Who are you in the digital public sphere? Are you »a woman«, »a man« or a robot? Are you classified as »right-wing« or »left-wing«? Dutch researcher Miriam Rasch writes about how twisted alternatives for bodies and identities are created online. A translation of the essay was originally published in 10TAL #32.
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
Rationale: Patients with cancer of the upper gastrointestinal tract or lung are more likely to present with malnutrition at diagnosis than, for instance, patients with melanoma. Low muscle mass is an indicator of malnutrition and can be determined by computed tomography (CT) analysis of the skeletal muscle index (SMI) at the 3rd lumbar vertebra (L3) level. However, CT images at L3 are not always available. At each vertebra level, we determined if type of cancer, i.e., head and neck cancer (HNC), oesophageal cancer (OC) or lung cancer (LC) vs. melanoma (ME) was associated with lower SMI. Methods: CT images from adult patients with HNC, OC, LC or ME were included and analyzed. Scans were performed in the patient’s initial staging after diagnosis. MIM software version 7.0.1 was used to contour the muscle areas for all vertebra levels. Skeletal muscle area was corrected for stature to calculate SMI (cm2/m2). We tested for the association of HNC, OC, or LC diagnosis vs ME with SMI by univariate and multivariate linear regression analyses. In the multivariate analyses, age (years), sex, and body mass index (BMI; kg/m2) were included. Betas (B;95%CI) were calculated and statistical significance was set at p
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
De kledingindustrie heeft een slecht duurzaamheidsimago. Tijdens het maakproces gaat ongeveer 30% van het basismateriaal als snijafval verloren. Daarnaast is er een grote reststroom van niet verkocht materiaal. In dit project werken de bedrijven NEFFA, gespecialiseerd in het ontwerpen van modecollecties en textiele producten, East4West, strategisch adviesbureau voor de kledingindustrie en het Centre of Expertise Biobased Economy samen bij het ontwikkelen van een nieuwe maaktechniek waarbij gebruik wordt gemaakt van mycelium als grondstof voor een kledingstuk dat met de mal wordt geproduceerd. Via een bodyscan die over te brengen is op een flexibele mal is de ideale pasvorm te maken. Hierop kan een gegroeid schimmelmycelium aangebracht worden dat door zijn sterke bindings- eigenschappen het mogelijk maakt dat grotere vlakken kunnen worden samengesteld. Via deze techniek wordt dan een gepersonaliseerd kledingstuk gemaakt zonder reststromen dat aan het einde van de gebruiksduur ook composteerbaar is zonder milieubelasting. Het doel van deze nieuw te ontwikkelen techniek is het terugdringen van afvalverlies en het benutten van mycelium als potentiele grondstof voor kleding.
Along with the rapidly growing number of disabled people participating in competitive sports, there is an increased need for (para)medical support in disability sports. Disabled athletes experience differences in body composition, metabolism, training load and habitual activity patterns compared with non-disabled athletes. Moreover, it has been suggested that the well-recognized athlete triad, and low energy availability and low bone mineral density in particular, is even a greater challenge in disabled athletes. Therefore, it is not surprising that sport nutritionists of disabled athletes have expressed an urgency for increased knowledge and insights on the nutritional demands of this group. This project aims to investigate energy expenditure, dietary intake, body composition and bone health of disabled athletes, ultimately leading to nutritional guidelines that promote health and optimal sports performance for this unique population. For this purpose, we will conduct a series of studies and implementation activities that are inter-related and build on the latest insights from sports practice, technology and science. Our international consortium is highly qualified to achieve this goal. It consists of knowledge institutes including world-leading experts in sport and nutrition research, complemented with practical insights from nutritionists working with disabled athletes and the involvement of athletes and teams through the Dutch and Norwegian Olympic committees. The international collaboration, which is a clear strength of this project, is not only focused on research, but also on the optimization of professional practice and educational activities. In this regard, the outcomes of this project will be directly available for practical use by the (para)medical staff working with disabled athletes, and will be extensively communicated to sport teams to ensure that the new insights are directly embedded into daily practice. The project outcomes will also be incorporated in educational activities for dietetics and sport and exercise students, thereby increasing knowledge of future practitioners.
