Simple Summary The treatment outcomes in patients with head and neck cancer vary greatly, and serious side effects are often observed. Being able to predict therapy effects is therefore crucial for choosing the best treatment option for each patient. In this study, we developed an assay to evaluate how head and neck tumor cells respond to radiation and chemotherapy. Treatment of thin patient-derived cancer tissue slices in the laboratory (in vitro) resulted in large differences in individual tumor’s reactions to treatment. In the sensitive tumors, cancer cells repaired the DNA damage inflicted by therapy only partially, stopped multiplying, and showed increased levels of cell death. On the other hand, resistant tumors were able to recover from the damage caused by the treatment. The next crucial step is to investigate whether the differences we observed in vitro can indeed predict the treatment outcomes; this is currently being tested in an ongoing clinical trial. Abstract Background: Head and neck squamous cell carcinoma (HNSCC) displays a large heterogeneity in treatment response, and consequently in patient prognosis. Despite extensive efforts, no clinically validated model is available to predict tumor response. Here we describe a functional test for predicting tumor response to radiation and chemotherapy on the level of the individual patient. Methods: Resection material of 17 primary HNSCC patients was cultured ex vivo, irradiated or cisplatin-treated, after which the effect on tumor cell vitality was analyzed several days after treatment. Results: Ionizing radiation (IR) affected tumor cell growth and viability with a clear dose-response relationship, and marked heterogeneity between tumors was observed. After a single dose of 5Gy, proliferation in IR-sensitive tumors dropped below 30% of the untreated level, while IR-resistant tumors maintained at least 60% of proliferation. IR-sensitive tumors showed on average a twofold increase in apoptosis, as well as an increased number and size of DNA damage foci after treatment. No differences in the homologous recombination (HR) proficiency between IR-sensitive and –resistant tumors were detected. Cisplatin caused a decrease in proliferation, as well as induction of apoptosis, again with marked variation between the samples. Conclusions: Our functional ex vivo assay discriminated between IR-sensitive and IR-resistant HNSCC tumors, and may also be suitable for predicting response to cisplatin. Its predictive value is currently under investigation in a prospective clinical study.

Edge computing is one of the key features of the 5G technology-scape that is realizing new and enhanced automotive use cases for improving road safety and emergency response management. Back Situation Awareness (BSA) is such a use case that provides an advance notification to the vehicles of an arriving emergency vehicle (EmV). This paper presents an algorithm for enhancing the accuracy of the advanced Estimated Time of Arrival (ETA) notification of an approaching EmV towards the other vehicles on the highway. The notification is expected to ensure timely reaction by the vehicles to create a clear corridor for the EmV to pass through unhindered, thereby saving critical time to reach the emergency event in a safe manner. The main features of the presented solution are i) the self-correcting algorithm, ii) adaptive and dynamic dissemination areas size allocation, as a response to traffic changes, and iii) the evaluation of the ETA estimation accuracy. We have used the real travel time data measurements collected on the E313 highway (Antwerp, Belgium), to evaluate the performance of the algorithm. The performance is evaluated and compared in terms of accuracy and run-time complexity, using different methods such as Kalman filter, Filter-less method, Moving Average, and Exponential Moving Average filters. It is observed that the Kalman filter provides better accuracy on the ETA estimation, thereby reducing the estimation error by around 14% on average.

Computed tomography (CT) is a diagnostic imaging process that uses ionising radiation to obtain information about the interior anatomic structure of the human body. Considering that the medical use of ionising radiation implies exposing patients to radiation that may lead to unwanted stochastic effects and that those effects are less probable at lower doses, optimising imaging protocols is of great importance. In this paper, we used an assembled 3D-printed infant head phantom and matched its image quality parameters with those obtained for a commercially available adult head phantom using the imaging protocol dedicated for adult patients. In accordance with the results, an optimised scanning protocol was designed which resulted in dose reductions for paediatric patients while keeping image quality at an adequate level.

