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Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many of the toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes.

In this paper, a long short-term memory (LSTM)-based method with a multi-input tensor approach is used for the classification of events that affect the power quality (PQ) in power systems with distributed generation sources. The considered events are line faults (one line, two lines, and three lines faulted), islanding events, sudden load variations, and generation tripping. The proposed LSTM-based method was trained and tested using the signals produced by the events simulated in a study system with distributed generation sources via PSCAD®. Then, noise with different levels was added to the testing set for a thorough assessment, and the results were compared with other well-known methods such as convolutional and simple recurrent neuronal networks. The LSTM-based method with multi-input proved to be effective for event classification, achieving remarkable classification performance even in noisy conditions.

In recent years, the global production and industrialization of avocados has led to the generation of large numbers of peel, seeds, and leaf by-products with significant environmental implications. Current efforts, including the Sustainable Development Goals, aim towards the development of sustainable operations through the valorization of waste. Previous research has focused on studying the rich chemical composition of these avocado by-products. Current studies are working on the isolation of polyphenols, flavonoids, phenolic acids and other bioactive compounds found in avocado peel, seeds and leaves for applications in nutraceutical products in the food, pharmaceuticals and cosmetics industries. The inclusion of these extracts in industrial matrices often requires stabilization technologies such as encapsulation emulsions to ensure the delivery and bioactivity of these target compounds. This review will discuss the global production volumes of avocado and its by-products as well as the potential inclusion in various industries based on their chemical compositions. Additionally, this work addresses the various applications that have been previously proposed for the wastes and their extracts. This review also covers the stabilization techniques previously employed in avocado extract treatment, their applications, and the current challenges and opportunities associated with avocado by-products.

In this study, we used machine learning (ML) to estimate time-lapse velocity variations in a reservoir region using seismic data. To accomplish this task, we needed an adequate training set that could map seismic data to velocity perturbation. We generated a synthetic seismic database by simulating reservoirs of varying velocities using a 2D velocity model typical of the Brazilian pre-salt ocean bottom node (OBN) acquisition, located in the Santos basin, Brazil. The largest velocity change in the injector well was around 3% of the empirical velocity model, which mimicked a realistic scenario. The acquisition geometry was formed by the geometry of 1 shot and 49 receivers. For each synthetic reservoir, the corresponding seismic data were obtained by estimating a one-shot forward-wave propagation using acoustic approximation. We studied the reservoir illumination to optimize the input data of the ML inversion. We split the set of synthetic reservoirs into two subsets: training (80%) and testing (20%) sets. We point out that the ML inversion was restricted to the reservoir zone, which means that it was inversion-oriented to a target. We obtained a good similarity between true and ML-inverted reservoir anomalies. The similarity diminished for a situation with non-repeatability noise.

The development of novel tools to tackle viral processes has become a central focus in global health, during the COVID-19 pandemic. The spike protein is currently one of the main SARS-CoV-2 targets, owing to its key roles in infectivity and virion formation. In this context, exploring innovative strategies to block the activity of essential factors of SARS-CoV-2, such as spike proteins, will strengthen the capacity to respond to current and future threats. In the present work, we developed and tested novel bispecific molecules that encompass: (i) oligonucleotide aptamers S901 and S702, which bind to the spike protein through its S1 domain, and (ii) hydrophobic tags, such as adamantane and tert-butyl-carbamate-based ligands. Hydrophobic tags have the capacity to trigger the degradation of targets recruited in the context of a proteolytic chimera by activating quality control pathways. We observed that S901-adamantyl conjugates promote the degradation of the S1 spike domain, stably expressed in human cells by genomic insertion. These results highlight the suitability of aptamers as target-recognition molecules and the robustness of protein quality control pathways triggered by hydrophobic signals, and place aptamer-Hytacs as promising tools for counteracting coronavirus progression in human cells.

Polycarboxylate ether (PCE) superplasticisers have been widely used in cement formulations. However, it is not until recently that several studies have analysed the relationship between the properties and the molecular structure. In the present work, PCEs with different side chain lengths and charge densities synthesised through free radical copolymerisation are used to analyse the effect they have on the hydration of ordinary Portland cement (OPC). It was found that the addition method of these PCEs to the OPC significantly affects the hydration kinetics of the cement paste. When PCEs are added through the direct addition method, a linear dependency between the retardation of hydration and the microstructure of the used PCEs is observed. On the contrary, when PCEs are added through the delayed addition method (PCEs are added to the cement paste 5 min after water), no retardation in hydration is observed, but the rate of hydration is reduced.

