Novadip (Belgium)

Chimeric antigen receptor (CAR)-T cell therapy revolutionized treatment of hematological malignancies, but translation to solid tumors remains difficult. Therefore, different avenues are explored to develop novel CAR-T cell products. Nanobodies have advantageous features for CAR design, such as ease of engineering and low immunogenicity, but predictive values on nanobody characteristics for optimal CAR-T cell function remain unclear. We performed a side-by-side evaluation of 12 nanobody-based (nano)CARs against human epidermal growth factor receptor 2 (HER2). All nanoCARs showed the ability to activate a reporter T cell line upon recognition of HER2pos cells. When evaluated in primary T cells, 6/12 nanoCARs showed the strongest reactivity and cytotoxicity against HER2pos glioblastoma and breast cancer cells. Moreover, only 1/12 was able to confer potent cytotoxicity against HER2pos melanoma cells. These data add to the current view that nanobody selection for CAR design remains subject to extensive side-by-side screening. Still, superior cytotoxicity across multiple solid tumor cell lines—determined using in vitro assays evaluating activation, cytokine secretion, and target cell-specific killing—led to selection and further characterization of a lead nanoCAR 1R59b, showing tumor control in an in vivo xenograft model, providing a promising HER2 nanoCAR for further (pre)clinical investigation.

The European Quality In Preclinical Data (EQIPD) consortium was born from the fact that publications report challenges with the robustness, rigor, and/or validity of research data, which may impact decisions about whether to proceed with further preclinical testing or to advance to clinical testing, as well as draw conclusions on the predictability of preclinical models. To address this, a consortium including multiple research laboratories from academia and industry participated in a series of electroencephalography (EEG) experiments in mice aimed to detect sources of variance and to gauge how protocol harmonisation and data analytics impact such variance. Ultimately, the goal of this first ever between-laboratory comparison of EEG recordings and analyses was to validate the principles that supposedly increase data quality, robustness, and comparability. Experiments consisted of a Localisation phase, which aimed to identify the factors that influence between-laboratory variability, a Harmonisation phase to evaluate whether harmonisation of standardized protocols and centralised processing and data analysis reduced variance, and a Ring-Testing phase to verify the ability of the harmonised protocol to generate consistent findings. Indeed, between-laboratory variability reduced from Localisation to Harmonisation and this reduction remained during the Ring-Testing phase. Results obtained in this multicentre preclinical qEEG study also confirmed the complex nature of EEG experiments starting from the surgery and data collection through data pre-processing to data analysis that ultimately influenced the results and contributed to variance in findings across laboratories. Overall, harmonisation of protocols and centralized data analysis were crucial in reducing laboratory-to-laboratory variability. To this end, it is recommended that standardized guidelines be updated and followed for collection and analysis of preclinical EEG data.

The JPEG XS [1,2,3] activity started around 2015 to address the problem of the ever- increasing bandwidth requirements for transmitting video content in professional production environments. In such environments, ultra-low latency, low complexity, and visually lossless image quality are key features, ruling out the existing state of the art video compression technology. A paradigm shift was needed to find a solution where these three key features were prioritized over the pure compression performance.

Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias.

Digital technologies are everywhere, and everyone is using them and interacting with them in everyday life. However, not everyone is aware of the impacts that their usage does have on the environment, the climate, their health and well-being, should they be positive or negative. This work-in-progress paper presents how a quiz-based challenge has been designed for young pupils from secondary schools and students in higher education to raise their own awareness about these impacts. More precisely, this paper presents how the challenge has been defined, including young students in engineering in the team. It also details its structure and how the questions set has been designed. The paper then presents preliminary results obtained from the analysis of data collected during a first edition of the proposed challenge, run in November 2022 in the French-speaking part of Belgium. The analysis highlights that negative impacts are better known than positive ones. It also reveals that questions related to energy and CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>emissions are the best-known. Analysing the profiles of the participants also showed that a large majority are paying attention to their digital consumption and would be ready to adapt their behaviour to reduce their negative impacts. The paper concludes with a discussion about how the challenge could be used in engineering schools, as an introduction to courses related to the development of digital technologies, to make students future responsible developers.

Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use different methods and spectral bands introduces various systematic errors. This paper evaluates the use of a Convolutional Neural Network (CNN) to reliably and accurately infer the masses of galaxy clusters from the Compton-y parameter maps provided by the Planck satellite. The CNN is trained with mock images generated from hydrodynamic simulations of galaxy clusters, with Planck's observational limitations taken into account. We observe that the CNN approach is not subject to the usual observational assumptions, and so is not affected by the same biases. By applying the trained CNNs to the real Planck maps, we find cluster masses compatible with Planck measurements within a 15% bias. Finally, we show that this mass bias can be explained by the well known hydrostatic equilibrium assumption in Planck masses, and the different parameters in the Y500-M500 scaling laws. This work highlights that CNNs, supported by hydrodynamic simulations, are a promising and independent tool for estimating cluster masses with high accuracy, which can be extended to other surveys as well as to observations in other bands.

BackgroundPridopidine is a selective, potent Sigma-1 Receptor (S1R) agonist in clinical development for HD and ALS. S1R agonists enhance neuroprotective pathways in a biphasic manner, demonstrated in numerous preclinical and clinical studies.ResultsPridopidine demonstrates the biphasic dose response typical of S1R agonists in preclinical HD models. It increases BDNF secretion, rescues mitochondrial function and increases synaptic plasticity with maximal effect at 1μM. Higher and lower concentrations show diminished effect. Pridopidine also demonstrates a biphasic dose response in clinical trials. The PRIDE-HD 52-week Ph2 trial evaluated pridopidine 45, 67.5, 90 and 112.5 mg bid in HD patients. Increased levels of neurofilament light chain (NfL) indicate neuronal injury and serve as a biomarker correlating with longitudinal decline in Total Functional Capacity (TFC). Post-hoc analysis examined the effect of pridopidine on plasma NfL, and its association with TFC in early HD (TFC 7-13). 45 mg bid shows highest efficacy for TFC maintenance ΔTFC +0.09 vs -1.0, placebo, (p=0.0006). Combined higher doses were less efficacious (ΔTFC -0.55, p=0.002). 45 mg bid was most efficacious showing stabilization of plasma NfL levels (ΔNfL -0.06 log2pg/ml, n=31). Higher doses show less effect (ΔNfL -0.009 log2pg/ml, n=82). We used S1R-BiP dissociation and S1R oligomerization assays to assess pridopidine’s biphasic response. S1R agonists facilitate S1R-BiP dissociation, freeing S1R to act on different targets. Pridopidine causes S1R-BiP dissociation in a biphasic manner with maximal effect at 1μM.ConclusionsPridopidine’s effect on plasma NfL levels associates with TFC and adheres to classic S1R agonist biphasic dose response.