Die Bedeutung der Expression von CD44 und dessen Splice-Varianten CD44v5 und CD44v6 in hypertrophiertem Ligamentum flavum für die Ausprägung einer lumbalen Spinalkanalstenose

Einleitung: Die lumbale Spinalkanalstenose (LSS) stellt die häufigste Indikation für operative Eingriffe im Bereich der Lendenwirbelsäule dar. Die lumbale Einengung des Spinalkanals geht zum überwiegenden Teil auf eine Hypertrophie des Ligamentum flavum (LF) zurück. Trotz zahlreicher vorangegangener Studien sind die zugrundeliegenden pathophysiologischen Mechanismen der Hypertrophie in ihrer Komplexität nach wie vor unbekannt. Das Ziel dieser experimentellen Studie besteht in der immunhistochemischen Detektion einer eventuell vorliegenden Überexpression von CD44 und seiner Splice-Varianten CD44v5 und CD44v6 in hypertrophiertem Ligamentum flavum bei Patienten mit LSS. Material und Methoden: 38 Patienten mit LSS wurden im Rahmen einer opera-tiven spinalen Dekompression Proben des LF entnommen. 12 weitere LF-Proben wurden Patienten mit Discusprolaps ohne magnetresonanztomogra-phisch nachweisbare degenerative LSS im Zuge einer Nukleotomie entnom-men. Die Proben wurden in Paraffin eingebettet, geschnitten und mittels Anti-körpern gegen CD44, CD44v5 und CD44v6 mit DAB gefärbt. Zusätzlich wurde in präoperativ angefertigten T1-gewichteten MRT-Aufnahmen dieser Patienten auf Höhe L4/5 die LF-Dicke sowie die minimal Cross-Sectional Area (mCSA) des Duralsacks bestimmt. Ergebnisse: Flavum-Dicke, CD44- und CD44v5-Expression waren auf Seiten der LSS-Gruppe signifikant erhöht, die mCSA signifikant verringert. Bezüglich der CD44v6-Expression ließ sich kein Unterschied zwischen beiden Gruppen nachweisen. Es konnte eine Korrelation zwischen LF-Dicke, mCSA, CD44- und CD44v5-Expression nachgewiesen werden. Schlussfolgerung: Die Entstehung einer LSS beruht auf einer Hypertrophie des LF und ist mit einer Überexpression von CD44 und CD44v5 assoziiert. CD44v6 ist daran nicht beteiligt. Die Studie zeigt einen möglichen molekularen Mechanismus der strukturellen Veränderungen des LF auf, die nicht durch morphologische Merkmale allein erklärbar sind

Environmental impacts of a digital health and well-being service in elderly living schemes

Over the past decade, digitalization and digital technologies (DTs) have undergone rapid evolution, transforming how goods and services are produced and consumed in modern societies. Health and well-being sectors have embraced this digital revolution. Besides the economic and social benefits, digitalization can significantly enhance patient diagnostics and prognostics while improving overall service efficiency. To ensure long-term sustainability, it is important to assess and reduce the environmental impacts of digital services. This article examines the life cycle impacts of a digital service implemented in three elderly living schemes (ELSs) located in the United Kingdom (UK). The digital service consists of six electronic devices (EDs) that enable communication between residents, visitors, staff, and offsite monitoring (OM). The equipment is connected using Power over Ethernet (PoE) technology, which includes smart network switch and uninterruptable power supply units. The digital service's global warming potential (GWP) was estimated at 718–741 kg CO2 eq./resident for two of the ELSs and 1509 kg CO2 eq./resident for a third ELS, considering a service period of 20 years. The reason for the significant difference is the greater use of air conditioner (A/C) units to cool down server rooms and fewer residents in the third scheme. The consumption of electricity was found to be the main contributor to most of the environmental impacts. However, in certain categories such as mineral resource scarcity, freshwater eutrophication, and freshwater and marine ecotoxicity potentials, printed circuit boards (PCBs) were the main contributors. A sensitivity analysis considering different national electricity mixes demonstrated that the French electricity grid promoted the reduction in 14 impact categories, whereas the German, Italian, Spanish and Japanese grids increased on average impacts on most categories. Another sensitivity analysis demonstrates that reducing A/C unit running time by 28% resulted in an average impact reduction of 5.5%, becoming equivalent to the results obtained for the French electricity grid. Finally, extending the expected lifespan of electronic equipment by 20% yielded the highest average decrease in environmental impacts (8.1%). While digitalization has the potential to enhance patient healthcare and reduce costs, it is crucial to carefully assess its environmental impacts and implement mitigation strategies to ensure sustainable development in the healthcare sector.<br/