The major factors affecting the in vitro immature embryo rescue efficiencies from Prunus persica or P. armeniaca accessions have been identified, along with improving the feasibility. Variations in the woody plant medium (WPM) were used depending on the embryo size. Embryos less than 5 mm long were cultured in WPM supplemented with 1 μM BAP and 1 μM GA3, while embryos bigger than 5 mm long were cultured in hormone-free medium, with or without vermiculite. The environmental in vitro culture conditions consisted of three phases: a (I) stratification at 4 °C during a 3- to 5-month-long period in the dark, followed by (II) growth of germinated embryos at 14 °C for a 4-week-long period, with 12 h light a day, which favors plantlet development, and finally, (III) growth at 24 °C, with 16 h light a day, until the plantlets were acclimatized in the greenhouse. The germination of smaller embryos, at the end of phase I, ranged from 82.2% to 22.1% for apricots and flat peaches, respectively, whereas for bigger embryos, the germination varied from 97.3% to 53.2% for the same species. The embryo germination for peaches and nectarines ranged from 40.1% to 30.3% for smaller embryos, and from 91.9% to 63.0% for bigger embryos. Endo- and epiphytic contamination, affecting from 7.4% to 52.9% of cultured embryos, depending on the fruit type and conservation conditions, and the capacity to acclimate to soil conditions, ranging from 50.4% to 93.2%, were the two most important factors influencing the protocol’s efficiency and feasibility. Considering the overall efficiencies, expressed as hardened plants transferred to field plots over clean uncontaminated embryo, the values ranged from 55.8% for nectarines, 54.0% for peaches, 45.6% for apricots, and 23.3% for flat fruits. The addition of vermiculite to the culture medium significantly improved the plantlet development, avoiding subculture to fresh medium when an extension of phase III was required before acclimatization. Compared to laboratory glassware, the use of food glass containers with air-permeable sealing film, along with vermiculite-containing medium, significantly reduced the costs when handling the large number of embryos required for breeding programs.

Background: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n = 5844) was used to infer a convolutional neural network for wheal segmentation (ML model). Three methods for inferring wheal dimensions were evaluated: the ML model; the standard protocol (MA1); and approximation of the area as an ellipse using diameters measured by an allergist (MA2). The results were compared with assisted image segmentation (AIS), the most accurate method. Bland–Altman analysis, distribution analyses, and correlation tests were applied to compare the methods. This study also compared the percentage deviation among these methods in determining the area of wheals with regular geometric shapes (n = 150) and with irregular shapes (n = 150). Results: The Bland–Altman analysis showed that the difference between methods was not correlated with the absolute area. The ML model achieved a segmentation accuracy of 85.88% and a strong correlation with the AIS method (ρ = 0.88), outperforming all other methods. Additionally, MA1 showed significant error (13.44 ± 13.95%) for pseudopods. Conclusions: The ML protocol can potentially automate the reading of SPT, offering greater accuracy than the standard protocol.

Advancements in robotics are increasingly essential in addressing complex challenges associated with motion planning and control, particularly in environments that require physical interaction with various elements [...]

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices.

This sequential explanatory mixed-methods study investigated the differences in persistence between students from the Sherman STEM Teacher Scholars Program (STEP), a STEM teacher scholarship and career preparation program, and STEM majors not in the program. Quantitative results indicated that STEP participants had higher levels of academic integration, women scored higher on persistence factors than men, and White students had a higher degree commitment than students of color. Qualitative findings indicated that STEP provided a family atmosphere and connected their coursework to their career aspirations. Women of color felt stereotyped by White classmates in STEM courses, which impacted their degree of commitment, and students of color in STEP relied on the program as a counterspace to racially insensitive STEM classrooms.

Biological decontamination strategies using microorganisms to adsorb aflatoxins have shown promising results for reducing the dietary exposure to these contaminants. In this study, the ability of inactivated biomasses of Lacticaseibacillus rhamnosus (LRB) and Saccharomyces cerevisiae (SCB) incorporated alone or in combination into functional yogurts (FY) at 0.5–4.0% (w/w) to adsorb aflatoxin B1 (AFB1) was evaluated in vitro. Higher adsorption percentages (86.9–91.2%) were observed in FY containing 1.0% LR + SC or 2.0% SC (w/w). The survival of mouse embryonic fibroblasts increased after exposure to yogurts containing LC + SC at 1.0–4.0% (w/w). No significant differences were noted in the physicochemical and sensory characteristics between aflatoxin-free FY and control yogurts (no biomass) after 30 days of storage. The incorporation of combined LRB and SCB into yogurts as vehicles for these inactivated biomasses is a promising alternative for reducing the exposure to dietary AFB1. The results of this trial support further studies to develop practical applications aiming at the scalability of using the biomasses evaluated in functional foods to mitigate aflatoxin exposure.

This work studies the Hotelling game with sequential choice of prices, that is, the Stackelberg–Hotelling (SHOT) game. The game is studied through numerical simulation, which provides the subgame perfect equilibrium solution not only in the unrestricted game but also in the game with reservation cost and with elastic demand. The simulation technique is tested first in the unconstrained game, where the analytical subgame perfect equilibrium solution was already known. Then, the numerical procedure is generalized to cope with the SHOT game with reservation cost and with elastic demand. These enriched formulations of the SHOT game have not been studied so far, so this article provides an exploratory study of them.

The economic cost of repairing corrosion-affected reinforced concrete structures (RCSs) means that reliable and accurate assessment and early detection methods must be sought after. Conventional techniques, such as visual inspections, or measuring either cover layer resistivity or the corrosion potential, are methods that require accessibility and involve personnel having to travel to take in situ measurements. Monitoring by embedded sensors is a much more efficient approach that allows early detection by remote sensing. This work presents the implementation of a new measurement protocol regarding the existing monitoring system called INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring). Along with the corrosion intensity measurement in embedded sensors, it also proposes monitoring the double layer capacity of the sensors’ responses. It aims to determine, along with the rebars’ corrosion rate, the triggering agent of the corrosion process. This study was carried out using three reinforced concrete scaled columns that were exposed to different environments. The results demonstrate with this new protocol that the remote INESSCOM monitoring system can establish the corrosion rate and identify the precursor agent of corrosion (carbonation or chlorides), even when the recorded corrosion rates are similar.

