Pub Date : 2024-12-02DOI: 10.1088/2516-1091/ad9530
Poh Soo Lee, Kiran K Sriperumbudur, Jonathan Dawson, Ursula van Rienen, Revathi Appali
The role of bioelectricity in regulating various physiological processes has attracted increasing scientific interest in implementing exogenous electrical stimulations as a therapeutic approach. In particular, electrical stimuli are used clinically in pre-/post-surgery patient care for the musculoskeletal tissues. The reported potential of electric fields (EF) to regulate bone cell homeostasis and kineticsin vitrohas further provoked more studies in this field of research. Various customised apparatuses have been developed, and a range of parameters for the applied EFs have been investigatedin vitrowith bone cells or mesenchymal stem cells. Additionally, biomaterials with conductive or piezo-electric properties have been designed to complement the enhancing effects of the EF on bone regeneration. Despite much research, there remained a significant gap in knowledge due to the diverse range of EF parameters available. Mathematical models are built to facilitate further understanding and zero in on an effective range of EF parametersin silico. However, the diverse range of EF parameters, experimental conditions, and reported analytical output of different works of literature were reported to possess significant variance, making it challenging to accurately model the fieldin silico. This review categorises the existing experimental approaches and the parameters used to distinguish the potential variables that apply to mathematical modelling. Furthermore, we will discuss existing modelling approaches and models available in the literature. With this, we will concisely highlight the need to categorise EF parameters, osteogenic differentiation initiators and research output.
{"title":"Mathematical models on bone cell homeostasis and kinetics in the presence of electric fields: a review.","authors":"Poh Soo Lee, Kiran K Sriperumbudur, Jonathan Dawson, Ursula van Rienen, Revathi Appali","doi":"10.1088/2516-1091/ad9530","DOIUrl":"10.1088/2516-1091/ad9530","url":null,"abstract":"<p><p>The role of bioelectricity in regulating various physiological processes has attracted increasing scientific interest in implementing exogenous electrical stimulations as a therapeutic approach. In particular, electrical stimuli are used clinically in pre-/post-surgery patient care for the musculoskeletal tissues. The reported potential of electric fields (EF) to regulate bone cell homeostasis and kinetics<i>in vitro</i>has further provoked more studies in this field of research. Various customised apparatuses have been developed, and a range of parameters for the applied EFs have been investigated<i>in vitro</i>with bone cells or mesenchymal stem cells. Additionally, biomaterials with conductive or piezo-electric properties have been designed to complement the enhancing effects of the EF on bone regeneration. Despite much research, there remained a significant gap in knowledge due to the diverse range of EF parameters available. Mathematical models are built to facilitate further understanding and zero in on an effective range of EF parameters<i>in silico</i>. However, the diverse range of EF parameters, experimental conditions, and reported analytical output of different works of literature were reported to possess significant variance, making it challenging to accurately model the field<i>in silico</i>. This review categorises the existing experimental approaches and the parameters used to distinguish the potential variables that apply to mathematical modelling. Furthermore, we will discuss existing modelling approaches and models available in the literature. With this, we will concisely highlight the need to categorise EF parameters, osteogenic differentiation initiators and research output.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"7 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1088/2516-1091/ad879a
Ali H Foroughi, Caleb Valeri, Mir Jalil Razavi
The design and optimization of bone scaffolds are critical for the success of bone tissue engineering (BTE) applications. This review paper provides a comprehensive analysis of computational optimization methods for bone scaffold architecture, focusing on the balance between mechanical stability, biological compatibility, and manufacturability. Finite element method (FEM), computational fluid dynamics (CFD), and various optimization algorithms are discussed for their roles in simulating and refining scaffold designs. The integration of multiobjective optimization and topology optimization has been highlighted for developing scaffolds that meet the multifaceted requirements of BTE. Challenges such as the need for consideration of manufacturing constraints and the incorporation of degradation and bone regeneration models into the optimization process have been identified. The review underscores the potential of advanced computational tools and additive manufacturing techniques in evolving the field of BTE, aiming to improve patient outcomes in bone tissue regeneration. The reliability of current optimization methods is examined, with suggestions for incorporating non-deterministic approaches andin vivovalidations to enhance the practical application of optimized scaffolds. The review concludes with a call for further research into artificial intelligence-based methods to advance scaffold design and optimization.
