcare from reactive to predictive and preventive. Additionally, MMAI can lead to improved operational efficiency by enabling automated reporting and streamlining clinical workflows, helping to reduce clinician burnout and accelerate diagnostic turnaround times. Despite significant advancements, several challenges hinder the widespread adoption of MMAI, including data fragmentation, interoperability issues, computational demands, and the need for explainable AI in clinical decision-making. This opinion paper explores four key aspects driving the future of MMAI in healthcare: (1) the evolution of multimodal data; (2) advancements in AI models and fusion strategies for extracting insights from heterogeneous data streams; (3) major challenges such as synchronization across modalities, interpretability, and regulatory constraints; and (4) emerging future directions, including the role of digital twins, automated clinical reporting, and precision medicine.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100632"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-26DOI: 10.1016/j.cobme.2025.100638
M. Chandru , M. Abinesh , M. Siva Ananth , Arti Vashist , Pandiaraj Manickam
This review article explores the significant advancements of artificial intelligence (AI) as a transformative tool for the early detection of major neurodegenerative diseases, specifically Alzheimer’s disease (AD) and Parkinson’s disease (PD). Traditional diagnostic techniques, including standardized clinical cognitive assessments, molecular biomarker analysis, and neuroimaging, remain crucial in clinical assessment. However, their utility is often limited by their accessibility, cost, and insufficient sensitivity. In the recent years, AI-driven techniques have emerged as a promising tool in advancing the early detection of AD and PD. These techniques play a crucial role in diagnosis by analyzing complex dataset derived from neuroimaging, integrated wearable sensors, and various digital biomarkers. By integrating multimodal data analysis with digital phenotyping and digital biomarkers discovery, a personalized therapeutic regime can be developed. Challenges, including the need for standardized data acquisition, improving model interpretability, and addressing ethical concerns related to data privacy and equitable access, are also highlighted.
{"title":"Artificial intelligence in neurodegenerative disease diagnosis: Advancing Alzheimer’s and Parkinson’s diseases","authors":"M. Chandru , M. Abinesh , M. Siva Ananth , Arti Vashist , Pandiaraj Manickam","doi":"10.1016/j.cobme.2025.100638","DOIUrl":"10.1016/j.cobme.2025.100638","url":null,"abstract":"<div><div>This review article explores the significant advancements of artificial intelligence (AI) as a transformative tool for the early detection of major neurodegenerative diseases, specifically Alzheimer’s disease (AD) and Parkinson’s disease (PD). Traditional diagnostic techniques, including standardized clinical cognitive assessments, molecular biomarker analysis, and neuroimaging, remain crucial in clinical assessment. However, their utility is often limited by their accessibility, cost, and insufficient sensitivity. In the recent years, AI-driven techniques have emerged as a promising tool in advancing the early detection of AD and PD. These techniques play a crucial role in diagnosis by analyzing complex dataset derived from neuroimaging, integrated wearable sensors, and various digital biomarkers. By integrating multimodal data analysis with digital phenotyping and digital biomarkers discovery, a personalized therapeutic regime can be developed. Challenges, including the need for standardized data acquisition, improving model interpretability, and addressing ethical concerns related to data privacy and equitable access, are also highlighted.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100638"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-26DOI: 10.1016/j.cobme.2025.100634
Brian I. Molina Diaz, Pei Zhuang, Xiaoshu Pan, George Doctsch, Mei He
Extracellular vesicles (EVs), the so-called nanosized vesicles shedding out from cells, have emerged as promising nanocarriers for cancer therapy given their high biocompatibility and low immunogenicity. However, their clinical utility remains limited by challenges such as off-target, premature drug release and rapid clearance. In solid tumors, these issues are further compounded by the hostile biomechanical environment, including stiff extracellular matrix, elevated interstitial fluid pressure, and abnormal vasculatures, which further complicates drug delivery and therapeutic efficacy. To overcome these limitations, recent efforts have focused on engineering stimuli-responsive EVs that respond to internal stimuli (e.g. pH, enzymatic activity, and redox imbalance) or external stimuli (e.g. magnetic fields, light, ultrasound, and temperature), as well as combinations thereof. These smart nanoplatforms have demonstrated a superior capacity in achieving controlled drug release, enhancing tumor targeting, and improving deep tissue penetration. In this minireview, we highlight how stimuli-responsive EVs surpass tumor biomechanics for cancer therapy and discuss key considerations for future development and clinical translation.