Lactic acid bacteria (LABs) are microorganisms of significant scientific and industrial importance and have great potential for application in meat and meat products. This comprehensive review addresses the main characteristics of LABs, their nutritional, functional, and technological benefits, and especially their importance not only as starter cultures. LABs produce several metabolites during their fermentation process, which include bioactive compounds, such as peptides with antimicrobial, antidiabetic, antihypertensive, and immunomodulatory properties. These metabolites present several benefits as health promoters but are also important from a technological point of view. For example, bacteriocins, organic acids, and other compounds are of great importance, whether from a sensory or product quality or a safety point of view. With the production of GABA, exopolysaccharides, antioxidants, and vitamins are beneficial metabolites that influence safety, technological processes, and even health-promoting consumer benefits. Despite the benefits, this review also highlights that some LABs may present virulence properties, requiring critical evaluation for using specific strains in food formulations. Overall, this review hopes to contribute to the scientific literature by increasing knowledge of the various benefits of LABs in meat and meat products.

Background: To enhance patient safety in healthcare, it is crucial to address the underreporting of issues in Critical Incident Reporting Systems (CIRSs). This study aims to evaluate the effectiveness of generative Artificial Intelligence and Natural Language Processing (AI/NLP) in reviewing CIRS cases by comparing its performance with human reviewers and categorising these cases into relevant topics. Methods: A case–control feasibility study was conducted using CIRS cases from the German CIRS-Anaesthesiology subsystem. Each case was reviewed by a human expert and by an AI/NLP model (ChatGPT-3.5). Two CIRS experts blindly assessed these reviews, rating them on linguistic quality, recognisable expertise, logical derivability, and overall quality using six-point Likert scales. Results: On average, the CIRS experts correctly classified 80% of human CIRS reviews as created by a human and misclassified 45.8% of AI reviews as written by a human. Ratings on a scale of 1 (very good) to 6 (failed) revealed a comparable performance between human- and AI-generated reviews across the dimensions of linguistic expression (p = 0.39), recognisable expertise (p = 0.89), logical derivability (p = 0.84), and overall quality (p = 0.87). The AI model was able to categorise the cases into relevant topics independently. Conclusions: This feasibility study demonstrates the potential of generative AI/NLP in analysing and categorising cases from the CIRS. This could have implications for improving incident reporting in healthcare. Therefore, additional research is required to verify and expand upon these discoveries.

This work investigated the gelation kinetics and mechanical properties of agarose hydrogels studied at different concentrations (in the range 1–5 g/L) and temperatures. Rheological measurements were performed by diffusing wave spectroscopy (DWS) using polystyrene and titanium dioxide particles as probes. The study emphasized the influence of gelation kinetics on the mechanical behavior of the hydrogels. The results showed that the gel properties were closely related to the thermal history and aging time of the samples. The insights gained from this study are critical for optimizing the performance of agarose hydrogels in specific applications and highlight the importance of controlling the concentration and thermal conditions during hydrogel preparation.

The historical and cultural significance of artistic works and archaeological artifacts underscores the imperative use of non-destructive testing methods in cultural heritage objects. Analyzing pigments in artwork poses a specific analytical challenge that demands a combination of various techniques to accurately determine chemical compositions. In this context, our work focused on the multi-analytical characterization of samples derived from fragments of a Roman-era Egyptian mummy named Kherima, dating back to around 200 AD. To identify the layers and elemental composition of the pigments used in the decoration, various techniques were employed: X-ray microfluorescence (µXRF), X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), high-resolution optical microscopy (OM), and X-ray computed microtomography (microCT). This multi-analytical approach facilitated the identification of the original pigments in the analyzed mummy fragments, along with insights into the materials used in the ground layer and the techniques applied in artifact manufacturing, indicating their accordance with the historical period and region to which they originally belonged.

Nanoparticles (NPs) are receiving increasing interest in biomedical applications. However, due to their large surface area, in physiological environments, they tend to interact with plasma proteins, inducing their agglomeration and ultimately resulting in a substantial efficiency decrease in diagnostic and therapeutic applications. To overcome such problems, NPs are typically coated with a layer of hydrophilic and biocompatible polymers, such as PEG chains. However, few examples exist in which this property could be systematically fine-tuned and combined with added properties, such as emission. Herein, we report a novel mussel-inspired catechol-based strategy to obtain biocompatible and multifunctional coatings, using a previously developed polymerization methodology based on the formation of disulfide bridges under mild oxidative conditions. Two families of NPs were selected as the proof of concept: mesoporous silica NPs (MSNPs), due to their stability and known applications, and magnetite NPs (Fe3O4 NPs), due to their small size (

Mead is a fermented alcoholic beverage produced from a diluted solution of honey and yeast activity. The objectives of this study were to produce a potentially probiotic mead through mixed fermentation by Saccharomyces cerevisiae var. boulardii and kombucha microorganisms and to evaluate fermentation kinetics, microbial cell survival and their in vitro resistance to simulated gastrointestinal transit, color parameters and the phenolic and antioxidant potential of the product. The main results of this study show that in order to develop a potentially probiotic mead utilizing the mixed fermentation of S. boulardii and kombucha microorganisms, the best condition was a concentration of 25 mL/L (v/v) of kombucha and 0.75 g/L (w/v) of S. boulardii with fermentation for 9 days at a temperature of 25 °C. In addition, at the end of fermentation, mead with kombucha and S. boulardii presented physicochemical characteristics with a pH of 3.48, 0.67% total acidity, 18.76 °Brix soluble solids and 4.77% alcohol content. The S. boulardii and lactic acid bacteria (LAB) present in the mead survived conditions reproducing those of the gastrointestinal tract, with counts of more than 6 Log10 CFU/mL for both microorganisms after the intestinal phase. In the color analysis, the mead with kombucha and S. boulardii had a yellow color with the b* parameter corresponding to 35.93, luminosity (L*) equal to 76.09 and 1.82 for a*. In addition, the mead we produced contains quantities of phenolics and antioxidants. In conclusion, kombucha and S. boulardii are presented as alternative microbial sources for obtaining potentially probiotic mead.

Eucalyptus-based glued laminated timber (glulam) was produced to determine the feasibility of a non-destructive method (drilling resistance) to predict the properties of structural elements and add value to lower-quality hardwood species. Glulam was manufactured with formaldehyde (Resorcinol), reference condition, and bio-based (Castor oil-based) adhesives in two assembly schemes, the core composed either of two continuous lamellae each 105 cm long, or of two formed by the juxtaposition of shorter boards (35 and 55 cm). The shear strength of the glue line (fv0), modulus of elasticity (Ec90), and strength (fc90) in compression perpendicular to the grain; delamination (DL); and main and extended glue line thicknesses were evaluated. The Resistograph equipment was used to perform the perforation perpendicular to the glue line (samples extracted from the glulam elements) to correlate the properties. The results of this research demonstrate that the scheme of the boards had little effect on the physical and mechanical properties evaluated (except the main glue line and delamination), and the drilling resistance (DR) presents a significant correlation with practically all properties evaluated (variations in density values and other properties are explained by variations in DR values), making it possible to estimate Ec90 and fc90 with desired precision (R2adj ≈ 80%). This highlights the feasibility of using this methodology in the quality control of glulam elements. It is concluded that regardless of the adhesive, elements comprising a 105 cm-length core and external lamellae (T1 and control) are indicated for external use, presenting low delamination. Short-length central lamellae adhesively glued with PUR (T2) are not recommended for external applications due to their susceptibility to delamination. However, T2 is indicated for internal environments due to its low production cost. This study also proved the efficiency of using models based on drilling resistance to estimate wood density and its resistance to compression perpendicular to the fiber.

This research explores the application of wireless sensor networks for the non-invasive monitoring of sleep quality and vital signs in elderly individuals, addressing significant challenges faced by the aging population. The study implemented and evaluated WSNs in home environments, focusing on variables such as breathing frequency, deep sleep, snoring, heart rate, heart rate variability (HRV), oxygen saturation, Rapid Eye Movement (REM sleep), and temperature. The results demonstrated substantial improvements in key metrics: 68% in breathing frequency, 68% in deep sleep, 70% in snoring reduction, 91% in HRV, and 85% in REM sleep. Additionally, temperature control was identified as a critical factor, with higher temperatures negatively impacting sleep quality. By integrating AI with WSN data, this study provided personalized health recommendations, enhancing sleep quality and overall health. This approach also offered significant support to caregivers, reducing their burden. This research highlights the cost-effectiveness and scalability of WSN technology, suggesting its feasibility for widespread adoption. The findings represent a significant advancement in geriatric health monitoring, paving the way for more comprehensive and integrated care solutions.

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0–8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear–quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

Lichen planus (LP) is a highly prevalent inflammatory skin disease. While various clinical subtypes have been defined, detailed comparisons of these variants are lacking. This study aimed to elucidate differences in gene expression and cellular composition across LP subtypes. Lesional skin biopsies from 28 LP patients (classical, oral, genital, and lichen planopilaris) and seven non-diseased skin controls (NDC) were analyzed. Gene expression profiling of 730 inflammation-related genes was conducted using NanoString. Immune cell compositions were assessed by multiplex immunohistochemistry. Gene expression profiles revealed unique inflammatory signatures for each LP subtype. Lichen planopilaris exhibited the most divergence, with downregulated gene expression and upregulation of complement pathway genes (C5-7), along with elevated M2 macrophages. Oral and genital LP demonstrated similar profiles with strong upregulation of TNF-related and Toll-like receptor-associated genes. Oral LP showed the highest upregulation of cytotoxicity-associated genes, as well as high numbers of CD8+ IL-17A+ (Tc17) cells (8.02%). Interferon gene signatures were strongly upregulated in oral and classical LP. The study highlights distinct differences in inflammatory gene expression and cell composition across LP subtypes, emphasizing the need for tailored therapeutic approaches.

Positive Youth Development (PYD) emerged as a holistic and strength-based perspective that focuses on the fact that young people may have the internal and external resources for healthy and successful development through five dimensions (5Cs) that empower them: Perceived Competence, Confidence, Character, Connection, and Caring. The aim of this study was to examine the relationship between the overall PYD factor, the 5Cs, and risk behaviors, in addition to analyzing gender differences. This study showed the results of a cross-sectional study of 1044 emerging adults from 11 Spanish universities in 2021. Data collection was performed by applying an online self-report measure. The results showed that the Character was protective against substance abuse, mainly in women, while the connection was related to the participation of betting money and online betting in men. Caring was protective against money bets in the men’s sample. However, controversial results were found regarding Perceived competence, which had a positive association with substance abuse, money bets, and drunk driving. It seems that high levels of Perceived competence, rather than objective competence, were associated with engagement in various risk behaviors. Concerning gender differences, men showed more risky behaviors than women. A partial mediation model pointed out that lower character and higher perceived competence in men partly explained the higher presence of risky behavior compared to women. These results underline the need to promote PYD within the university context to prevent risky behaviors by addressing gender differences and the separate role of the 5Cs.

