Activation of Retinoid X Receptor increases dopamine cell survival in models for Parkinson's disease

<p>Abstract</p> <p>Background</p> <p>Parkinson's disease (PD) is caused by degeneration of dopamine (DA) neurons in the ventral midbrain (vMB) and results in severely disturbed regulation of movement. The disease inflicts considerable suffering for the affected and their families. Today, the opportunities for pharmacological treatment are meager and new technologies are needed. Previous studies have indicated that activation of the nuclear receptor Retinoid X Receptor (RXR) provides trophic support for DA neurons. Detailed investigations of these neurotrophic effects have been hampered by the lack of readily available DA neurons <it>in vitro</it>. The aim of this study was to further describe the potential neurotrophic actions of RXR ligands and, for this and future purposes, develop a suitable <it>in vitro</it>-platform using mouse embryonic stem cells (mESCs).</p> <p>Results</p> <p>We studied the potential neurotrophic effects of the RXR ligand LG100268 (LG268) and the RXR-Nurr1 ligand XCT0139508 (XCT) in neuronal cultures derived from rat primary vMB and mESCs. RXR ligands protect DA neurons from stress, such as that induced by the PD-modeling toxin 6-hydroxy dopamine (6-OHDA) and hypoxia, but not from stress induced by oxidative hydrogen peroxide (H₂O₂) or the excitotoxic agent kainic acid (KA). The neurotrophic effect is selective for DA neurons. DA neurons from rat primary vMB and mESCs behaved similarly, but the mESC-derived cultures contained a much higher fraction of DA cells and thus provided more accessible experimental conditions.</p> <p>Conclusions</p> <p>RXR ligands rescue DA neurons from degeneration caused by the PD simulating 6-OHDA as well as hypoxia. Thus, RXR is a novel promising target for PD research. mESC-derived DA cells provide a valid and accessible <it>in vitro</it>-platform for studying PD inducing toxins and potential trophic agents.</p

The Notion of Presence in a Telematic Cross-Disciplinary Program for Music, Communication and Technology

open access bookThis chapter examines how students in a two-campus, cross-disciplinary program in Music, Communication and Technology (MCT) experience the sense of presence of peer students and teachers, some physically co-localized while others are present via an audiovisual communications system. The chapter starts by briefly delineating the MCT program, the audiovisual communications system and the learning space built around it, named the Portal, and the research project SALTO which frames the current study. We then review research literature on presence relevant to this particular context and use this as a basis for the design of an online survey using a combination of Likert items and free text response. Our main findings, based on responses from the 16 students who participated in the survey, are that the mediating technologies of the Portal affect the experience of presence negatively, but that formal learning scenarios are less affected than informal scenarios that require social interaction

An econometric estimation of the Swedish hog market : estimation of short and long run elasticites

The Swedish aggregated hog supply has been in a steadily decreasing trend during the last twenty years. In this paper the Swedish price supply relationship is examined to find elasticities for the market. The estimation of the supply function is made using AR (1) regression. The prices of inputs are made out of feed prices and the prices of output are the price paid to the producer. A risk variable is introduced to account for short term price fluctuations, however the risk variable shows no significance. The period that is studied is 1996-2015 and analyses quarterly data. The results suggest that the Swedish elasticities are in line with prior estimates in other hog markets in the world. The conclusion drawn is that the relationship between prices and supply are positive. Further research is proposed with further refined methods and more variables included to confirm the results of this paper.Det svenska aggregerade utbudet av fläskkött har varit i en ständigt nedgående trend under de senaste tjugo åren. I denna uppsats undersöks den svenska pris- och utbudsrelationen och elasticiteten för den svenska grismarknaden föreslås. Estimeringen av den svenska utbudsfunktionen utförs genom AR(1) regression. Inputpriserna utgörs av foderpriser och produktpriserna utgörs av slaktsvinspriser. En riskvariabel introduceras för att räkna på korta fluktuationer i priserna och producenternas reaktioner kring detta, riskvariabeln visade ingen signifikans i resultatet. Studien sträcker sig mellan 1996-2015 och analyserar kvartalsdata. Resultaten visar på att elasticiteten på den svenska grismarknaden ligger i samma spann som internationella marknader. Slutsatsen som dras är att pris och utbudsrelationen är positiv. Det föreslås ytterligare undersökningar med fler variabler och en mer utvecklad metod för att bekräfta dessa resultat

Skriften på veggen – eller forbrytelser lønner seg sjelden

Artikkelen tar for seg ett av arbeidsfeltene ved Kriminalpolitisentralen i Oslo, nemlig Dokument- og skriftavsnittet ved Laboratorieavdelingen. Som tidligere ansatt ved Kripos, har forfatteren gjennom sitt virke som skriftgransker arbeidet med falske dokumenter av ulike slag, og sett hvordan forfalskninger kan avsløres ved hjelp av moderne teknologi og avanserte instrumenter. Forbryterne blir stadig mer utspekulerte, men ettersom politiet benytter sofistikert teknologi, blir det ikke lett å gjennomføre den perfekte forbrytelse

«Det minnet mest om en fremmed, kald og nesten død planet.» En lesning av Thomas Engers Enkebyen-de bortførte(2019) i et didaktisk perspektiv.

Master´s thesis in Advanced Teacher Educatio

Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data

Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub