3D printed tissue engineered model for bone invasion of oral cancer

Recent advances in three-dimensional printing technology have led to a rapid expansion of its applications in tissue engineering. The present study was designed to develop and characterize an in vitro multi-layered human alveolar bone, based on a 3D printed scaffold, combined with tissue engineered oral mucosal model. The objective was to incorporate oral squamous cell carcinoma (OSCC) cell line spheroids to the 3D model at different anatomical levels to represent different stages of oral cancer. Histological evaluation of the 3D tissue model revealed a tri-layered structure consisting of distinct epithelial, connective tissue, and bone layers; replicating normal oral tissue architecture. The mucosal part showed a well-differentiated stratified oral squamous epithelium similar to that of the native tissue counterpart, as demonstrated by immunohistochemistry for cytokeratin 13 and 14. Histological assessment of the cancerous models demonstrated OSCC spheroids at three depths including supra-epithelial level, sub-epithelial level, and deep in the connective tissue-bone interface. The 3D tissue engineered composite model closely simulated the native oral hard and soft tissues and has the potential to be used as a valuable in vitro model for the investigation of bone invasion of oral cancer and for the evaluation of novel diagnostic or therapeutic approaches to manage OSCC in the future

MicroMotility: State of the art, recent accomplishments and perspectives on the mathematical modeling of bio-motility at microscopic scales

Mathematical modeling and quantitative study of biological motility (in particular, of motility at microscopic scales) is producing new biophysical insight and is offering opportunities for new discoveries at the level of both fundamental science and technology. These range from the explanation of how complex behavior at the level of a single organism emerges from body architecture, to the understanding of collective phenomena in groups of organisms and tissues, and of how these forms of swarm intelligence can be controlled and harnessed in engineering applications, to the elucidation of processes of fundamental biological relevance at the cellular and sub-cellular level. In this paper, some of the most exciting new developments in the fields of locomotion of unicellular organisms, of soft adhesive locomotion across scales, of the study of pore translocation properties of knotted DNA, of the development of synthetic active solid sheets, of the mechanics of the unjamming transition in dense cell collectives, of the mechanics of cell sheet folding in volvocalean algae, and of the self-propulsion of topological defects in active matter are discussed. For each of these topics, we provide a brief state of the art, an example of recent achievements, and some directions for future research

Maintaining safe lung cancer surgery during the COVID-19 pandemic in a global city.

Background: SARS-CoV-2 has challenged health service provision worldwide. This work evaluates safe surgical pathways and standard operating procedures implemented in the high volume, global city of London during the first wave of SARS-CoV-2 infection. We also assess the safety of minimally invasive surgery(MIS) for anatomical lung resection. Methods: This multicentre cohort study was conducted across all London thoracic surgical units, covering a catchment area of approximately 14.8 Million. A Pan-London Collaborative was created for data sharing and dissemination of protocols. All patients undergoing anatomical lung resection 1st March-1st June 2020 were included. Primary outcomes were SARS-CoV-2 infection, access to minimally invasive surgery, post-operative complication, length of intensive care and hospital stay (LOS), and death during follow up. Findings: 352 patients underwent anatomical lung resection with a median age of 69 (IQR: 35-86) years. Self-isolation and pre-operative screening were implemented following the UK national lockdown. Pre-operative SARS-CoV-2 swabs were performed in 63.1% and CT imaging in 54.8%. 61.7% of cases were performed minimally invasively (MIS), compared to 59.9% pre pandemic. Median LOS was 6 days with a 30-day survival of 98.3% (comparable to a median LOS of 6 days and 30-day survival of 98.4% pre-pandemic). Significant complications developed in 7.3% of patients (Clavien-Dindo Grade 3-4) and 12 there were re-admissions(3.4%). Seven patients(2.0%) were diagnosed with SARS-CoV-2 infection, two of whom died (28.5%). Interpretation: SARS-CoV-2 infection significantly increases morbidity and mortality in patients undergoing elective anatomical pulmonary resection. However, surgery can be safely undertaken via open and MIS approaches at the peak of a viral pandemic if precautionary measures are implemented. High volume surgery should continue during further viral peaks to minimise health service burden and potential harm to cancer patients. Funding: This work did not receive funding

Zinc Coordination Is Required for and Regulates Transcription Activation by Epstein-Barr Nuclear Antigen 1

Epstein-Barr Nuclear Antigen 1 (EBNA1) is essential for Epstein-Barr virus to immortalize naïve B-cells. Upon binding a cluster of 20 cognate binding-sites termed the family of repeats, EBNA1 transactivates promoters for EBV genes that are required for immortalization. A small domain, termed UR1, that is 25 amino-acids in length, has been identified previously as essential for EBNA1 to activate transcription. In this study, we have elucidated how UR1 contributes to EBNA1's ability to transactivate. We show that zinc is necessary for EBNA1 to activate transcription, and that UR1 coordinates zinc through a pair of essential cysteines contained within it. UR1 dimerizes upon coordinating zinc, indicating that EBNA1 contains a second dimerization interface in its amino-terminus. There is a strong correlation between UR1-mediated dimerization and EBNA1's ability to transactivate cooperatively. Point mutants of EBNA1 that disrupt zinc coordination also prevent self-association, and do not activate transcription cooperatively. Further, we demonstrate that UR1 acts as a molecular sensor that regulates the ability of EBNA1 to activate transcription in response to changes in redox and oxygen partial pressure (pO2). Mild oxidative stress mimicking such environmental changes decreases EBNA1-dependent transcription in a lymphoblastoid cell-line. Coincident with a reduction in EBNA1-dependent transcription, reductions are observed in EBNA2 and LMP1 protein levels. Although these changes do not affect LCL survival, treated cells accumulate in G0/G1. These findings are discussed in the context of EBV latency in body compartments that differ strikingly in their pO2 and redox potential

Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors

The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods

Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors

1089-778X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted

Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

© 2016 IEEE. In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods