Towards a Diagnostic Tool for Diagnosing Joint Pathologies: Supervised Learning of Acoustic Emission Signals

Acoustic emission (AE) testing detects the onset and progression of mechanical flaws. AE as a diagnostic tool is gaining traction for providing a tribological assessment of human joints and orthopaedic implants. There is potential for using AE as a tool for diagnosing joint pathologies such as osteoarthritis and implant failure, but the signal analysis must differentiate between wear mechanisms—a challenging problem! In this study, we use supervised learning to classify AE signals from adhesive and abrasive wear under controlled joint conditions. Uncorrelated AE features were derived using principal component analysis and classified using three methods, logistic regression, k-nearest neighbours (KNN), and back propagation (BP) neural network. The BP network performed best, with a classification accuracy of 98%, representing an exciting development for the clustering and supervised classification of AE signals as a bio-tribological diagnostic tool.

All roads lead to Rome, but someone are safer

Since the first in-human implantation, trans-catheter aortic valve replacement (TAVR) has shown an exciting development in both technical and technological terms, becoming the standard of care for many patients, even not only inoperable ones. Although trans-femoral (TF) access has the scepter of first-line route for TAVR, in some cases, this access is not feasible, so several alternative routes were introduces over time. The network meta-analysis by Hameed et al has the great merit to provide a comprehensive picture. Hence, through either direct and indirect comparison, the authors confirmed as TF is the gold standard as access, followed by trans-carotid and trans-subclavian. Conversely, trans-thoracic (trans apical and trans-aortic) routes are the least safe and should be reserved only to sporadic cases.

Precision Medicine Trials in Retinal Degenerations

The beginning of the twenty-first century was marked by the innovative use of pharmacochemical interventions, which have since expanded to include gene-based molecular therapies. For years, treatment has focused on tackling the pathophysiology of monogenic orphan diseases, and one of the first applications of these novel genome editing technologies was the treatment of rare inherited retinal dystrophies. In this review, we present recent, ongoing, and future gene therapy–based treatment trials for choroideremia, X-linked retinitis pigmentosa, Stargardt disease, and age-related macular degeneration. As these trials pave the way toward halting the progression of such devastating diseases, we will begin to see the exciting development of newer, cutting-edge strategies including base editing and prime editing, ushering in a new era of precision medicine.

Cancer-Associated Fibroblasts in Oral Cancer: A Current Perspective on Function and Potential for Therapeutic Targeting

The role of the tumour microenvironement (TME) in cancer progression and resistance to therapies is now widely recognized. The most prominent non-immune cell type in the microenvironment of oral cancer (OSCC) is cancer-associated fibroblasts (CAF). Although CAF are a poorly characterised and heterogenous cell population, those with an “activated” myofibroblastic phenotype have been shown to support OSCC progression, promoting growth, invasion and numerous other “hallmarks of malignancy.” CAF also confer broad resistance to different types of therapy, including chemo/radiotherapy and EGFR inhibitors; consistent with this, CAF-rich OSCC are associated with poor prognosis. In recent years, much CAF research has focused on their immunological role in the tumour microenvironment, showing that CAF shield tumours from immune attack through multiple mechanisms, and particularly on their role in promoting resistance to anti-PD-1/PD-L1 checkpoint inhibitors, an exciting development for the treatment of recurrent/metastatic oral cancer, but which fails in most patients. This review summarises our current understanding of CAF subtypes and function in OSCC and discusses the potential for targeting these cells therapeutically.

How To Ensure Senior Management Approval For Your Next Regulated IT System

Senior Leadership has to make hard decisions about investments. We see headlines about mergers, acquisitions, partnerships, opting in on products that have exciting development or preclinical results. The headlines may also include significant investment in internal infrastructure. Hundreds of millions of dollars may be directed to building a new facility to manage the increased demand of an existing product or to prepare for a clinical candidate showing a high probability of success.

How To Ensure Senior Management Approval For Your Next Regulated IT System

Senior Leadership has to make hard decisions about investments. We see headlines about mergers, acquisitions, partnerships, opting in on products that have exciting development or preclinical results. The headlines may also include significant investment in internal infrastructure. Hundreds of millions of dollars may be directed to building a new...

Estimation of the Bid-Ask Prices for the European Discrete Geometric Average and Arithmetic Average Asian Options

Conic finance is a new and exciting development in quantitative finance, which is widely applied to several topics in finance. The theory of conic finance extends the law of one price to the law of two prices, which yields closed forms for bid-ask prices of European options. In this paper, within the framework of conic finance, we derive effective, explicit, approximate formulas to estimate the bid-ask prices for the European discrete geometric average and arithmetic average Asian options. Finally, we give two examples to demonstrate and validate that the approximate closed-form solutions are efficient and accurate.

Articulating the canon: The sociology of medical education from 1980 to 2000

An exciting development in the sociology of medical education has been its recent return as a distinct scholarly conversation in medical sociology. During the 1980s and 1990s, the sociology of medical education, an historically prominent subfield in sociology, seemed to disappear from the scholarly conversation despite ongoing development in this area. In this narrative review I describe this “missing period” of sociology of medical education, discussing complementary explanations for why it receded and describing what research activity did take place during those decades. In reviewing this work, I argue that articulating theoretical advances made within sociology of medical education research during these decades allows us to link foundational research from the 1950s and 1960s with the renaissance of this subfield in the early 2000s. Fundamentally, understanding the intellectual history and development of this subfield supports a broader movement to understand the import of studies of medical training for exploring questions of interest in general sociology.

Categorization of Text Documents Based on Fuzzy Attitude and Neural Networks

With the exciting development of Internet and the increasing use of it for providing or acquiring information, we are witnessing an enormous volume of text documents and online images. This is considered as information redundancy, which is one of the prominent features of modern day life. In this regard, fast and accurate access to important and favorite resources is one of the concerns of users of these enormous resources of information. Today, what is of great importance is the lack of methods to find and optimally exploit the information available, rather than the shortage or lack of information. The problem with big image data, the effort to eliminate noise and visual disturbances such as parameters from inappropriate light sources, the inadequacy of color combinations, and many other factors in received images, are very important issues in working on images and processing them. In this regard, the method of classification of the texts from the images using a fuzzy system and neural network based algorithm is suggested. In this method, the location of the fuzzy system is introduced at the begin and end of the neural network synchronized with fuzzification operation and fuzzy inversion. In fact, the main idea in this article is to eliminate or minimize noise in classifying the documents with high inaccuracy.