61 research outputs found
The use of porcine small intestinal submucosa mesh (SURGISIS) as a pelvic sling in a man and a woman with previous pelvic surgery: two case reports
<p>Abstract</p> <p>Introduction</p> <p>Closing the pelvic peritoneum to prevent the small bowel dropping into the pelvis after surgery for locally recurrent rectal cancer is important to prevent adhesions deep in the pelvis or complications of adjuvant radiotherapy. Achieving this could be difficult because sufficient native tissue is unavailable; we report on the use of small intestine submucosa extra-cellular matrix mesh in the obliteration of the pelvic brim.</p> <p>Case presentation</p> <p>We describe two cases in which submucosa extra-cellular matrix mesh was used to obliterate the pelvic brim following resection of a recurrent rectal tumour; the first patient, a 78-year-old Caucasian man, presented with small bowel obstruction caused by adhesions to a recurrent rectal tumour. The second patient, an 84-year-old Caucasian woman, presented with vaginal discharge caused by an entero-vaginal fistula due to a recurrent rectal tumour.</p> <p>Conclusion</p> <p>We report on the use of submucosa extra-cellular matrix mesh as a pelvic sling in cases where primary closure of the pelvic peritoneum is unfeasible. Its use had no infective complications and added minimal morbidity to the postoperative period. This is an original case report that would be of interest to general and colorectal surgeons.</p
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
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
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
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
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