Pub Date : 2024-05-13DOI: 10.1016/j.slast.2024.100145
Haewon Byeon , Prashant GC , Shaikh Abdul Hannan , Faisal Yousef Alghayadh , Arsalan Muhammad Soomar , Mukesh Soni , Mohammed Wasim Bhatt
Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ability to increase accuracy by extending the network's depth and modifying weights through gradient descent. Traditional approaches, however, are hindered by issues such as gradient instability and delayed training. As a substitute, the Broad Learning (BL) system is introduced, which avoids gradient descent in favor of quick reconstruction by incremental learning. However, BL has trouble extracting complicated features from medical data, which makes it perform poorly in cases involving complex healthcare. We suggest ABL, which combines the effectiveness of BL with the noise reduction of Denoising Auto Encoder (AE), to address this. Robust feature extraction is an area in which the hybrid model shines, especially in intricate medical environments. Accuracy of up to 98.50 % is achieved by remarkable results from validation using a variety of datasets. The ability of ABL to quickly adapt through incremental learning suggests that it may be used to forecast diseases in complicated healthcare contexts with agility and accuracy.
{"title":"Deep neural network model for enhancing disease prediction using auto encoder based broad learning","authors":"Haewon Byeon , Prashant GC , Shaikh Abdul Hannan , Faisal Yousef Alghayadh , Arsalan Muhammad Soomar , Mukesh Soni , Mohammed Wasim Bhatt","doi":"10.1016/j.slast.2024.100145","DOIUrl":"10.1016/j.slast.2024.100145","url":null,"abstract":"<div><p>Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ability to increase accuracy by extending the network's depth and modifying weights through gradient descent. Traditional approaches, however, are hindered by issues such as gradient instability and delayed training. As a substitute, the Broad Learning (BL) system is introduced, which avoids gradient descent in favor of quick reconstruction by incremental learning. However, BL has trouble extracting complicated features from medical data, which makes it perform poorly in cases involving complex healthcare. We suggest ABL, which combines the effectiveness of BL with the noise reduction of Denoising Auto Encoder (AE), to address this. Robust feature extraction is an area in which the hybrid model shines, especially in intricate medical environments. Accuracy of up to 98.50 % is achieved by remarkable results from validation using a variety of datasets. The ability of ABL to quickly adapt through incremental learning suggests that it may be used to forecast diseases in complicated healthcare contexts with agility and accuracy.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S247263032400027X/pdfft?md5=5aae56812112d7e53aa401270769e044&pid=1-s2.0-S247263032400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946443","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}
Pub Date : 2024-05-11DOI: 10.1016/j.slast.2024.100143
Ting Qin, Sergio Ernesto Ruiz Hernandez, Jason Shiers, Matthew Crittall, Andrew Novak, Colin Sambrook Smith
Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.
{"title":"A decentralized solid compound storage facility managed by a centralized electronic platform at a growing drug discovery company","authors":"Ting Qin, Sergio Ernesto Ruiz Hernandez, Jason Shiers, Matthew Crittall, Andrew Novak, Colin Sambrook Smith","doi":"10.1016/j.slast.2024.100143","DOIUrl":"10.1016/j.slast.2024.100143","url":null,"abstract":"<div><p>Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000256/pdfft?md5=306a2af7cbf3495f98a6adfa941cbe44&pid=1-s2.0-S2472630324000256-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917481","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}
Pub Date : 2024-05-08DOI: 10.1016/j.slast.2024.100140
Mohammed Faisal , Abdullah Alharbi , Amnah Alhamadi , Sarah Almutairi , Shaikhah Alenezi , Anfal Alsulaili , Murad Khan , Faheem Khan
Alzheimer's is a progressive and debilitating neurological disorder characterized by cognitive decline, memory loss, and impaired daily functioning. It is an irreversible brain disease that destroys memory, thinking, and the ability to carry out daily activities. It poses significant challenges for patients and healthcare providers. Modern societies are trying to enhance the quality of people's lives, including Alzheimer's patients. In this study, we explored the potential of social robots to provide emotional support, improve cognitive function, and facilitate communication among Alzheimer's patients. This was achieved by initiating conversations on various topics such as family, relationships, and daily activities. This paper contributes to the literature by introducing a novel and well-organized framework for building an Alzheimer's care robot. Further, this study enriches the literature by introducing the Alzheimer Care Companion Robot (ACCR), designed to identify Alzheimer's patients. The ACCR initiates conversations in the native Arab-Kuwaiti dialect, displaying relevant memories through images and videos on its screen to assist in memory recall based on the individuals' life experiences. The proposed ACCR consists of 271 conversations belonging to three main categories: active, proactive, and graphical user interface (GUI) dialogs comprising 112 dialogs, 109 dialogs, and 50 dialogs for active, proactive, and GUI, respectively. The experimental result illustrated the success of the proposed solution.
{"title":"Robot-based solution for helping Alzheimer patients","authors":"Mohammed Faisal , Abdullah Alharbi , Amnah Alhamadi , Sarah Almutairi , Shaikhah Alenezi , Anfal Alsulaili , Murad Khan , Faheem Khan","doi":"10.1016/j.slast.2024.100140","DOIUrl":"10.1016/j.slast.2024.100140","url":null,"abstract":"<div><p>Alzheimer's is a progressive and debilitating neurological disorder characterized by cognitive decline, memory loss, and impaired daily functioning. It is an irreversible brain disease that destroys memory, thinking, and the ability to carry out daily activities. It poses significant challenges for patients and healthcare providers. Modern societies are trying to enhance the quality of people's lives, including Alzheimer's patients. In this study, we explored the potential of social robots to provide emotional support, improve cognitive function, and facilitate communication among Alzheimer's patients. This was achieved by initiating conversations on various topics such as family, relationships, and daily activities. This paper contributes to the literature by introducing a novel and well-organized framework for building an Alzheimer's care robot. Further, this study enriches the literature by introducing the Alzheimer Care Companion Robot (ACCR), designed to identify Alzheimer's patients. The ACCR initiates conversations in the native Arab-Kuwaiti dialect, displaying relevant memories through images and videos on its screen to assist in memory recall based on the individuals' life experiences. The proposed ACCR consists of 271 conversations belonging to three main categories: active, proactive, and graphical user interface (GUI) dialogs comprising 112 dialogs, 109 dialogs, and 50 dialogs for active, proactive, and GUI, respectively. The experimental result illustrated the success of the proposed solution.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000220/pdfft?md5=d4e634b6d96bac2291471d7a78ce705c&pid=1-s2.0-S2472630324000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905191","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}
Pub Date : 2024-05-03DOI: 10.1016/j.slast.2024.100135
Simon D. Rihm , Yong Ren Tan , Wilson Ang , Markus Hofmeister , Xinhong Deng , Michael Teguh Laksana , Hou Yee Quek , Jiaru Bai , Laura Pascazio , Sim Chun Siong , Jethro Akroyd , Sebastian Mosbach , Markus Kraft
Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability.
{"title":"The digital lab manager: Automating research support","authors":"Simon D. Rihm , Yong Ren Tan , Wilson Ang , Markus Hofmeister , Xinhong Deng , Michael Teguh Laksana , Hou Yee Quek , Jiaru Bai , Laura Pascazio , Sim Chun Siong , Jethro Akroyd , Sebastian Mosbach , Markus Kraft","doi":"10.1016/j.slast.2024.100135","DOIUrl":"10.1016/j.slast.2024.100135","url":null,"abstract":"<div><p>Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000177/pdfft?md5=442d6f9f67be7dd5616625f73e8202e6&pid=1-s2.0-S2472630324000177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140860144","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}
Pub Date : 2024-04-24DOI: 10.1016/j.slast.2024.100134
Shuo Jiang , Daniel Evans-Yamamoto , Dennis Bersenev , Sucheendra K. Palaniappan , Ayako Yachie-Kinoshita
Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols into intermediate representation formats which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69–100 % depending on the information content. In all tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at https://curation.taxila.io/ProtoCode/.
{"title":"ProtoCode: Leveraging large language models (LLMs) for automated generation of machine-readable PCR protocols from scientific publications","authors":"Shuo Jiang , Daniel Evans-Yamamoto , Dennis Bersenev , Sucheendra K. Palaniappan , Ayako Yachie-Kinoshita","doi":"10.1016/j.slast.2024.100134","DOIUrl":"10.1016/j.slast.2024.100134","url":null,"abstract":"<div><p>Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols into intermediate representation formats which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69–100 % depending on the information content. In all tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at <span>https://curation.taxila.io/ProtoCode/</span><svg><path></path></svg>.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000165/pdfft?md5=d470a9ecd45aada315855fbaeaba12b8&pid=1-s2.0-S2472630324000165-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140768345","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}
After haematology, urinalysis is the most common biological test performed in clinical settings. Hence, simplified workflow and automated analysis of urine elements are of absolute necessities. In the present work, a novel lab-on-chip cartridge (Gravity Sedimentation Cartridge) for the auto analysis of urine elements is developed. The GSC consists of a capillary chamber that uptakes a raw urine sample by capillary force and performs particles and cells enrichment within 5 min through a gravity sedimentation process for the microscopic examination. Centrifugation, which is necessary for enrichment in the conventional method, was circumvented in this approach. The AI100 device (Image based autoanalyzer) captures microscopic images from the cartridge at 40x magnification and uploads them into the cloud. Further, these images were auto-analyzed using an AI-based object detection model, which delivers the reports. These reports were available for expert review on a web-based platform that enables evidence-based tele reporting. A comparative analysis was carried out for various analytical parameters of the data generated through GSC (manual microscopy, tele reporting, and AI model) with the gold standard method. The presented approach makes it a viable product for automated urinalysis in point-of-care and large-scale settings.
{"title":"AI Driven Lab-on-Chip Cartridge for Automated Urinalysis","authors":"Avinash Sahu, Srinivasan Kandaswamy, Dhanu Vardhan Singh, Eshwarmurthy Thyagarajan, Arun Koushik Parthasarathy, Sharitha Naganna, Tathagato Rai Dastidar","doi":"10.1016/j.slast.2024.100137","DOIUrl":"10.1016/j.slast.2024.100137","url":null,"abstract":"<div><p>After haematology, urinalysis is the most common biological test performed in clinical settings. Hence, simplified workflow and automated analysis of urine elements are of absolute necessities. In the present work, a novel lab-on-chip cartridge (Gravity Sedimentation Cartridge) for the auto analysis of urine elements is developed. The GSC consists of a capillary chamber that uptakes a raw urine sample by capillary force and performs particles and cells enrichment within 5 min through a gravity sedimentation process for the microscopic examination. Centrifugation, which is necessary for enrichment in the conventional method, was circumvented in this approach. The AI100 device (Image based autoanalyzer) captures microscopic images from the cartridge at 40x magnification and uploads them into the cloud. Further, these images were auto-analyzed using an AI-based object detection model, which delivers the reports. These reports were available for expert review on a web-based platform that enables evidence-based tele reporting. A comparative analysis was carried out for various analytical parameters of the data generated through GSC (manual microscopy, tele reporting, and AI model) with the gold standard method. The presented approach makes it a viable product for automated urinalysis in point-of-care and large-scale settings.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000190/pdfft?md5=add5eea0974eb49916491074aa244764&pid=1-s2.0-S2472630324000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140791771","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}
Pub Date : 2024-04-04DOI: 10.1016/j.slast.2024.100132
Khin The Nu Aye , Joao N. Ferreira , Chayanit Chaweewannakorn , Glauco R. Souza
Background
The field of tissue engineering has remarkably progressed through the integration of nanotechnology and the widespread use of magnetic nanoparticles. These nanoparticles have resulted in innovative methods for three-dimensional (3D) cell culture platforms, including the generation of spheroids, organoids, and tissue-mimetic cultures, where they play a pivotal role. Notably, iron oxide nanoparticles and superparamagnetic iron oxide nanoparticles have emerged as indispensable tools for non-contact manipulation of cells within these 3D environments. The variety and modification of the physical and chemical properties of magnetic nanoparticles have profound impacts on cellular mechanisms, metabolic processes, and overall biological function. This review article focuses on the applications of magnetic nanoparticles, elucidating their advantages and potential pitfalls when integrated into 3D cell culture systems. This review aims to shed light on the transformative potential of magnetic nanoparticles in terms of tissue engineering and their capacity to improve the cultivation and manipulation of cells in 3D environments.