Abstract Hypoxia induces changes in the secretion of extracellular vesicles (EVs) in several non‐neuronal cells and pathological conditions. EVs are packed with biomolecules, such as microRNA(miR)‐21‐5p, which respond to hypoxia. However, the true EV association of miR‐21‐5p, and its functional or biomarker relevance, are inadequately characterised. Neurons are extremely sensitive cells, and it is not known whether the secretion of neuronal EVs and miR‐21‐5p are altered upon hypoxia. Here, we characterised the temporal EV secretion profile and cell viability of neurons under hypoxia. Hypoxia induced a rapid increase of miR‐21a‐5p secretion in the EVs, which preceded the elevation of hypoxia‐induced tissue or cellular miR‐21a‐5p. Prolonged hypoxia induced cell death and the release of morphologically distinct EVs. The EVs protected miR‐21a‐5p from enzymatic degradation but a remarkable fraction of miR‐21a‐5p remained fragile and non‐EV associated. The increase in miR‐21a‐5p secretion may have biomarker potential, as high blood levels of miR‐21‐5p in stroke patients were associated with significant disability at hospital discharge. Our data provides an understanding of the dynamic regulation of EV secretion from neurons under hypoxia and provides a candidate for the prediction of recovery from ischemic stroke.

Purpose Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device. Methods This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices. Results Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm. Conclusion This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions. Trial registration ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1 .

Artemisia annua L. has long been known for its medicinal properties and isolation of ingredients whose derivatives are used for therapeutic purposes. The CYP2B6 and CYP3A4 enzymes belong to a large family of cytochrome P450 enzymes. These enzymes are involved in the metabolism of drugs and other xeonobiotics. It is known that various compounds can induce or inhibit the activity of these enzymes. The aim of this study was to investigate the nature of the inhibitory effect of Artemisia annua extract on CYP2B6 and CYP3A4 enzymes, as well as the type of inhibition, the presence of reversible or pseudo-irreversible inhibition, and the possible heme destruction. The methanolic extract of Artemisia annua showed an inhibitory effect on CYP2B6 (by almost 90%) and CYP3A4 enzymes (by almost 70%). A significant decrease in heme concentration by 46.8% and 38.2% was observed in different assays. These results clearly indicate that the studied plant extracts significantly inhibited the activity of CYP2B6 and CYP3A4 enzymes. Moreover, they showed irreversible inhibition, which is even more important for possible interactions with drugs and dietary supplements.

With the goal of enhancing the quality of the environment, urban green infrastructure (UGI) is an essential element in sustainable cities, and nature-based solutions (NBS) are being carried out as new infrastructure solutions that increase the resilience of cities. In this research, the method of theoretical analysis and the content analysis as the basic fact-gathering technique was applied to answer to following questions: What are the hindrances and bottlenecks in implementing NBS? Are the current decision-making mechanisms helping NBS get in route to shape cities? Is there any binding policy in practice that promotes NBS? In Belgrade is planned Type 3 of the degree of intervention/level and engineering type—Creation and new ecosystem management in the classifications of intensive urban green space management; urban planning strategies; urban water management; ecological restoration of degraded terrestrial ecosystems; and restoration and creation of semi-natural water bodies and hydrographic networks. In the future, it is essential to implement policies and incentives on national, regional, and local scales that help encourage the usage of NBS in the development of urban infrastructure.

Purpose: Total elbow arthroplasty (TEA) is rarely performed compared to other arthroplasties. For many surgical procedures, literature shows better outcomes when they are performed by experienced surgeons and in so-called ‘high-volume’ hospitals. We systematically reviewed the literature on the relationship between surgical volume and outcomes following TEA. Methods: A literature search was performed using the MEDLINE, EMBASE and CINAHL databases. The literature was systematically reviewed for original studies comparing TEA outcomes among hospitals or surgeons with different annual or career volumes. For each study, data were collected on study design, indications for TEA, number of included patients, implant types, cut-off values for volume, number and types of complications, revision rate and functional outcome measures. The methodological quality of the included studies was assessed using the Newcastle–Ottawa Scale. Results: Two studies, which included a combined 2301 TEAs, found that higher surgeon volumes were associated with lower revision rates. The examined complication rates did not differ between high- and low-volume surgeons. In one study, low-hospital volume is associated with an increased risk of revision compared to high-volume hospitals, but for other complication types, no difference was found. Conclusions: Based on the results, the evidence suggests that high-volume centers have a lower revision rate in the long term. No minimum amount of procedures per year can be advised, as the included studies have different cut-off values between groups. As higher surgeon- and center-volume, (therefore presumably experience) appear to yield better outcomes, centralization of total elbow arthroplasty should be encouraged.

