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The use of substances to improve sports performance is a widespread phenomenon among adolescents. Several anti-doping programs have been developed, mainly based on knowledge-based evidence. The main aim of the present study was to implement an anti-doping intervention in sports high school students, based on a psychological framework, such as Socio-Cognitive Theory, through the development of a Serious Game (SG), i.e., digital learning based on the game. The experimental design included an intervention group (n = 167; F = 37.7%; Meanage = 17.5 years; SD = 0.58) and a control group (n = 112; F = 42%; Meanage = 17.6; SD = 1). Both of the groups completed the same questionnaire on two different occasions (i.e., time 1 and time 2) for measuring doping intention, self-regulatory efficacy to resist social pressure for the use of substances, moral disengagement, and doping knowledge. Data were analyzed through repeated measures of Group X Time ANOVA, demonstrating some degree of efficacy of the intervention, in particular in terms of the decrease in doping intention and the strengthening of doping knowledge. Moreover, the study demonstrated that the score obtained during the implementation of the SG could partially represent a coherent measure of the participants’ beliefs regarding doping. These results could be considered a starting point for future research to better develop technological anti-doping interventions.

Background/Objective: There are several questionnaires for the challenge of anticipating opioid use disorder (OUD). However, many are not specific for chronic non-cancer pain (CNCP) or have been developed in the American population, whose sociodemographic factors are very different from the Spanish population, leading to scarce translation into clinical practice. Thus, the aim of this study is to prospectively validate a predictive model for OUD in Spanish patients under long-term opioids. Methods: An innovative two-stage predictive model was developed from retrospective (n = 129) and non-overlapping prospective (n = 100) cohorts of real-world CNCP outpatients. All subjects used prescribed opioids for 6 or more months. Sociodemographic, clinical and pharmacological covariates were registered. Mu-opioid receptor 1 (OPRM1, A118G, rs1799971) and catechol-O-methyltransferase (COMT, G472A, rs4680) genetic variants plus cytochrome P450 2D6 (CYP2D6) liver enzyme phenotypes were also analyzed. The model performance and diagnostic accuracy were calculated. Results: The two-stage model comprised risk factors related to OUD (younger age, work disability and high daily opioid dose) and provided new useful information about other risk factors (low quality of life, OPRM-G allele and CYP2D6 extreme phenotypes). The validation showed a satisfactory accuracy (70% specificity and 75% sensitivity) for our predictive model with acceptable discrimination and goodness of fit. Conclusions: Our study presents the results of an innovative model for predicting OUD in our setting. After external validation, it could represent a change in the paradigm of opioid treatment, helping clinicians to better identify and manage the risks and reduce the side effects and complications.

The application of venovenous (VV) extracorporeal membrane oxygenation (ECMO) has gained wide acceptance for the treatment of acute severe respiratory failure. Since no rat model of VV ECMO therapy with femoral drainage has yet been described, although this cannulation strategy is commonly used in humans, this study aimed to establish such a model. Twenty male Lewis rats were randomly assigned to receive a sham procedure or VV ECMO therapy. After the inhalative induction of anesthesia, animals were intubated and the vascular accesses were placed surgically. While venous drainage was achieved through a modified multi-orifice 18 G cannula that was placed in the inferior vena cava through the femoral vein over a guide wire with an ultra-flexible tip, the venous return was realized via a shortened 20 G cannula into the jugular vein. Hemodynamic data were obtained from a tail artery and left ventricular pressure–volume catheter. Repetitive blood gas analyses were carried out, and systemic inflammation was measured using an enzyme-linked immunosorbent assay. While animals in the ECMO group showed adequate oxygenation and decarboxylation, there was no evidence of recirculation. VV ECMO therapy increased stroke volume (SV), cardiac output (CO), and left ventricular end-diastolic volume (LVEDV). ECMO-induced inflammation was reflected in increased levels of tumor necrosis factor alpha. However, no differences in interleukins 6 and 10 were seen. This study describes a frequently used cannulation strategy in humans for a rat model of VV ECMO. Despite successful oxygenation and decarboxylation, the oxygenated blood may reduce pulmonary vascular resistance and lead to an increased LVEDV, which is associated with increased SV and CO. This model allows us to answer research questions about topics such as intestinal microcirculation in further studies.