{"title":"Engineering stimuli-responsive extracellular vesicles for enhanced anticancer therapeutics","authors":"Brian I. Molina Diaz, Pei Zhuang, Xiaoshu Pan, George Doctsch, Mei He","doi":"10.1016/j.cobme.2025.100634","DOIUrl":"10.1016/j.cobme.2025.100634","url":null,"abstract":"<div><div>Extracellular vesicles (EVs), the so-called nanosized vesicles shedding out from cells, have emerged as promising nanocarriers for cancer therapy given their high biocompatibility and low immunogenicity. However, their clinical utility remains limited by challenges such as off-target, premature drug release and rapid clearance. In solid tumors, these issues are further compounded by the hostile biomechanical environment, including stiff extracellular matrix, elevated interstitial fluid pressure, and abnormal vasculatures, which further complicates drug delivery and therapeutic efficacy. To overcome these limitations, recent efforts have focused on engineering stimuli-responsive EVs that respond to internal stimuli (e.g. pH, enzymatic activity, and redox imbalance) or external stimuli (e.g. magnetic fields, light, ultrasound, and temperature), as well as combinations thereof. These smart nanoplatforms have demonstrated a superior capacity in achieving controlled drug release, enhancing tumor targeting, and improving deep tissue penetration. In this minireview, we highlight how stimuli-responsive EVs surpass tumor biomechanics for cancer therapy and discuss key considerations for future development and clinical translation.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100634"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-09DOI: 10.1016/j.cobme.2025.100643
Yu Shrike Zhang
{"title":"Biosensors for in vitro modeling of cell and tissue functions","authors":"Yu Shrike Zhang","doi":"10.1016/j.cobme.2025.100643","DOIUrl":"10.1016/j.cobme.2025.100643","url":null,"abstract":"","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100643"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-11DOI: 10.1016/j.cobme.2025.100644
Nadia Soulioti, Omolola Ajayi, Delphine Gourdon
Obesity triggers ample reorganization of adipose tissue through mechanical and biological mechanisms that extend beyond simple energy storage. Here, we analyse how the extracellular matrix (ECM) mechanically controls obesity's effects on tissue function. We examine three interconnected mechanisms: first, how obesity triggers cellular adaptations through hypertrophy, hyperplasia, and phenotypic transitions; second, how these changes activate specific mechanosensitive pathways through ECM-generated forces; and third, how the resulting matrix alterations promote breast cancer progression through coordinated changes in adipocyte phenotype and tissue mechanics. We critically evaluate current in vitro models for studying these processes and discuss their limitations in recapitulating the complex adipose-tumour microenvironment. By elucidating these ECM-mediated mechanisms, we identify potential therapeutic targets for obesity-related pathologies, particularly breast cancer, while noting the broader applicability of these mechanobiological principles to other adipose-associated malignancies.
{"title":"Mechano-adaptation of adipose tissue: ECM-mediated control of obesity and breast cancer progression","authors":"Nadia Soulioti, Omolola Ajayi, Delphine Gourdon","doi":"10.1016/j.cobme.2025.100644","DOIUrl":"10.1016/j.cobme.2025.100644","url":null,"abstract":"<div><div>Obesity triggers ample reorganization of adipose tissue through mechanical and biological mechanisms that extend beyond simple energy storage. Here, we analyse how the extracellular matrix (ECM) mechanically controls obesity's effects on tissue function. We examine three interconnected mechanisms: first, how obesity triggers cellular adaptations through hypertrophy, hyperplasia, and phenotypic transitions; second, how these changes activate specific mechanosensitive pathways through ECM-generated forces; and third, how the resulting matrix alterations promote breast cancer progression through coordinated changes in adipocyte phenotype and tissue mechanics. We critically evaluate current in vitro models for studying these processes and discuss their limitations in recapitulating the complex adipose-tumour microenvironment. By elucidating these ECM-mediated mechanisms, we identify potential therapeutic targets for obesity-related pathologies, particularly breast cancer, while noting the broader applicability of these mechanobiological principles to other adipose-associated malignancies.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100644"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-31DOI: 10.1016/j.cobme.2025.100648
Sayed Maeen Badshah , Ndumiso Vukile Mdlovu , Chun-Ming Wu , Kuen-Song Lin , Ming-Tao Yang
Transdermal nanomedicine integrates nanotechnology with drug-delivery science to overcome the formidable skin barrier, offering a noninvasive, patient-centric alternative to oral and injectable routes. This review traces its evolution from conventional patches to nano-enabled systems, critically appraising lipid and polymeric nanoparticles, nanogels, solid and nanostructured lipid carriers, and microneedle-assisted delivery. Key permeation strategies surface engineering, follicular targeting, and stimuli-responsive designs are discussed alongside bioinspired carriers such as cell-membrane-coated, exosome-derived, and virus-like particles that enhance precision and biocompatibility. Structural biomimetics, including adhesive and proboscis-inspired microneedles, further advance patch design. Persistent challenges reproducibility, chronic toxicity, scale-up, and regulatory heterogeneity are analyzed with emphasis on standardization and long-term safety. Emerging solutions combine microfluidic and 3D-printed fabrication, self-assembling nanostructures, and advanced characterization (SANS/SAXS) under Quality-by-Design and AI-guided frameworks. By integrating materials science, skin biology, and regulatory insight, this review delineates a roadmap toward clinically translatable, personalized transdermal therapeutics.
