5G AI-IoT System for Bird Species Monitoring and Song Classification.

Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds' protection and the evaluation of the environmental quality of different ecosystems. In this case, the use of machine learning and deep learning techniques has produced big progress in birdsong identification. To make an approach from AI-IoT, we have used different approaches based on image feature comparison (through CNNs trained with Imagenet weights, such as EfficientNet or MobileNet) using the feature spectrogram for the birdsong, but also the use of the deep CNN (DCNN) has shown good performance for birdsong classification for reduction of the model size. A 5G IoT-based system for raw audio gathering has been developed, and different CNNs have been tested for bird identification from audio recordings. This comparison shows that Imagenet-weighted CNN shows a relatively high performance for most species, achieving 75% accuracy. However, this network contains a large number of parameters, leading to a less energy efficient inference. We have designed two DCNNs to reduce the amount of parameters, to keep the accuracy at a certain level, and to allow their integration into a small board computer (SBC) or a microcontroller unit (MCU).

Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks.

In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation.

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios.

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.

Oncological outcomes after attempted nerve-sparing radical prostatectomy (NSRP) in patients with high-risk prostate cancer are comparable to standard non-NSRP: a longitudinal long-term propensity-matched single-centre study.

OBJECTIVE: To assess the long-term safety of nerve-sparing radical prostatectomy (NSRP) in men with high-risk prostate cancer (PCa) by comparing survival outcomes, disease recurrence, the need for additional therapy, and perioperative outcomes of patients undergoing NSRP to those having non-NSRP. PATIENTS AND METHODS: We included consecutive patients at a single, academic centre who underwent open RP for high-risk PCa, defined as preoperative prostate-specific antigen level of > 20 ng/mL and/or postoperative International Society of Urological Pathology Grade Group 4 or 5 (i.e., Gleason score ≥ 8) and/or ≥pT3 and/or pN1 assessing the RP and lymph node specimen. We calculated a propensity score and used inverse probability of treatment weighting to match baseline characteristics of patients with high-risk PCa who underwent NSRP vs non-NSRP. We analysed oncological outcome as time-to-event and calculated hazard ratios (HRs). RESULTS: A total of 726 patients were included in this analysis of which 84% (n = 609) underwent NSRP. There was no evidence for the positive surgical margin rate being different between the NSRP and non-NSRP groups (47% vs 49%, P = 0.64). Likewise, there was no evidence for the need for postoperative radiotherapy being different in men who underwent NSRP from those who underwent non-NSRP (HR 0.78, 95% confidence interval [CI] 0.53-1.15). NSRP did not impact the risk of any recurrence (HR 0.99, 95% CI 0.73-1.34, P = 0.09) and there was no evidence for survival being different in men who underwent NSRP to those who underwent non-NSRP (HR 0.65, 95% CI 0.39-1.08). There was also no evidence for the cancer-specific survival (HR 0.56, 95% CI 0.29-1.11) or progression-free survival (HR 0.99, 95% CI 0.73-1.34) being different between the groups. CONCLUSION: In patients with high-risk PCa, NSRP can be attempted without compromising long-term oncological outcomes provided a comprehensive assessment of objective (e.g., T Stage) and subjective (e.g., intraoperative appraisal of tissue planes) criteria are conducted.

AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems.

The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection and analysis process is complex and prone to errors. Although these sensors do not meet the performance requirements for reference regulatory-equivalent monitoring, they can provide informative measurements and more if we can adjust and add further processing to their raw measurements. Therefore, the integration of these sensors aims to facilitate real-time monitoring and achieve a higher spatial and temporal sampling density, particularly in urban areas, where there is a strong interest in providing AQ surveillance services since there is an increase in respiratory/allergic issues among the population. Leveraging a network of low-cost sensors, supported by 5G communications in combination with Artificial Intelligence (AI) techniques (using Convolutional and Deep Neural Networks (CNN and DNN)) to predict 24-h-ahead readings is the goal of this article in order to be able to provide early warnings to the populations of hazards areas. We have evaluated four different neural network architectures: Multi-Linear prediction (with a dense Multi-Linear Neural Network (NN)), Multi-Dense network prediction, Multi-Convolutional network prediction, and Multi-Long Short-Term Memory (LSTM) network prediction. To perform the training of the prediction of the readings, we have prepared a significant dataset that is analyzed and processed for training and testing, achieving an estimation error for most of the predicted parameters of around 7.2% on average, with the best option being the Multi-LSTM network in the forthcoming 24 h. It is worth mentioning that some pollutants achieved lower estimation errors, such as CO2 with 0.1%, PM10 with 2.4% (as well as PM2.5 and PM1.0), and NO2 with 6.7%.