Background and Objectives: Parathyroid adenoma is a distinct cause of primary hyperparathyroidism, with the vast majority being sporadic ones. Proteomic analysis of parathyroid adenomas has proposed a large number of related proteins. The aim of this study is to evaluate the immunohistochemical staining of ANXA2, MED12, MAPK1 and VDR in parathyroid adenoma tissue. Materials and Methods: Fifty-one parathyroid adenomas were analyzed for ANXA2, MED12, MAPK1 and VDR expressions. Tissue was extracted from formalin-fixed paraffin-embedded parathyroid adenoma specimens; an immunohistochemical study was applied, and the percentage of allocation and intensity were evaluated. Results: ANXA2 stained positively in 60.8% of all cell types, while MED12 had positive staining in 66%. MAPK1 expression was found to be negative in total, although a specific pattern for oxyphil cells was observed, as they stained positive in 17.7%. Finally, VDR staining was positive at 22.8%, based on nuclear staining. Conclusions: These immunohistochemical results could be utilized as biomarkers for the diagnosis of sporadic parathyroid adenoma. It is of great importance that a distinct immunophenotype of nodule-forming cells in a positive adenoma could suggest a specific pattern of adenoma development, as in hereditary patterns.

Glass-ceramics containing pyroelectric Sr2TiSi2O8 (STS) crystals are produced from parent glasses of compositions STS + 0.8 SiO2 + (0.2 − x) Na2O + x CaO, with x = 0; 0.05; 0.10; and 0.15. The aim of this work is to investigate the effect of the additions to the stoichiometric STS composition on the crystallization mechanisms and on the piezoelectric properties of the glass-ceramic. The DSC analyses evidence that the glass transition temperatures Tg, the onset temperature of the crystallization peak To and the temperature Tc of the maximum of this peak increase with the CaO/Na2O ratio. On the basis of the DSC data, the crystallization of the parent glass samples was operated by thermal treatment. The observation of the cross-sections of the heat-treated samples highlights that the competition between the surface and volume crystallization mechanisms is influenced by the CaO/Na2O ratio and the temperature. For all the samples, the XRD analyses performed on the surfaces as obtained after the crystallization treatment evidenced a preferential orientation of the STS crystals with the plans (00l) parallel to the surface. The XRD analyses performed after grinding the surface show that only the surface crystallized layers are preferentially oriented. Moreover, changes in preferential orientation to plans (202) or (201) are observed over the depth, depending on the composition and the temperature of the thermal treatment. These changes influence the polarization of the surface crystallized layer and, consequently, its piezoelectric charge coefficient d33. The highest values of d33 were measured on the glass-ceramic samples exhibiting mainly a (202) preferential orientation over their thickness.

T-cell immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a central role in the control of the virus. In this study, we evaluated the performance of T-Track® SARS-CoV-2, a novel CE-marked quantitative reverse transcription-polymerase chain reaction (RT-qPCR) assay, which relies on the combined evaluation of IFNG and CXCL10 mRNA levels in response to the S1 and NP SARS-CoV-2 antigens, in 335 participants with or without a history of SARS-CoV-2 infection and vaccination, respectively. Of the 62 convalescent donors, 100% responded to S1 and 88.7% to NP antigens. In comparison, of the 68 naïve donors, 4.4% were reactive to S1 and 19.1% to NP. Convalescent donors ® SARS-CoV-2 and IgG serology by recomLine SARS-CoV-2 IgG according to the time from the last immunisation (by vaccination or viral infection) were comparable. Both assays showed a persistent cellular and humoral response for at least 36 weeks post immunisation in vaccinated and convalescent donors. Our results demonstrate the very good performance of the T-Track® SARS-CoV-2 molecular assay and suggest that it might be suitable to monitor the SARS-CoV-2-specific T-cell response in COVID-19 vaccinations trials and cross-reactivity studies.

With the umbrella term ‘neurodevelopmental disorders’ (NDDs) we refer to a plethora of congenital pathological conditions generally connected with cognitive, social behavior, and sensory/motor alterations. Among the possible causes, gestational and perinatal insults have been demonstrated to interfere with the physiological processes necessary for the proper development of fetal brain cytoarchitecture and functionality. In recent years, several genetic disorders caused by mutations in key enzymes involved in purine metabolism have been associated with autism-like behavioral outcomes. Further analysis revealed dysregulated purine and pyrimidine levels in the biofluids of subjects with other NDDs. Moreover, the pharmacological blockade of specific purinergic pathways reversed the cognitive and behavioral defects caused by maternal immune activation, a validated and now extensively used rodent model for NDDs. Furthermore, Fragile X and Rett syndrome transgenic animal models as well as models of premature birth, have been successfully utilized to investigate purinergic signaling as a potential pharmacological target for these diseases. In this review, we examine results on the role of the P2 receptor signaling in the etiopathogenesis of NDDs. On this basis, we discuss how this evidence could be exploited to develop more receptor-specific ligands for future therapeutic interventions and novel prognostic markers for the early detection of these conditions.