Food safety is a major concern for commercial poultry producers and consumers. Currently, there is also pressure from retailers and legislators to increase the space per hen in cages. Five different density treatments consisting of six (208 in2/bird), nine (139 in2/bird), twelve (104 in2/bird), fifteen (83 in2/bird), and eighteen birds (69 in2/bird) per cage were examined in colony cage environments. Microbiological tests were performed at 39, 55, and 68 weeks of age. The populations of total aerobic bacteria; E. coli/coliform; Enterobacteriaceae; and yeasts and molds from an eggshell rinse, egg content, and cloacal swabs were enumerated. The prevalence of Salmonella spp. in these samples was also monitored. Overall, no bacteria were detected in any of the egg content, and there were no differences (p > 0.05) between treatments for the shell rinse. Stocking density did not influence the eggshell microbiota of the hens. Hens housed at 104 in2 per hen showed higher levels of total aerobic bacterial counts from the cloaca compared to hens at 208 in2 and 69 in2 per hen. Hens housed at 139 in2 per hen had the highest level of cloacal molds. This research demonstrates that stocking density does not influence eggshell microbiota or Salmonella contamination of the eggshell or cloaca, thereby indicating that allowing more space per hen will not positively or negatively affect the prevalence or concentration of foodborne pathogen-associated bacteria in or on the eggs.

Worldwide, 3.9 million individuals rely on kidney replacement therapy. They experience heightened susceptibility to cardiovascular diseases and mortality, alongside an increased risk of infections and malignancies, with inflammation being key to explaining this intensified risk. This study utilized semi-targeted metabolomics to explore novel metabolic pathways related to inflammation in this population. We collected pre- and post-session blood samples of patients who had already undergone one year of chronic hemodialysis and used liquid chromatography and high-resolution mass spectrometry to perform a metabolomic analysis. Afterwards, we employed both univariate (Mann–Whitney test) and multivariate (logistic regression with LASSO regularization) to identify metabolites associated with inflammation. In the univariate analysis, indole-3-acetaldehyde, 2-ketobutyric acid, and urocanic acid showed statistically significant decreases in median concentrations in the presence of inflammation. In the multivariate analysis, metabolites positively associated with inflammation included allantoin, taurodeoxycholic acid, norepinephrine, pyroglutamic acid, and L-hydroorotic acid. Conversely, metabolites showing negative associations with inflammation included benzoic acid, indole-3-acetaldehyde, methionine, citrulline, alphaketoglutarate, n-acetyl-ornithine, and 3-4-dihydroxibenzeneacetic acid. Non-inflamed patients exhibit preserved autophagy and reduced mitochondrial dysfunction. Understanding inflammation in this group hinges on the metabolism of arginine and the urea cycle. Additionally, the microbiota, particularly uricase-producing bacteria and those metabolizing tryptophan, play critical roles.

The efficient use of depleted gas reservoirs for hydrogen storage is a promising solution for transitioning to carbon-neutral energy sources. This study proposes an analytical framework for estimating hydrogen storage capacity using a comprehensive material balance approach in depleted gas reservoirs. The methodology integrates basic reservoir engineering principles with thermodynamic considerations to accurately estimate hydrogen storage capacity in both volumetric drive and water drive gas reservoirs through an iterative approach based on mass conservation and the real gas law. This framework is implemented in a Python program, using the CoolProp library for phase behavior modeling with the Soave–Redlich–Kwong (SRK) equation of state. The methodology is validated with numerical simulations of a tank model representing the two reservoir drive mechanisms discussed. Also, a case study of a synthetic complex reservoir demonstrates the applicability of the proposed approach to real-world scenarios. The findings suggest that precise modeling of fluid behavior is crucial for reliable capacity estimations. The proposed analytical framework achieves an impressive accuracy, with deviations of less than 1% compared to estimates obtained through numerical simulations. Insights derived from this study can significantly contribute to the assessment of strategic decisions for utilizing depleted gas reservoirs for hydrogen storage.

Arrhythmogenic cardiomyopathy is an inherited entity characterized by irregular cell–cell adhesion, cardiomyocyte death and fibro-fatty replacement of ventricular myocytes, leading to malignant ventricular arrythmias, contractile dysfunction and sudden cardiac death. Pathogenic variants in genes that encode desmosome are the predominant cause of arrhythmogenic cardiomyopathy. Moreover, signalling pathways such as Wnt/ß-catenin and transforming growth factor-β have been involved in the disease progression. However, still little is known about the molecular pathophysiological mechanisms that underlie arrhythmogenic cardiomyopathy pathogenesis. We used mRNA and small RNA sequencing to analyse the transcriptome of health and arrhythmogenic cardiomyopathy of autopsied human hearts. Our results showed 697 differentially expressed genes and eight differentially expressed miRNAs. Functional enrichment revealed mitochondrial respiratory-related pathways, impaired response to oxidative stress, apoptotic signalling pathways and inflammatory response-related and extracellular matrix response pathways. Furthermore, analysis of the miRNA–mRNA interactome identified eleven negatively correlated miRNA-target pairs for arrhythmogenic cardiomyopathy. Our finding revealed novel arrhythmogenic cardiomyopathy-related miRNAs with important regulatory function in disease pathogenesis, highlighting their value as potential key targets for therapeutic approaches.