Dynamic-Distance-Based Thresholding for UAV-Based Face Verification Algorithms.

Face verification, crucial for identity authentication and access control in our digital society, faces significant challenges when comparing images taken in diverse environments, which vary in terms of distance, angle, and lighting conditions. These disparities often lead to decreased accuracy due to significant resolution changes. This paper introduces an adaptive face verification solution tailored for diverse conditions, particularly focusing on Unmanned Aerial Vehicle (UAV)-based public safety applications. Our approach features an innovative adaptive verification threshold algorithm and an optimised operation pipeline, specifically designed to accommodate varying distances between the UAV and the human subject. The proposed solution is implemented based on a UAV platform and empirically compared with several state-of-the-art solutions. Empirical results have shown that an improvement of 15% in accuracy can be achieved.

Dual-Action Effect of Gallium and Silver Providing Osseointegration and Antibacterial Properties to Calcium Titanate Coatings on Porous Titanium Implants.

Previously, functional coatings on 3D-printed titanium implants were developed to improve their biointegration by separately incorporating Ga and Ag on the biomaterial surface. Now, a thermochemical treatment modification is proposed to study the effect of their simultaneous incorporation. Different concentrations of AgNO3 and Ga(NO3)3 are evaluated, and the obtained surfaces are completely characterized. Ion release, cytotoxicity, and bioactivity studies complement the characterization. The provided antibacterial effect of the surfaces is analyzed, and cell response is assessed by the study of SaOS-2 cell adhesion, proliferation, and differentiation. The Ti surface doping is confirmed by the formation of Ga-containing Ca titanates and nanoparticles of metallic Ag within the titanate coating. The surfaces generated with all combinations of AgNO3 and Ga(NO3)3 concentrations show bioactivity. The bacterial assay confirms a strong bactericidal impact achieved by the effect of both Ga and Ag present on the surface, especially for Pseudomonas aeruginosa, one of the main pathogens involved in orthopedic implant failures. SaOS-2 cells adhere and proliferate on the Ga/Ag-doped Ti surfaces, and the presence of gallium favors cell differentiation. The dual effect of both metallic agents doping the titanium surface provides bioactivity while protecting the biomaterial from the most frequent pathogens in implantology.

Machine-Learning-Based Carbon Dioxide Concentration Prediction for Hybrid Vehicles.

The current understanding of CO2 emission concentrations in hybrid vehicles (HVs) is limited, due to the complexity of the constant changes in their power-train sources. This study aims to address this problem by examining the accuracy, speed and size of traditional and advanced machine learning (ML) models for predicting CO2 emissions in HVs. A new long short-term memory (LSTM)-based model called UWS-LSTM has been developed to overcome the deficiencies of existing models. The dataset collected includes more than 20 parameters, and an extensive input feature optimization has been conducted to determine the most effective parameters. The results indicate that the UWS-LSTM model outperforms traditional ML and artificial neural network (ANN)-based models by achieving 97.5% accuracy. Furthermore, to demonstrate the efficiency of the proposed model, the CO2-concentration predictor has been implemented in a low-powered IoT device embedded in a commercial HV, resulting in rapid predictions with an average latency of 21.64 ms per prediction. The proposed algorithm is fast, accurate and computationally efficient, and it is anticipated that it will make a significant contribution to the field of smart vehicle applications.

Design, Implementation, and Empirical Validation of a Framework for Remote Car Driving Using a Commercial Mobile Network.

Despite the fact that autonomous driving systems are progressing in terms of their automation levels, the achievement of fully self-driving cars is still far from realization. Currently, most new cars accord with the Society of Automotive Engineers (SAE) Level 2 of automation, which requires the driver to be able to take control of the car when needed: for this reason, it is believed that between now and the achievement of fully automated self-driving car systems, there will be a transition, in which remote driving cars will be a reality. In addition, there are tele-operation-use cases that require remote driving for health or safety reasons. However, there is a lack of detailed design and implementation available in the public domain for remote driving cars: therefore, in this work we propose a functional framework for remote driving vehicles. We implemented a prototype, using a commercial car. The prototype was connected to a commercial 4G/5G mobile network, and empirical experiments were conducted, to validate the prototype's functions, and to evaluate its performance in real-world driving conditions. The design, implementation, and empirical evaluation provided detailed technical insights into this important research and innovation area.

Surface competition between osteoblasts and bacteria on silver-doped bioactive titanium implant.

The rapid integration in the bone tissue and the prevention of bacterial infection are key for the success of the implant. In this regard, a silver (Ag)-doped thermochemical treatment that generate an Ag-doped calcium titanate layer on titanium (Ti) implants was previously developed by our group to improve the bone-bonding ability and provide antibacterial activity. In the present study, the biological and antibacterial potential of this coating has been further studied. In order to prove that the Ag-doped layer has an antibacterial effect with no detrimental effect on the bone cells, the behavior of osteoblast-like cells in terms of cell adhesion, morphology, proliferation and differentiation was evaluated, and the biofilm inhibition capacity was assessed. Moreover, the competition by the surface between cell and bacteria was carried out in two different co-culture methods. Finally, the treatment was applied to porous Ti implants to study in vivo osteointegration. The results show that the incorporation of Ag inhibits the biofilm formation and has no effect on the performance of osteoblast-like cells. Therefore, it can be concluded that the Ag-doped surface is capable of preventing bone bacterial infection and providing suitable osseointegration.

Intelligent tutoring systems for word problem solving in COVID-19 days: could they have been (part of) the solution?

The COVID-19 pandemic led to the lockdown of schools in many countries, forcing teachers and students to carry out educational activities remotely. In the case of mathematics, developing remote instruction based on both synchronous and asynchronous technological solutions has proven to be an extremely complex challenge. Specifically, this was the case in topics such as word problem solving, as this domain requires intensive supervision and feedback from the teacher. In this piece of research, we present an evaluation of how technology is employed in the teaching of mathematics, with particular relevance to learning during the pandemic. For that purpose, we conducted a systematic review, revealing the almost complete absence of experiments in which the use of technology is not mediated by the teacher. These results reflect a pessimistic vision within the field of mathematics education about the possibilities of learning when the student uses technology autonomously. Bringing good outcomes out of a bad situation, the pandemic crisis may represent a turning point from which to start directing the research gaze towards technological environments such as those mediated by artificial intelligence. As an example, we provide a study illustrating to what extent intelligent tutoring systems can be cost-effective compared to one-to-one human tutoring and mathematic learning-oriented solutions for intensive supervision in the teaching of word problem solving, especially appropriate for remote settings. Despite the potential of these technologies, the experience also showed that student socioeconomic level was a determining factor in the participation rate with an intelligent tutoring system, regardless of whether or not the administration guaranteed students' access to technological resources during the COVID-19 situation.

Lymphovascular Invasion at the Time of Radical Prostatectomy Adversely Impacts Oncological Outcomes.