Measuring the Latency of Graphics Frameworks on X11 Based Systems

Latency is an intrinsic property of all human-computer systems. As it can affect user experience and performance, it should be kept as low as possible for real-time applications. To identify the source of latency, measuring partial latencies is necessary. We present a new method for measuring the latency of graphics frameworks on X11-based systems. Our tool measures the time between an input event arriving at the kernel until a pixel is updated in graphics memory. In a systematic evaluation with 36 test applications, we found that our method delivers consistent results for most tested frameworks, and does not add a significant amount of additional end-to-end latency. Even though further investigation is required to explain inconsistencies with Qt-based frameworks, our method measures the latency of graphics frameworks reliably and accurately in all other cases

Sketching with Hardware: A Course for Teaching Interactive Hardware Prototyping to Computer Science Students

Sketching with Hardware is an undergraduate university course with the goal to teach students how to build prototypes for tangible user interfaces. Goals of this course are to create awareness for tangible interaction among students and prepare them to realize advanced projects like bachelor's and master's theses in this field. In this paper, authors share their experience teaching the concepts of tangible interaction, electronics and prototyping to computer science students in a two week course. The course's content, structure and goals are explained, and needed material and infrastructure are described. The long-term effect of the course has been evaluated by conducting a survey among former participants

Methods and tools for mining the transcriptomic landscape of human tissue and disease

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from PDF version of thesis.Includes bibliographical references (p. 343-356).Although there are a variety of high-throughput technologies used to perform biological experiments, DNA microarrays have become a standard tool in the modern biologist's arsenal. Microarray experiments provide measurements of thousands of genes simultaneously, and offer a snapshot view of transcriptomic activity. With the rapid growth of public availability of transcriptomic data, there is increasing recognition that large sets of such data can be mined to better understand disease states and mechanisms. Unfortunately, several challenges arise when attempting to perform such large-scale analyses. For instance, public repositories to which the data is being submitted to were designed around the simple task of storage rather than that of data mining. As such, the seemingly simple task of obtaining all data relating to a particular disease becomes an arduous task. Furthermore, prior gene expression analyses, both large and small, have been dichotomous in nature, in which phenotypes are compared using clearly defined controls. Such approaches may require arbitrary decisions about what are considered "normal" phenotypes, and what each phenotype should be compared to. Addressing these issues, we introduce methods for creating a large curated gene expression database geared towards data mining, and explore methods for efficiently expanding this database using active learning. Leveraging our curated expression database, we adopt a holistic approach in which we characterize phenotypes in the context of a myriad of tissues and diseases. We introduce scalable methods that associate expression patterns to phenotypes in order to assign phenotype labels to new expression samples and to select phenotypically meaningful gene signatures. By using a nonparametric statistical approach, we identify signatures that are more precise than those from existing approaches and accurately reveal biological processes that are hidden in case vs. control studies. We conclude the work by exploring the applicability of the heterogeneous expression database in analyzing clinical drugs for the purpose of drug repurposing.by Patrick Raphael Schmid.Ph.D

Large scale disease prediction

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (leaves 69-73).The objective of this thesis is to present the foundation of an automated large-scale disease prediction system. Unlike previous work that has typically focused on a small self-contained dataset, we explore the possibility of combining a large amount of heterogeneous data to perform gene selection and phenotype classification. First, a subset of publicly available microarray datasets was downloaded from the NCBI Gene Expression Omnibus (GEO) [18, 5]. This data was then automatically tagged with Unified Medical Language System (UMLS) concepts [7]. Using the UMLS tags, datasets related to several phenotypes were obtained and gene selection was performed on the expression values of this tagged microarray data. Using the tagged datasets and the list of genes selected in the previous step, classifiers that can predict whether or not a new sample is also associated with a given UMLS concept based solely on the expression data were created. The results from this work show that it is possible to combine a large heterogeneous set of microarray datasets for both gene selection and phenotype classification, and thus lays the foundation for the possibility of automatic classification of disease types based on gene expression data in a clinical setting.by Patrick R. Schmid.S.M

Des opportunités de diversification existent dans les grandes cultures

En bio, la diversité des cultures de la rotation joue un rôle essentiel. Pour accompagner le développement de nouvelles cultures ou variétés, le FiBL met en place des dispositifs expérimentaux. Tour d’horizon des essais 2020