{"title":"A review of computational optimization of bone scaffold architecture: methods, challenges, and perspectives.","authors":"Ali H Foroughi, Caleb Valeri, Mir Jalil Razavi","doi":"10.1088/2516-1091/ad879a","DOIUrl":"10.1088/2516-1091/ad879a","url":null,"abstract":"<p><p>The design and optimization of bone scaffolds are critical for the success of bone tissue engineering (BTE) applications. This review paper provides a comprehensive analysis of computational optimization methods for bone scaffold architecture, focusing on the balance between mechanical stability, biological compatibility, and manufacturability. Finite element method (FEM), computational fluid dynamics (CFD), and various optimization algorithms are discussed for their roles in simulating and refining scaffold designs. The integration of multiobjective optimization and topology optimization has been highlighted for developing scaffolds that meet the multifaceted requirements of BTE. Challenges such as the need for consideration of manufacturing constraints and the incorporation of degradation and bone regeneration models into the optimization process have been identified. The review underscores the potential of advanced computational tools and additive manufacturing techniques in evolving the field of BTE, aiming to improve patient outcomes in bone tissue regeneration. The reliability of current optimization methods is examined, with suggestions for incorporating non-deterministic approaches and<i>in vivo</i>validations to enhance the practical application of optimized scaffolds. The review concludes with a call for further research into artificial intelligence-based methods to advance scaffold design and optimization.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"7 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1088/2516-1091/ad8fe7
Phattalapol Lhaglham, Luksika Jiramonai, Xing-Jie Liang, Bingchuan Liu, Fangzhou Li
Schizophrenia is a complex and chronic psychiatric disorder that significantly impacts patients' quality of life. Ranking 12th among 310 diseases and injuries that result in disability, the number of patients suffering from schizophrenia continues to rise, emphasizing the urgent need for developing effective treatments. Despite the availability of effective antipsychotic drugs, over 80% of patients taking oral antipsychotics experience relapses, primarily caused by non-adherence as the high dosing frequency is required. In this review, we discuss about schizophrenia, its incidence, pathological causes, influencing factors, and the challenges of the current medications. Specifically, we explore nanocrystal technology and its application to paliperidone, making it one of the most successful long-acting antipsychotic drugs introduced to the market. We highlight the clinical advantages of paliperidone nanocrystals, including improved adherence, efficacy, long-term outcomes, patient satisfaction, safety, and cost-effectiveness. Additionally, we address the physicochemical factors influencing the drug's half-life, which crucially contribute to long-acting medications. Further studies on nanocrystal-based long-acting medications are crucial for enhancing their effectiveness and reliability. The successful development of paliperidone nanocrystals holds great promise as a significant approach for drug development, with potential applications for other chronic disease management.
{"title":"The development of paliperidone nanocrystals for the treatment of schizophrenia.","authors":"Phattalapol Lhaglham, Luksika Jiramonai, Xing-Jie Liang, Bingchuan Liu, Fangzhou Li","doi":"10.1088/2516-1091/ad8fe7","DOIUrl":"10.1088/2516-1091/ad8fe7","url":null,"abstract":"<p><p>Schizophrenia is a complex and chronic psychiatric disorder that significantly impacts patients' quality of life. Ranking 12th among 310 diseases and injuries that result in disability, the number of patients suffering from schizophrenia continues to rise, emphasizing the urgent need for developing effective treatments. Despite the availability of effective antipsychotic drugs, over 80% of patients taking oral antipsychotics experience relapses, primarily caused by non-adherence as the high dosing frequency is required. In this review, we discuss about schizophrenia, its incidence, pathological causes, influencing factors, and the challenges of the current medications. Specifically, we explore nanocrystal technology and its application to paliperidone, making it one of the most successful long-acting antipsychotic drugs introduced to the market. We highlight the clinical advantages of paliperidone nanocrystals, including improved adherence, efficacy, long-term outcomes, patient satisfaction, safety, and cost-effectiveness. Additionally, we address the physicochemical factors influencing the drug's half-life, which crucially contribute to long-acting medications. Further studies on nanocrystal-based long-acting medications are crucial for enhancing their effectiveness and reliability. The successful development of paliperidone nanocrystals holds great promise as a significant approach for drug development, with potential applications for other chronic disease management.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"7 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1088/2516-1091/ad811e
Luke E Hallum, Shaun L Cloherty
Retinal implantation of an electrode array is an emerging treatment for vision loss caused by outer-retinal degeneration. This article collects and analyses harms associated with the treatment reported in the peer-reviewed literature, thus enabling informed decision-making by patients, clinicians, researchers, engineers, and policymakers. We searched MEDLINE, Embase, and clinical trials registries for peer-reviewed journal articles reporting harms outcomes. We extracted data from articles including study design, definitions of 'serious adverse event', and timing of adverse events. We applied the McMaster tool to these articles to assess the risk of bias in harms assessment and reporting. Our searches returned 585 abstracts. We reviewed the full text of 59 articles describing 11 different devices. McMaster scores ranged from 3 to 12 (maximum 15; higher scores indicate less risk). We compiled a comprehensive list of all serious and non-serious adverse events associated with retinal implantation. Several harms were common across devices. Our meta-analysis showed that serious adverse events are log-uniformly distributed throughout follow-up. Improved reporting and further clinical studies are needed to develop a reliable safety profile of retinal implantation. Our findings will help guide the design, conduct, and reporting of future clinical trials of retinal implantation and other emerging treatments for vision loss. (PROSPERO registration: CRD42022308123.).
{"title":"Harms associated with retinal implantation of a stimulating electrode array to treat outer-retinal degeneration: a systematic review and meta-analysis of safety.","authors":"Luke E Hallum, Shaun L Cloherty","doi":"10.1088/2516-1091/ad811e","DOIUrl":"10.1088/2516-1091/ad811e","url":null,"abstract":"<p><p>Retinal implantation of an electrode array is an emerging treatment for vision loss caused by outer-retinal degeneration. This article collects and analyses harms associated with the treatment reported in the peer-reviewed literature, thus enabling informed decision-making by patients, clinicians, researchers, engineers, and policymakers. We searched MEDLINE, Embase, and clinical trials registries for peer-reviewed journal articles reporting harms outcomes. We extracted data from articles including study design, definitions of 'serious adverse event', and timing of adverse events. We applied the McMaster tool to these articles to assess the risk of bias in harms assessment and reporting. Our searches returned 585 abstracts. We reviewed the full text of 59 articles describing 11 different devices. McMaster scores ranged from 3 to 12 (maximum 15; higher scores indicate less risk). We compiled a comprehensive list of all serious and non-serious adverse events associated with retinal implantation. Several harms were common across devices. Our meta-analysis showed that serious adverse events are log-uniformly distributed throughout follow-up. Improved reporting and further clinical studies are needed to develop a reliable safety profile of retinal implantation. Our findings will help guide the design, conduct, and reporting of future clinical trials of retinal implantation and other emerging treatments for vision loss. (PROSPERO registration: CRD42022308123.).</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"7 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1088/2516-1091/ad819c
Franziska Jurosch, Nicolai Kröger, Sven Kolb, Fidan Mehmeti, Eimo Martens, Stefanie Speidel, Wolfgang Kellerer, Dirk Wilhelm, Jonas Fuchtmann
Technical setups in today's operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks. While these initial implementations of sensor fused ORs have shown to be promising, all current proposals lack a scalable networking backbone that serves the needs of future applications. We therefore discuss how the coming 6G standard's envisioned advancements can provide a flexible and intelligent platform to enable the fully integrated OR of the future.
{"title":"6G networks for the operating room of the future.","authors":"Franziska Jurosch, Nicolai Kröger, Sven Kolb, Fidan Mehmeti, Eimo Martens, Stefanie Speidel, Wolfgang Kellerer, Dirk Wilhelm, Jonas Fuchtmann","doi":"10.1088/2516-1091/ad819c","DOIUrl":"10.1088/2516-1091/ad819c","url":null,"abstract":"<p><p>Technical setups in today's operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks. While these initial implementations of sensor fused ORs have shown to be promising, all current proposals lack a scalable networking backbone that serves the needs of future applications. We therefore discuss how the coming 6G standard's envisioned advancements can provide a flexible and intelligent platform to enable the fully integrated OR of the future.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"6 4","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1088/2516-1091/ad8530
Vishnu K N, Cota Navin Gupta
This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.
{"title":"Systematic review of experimental paradigms and deep neural networks for electroencephalography-based cognitive workload detection.","authors":"Vishnu K N, Cota Navin Gupta","doi":"10.1088/2516-1091/ad8530","DOIUrl":"10.1088/2516-1091/ad8530","url":null,"abstract":"<p><p>This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"6 4","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1088/2516-1091/ad776b
Joana Cristo Santos, Miriam Seoane Santos, Pedro Henriques Abreu
Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.
{"title":"Enhancing mammography: a comprehensive review of computer methods for improving image quality.","authors":"Joana Cristo Santos, Miriam Seoane Santos, Pedro Henriques Abreu","doi":"10.1088/2516-1091/ad776b","DOIUrl":"10.1088/2516-1091/ad776b","url":null,"abstract":"<p><p>Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"6 4","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1088/2516-1091/ad0b19
Martin Niemiec, Kyungjin Kim
Abstract While the importance of thin form factor and mechanical tissue biocompatibility has been made clear for next generation bioelectronic implants, material systems meeting these criteria still have not demonstrated sufficient long-term durability. This review provides an update on the materials used in modern bioelectronic implants as substrates and protective encapsulations, with a particular focus on flexible and conformable devices. We review how thin film encapsulations are known to fail due to mechanical stresses and environmental surroundings under processing and operating conditions. This information is then reflected in recommending state-of-the-art encapsulation strategies for designing mechanically reliable thin film bioelectronic interfaces. Finally, we assess the methods used to evaluate novel bioelectronic implant devices and the current state of their longevity based on encapsulation and substrate materials. We also provide insights for future testing to engineer long-lived bioelectronic implants more effectively and to make implantable bioelectronics a viable option for chronic diseases in accordance with each patient’s therapeutical timescale.
