The bottom-up synthesis of cell-like entities or from inanimate molecules and materials is one of the grand challenges of our time. In the past decade, researchers in the emerging field of have developed different protocell models and engineered them to mimic one or more abilities of biological cells, such as information transcription and translation, adhesion, and enzyme-mediated metabolism. Whilst thus far efforts have focused on increasing the biochemical complexity of individual protocells, an emerging challenge in bottom-up synthetic biology is the development of networks of communicating synthetic protocells. The possibility of engineering multi-protocellular systems capable of sending and receiving chemical signals to trigger individual or collective programmed cell-like behaviours or for communicating with living cells and tissues would lead to major scientific breakthroughs with important applications in biotechnology, tissue engineering and regenerative medicine. This mini-review will discuss this new, emerging area of bottom-up synthetic biology and will introduce three types of bioinspired networks of communicating synthetic protocells that have recently emerged.
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http://dx.doi.org/10.3389/fmolb.2021.804717 | DOI Listing |
Death Stud
January 2025
School of Health Sciences, The University of Manchester, Manchester, UK.
Following a perinatal death, parents can experience mental health difficulties and social stigma around the loss that can lead to increased feelings of isolation. This meta-synthesis aimed to explore partners' experiences of perinatal death following miscarriage, stillbirth and neonatal death. A search of six electronic databases resulted in the inclusion of 18 studies involving over 300 fathers.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
Background: Unwarranted clinical variation presents a major challenge in contemporary healthcare, indicating potential inequalities and inefficiencies, and unrealised potential for better outcomes. Despite an increasing focus on unwarranted clinical variation, and consideration of efforts to address this challenge, evidence-based strategies which achieve this are limited. Audit and feedback of healthcare processes (process auditing) and clinician engagement are important tools which may help to reduce unwarranted clinical variation, however their application in maternity care is yet to be thoroughly explored.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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