Lymphovascular invasion, whereby tumour cells or cell clusters are identified in the lumen of lymphatic or blood vessels, is thought to be an essential step in disease dissemination. It has been established as an independent negative prognostic indicator in a range of cancers. We therefore aimed to assess the impact of lymphovascular invasion at the time of prostatectomy on oncological outcomes. We performed a multicentre, retrospective cohort study of 3495 men who underwent radical prostatectomy for localised prostate cancer. Only men with negative preoperative staging were included. We assessed the relationship between lymphovascular invasion and adverse pathological features using multivariable logistic regression models. Kaplan-Meier curves and Cox proportional hazard models were created to evaluate the impact of lymphovascular invasion on oncological outcomes. Lymphovascular invasion was identified in 19% (n = 653) of men undergoing prostatectomy. There was an increased incidence of lymphovascular invasion-positive disease in men with high International Society of Urological Pathology (ISUP) grade and non-organ-confined disease (p < 0.01). The presence of lymphovascular invasion significantly increased the likelihood of pathological node-positive disease on multivariable logistic regression analysis (OR 15, 95%CI 9.7-23.6). The presence of lymphovascular invasion at radical prostatectomy significantly increased the risk of biochemical recurrence (HR 2.0, 95%CI 1.6-2.4). Furthermore, lymphovascular invasion significantly increased the risk of metastasis in the whole cohort (HR 2.2, 95%CI 1.6-3.0). The same relationship was seen across D'Amico risk groups. The presence of lymphovascular invasion at the time of radical prostatectomy is associated with aggressive prostate cancer disease features and is an indicator of poor oncological prognosis.

Gender biases in the training methods of affective computing: Redesign and validation of the Self-Assessment Manikin in measuring emotions via audiovisual clips.

Audiovisual communication is greatly contributing to the emerging research field of affective computing. The use of audiovisual stimuli within immersive virtual reality environments is providing very intense emotional reactions, which provoke spontaneous physical and physiological changes that can be assimilated into real responses. In order to ensure high-quality recognition, the artificial intelligence (AI) system must be trained with adequate data sets, including not only those gathered by smart sensors but also the tags related to the elicited emotion. Currently, there are very few techniques available for the labeling of emotions. Among them, the Self-Assessment Manikin (SAM) devised by Lang is one of the most popular. This study shows experimentally that the graphic proposal for the original SAM labelling system, as devised by Lang, is not neutral to gender and contains gender biases in its design and representation. Therefore, a new graphic design has been proposed and tested according to the guidelines of expert judges. The results of the experiment show an overall improvement in the labeling of emotions in the pleasure-arousal-dominance (PAD) affective space, particularly, for women. This research proves the relevance of applying the gender perspective in the validation of tools used throughout the years.

Aberrant hyperexpression of the RNA binding protein FMRP in tumors mediates immune evasion.

Many human cancers manifest the capability to circumvent attack by the adaptive immune system. In this work, we identified a component of immune evasion that involves frequent up-regulation of fragile X mental retardation protein (FMRP) in solid tumors. FMRP represses immune attack, as revealed by cancer cells engineered to lack its expression. FMRP-deficient tumors were infiltrated by activated T cells that impaired tumor growth and enhanced survival in mice. Mechanistically, FMRP's immunosuppression was multifactorial, involving repression of the chemoattractant C-C motif chemokine ligand 7 (CCL7) concomitant with up-regulation of three immunomodulators-interleukin-33 (IL-33), tumor-secreted protein S (PROS1), and extracellular vesicles. Gene signatures associate FMRP's cancer network with poor prognosis and response to therapy in cancer patients. Collectively, FMRP is implicated as a regulator that orchestrates a multifaceted barrier to antitumor immune responses.

Water IoT Monitoring System for Aquaponics Health and Fishery Applications.

Aquaponic health is a very important in the food industry field, as currently there is a huge amount of fishing farms, and the demands are growing in the whole world. This work examines the process of developing an innovative aquaponics health monitoring system that incorporates high-tech back-end innovation sensors to examine fish and crop health and a data analytics framework with a low-tech front-end approach to feedback actions to farmers. The developed system improves the state-of-the-art in terms of aquaponics life cycle monitoring metrics and communication technologies, and the energy consumption has been reduced to make a sustainable system.

Reducing bacterial adhesion to titanium surfaces using low intensity alternating electrical pulses.

BACKGROUND: Orthopedic implant-related infection remains one of the most serious complications after orthopedic surgery. In recent years, there has been an increased scientific interest to improve prevention and treatment strategies. However, many of these strategies have focused on chemical measures. AIM: To analyze the effect of alternating current electrical fields on bacterial adherence to titanium surfaces. METHODS: Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) were exposed to 6.5 V electrical currents at different frequencies: 0.5 Hz, 0.1 Hz, and 0.05 Hz. After exposure, a bacterial count was then performed and compared to the control model. Other variables registered included the presence of electrocoagulation of the medium, electrode oxidation and/or corrosion, and changes in pH of the medium. RESULTS: The most effective electrical model for reducing S. aureus adhesion was 6.5 V alternating current at 0.05 Hz achieving a 90% adhesion reduction rate. For E. coli, the 0.05 Hz frequency model also showed the most effective results with a 53% adhesion reduction rate, although these were significantly lower than S. aureus. Notable adhesion reduction rates were observed for S. aureus and E.coli in the studied conditions. However, the presence of electrode oxidation makes us presume these conditions are not optimal for in vivo use. CONCLUSION: Although our findings suggest electrical currents may be useful in preventing bacterial adhesion to metal surfaces, further research using other electrical conditions must be examined to consider their use for in vivo trials.

On-Growth and In-Growth Osseointegration Enhancement in PM Porous Ti-Scaffolds by Two Different Bioactivation Strategies: Alkali Thermochemical Treatment and RGD Peptide Coating.

A lack of primary stability and osteointegration in metallic implants may result in implant loosening and failure. Adding porosity to metallic implants reduces the stress shielding effect and improves implant performance, allowing the surrounding bone tissue to grow into the scaffold. However, a bioactive surface is needed to stimulate implant osteointegration and improve mechanical stability. In this study, porous titanium implants were produced via powder sintering to create different porous diameters and open interconnectivity. Two strategies were used to generate a bioactive surface on the metallic foams: (1) an inorganic alkali thermochemical treatment, (2) grafting a cell adhesive tripeptide (RGD). RGD peptides exhibit an affinity for integrins expressed by osteoblasts, and have been reported to improve osteoblast adhesion, whereas the thermochemical treatment is known to improve titanium implant osseointegration upon implantation. Bioactivated scaffolds and control samples were implanted into the tibiae of rabbits to analyze the effect of these two strategies in vivo regarding bone tissue regeneration through interconnected porosity. Histomorphometric evaluation was performed at 4 and 12 weeks after implantation. Bone-to-implant contact (BIC) and bone in-growth and on-growth were evaluated in different regions of interest (ROIs) inside and outside the implant. The results of this study show that after a long-term postoperative period, the RGD-coated samples presented higher quantification values of quantified newly formed bone tissue in the implant's outer area. However, the total analyzed bone in-growth was observed to be slightly greater in the scaffolds treated with alkali thermochemical treatment. These results suggest that both strategies contribute to enhancing porous metallic implant stability and osteointegration, and a combination of both strategies might be worth pursuing.

Olean (1,7-dioxaspiro[5.5]undecane): A Novel Intraspecific Chemical Cue in Coraebus undatus (F.) (Coleoptera: Buprestidae).

The main aim of this work was to identify semiochemicals from the jewel beetle Coraebus undatus (F.) (Coleoptera: Buprestidae) that may aid in the improvement of current monitoring tools. First, HS-SPME collections revealed that individually sampled adults (>7 days old) of both sexes release the spiroacetal 1,7-dioxaspiro[5.5]undecane (olean). Electroantennographic recordings from both sexes exposed to increasing amounts of olean followed a dose-dependent pattern, with females being more responsive than males to the highest amount of the compound (100 µg). In double-choice assays, adults older than seven days were significantly attracted to olean, whereas this attraction was not detected in insects aged less than seven days. Indeed, a repellent effect was observed in young females. Subsequent field trials employing sticky purple prism traps revealed that there were no differences among the number of insects caught in control and olean-baited traps at two different release rates (0.75 and 3.75 mg/day). Interestingly, all the trapped specimens were determined as mated females, regardless of the presence of olean. Overall, these findings provide a basis for unraveling the chemical ecology of the species, although further research is still needed to determine the specific role of this compound within the chemical communication of the species.

