Human tumour-associated cell adhesion protein MN/CA IX: identification of M75 epitope and of the region mediating cell adhesion

MN/CA IX is a cell surface protein, strongly associated with several types of human carcinomas. It exerts activity of carbonic anhydrase and capacity of binding to cell surface receptors. In the present work, we used affinity purified MN/CA IX protein to demonstrate that the cells adhere to immobilized MN/CA IX and that the monoclonal antibody M75 abrogates cell attachment to MN/CA IX. Using synthetic oligopeptides, we identified M75 epitope and located it in the proteoglycan domain, which contains a sixfold tandem repeat of six amino acids GEEDLP. From phage display library of random heptapeptides we identified and chemically synthesized those which compete for the epitope with M75 and inhibit adhesion of cells to MN/CA IX. These heptapeptides might serve as lead compounds for drug design. © 2000 Cancer Research Campaig

EU Agro Biogas Project

EU-AGRO-BIOGAS is a European Biogas initiative to improve the yield of agricultural biogas plants in Europe, to optimise biogas technology and processes and to improve the efficiency in all parts of the production chain from feedstock to biogas utilisation. Leading European research institutions and universities are cooperating with key industry partners in order to work towards a sustainable Europe. Fourteen partners from eight European countries are involved. EU-AGRO-BIOGAS aims at the development and optimisation of the entire value chain – to range from the production of raw materials, the production and refining of biogas to the utilisation of heat and electricity

Ariel – a window to the origin of life on early earth?

Is there life beyond Earth? An ideal research program would first ascertain how life on Earth began and then use this as a blueprint for its existence elsewhere. But the origin of life on Earth is still not understood, what then could be the way forward? Upcoming observations of terrestrial exoplanets provide a unique opportunity for answering this fundamental question through the study of other planetary systems. If we are able to see how physical and chemical environments similar to the early Earth evolve we open a window into our own Hadean eon, despite all information from this time being long lost from our planet’s geological record. A careful investigation of the chemistry expected on young exoplanets is therefore necessary, and the preparation of reference materials for spectroscopic observations is of paramount importance. In particular, the deduction of chemical markers identifying specific processes and features in exoplanetary environments, ideally “uniquely”. For instance, prebiotic feedstock molecules, in the form of aerosols and vapours, could be observed in transmission spectra in the near future whilst their surface deposits could be observed from reflectance spectra. The same detection methods also promise to identify particular intermediates of chemical and physical processes known to be prebiotically plausible. Is Ariel truly able to open a window to the past and answer questions concerning the origin of life on our planet and the universe? In this paper, we discuss aspects of prebiotic chemistry that will help in formulating future observational and data interpretation strategies for the Ariel mission. This paper is intended to open a discussion and motivate future detailed laboratory studies of prebiotic processes on young exoplanets and their chemical signatures

Characterization of Carbonic Anhydrase IX Interactome Reveals Proteins Assisting Its Nuclear Localization in Hypoxic Cells

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Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment