RISK ASSESSMENT IN PET RADIOPHARMACEUTICALS PRODUCTION: PLANNING THE IMPLEMENTATION OF A PRODUCTION LINE COMPLIANT WITH GMP REGULATION

The aim of this paper is to provide some indications for carrying out the risk assessment for the activation of a production line of a radiopharmaceutical containing a positrons emitting radionuclide. The risk analysis was performed by following the ICH Q10 guideline and ISO 9001:2015 standards and by using the risk-based thinking approach applied to the entire production cycle. The overall analysis has shown that hard and soft skills of the expert group are key factors of success both in technical and radiopharmaceuticals-related preparations as well as in risk management methodologies

Liver and White/Brown Fat Dystrophy Associates with Gut Microbiota and Metabolomic Alterations in 3xTg Alzheimer's Disease Mouse Model

Metabolic impairments and liver and adipose depots alterations were reported in subjects with Alzheimer's disease (AD), highlighting the role of the liver-adipose-tissue-brain axis in AD pathophysiology. The gut microbiota might play a modulating role. We investigated the alterations to the liver and white/brown adipose tissues (W/BAT) and their relationships with serum and gut metabolites and gut bacteria in a 3xTg mouse model during AD onset (adulthood) and progression (aging) and the impact of high-fat diet (HFD) and intranasal insulin (INI). Glucose metabolism (18FDG-PET), tissue radiodensity (CT), liver and W/BAT histology, BAT-thermogenic markers were analyzed. 16S-RNA sequencing and mass-spectrometry were performed in adult (8 months) and aged (14 months) 3xTg-AD mice with a high-fat or control diet. Generalized and HFD resistant deficiency of lipid accumulation in both liver and W/BAT, hypermetabolism in WAT (adulthood) and BAT (aging), abnormal cytokine-hormone profiles, and liver inflammation were observed in 3xTg mice; INI could antagonize all these alterations. Specific gut microbiota-metabolome profiles correlated with a significant disruption of the gut-microbiota-liver-adipose axis in AD mice. In conclusion, fat dystrophy in liver and adipose depots contributes to AD progression, and associates with altered profiles of the gut microbiota, which candidates as an appealing early target for preventive intervention.This study was conducted within the JPI-HDHL-INTIMIC Knowledge Platform of Food, Diet, Intestinal Microbiomics, and Human Health (sub-project no. KP-778 MISVILUPPO, Italian Ministry of Agricultural, Food and Forestry Policies, Ministry Decree 23092/7303/19), and the JPI-HDHL-INTIMIC Joint Transnational Research program (project no. INTIMIC-085 GUTMOM, Italian Ministry of Education, University and Research, Ministry Decree no. 946/2019). The funders had no role in study design, data collection and analysis, or preparation of the manuscript. Projects supported by the Joint Action “European Joint Programming Initiative: A Healthy Diet for a Healthy Life (JPI HDHL)” are funded by the respective national/regional funding organisations: Fund for Scientific Research (FRS—FNRS, Belgium); Research Foundation—Flanders (FWO, Belgium); INSERM Institut National de la Santé et de la Recherche Médicale (France); Federal Ministry of Food and Agriculture (BMEL) represented by Federal Office for Agriculture and Food (BLE, Germany); Ministry of Education, University and Research (MIUR), Ministry of agricultural, food, and forestry policies (MiPAAF), National Institute of Health (ISS) on behalf of the Ministry of Health (Italy); the National Institute of Health Carlos III (Spain); The Netherlands Organisation for Health Research and Development (ZonMw, The Netherlands), Austrian Research Promotion Agency (FFG) on behalf of the Austrian Federal Ministry for Education, Science, and Research (BMBWF), Ministry of Science and Technology (Israel), Formas (Sweden). DM gratefully acknowledges funding from the Ministry of Science and Innovation of Spain (ACPIN2017-117 and PID2019-108973RB-C22).Peer reviewe

Identification of MEMS Geometric Uncertainties through Homogenization

Fabrication imperfections strongly influence the functioning of Micro-Electro-Mechanical Systems (MEMS) if not taken into account during the design process. They must be indeed identified or precisely predicted to guarantee a proper compensation during the calibration phase or directly in operation. In this work, we propose an efficient approach for the identification of geometric uncertainties of MEMS, exploiting the asymptotic homogenization technique. In particular, the proposed strategy is experimentally validated on a MEMS filter, a device constituted by a complex periodic geometry, which would require high computational costs if simulated through full-order models. The complex periodic structure is replaced by an equivalent homogeneous medium, allowing a fast optimization procedure to identify imperfections by comparing a simplified analytical model with the experimental data available for the MEMS filter. The actual over-etch, obtained after the release phase, and the electrode offset of a fabricated MEMS filter are effectively identified through the proposed strategy

Machine learning for monitoring and predictive maintenance of cutting tool wear for clean-cut machining machines

This paper focuses on the study and development of learning algorithms oriented to wear classification and predictive maintenance (PdM) of the cutting tool (CT) of a clamping machine for producing structural steel bars. While several works dedicated to CTs for turning and milling operations, or in general for metal removal operations, also known as subtractive manufacturing processes, can be found in the literature, the phenomena related to cutting with a cutting knife have not been widely treated in the literature. This article intends to focus on the analysis of the latter problem. The objective is to estimate the wear of the CT, a critical component of the steel bar cutting machine. The SVM classifiers were therefore used to classify the wear. For the predictive maintenance purpose, two algorithms were implemented for the prediction of the remaining service life, based on the Degradation Model and the Similarity Model respectively; in the first method, a prediction and state update function were used, while in the second method, a Long Short-Term Memory (LSTM) Neural Network (NN) was used

Dynamic Non-Linear Behaviour of Torsional Resonators in MEMS

This paper reports the theoretical and experimental characterization of the dynamic behavior of torsional resonators that can be applied to inertial sensors. For the correct operation of the devices it is necessary to model the dynamic behavior of the electrostatically actuated torsional resonators both in the linear and nonlinear range. A complete analytical model is developed in this work including nonlinear terms in the electrostatic stiffness. This provides clear quantitative information about the available linear range of operation and opens the way to exploit the nonlinear range. The model is validated through comparison with experimental data on two 22 μm thick polysilicon resonators having different distances from the underlying electrodes

A differential resonant micro accelerometer for out-of-plane measurements

This paper reports the theoretical and experimental characterization of a new z-axis silicon resonant micro accelerometer fabricated by the THELMA© surface micromachining technique, characterized by differential sensing and very small dimensions. The working principle of this device is based on the variation of the electrostatic stiffness of two torsional resonators. This work is a prosecution of the research on resonant accelerometers published in [1,2]

Risk Management in Good Manufacturing Practice (GMP) Radiopharmaceutical Preparations

Risk assessment and management during the entire production process of a radiopharmaceutical are pivotal factors in ensuring drug safety and quality. A methodology of quality risk assessment has been performed by integrating the advice reported in Eudralex, ICHQ, and ISO 9001, and its validity has been evaluated by applying it to real data collected in 21 months of activities of 18F-FDG production at Officina Farmaceutica, CNR-Pisa (Italy) to confirm whether the critical aspects that previously have been identified in the quality risk assessment were effective. The analysis of the results of the real data matched the hypotheses obtained from the model, and in particular, the most critical aspects were those related to human resources and staff organization with regard to management risk. Regarding the production process, the model of operational risk had predicted, as later confirmed by real data, that the most critical phase could be the synthesis and dispensing of the radiopharmaceuticals. So, the proposed method could be used by other similar radiopharmaceutical production sites to identify the critical phases of the production process and to act to improve performance and prevent failure in the entire cycle of radiopharmaceutical products