{"title":"Advances in the application of iron oxide nanoparticles (IONs and SPIONs) in three-dimensional cell culture systems","authors":"Khin The Nu Aye , Joao N. Ferreira , Chayanit Chaweewannakorn , Glauco R. Souza","doi":"10.1016/j.slast.2024.100132","DOIUrl":"https://doi.org/10.1016/j.slast.2024.100132","url":null,"abstract":"<div><h3>Background</h3><p>The field of tissue engineering has remarkably progressed through the integration of nanotechnology and the widespread use of magnetic nanoparticles. These nanoparticles have resulted in innovative methods for three-dimensional (3D) cell culture platforms, including the generation of spheroids, organoids, and tissue-mimetic cultures, where they play a pivotal role. Notably, iron oxide nanoparticles and superparamagnetic iron oxide nanoparticles have emerged as indispensable tools for non-contact manipulation of cells within these 3D environments. The variety and modification of the physical and chemical properties of magnetic nanoparticles have profound impacts on cellular mechanisms, metabolic processes, and overall biological function. This review article focuses on the applications of magnetic nanoparticles, elucidating their advantages and potential pitfalls when integrated into 3D cell culture systems. This review aims to shed light on the transformative potential of magnetic nanoparticles in terms of tissue engineering and their capacity to improve the cultivation and manipulation of cells in 3D environments.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000141/pdfft?md5=d1ec02e1b844f2e2681300eb004e2475&pid=1-s2.0-S2472630324000141-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540468","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}
Pub Date : 2024-04-01DOI: 10.1016/j.slast.2023.10.005
Patarasuda Chaisupa , R. Clay Wright
Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems—biosensor-based controllers—for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.
{"title":"State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering","authors":"Patarasuda Chaisupa , R. Clay Wright","doi":"10.1016/j.slast.2023.10.005","DOIUrl":"10.1016/j.slast.2023.10.005","url":null,"abstract":"<div><p>Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in <em>in vivo</em> biosensors and control systems—biosensor-based controllers—for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630323000638/pdfft?md5=14acbebb6d9a446683ec89b84f73a9b9&pid=1-s2.0-S2472630323000638-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429181","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}
Pub Date : 2024-04-01DOI: 10.1016/j.slast.2024.100121
Matthias Recktenwald , Evan Hutt , Leah Davis , James MacAulay , Nichole M. Daringer , Peter A. Galie , Mary M. Staehle , Sebastián L. Vega
A major aim in the field of synthetic biology is developing tools capable of responding to user-defined inputs by activating therapeutically relevant cellular functions. Gene transcription and regulation in response to external stimuli are some of the most powerful and versatile of these cellular functions being explored. Motivated by the success of chimeric antigen receptor (CAR) T-cell therapies, transmembrane receptor-based platforms have been embraced for their ability to sense extracellular ligands and to subsequently activate intracellular signal transduction. The integration of transmembrane receptors with transcriptional activation platforms has not yet achieved its full potential. Transient expression of plasmid DNA is often used to explore gene regulation platforms in vitro. However, applications capable of targeting therapeutically relevant endogenous or stably integrated genes are more clinically relevant. Gene regulation may allow for engineered cells to traffic into tissues of interest and secrete functional proteins into the extracellular space or to differentiate into functional cells. Transmembrane receptors that regulate transcription have the potential to revolutionize cell therapies in a myriad of applications, including cancer treatment and regenerative medicine. In this review, we will examine current engineering approaches to control transcription in mammalian cells with an emphasis on systems that can be selectively activated in response to extracellular signals. We will also speculate on the potential therapeutic applications of these technologies and examine promising approaches to expand their capabilities and tighten the control of gene regulation in cellular therapies.