Additive manufacturing (AM) or industrial 3D printing uses cutting-edge technologies and materials to produce a variety of complex products. However, the effects of the unintentionally emitted AM (nano)particles (AMPs) on human cells following inhalation, require further investigations. The physicochemical characterization of the AMPs, extracted from the filter of a Laser Powder Bed Fusion (L-PBF) 3D printer of iron-based materials, disclosed their complexity, in terms of size, shape, and chemistry. Cell Painting, a high-content screening (HCS) assay, was used to detect the subtle morphological changes elicited by the AMPs at the single cell resolution. The profiling of the cell morphological phenotypes, disclosed prominent concentration-dependent effects on the cytoskeleton, mitochondria, and the membranous structures of the cell. Furthermore, lipidomics confirmed that the AMPs induced the extensive membrane remodeling in the lung epithelial and macrophage co-culture cell model. To further elucidate the biological mechanisms of action, the targeted metabolomics unveiled several inflammation-related metabolites regulating the cell response to the AMP exposure. Overall, the AMP exposure led to the internalization, oxidative stress, cytoskeleton disruption, mitochondrial activation, membrane remodeling, and metabolic reprogramming of the lung epithelial cells and macrophages. We propose the approach of integrating Cell Painting with metabolomics and lipidomics, as an advanced nanosafety methodology, increasing the ability to capture the cellular and molecular phenotypes and the relevant biological mechanisms to the (nano)particle exposure.

ITk detector, the new ATLAS tracking system at High Luminosity LHC, will be equipped with 3D pixel sensor modules in the innermost layer (L0). The pixel cell dimensions will be either 25 × 100 μm2 (barrel) or 50 × 50 μm2 (endcap), with one read-out electrode at the centre of a pixel and four bias electrodes at the corners. Sensors from pre-production wafers (50 × 50 μm2) produced by FBK have been bump bonded to ITkPixV1.1 chips at IZM. Bare modules have been assembled in Genoa on Single Chip Cards and characterized in laboratory and on beam.

The Angle-of-Arrival (AoA)-based approach is an appealing solution for unmanned aerial vehicle (UAV) positioning, and has received significant interest recently. In this article, we propose a novel framework for UAV three-dimensional (3-D) positioning, the core of which is to measure the two-dimensional (2-D) Angle-of-Departure (2D-AoD) and 2D-AoA via a bistatic multiple-input multiple-output (MIMO) radar. Unlike the existing positioning architectures, the MIMO radar is equipped with polarized array antennas. An estimator based on the parallel factor (PARAFAC) decomposition is developed. It first obtains the direction matrices via performing the PARAFAC decomposition of the array data. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization response vector, from which the 2D-AoD, 2D-AoA, and polarization status of the UAVs are achieved via incorporating the vector cross-product method and the least squares (LSs) technique. Finally, the 3-D positions of the UAVs are easily calculated via the location relationship between the 2D-AoD, 2D-AoA, and the coordinates of transmitting/receiving (Tx/Rx) array. The proposed framework is computationally friendly, and is capable of positioning anonymous UAV. Moreover, it is insensitive to the geometry of the Tx/Rx array, indicating that the proposed framework supports configurable Tx/Rx antennas. Simulation results are provided to verify our theoretical advantages.

The requirements for the efficient replacement of fossil fuel, combined with the growing energy crisis, places focus on hydrogen production. Efficient and cost-effective electrocatalysts are needed for H2 production, and novel strategies for their discovery must be developed. Here, we utilized Kinetic Monte Carlo (KMC) simulations to demonstrate that hydrogen evolution reaction (HER) can be boosted via hydrogen spillover to the support when the catalyst surface is largely covered by adsorbed hydrogen under operating conditions. Based on the insights from KMC, we synthesized a series of reduced graphene-oxide-supported catalysts and compared their activities towards HER in alkaline media with that of corresponding pure metals. For Ag, Au, and Zn, the support effect is negative, but for Pt, Pd, Fe, Co, and Ni, the presence of the support enhances HER activity. The HER volcano, constructed using calculated hydrogen binding energies and measured HER activities, shows a positive shift of the strong binding branch. This work demonstrates the possibilities of metal–support interface engineering for producing effective HER catalysts and provides general guidelines for choosing novel catalyst–support combinations for electrocatalytic hydrogen production.