Interleukin (IL)-9 is present in atopic dermatitis (AD) lesions and is considered to be mainly produced by skin-homing T cells expressing the cutaneous lymphocyte-associated antigen (CLA). However, its induction by AD-associated triggers remains unexplored. Circulating skin-tropic CLA+ and extracutaneous/systemic CLA− memory T cells cocultured with autologous lesional epidermal cells from AD patients were activated with house dust mite (HDM) and staphylococcal enterotoxin B (SEB). Levels of AD-related mediators in response to both stimuli were measured in supernatants, and the cytokine response was associated with different clinical characteristics. Both HDM and SEB triggered heterogeneous IL-9 production by CLA+ and CLA− T cells in a clinically homogenous group of AD patients, which enabled patient stratification into IL-9 producers and non-producers, with the former group exhibiting heightened HDM-specific and total IgE levels. Upon allergen exposure, IL-9 production depended on the contribution of epidermal cells and class II-mediated presentation; it was the greatest cytokine produced and correlated with HDM-specific IgE levels, whereas SEB mildly induced its release. This study demonstrates that both skin-tropic and extracutaneous memory T cells produce IL-9 and suggests that the degree of allergen sensitization reflects the varied IL-9 responses in vitro, which may allow for patient stratification in a clinically homogenous population.

Congenital heart disease (CHD) can be complicated by pulmonary arterial hypertension (PAH). Cardiopulmonary bypass (CPB) for corrective surgery may cause endothelial dysfunction, involving endothelin-1 (ET-1), circulating endothelial cells (CECs), and endothelial progenitor cells (EPCs). These markers can gauge disease severity, but their levels in children’s peripheral blood still lack consensus for prognostic value. The aim of our study was to investigate changes in ET-1, cytokines, and the absolute numbers (Ɲ) of CECs and EPCs in children 24 h before and 48 h after CPB surgery to identify high-risk patients of complications. A cohort of 56 children was included: 41 cases with CHD-PAH (22 with high pulmonary flow and 19 with low pulmonary flow) and 15 control cases. We observed that Ɲ-CECs increased in both CHD groups and that Ɲ-EPCs decreased in the immediate post-surgical period, and there was a strong negative correlation between ET-1 and CEC before surgery, along with significant changes in ET-1, IL8, IL6, and CEC levels. Our findings support the understanding of endothelial cell precursors’ role in endogenous repair and contribute to knowledge about endothelial dysfunction in CHD.

The reaction of Mn(II) salts in the air with different R-salicylaldehyde oximes and the sodium or cesium salts of 9-anthracenecarboxylato (9-AC) allows for the isolation of new six polynuclear compounds: [Mn3NaO(salox)3(9-AC)2(EtOH)3H2O]n·2EtOH (1), [Mn3NaO(3-Me-salox)3(9-AC)2(EtOH)3H2O]n·EtOH (2), [Mn6O2(salox)6(9-AC)2(EtOH)2(H2O)2]·3EtOH (3), [Mn3O(3-Me-salox)3(9-AC)(EtOH)3(H2O)]·1.8EtOH·3H2O (4), [Mn6O2(Me-salox)6(9-AC)2(EtOH)4(H2O)2]·0.5H2O (5), and [Mn6O2(Et-salox)6(9-AC)2(EtOH)4(H2O)2]·3EtOH (6). H2salox is a salicylaldehyde oxime, H2(3-Me-salox) is a 3-methyl-salicylaldehyde oxime, H2Me-salox is a 1-(2-hydroxyphenyl)ethan-1-one oxime and a H2-Et-salox is 1-(2-hydroxyphenyl)propan-1-one oxime. Structurally, compounds 1 and 2 consist of chains of trinuclear {MnIII3(μ3-O)(salox)3}+ units connected by Na+ ions. Compounds 3, 5, and 6 are hexanuclear units formed by two parallel trinuclear units {MnIII3(μ3-O)(salox)3}+ or {MnIII3(μ3-O)(Me-salox)3}+ planes related through an inversion center. Compound 4 consists of two isolated [Mn3O(3-Me-salox)3(9-AC)(EtOH)3(H2O)] trinuclear molecules in the unit cell showing crystallographic differences. Magnetic studies reveal a set of antiferromagnetic interactions in compounds 1 and 2 and a combination of antiferromagnetic and ferromagnetic interactions in compounds 3, 5, and 6. In all cases, the magneto-structural correlation between the intramolecular MnIII-N-O-MnIII torsion angle and the magnetic exchange within these units have been confirmed. For compounds 5 and 6, ac magnetic measurements reveal the slow relaxation of magnetization with moderate energy barriers of 19.9 cm−1 and 31.1 cm−1, respectively. Absorbance and fluorescence measurements in solution show the transitions of the 9-anthracenecarboxylate chromophore for all the compounds.

The aim of our cross-sectional and longitudinal study is to assess the relationship between daytime and night-time sleep duration and health-related quality of life (HRQoL) in adults with metabolic syndrome after a 1-year healthy lifestyle intervention. Analysis of the data from 2119 Spanish adults aged 55–75 years from the PREDIMED-Plus study was performed. Sleep duration was assessed using a wrist-worn accelerometer. HRQoL was measured using the SF-36 questionnaire. Linear regression models adjusted for socioeconomic and lifestyle factors and morbidity were developed. In cross-sectional analyses, participants with extreme night-time sleep duration categories showed lower physical component summary scores in Models 1 and 2 [β-coefficient (95% confidence interval) p = 0.002. >9 h vs. 7–9 h: −1.1 (−2.0 to −0.3); p = 0.01]. Participants who sleep less than 7 h a night and take a nap are associated with higher mental component summary scores [β-coefficient (95% confidence interval) 6.3 (1.3 to 11.3); p = 0.01]. No differences between night-time sleep categories and 12-month changes in HRQoL were observed. In conclusion, in cross-sectional analyses, extremes in nocturnal sleep duration are related to lower physical component summary scores and napping is associated with higher mental component summary scores in older adults who sleep less than 7 h a night.

Studies addressing the supervised extraction of geospatial elements from aerial imagery with semantic segmentation operations (including road surface areas) commonly feature tile sizes varying from 256 × 256 pixels to 1024 × 1024 pixels with no overlap. Relevant geo-computing works in the field often comment on prediction errors that could be attributed to the effect of tile size (number of pixels or the amount of information in the processed image) or to the overlap levels between adjacent image tiles (caused by the absence of continuity information near the borders). This study provides further insights into the impact of tile overlaps and tile sizes on the performance of deep learning (DL) models trained for road extraction. In this work, three semantic segmentation architectures were trained on data from the SROADEX dataset (orthoimages and their binary road masks) that contains approximately 700 million pixels of the positive “Road” class for the road surface area extraction task. First, a statistical analysis is conducted on the performance metrics achieved on unseen testing data featuring around 18 million pixels of the positive class. The goal of this analysis was to study the difference in mean performance and the main and interaction effects of the fixed factors on the dependent variables. The statistical tests proved that the impact on performance was significant for the main effects and for the two-way interaction between tile size and tile overlap and between tile size and DL architecture, at a level of significance of 0.05. We provide further insights and trends in the predictions of the extensive qualitative analysis carried out with the predictions of the best models at each tile size. The results indicate that training the DL models on larger tile sizes with a small percentage of overlap delivers better road representations and that testing different combinations of model and tile sizes can help achieve a better extraction performance.

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was max and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.

Leishmaniasis is an infectious disease caused by protozoa of the genus Leishmania, which is endemic in certain areas of Europe, such as southern Spain. The disease manifests in various clinical phenotypes, including visceral, cutaneous, mucosal, or asymptomatic leishmaniasis. This diversity in clinical outcomes may be influenced by the host immune response, with human leukocyte antigen (HLA) molecules playing a crucial role in determining susceptibility and progression of the infection. This study explores the association between specific HLA variants and Leishmania infantum infection. We recruited four cohorts: a control group, asymptomatic individuals, patients with symptomatic disease, and cohabitants of infected individuals. HLA typing was performed for all participants, followed by an association analysis with infection status and disease progression. Our findings indicate that the HLA-B*38 and HLA-C*03 alleles are associated with protection against L. infantum infection. These results contribute to a better understanding of the disease’s progression, offer potential for new therapeutic approaches such as vaccines, and expand the existing knowledge in the literature.

Choosing the right metal AM equipment and material is a highly intricate process that forms a crucial part of every manufacturing company’s strategic plan. This study undertakes a comprehensive comparison of the performance and material properties of three Metal Additive Manufacturing (AM) technologies: Powder Bed Fusion (PBF), Metal Filament Deposition Modeling (MFDM), and Bound Metal Deposition (BMD). An automotive nozzle was selected and manufactured using all three technologies and three metallic materials to understand their respective advantages and disadvantages. The samples were then subjected to a series of tests and evaluations, including dimensional accuracy, mechanical properties, microstructure, defects, manufacturability, and cost efficiency. The nozzle combinations were PBF in aluminum, MFDM in stainless steel, and BMD in hard tool steel. The results underscore significant differences in functionality, material characteristics, product quality, lead time, and cost efficiency, all of which are crucial factors in making equipment investment decisions. The conclusions drawn in this paper aim to assist automotive industry equipment experts in making informed decisions about the technology and materials to use for parts with characteristics like these. Future studies will delve into other technologies, automotive components, and materials to further enhance our understanding of the application of metal AM in manufacturing.

Background: COVID-19 is an infectious disease caused by SARS-CoV-2. It is unclear whether influenza vaccination reduces the severity of disease symptoms. Previous studies have suggested a beneficial effect of influenza vaccination on the severity of COVID-19. The aim of this study was to evaluate the possible protective effect of the influenza vaccine on the occurrence of SARS-CoV-2 infection symptoms and prognosis in patients hospitalized with COVID-19. Methods: This was a retrospective cohort study of patients who tested positive for SARS-CoV-2, identified by quantitative real-time polymerase chain reaction. Chi-square tests, Kaplan–Meier analysis, and multivariate analysis were performed to assess the association between influenza vaccination and the presence of symptoms in hospitalized patients with COVID-19 and their outcome. Results: In this study, 1712 patients received positive laboratory tests for SARS-CoV-2; influenza vaccination was a protective factor against the presence of characteristic COVID-19 symptoms such as polypnea, anosmia, dysgeusia, and fever (p < 0.001). Influenza-vaccinated patients had fewer days of hospitalization (p = 0.029). Conclusions: The findings of this study support that influenza vaccination is associated with a decrease in the number of symptoms in patients hospitalized due to COVID-19, with fewer days of hospitalization, but not with the outcome of disease.