合成生物学领域的一个主要目标是开发能够对用户定义的输入做出反应的工具,激活治疗相关的细胞功能。基因转录和调控对外部刺激的反应是目前正在探索的这些细胞功能中最强大、最多才多艺的一些功能。在嵌合抗原受体(CAR)T 细胞疗法取得成功的推动下,基于跨膜受体的平台因其感知细胞外配体并随后激活细胞内信号转导的能力而受到欢迎。跨膜受体与转录激活平台的整合尚未充分发挥其潜力。质粒 DNA 的瞬时表达通常用于探索体外基因调控平台。然而,能够靶向治疗相关的内源性基因或稳定整合基因的应用更具临床意义。基因调控可使工程细胞进入感兴趣的组织,并向细胞外空间分泌功能蛋白或分化成功能细胞。调节转录的跨膜受体有可能在癌症治疗和再生医学等众多应用领域彻底改变细胞疗法。在这篇综述中,我们将探讨目前控制哺乳动物细胞转录的工程学方法,重点关注可根据细胞外信号选择性激活的系统。我们还将对这些技术的潜在治疗应用进行推测,并研究有前景的方法,以扩展这些技术的功能,加强细胞疗法中的基因调控。
{"title":"Engineering transcriptional regulation for cell-based therapies","authors":"Matthias Recktenwald , Evan Hutt , Leah Davis , James MacAulay , Nichole M. Daringer , Peter A. Galie , Mary M. Staehle , Sebastián L. Vega","doi":"10.1016/j.slast.2024.100121","DOIUrl":"10.1016/j.slast.2024.100121","url":null,"abstract":"<div><p>A major aim in the field of synthetic biology is developing tools capable of responding to user-defined inputs by activating therapeutically relevant cellular functions. Gene transcription and regulation in response to external stimuli are some of the most powerful and versatile of these cellular functions being explored. Motivated by the success of chimeric antigen receptor (CAR) T-cell therapies, transmembrane receptor-based platforms have been embraced for their ability to sense extracellular ligands and to subsequently activate intracellular signal transduction. The integration of transmembrane receptors with transcriptional activation platforms has not yet achieved its full potential. Transient expression of plasmid DNA is often used to explore gene regulation platforms <em>in vitro</em>. However, applications capable of targeting therapeutically relevant endogenous or stably integrated genes are more clinically relevant. Gene regulation may allow for engineered cells to traffic into tissues of interest and secrete functional proteins into the extracellular space or to differentiate into functional cells. Transmembrane receptors that regulate transcription have the potential to revolutionize cell therapies in a myriad of applications, including cancer treatment and regenerative medicine. In this review, we will examine current engineering approaches to control transcription in mammalian cells with an emphasis on systems that can be selectively activated in response to extracellular signals. We will also speculate on the potential therapeutic applications of these technologies and examine promising approaches to expand their capabilities and tighten the control of gene regulation in cellular therapies.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000037/pdfft?md5=e94309e5136cfa2a1a8ef713d4f90797&pid=1-s2.0-S2472630324000037-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139716669","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}