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality.

Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output–input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle.

Heat sterilization of glucose solutions can lead to the formation of various glucose degradation products (GDPs) due to oxidation, hydrolysis, and dehydration. GDPs can have toxic effects after parenteral administration due to their high reactivity. In this study, the application of the F0 concept to modify specific time/temperature models during heat sterilization and their influence on the formation of GDPs in parenteral glucose solutions was investigated using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Glucose solutions (10%, w/v) were autoclaved at 111 °C, 116 °C, and 121 °C for different durations. The GDPs glyoxal, methylglyoxal, glucosone, 3-deoxyglucosone/3-deoxygalactosone, 3,4-dideoxyglucosone-3-ene, and 5-hydroxymethylfurfural were quantified after derivatization with o-phenylenediamine by an optimized LC-MS/MS method. For all GDPs, the limit of detection was

Maximizing the value of each available data point in bioprocess development is essential in order to reduce the time-to-market, lower the number of expensive wet-lab experiments, and maximize process understanding. Advanced in silico methods are increasingly being investigated to accomplish these goals. Within this contribution, we propose a novel integrated process model procedure to maximize the use of development data to optimize the Stage 1 process validation work flow. We generate an integrated process model based on available data and apply two innovative Monte Carlo simulation-based parameter sensitivity analysis linearization techniques to automate two quality by design activities: determining risk assessment severity rankings and establishing preliminary control strategies for critical process parameters. These procedures are assessed in a case study for proof of concept on a candidate monoclonal antibody bioprocess after process development, but prior to process characterization. The evaluation was successful in returning results that were used to support Stage I process validation milestones and demonstrated the potential to reduce the investigated parameters by up to 24% in process characterization, while simultaneously setting up a strategy for iterative updates of risk assessments and process controls throughout the process life-cycle to ensure a robust and efficient drug supply.

Hyaluronan is an essential physiological bio macromolecule with different functions. One prominent area is the synovial fluid which exhibits remarkable lubrication properties. However, the synovial fluid is a multi-component system where different macromolecules interact in a synergetic fashion. Within this study we focus on the interaction of hyaluronan and phospholipids, which are thought to play a key role for lubrication. We investigate how the interactions and the association structures formed by hyaluronan (HA) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) are influenced by the molecular weight of the bio polymer and the ionic composition of the solution. We combine techniques allowing us to investigate the phase behavior of lipids (differential scanning calorimetry, zeta potential and electrophoretic mobility) with structural investigation (dynamic light scattering, small angle scattering) and theoretical simulations (molecular dynamics). The interaction of hyaluronan and phospholipids depends on the molecular weight, where hyaluronan with lower molecular weight has the strongest interaction. Furthermore, the interaction is increased by the presence of calcium ions. Our simulations show that calcium ions are located close to the carboxylate groups of HA and, by this, reduce the number of formed hydrogen bonds between HA and DPPC. The observed change in the DPPC phase behavior can be attributed to a local charge inversion by calcium ions binding to the carboxylate groups as the binding distribution of hyaluronan and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine is not changed.

The aim of the present study was to investigate sources of variation in bulk milk composition, somatic cell score, coagulation properties, and mineral content of Alpine Grey and Pinzgauer single-breed herds in the Italian alpine area. A total of 56,914 bulk milk samples from 461 farms located in the Bolzano province (Northeast Italy) were available for statistical analysis. Least squares means revealed that Pinzgauer herds produced milk with greater fat content and shorter rennet coagulation time than Alpine Grey herds, but the latter had greater milk protein content and lower somatic cell scores than Pinzgauer herds. The greatest content of Ca, protein, and casein and the most favorable coagulation properties were observed in milk sampled in autumn in both breeds. Results should serve to differentiate the milk of Alpine Grey cattle and Pinzgauer cattle from milk of other cattle breeds and thus provide necessary arguments for developing new production concepts that might expand future opportunities for further valorizing these local, dual-purpose breeds and therefore contribute long-term to their preservation.

Implantable assembly components that are biocompatible and highly miniaturized are an important objective for hearing aid development. We introduce a mechanical transducer, which could be suitable as part of a prospective fully-implantable hearing aid. The transducer comprises a sensor and an actuator unit in one housing, located in the joint gap between the middle ear ossicles, the incus and stapes. The setup offers the advantage of a minimally invasive and reversible surgical solution. However, feedback between actuator and sensor due to mechanical coupling limits the available stable gain. We show that the system can be stabilized by digital control algorithms. The transducer is tested both in a finite elements method simulation of the middle ear and a physical model of a human middle ear. First, we characterize the sensor and actuator elements separately. Then, the maximum stable gain (MSG) of the whole transducer is experimentally determined in the middle ear model. With digital feedback control (using a least mean squares algorithm) in place, the total signal gain is greater than 30 dB for frequencies of 1 kHz and above. This shows the potential of the transducer as a high frequency hearing aid.

Monazite is a common accessory phosphate mineral that occurs under a wide range of pressure and temperature conditions in sedimentary, metamorphic and igneous rocks. Monazite contains high amounts of Th and U, rendering single monazite grains suitable for in-situ U-Th/Pb dating using laser ablation inductively-coupled mass spectrometry (LA-ICP-MS). Two key aspects of monazite dating that are critical for accurate age data with maximum precision are (i) optimized instrumental conditions to minimize analytical scatter and (ii) a well characterized reference material to ensure the accuracy of the obtained aged. Here, we analyzed five monazite reference materials (USGS 44069, 94-222, MAdel, Moacir and Thompson Mine Monazite) for their U-Th/Pb ages using LA-ICP-MS technique and applied a variety of laser spot diameters and repetition rates to find the best operational conditions to achieve accurate age data while maintaining maximum precision. We find that a spot diameter of 10 µm and a repetition rate of 10 Hz yield the most precise ages with a deviation of ±2.0% from their respective high-precision U/Pb literature age data. Ages were reproduced in three different LA-ICP-MS laboratories using these parameters. Each reference material was tested for its suitability as a matrix-matched age reference material. For this, a rotating, iterative approach was adopted in which one reference monazite was used as calibration reference material against all others, which were treated as unknowns. The results reveal that USGS 44069, 94-222, Thompson Mine Monazite and MAdel all agree with their respective calculated ages and ID-TIMS reference ages and thus are suggested as suitable calibration reference materials. Moacir, however, appears slightly older than previously suggested (up to 4%), thus, caution is advised here when using Moacir as reference material for U-Th/Pb LA-ICP-MS dating in the absence of further absolute age calibration.

Data related to the performance of burst containments for high-speed rotating machines, such as flywheel energy storage systems (FESS), turbines or electric motors is scarce. However, development of optimized burst containment structures requires statistically significant data, which calls out for low-cost test methods as a strategic development tool. Consequently, a low-cost test rig (so called spin pit) for the investigation of burst containments was designed, with the goal to systematically investigate the performance of different containment structures and materials, in conjunction with the failure mechanisms of different rotors. The gathered data (e.g., burst speed, acceleration, temperature, ambient pressure, etc.) in combination with a post-mortem analysis was used to draw an energy balance and enabled the assessment of the effectiveness of various burst containments.

Identification of critical process parameters that impact product quality is a central task during regulatory requested process validation. Commonly, this is done via design of experiments and identification of parameters significantly impacting product quality (rejection of the null hypothesis that the effect equals 0). However, parameters which show a large uncertainty and might result in an undesirable product quality limit critical to the product, may be missed. This might occur during the evaluation of experiments since residual/un-modelled variance in the experiments is larger than expected a priori. Estimation of such a risk is the task of the presented novel retrospective power analysis permutation test. This is evaluated using a data set for two unit operations established during characterization of a biopharmaceutical process in industry. The results show that, for one unit operation, the observed variance in the experiments is much larger than expected a priori, resulting in low power levels for all non-significant parameters. Moreover, we present a workflow of how to mitigate the risk associated with overlooked parameter effects. This enables a statistically sound identification of critical process parameters. The developed workflow will substantially support industry in delivering constant product quality, reduce process variance and increase patient safety.

During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events.