{"title":"Transdermal nanomedicine: Emerging horizons, unresolved challenges, and the path forward","authors":"Sayed Maeen Badshah , Ndumiso Vukile Mdlovu , Chun-Ming Wu , Kuen-Song Lin , Ming-Tao Yang","doi":"10.1016/j.cobme.2025.100648","DOIUrl":"10.1016/j.cobme.2025.100648","url":null,"abstract":"<div><div>Transdermal nanomedicine integrates nanotechnology with drug-delivery science to overcome the formidable skin barrier, offering a noninvasive, patient-centric alternative to oral and injectable routes. This review traces its evolution from conventional patches to nano-enabled systems, critically appraising lipid and polymeric nanoparticles, nanogels, solid and nanostructured lipid carriers, and microneedle-assisted delivery. Key permeation strategies surface engineering, follicular targeting, and stimuli-responsive designs are discussed alongside bioinspired carriers such as cell-membrane-coated, exosome-derived, and virus-like particles that enhance precision and biocompatibility. Structural biomimetics, including adhesive and proboscis-inspired microneedles, further advance patch design. Persistent challenges reproducibility, chronic toxicity, scale-up, and regulatory heterogeneity are analyzed with emphasis on standardization and long-term safety. Emerging solutions combine microfluidic and 3D-printed fabrication, self-assembling nanostructures, and advanced characterization (SANS/SAXS) under Quality-by-Design and AI-guided frameworks. By integrating materials science, skin biology, and regulatory insight, this review delineates a roadmap toward clinically translatable, personalized transdermal therapeutics.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100648"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-26DOI: 10.1016/j.cobme.2025.100636
Raheel Ahmad , Sahbra Eldosougi , Shannon L. Stott
Extracellular vesicles (EVs) are membrane-bound particles secreted by cells into the extracellular space. EVs are recognized as nanotransporters of intercellular communication, particularly within the tumor microenvironment. In cancer, EVs are involved in disease progression by regulating signaling cascades, remodeling the extracellular matrix (ECM), and fostering invasive cell behavior. Recent evidence highlights the central role of mechanical signals of the ECM in regulating EV biogenesis, molecular cargo, and downstream functional effects. Elucidating these mechanobiological processes is crucial for the development of EV-based diagnostics and mechanotherapies. This review integrates new concepts on the biomechanics of EVs and ECM to shed light on their mutual regulation and collective influence on cancer progression. By linking the transport dynamics of EVs to the mechanics of the ECM and transmembrane modulators such as AQP1, it illustrates how biomechanical signals and osmotic gradients control the deformation, migration, and escape of EVs through the complex tumor matrix landscape.
{"title":"Mechanobiological modulation of extracellular vesicle function and transport dynamics in cancer progression","authors":"Raheel Ahmad , Sahbra Eldosougi , Shannon L. Stott","doi":"10.1016/j.cobme.2025.100636","DOIUrl":"10.1016/j.cobme.2025.100636","url":null,"abstract":"<div><div>Extracellular vesicles (EVs) are membrane-bound particles secreted by cells into the extracellular space. EVs are recognized as nanotransporters of intercellular communication, particularly within the tumor microenvironment. In cancer, EVs are involved in disease progression by regulating signaling cascades, remodeling the extracellular matrix (ECM), and fostering invasive cell behavior. Recent evidence highlights the central role of mechanical signals of the ECM in regulating EV biogenesis, molecular cargo, and downstream functional effects. Elucidating these mechanobiological processes is crucial for the development of EV-based diagnostics and mechanotherapies. This review integrates new concepts on the biomechanics of EVs and ECM to shed light on their mutual regulation and collective influence on cancer progression. By linking the transport dynamics of EVs to the mechanics of the ECM and transmembrane modulators such as AQP1, it illustrates how biomechanical signals and osmotic gradients control the deformation, migration, and escape of EVs through the complex tumor matrix landscape.</div></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100636"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-03DOI: 10.1016/j.cobme.2025.100642
Saba Rezakhani, Matthias Lutolf
{"title":"Engineering the organoid niche to build better human models","authors":"Saba Rezakhani, Matthias Lutolf","doi":"10.1016/j.cobme.2025.100642","DOIUrl":"10.1016/j.cobme.2025.100642","url":null,"abstract":"","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":"37 ","pages":"Article 100642"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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