{"title":"Lifetime engineering of bioelectronic implants with mechanically reliable thin film encapsulations","authors":"Martin Niemiec, Kyungjin Kim","doi":"10.1088/2516-1091/ad0b19","DOIUrl":"https://doi.org/10.1088/2516-1091/ad0b19","url":null,"abstract":"Abstract While the importance of thin form factor and mechanical tissue biocompatibility has been made clear for next generation bioelectronic implants, material systems meeting these criteria still have not demonstrated sufficient long-term durability. This review provides an update on the materials used in modern bioelectronic implants as substrates and protective encapsulations, with a particular focus on flexible and conformable devices. We review how thin film encapsulations are known to fail due to mechanical stresses and environmental surroundings under processing and operating conditions. This information is then reflected in recommending state-of-the-art encapsulation strategies for designing mechanically reliable thin film bioelectronic interfaces. Finally, we assess the methods used to evaluate novel bioelectronic implant devices and the current state of their longevity based on encapsulation and substrate materials. We also provide insights for future testing to engineer long-lived bioelectronic implants more effectively and to make implantable bioelectronics a viable option for chronic diseases in accordance with each patient’s therapeutical timescale.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135192315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1088/2516-1091/ad04c0
Aldo Badano, MIguel Lago, Elena Sizikova, Jana Delfino, Shuyue Guan, Mark A Anastasio, Berkman Sahiner
Abstract Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy status and for generating diseased cases and discuss briefly the role of augmentation methods. Finally, we discuss approaches for sampling digital cohorts and understanding the trade-offs and potential for study bias associated with selecting specific patient distributions.
{"title":"The stochastic digital human is now enrolling for in silico imaging trials – Methods and tools for generating digital cohorts","authors":"Aldo Badano, MIguel Lago, Elena Sizikova, Jana Delfino, Shuyue Guan, Mark A Anastasio, Berkman Sahiner","doi":"10.1088/2516-1091/ad04c0","DOIUrl":"https://doi.org/10.1088/2516-1091/ad04c0","url":null,"abstract":"Abstract Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy status and for generating diseased cases and discuss briefly the role of augmentation methods. Finally, we discuss approaches for sampling digital cohorts and understanding the trade-offs and potential for study bias associated with selecting specific patient distributions.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-11DOI: 10.1088/2516-1091/acef84
Z. Miri, S. Faré, Qianli Ma, H. Haugen
Polyurethanes (PUs) have properties that make them promising in biomedical applications. PU is recognized as one of the main families of blood and biocompatible materials. PU plays a vital role in the design of medical devices in various medical fields. The structure of PU contains two segments: soft and hard. Its elastomeric feature is due to its soft segment, and its excellent and high mechanical property is because of its hard segment. It is possible to achieve specific desirable and targeted properties by changing the soft and hard chemical structures and the ratio between them. The many properties of PU each draw the attention of different medical fields. This work reviews PU highlighted properties, such as biodegradability, biostability, shape memory, and improved antibacterial activity. Also, because PU has a variety of applications, this review restricts its focus to PU’s prominent applications in tissue engineering, cardiovascular medicine, drug delivery, and wound healing. In addition, it contains a brief review of PU’s applications in biosensors and oral administration.
{"title":"Updates on polyurethane and its multifunctional applications in biomedical engineering","authors":"Z. Miri, S. Faré, Qianli Ma, H. Haugen","doi":"10.1088/2516-1091/acef84","DOIUrl":"https://doi.org/10.1088/2516-1091/acef84","url":null,"abstract":"Polyurethanes (PUs) have properties that make them promising in biomedical applications. PU is recognized as one of the main families of blood and biocompatible materials. PU plays a vital role in the design of medical devices in various medical fields. The structure of PU contains two segments: soft and hard. Its elastomeric feature is due to its soft segment, and its excellent and high mechanical property is because of its hard segment. It is possible to achieve specific desirable and targeted properties by changing the soft and hard chemical structures and the ratio between them. The many properties of PU each draw the attention of different medical fields. This work reviews PU highlighted properties, such as biodegradability, biostability, shape memory, and improved antibacterial activity. Also, because PU has a variety of applications, this review restricts its focus to PU’s prominent applications in tissue engineering, cardiovascular medicine, drug delivery, and wound healing. In addition, it contains a brief review of PU’s applications in biosensors and oral administration.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44625083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}