Biological Responses of Ceramic Bone Spacers Produced by Green Processing of Additively Manufactured Thin Meshes.

Bone spacers are exclusively used for replacing the tissue after trauma and/or diseases. Ceramic materials bring positive opportunities to enhance greater osteointegration and performance of implants, yet processing of porous geometries can be challenging. Additive Manufacturing (AM) opens opportunities to grade porosity levels in a part; however, its productivity may be low due to its batch processing approach. The paper studies the biological responses yielded by hydroxyapatite with ß-TCP (tricalcium phosphate) ceramic porous bone spacers manufactured by robocasting 2-layer meshes that are rolled in green and sintered. The implants are assessed in vitro and in vivo for their compatibility. Human bone marrow mesenchymal stem cells attached, proliferated and differentiated on the bone spacers produced. Cells on the spacers presented alkaline phosphatase staining, confirming osteogenic differentiation. They also expressed bone-specific COL1A1, BGAP, BSP, and SPP1 genes. The fold change of these genes ranged between 8 to 16 folds compared to controls. When implanted into the subcutaneous tissue of rabbits, they triggered collagen fibre formation and mild fibroblastic proliferation. In conclusion, rolled AM-meshes bone spacers stimulated bone formation in vitro and were biocompatible in vivo. This technology may give the advantage to custom produce spacers at high production rates if industrially upscaled.

Safety, tolerability and clinical implementation of 'ready-to-use' 68gallium-DOTA0-Tyr3-octreotide (68Ga-DOTATOC) (SomaKIT TOC) for injection in patients diagnosed with gastroenteropancreatic neuroendocrine tumours (GEP-NETs).

BACKGROUND: 68Ga-DOTA0-Tyr3-octreotide (68Ga-DOTATOC) positron emission tomography-CT (PET-CT) has superior diagnostic performance compared to the licensed tracer OctreoScan single photon emission CT-CT in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs). A new preparation of 68Ga-DOTATOC using a new 'ready-to-use' 68Ga-DOTATOC formulation for injection has been developed (68Ga-DOTATOC (SomaKIT TOC)). OBJECTIVES: This study aimed to assess the safety and tolerability of 68Ga-DOTATOC (SomaKIT TOC) and evaluate the feasibility and robustness of implementing it in a NET clinical imaging service. METHODS: A first-in-human phase I/II multicentre, open-label study of a single dose of 68Ga-DOTATOC (SomaKIT TOC) 2 MBq/kg±10% (range 100-200 MBq) in patients with biopsy-proven grade 1-2 GEP-NETs. PET-CT was performed post injection. Patients were followed up for 28 days. We next implemented this new synthesis methodology in a clinical service assessed over 11 months. RESULTS: Twenty consenting patients were recruited; 14 males, 6 females; mean (SD) age 58 years (12); NET grade 1 (70%), grade 2 (30%); and 75% with stage IV disease. Twelve patients experienced at least one adverse event (AE) during the study with no grade 3-4 toxicities. Only four AEs were classified as possibly (headache (n=1; 4%), nausea (1; 4%)) or probably (dysgeusia (1; 4%), paraesthesia (1; 4%)) related to the study preparation. One hundred thirteen vials of 68Ga-DOTATOC (SomaKIT TOC) were synthesised with the 'kit' over a period of 11 months for clinical utility. Only 2/113 vials (1.77%) were rejected. CONCLUSIONS: The new ready-to-use preparation of 68Ga-DOTATOC (SomaKIT TOC) for injection was safe and well tolerated. This has led to the world's first (EMA) licensed 68Ga-DOTATOC (SomaKIT TOC) radiopharmaceutical for the utility of PET imaging in patients with NETs. This preparation can be robustly implemented into routine clinical practice.