This literature review explores cutting-edge microencapsulation techniques designed to enhance the antimicrobial efficacy of essential oils in dairy products. As consumer demand for natural preservatives rises, understanding the latest advancements in microencapsulation becomes crucial for improving the shelf life and safety of these products. The bibliometric analysis utilized in this review highlighted a large number of documents published on this topic in relation to the following keywords: essential oils, AND antimicrobials, AND dairy products, OR microencapsulation. The documents published in the last 11 years, between 2013 and 2023, showed a diversity of authors and countries researching this topic and the keywords commonly used. However, in the literature consulted, no study was identified that was based on bibliometric analysis and that critically evaluated the microencapsulation of essential oils and their antimicrobial potential in dairy products. This review synthesizes findings from diverse studies, shedding light on the various encapsulation methods employed and their impact on preserving the quality of dairy goods. Additionally, it discusses the potential applications and challenges associated with implementation in the dairy industry. This comprehensive analysis aims to provide valuable insights for researchers, food scientists, and industry professionals seeking to optimize the use of essential oils with antimicrobial properties in dairy formulations.

This study explored the impact of various label information (extrinsic attributes) and sociodemographic and attitudinal factors (intrinsic attributes) on Brazilian consumer choices, using simulated traditional and plant-based muçarela cheese as the model product. The research was conducted in two phases: the first involved a structured questionnaire assessing attitudinal dimensions such as Health Consciousness, Climate Change, Plant-based Diets, and Food Neophobia, along with sociodemographic data collection. The second phase comprised a discrete choice experiment with (n = 52) and without (n = 509) eye tracking. The term “Cheese” on labels increased choice probability by 7.6% in a general survey and 15.1% in an eye tracking study. A prolonged gaze at “Cheese” did not affect choice, while more views of “Plant-based product” slightly raised choice likelihood by 2.5%. Repeatedly revisiting these terms reduced the choice probability by 3.7% for “Cheese” and 1% for “Plant-based product”. Nutritional claims like “Source of Vitamins B6 and B12” and “Source of Proteins and Calcium” boosted choice probabilities by 4.97% and 5.69% in the general and 8.4% and 6.9% in the eye-tracking experiment, respectively. Conversely, front-of-package labeling indicating high undesirable nutrient content decreased choice by 13% for magnifying presentations and 15.6% for text. In a plant-based subsample, higher environmental concerns and openness to plant-based diets increased choice probabilities by 5.31% and 5.1%, respectively. These results highlight the complex dynamics between label information, consumer understanding, and decision-making.

This study explores the field of artificial intelligence (AI) through the lens of Stephen Hawking, who warned of its potential dangers. It aims to provide a comprehensive understanding of AI and its implications for food security using a qualitative approach and offering a contemporary perspective on the topic. The study explores the challenges and opportunities presented by AI in various fields with an emphasis on the global food reality. It also highlights the critical importance of striking a harmonious balance between technological progress and the preservation of local wisdom, cultural diversity, and environmental sustainability. In conclusion, the analysis argues that AI is a transformative force with the potential to address global food shortages and facilitate sustainable food production. However, it is not without significant risks that require rigorous scrutiny and ethical oversight.

The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI’s predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks.

In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this filter utilizing exclusively edge-preservation-oriented metrics such as the gradient conduction mean square error (GCMSE), the edge-based structural similarity (EBSSIM), and the multi-scale structural similarity (MS-SSIM); we conducted a statistical assessment of the performance of the filter based on those metrics by using the Berkeley segmentation dataset benchmark as a test corpus. Experimental data reveal that our approach achieves higher performance compared to conventional edge filters for all the metrics considered in this study. This is supported by the statistical analysis we carried out; specifically, the FCCF demonstrates a consistent enhancement in edge detection. We also conducted additional experiments for determining the main filter parameters, which we found to be optimal for a broad spectrum of images. The results underscore the FCCF’s potential to make significant contributions to the advancement of image processing techniques since many practical applications such as medical imaging, image enhancement, and computer vision rely heavily on edge detection filters.

In directional forming processes, such as rolling and extrusion, the grains can develop preferred crystal orientations. These preferred orientations—the texture—are the main cause for material anisotropy. This anisotropy leads to phenomena such as earing, which occur during further forming processes, e.g., during the deep drawing of sheet metal. Considering anisotropic properties in numerical simulations allows us to investigate the effects of texture-dependent defects in forming processes and the development of possible solutions. Purely phenomenological models for modeling anisotropy work by fitting material parameters or applying measured anisotropy properties to all elements of the part, which remain constant over the duration of the simulation. In contrast, crystal plasticity methods, such as the visco-plastic self-consistent (VPSC) model, provide a deeper insight into the development of the material microstructure. By experimentally measuring the initial texture and using it as an initial condition for the simulations, it is possible to predict the evolution of the microstructure and the resulting effect on the mechanical properties during forming operations. The results of the simulations with the VPSC model show a good agreement with corresponding compression tests and the earing phenomenon, which is typical for cup deep drawing.

Nowadays, including vegetable ingredients in fish diets without growth effects is common; however, their intestinal oxidative status under these conditions is less known. Five isonitrogenous and isolipidic diets with 75% vegetable oil (VO) inclusion were formulated for juvenile gilthead sea bream (Sparus aurata). As VO, one diet contained palm oil (diet P), another rapeseed oil (diet R), and the other three included linseed oil (L) combined with the above-mentioned VOs (named PL, RL and RPL diets). After 18 weeks, pyloric caeca (PC) and proximal intestine (PI) were analyzed for oxidative stress biomarkers, lipid peroxidation (LPO), and gene expression. Dietary linseed oil diminished the superoxide dismutase activity in both intestinal regions, catalase in PC and glutathione reductase in PI; rapeseed oil reduced the glutathione peroxidase (GPx) and glutathione-S-transferase activities in PC, and palm oil upregulated GPx activity in PI. The PL diet triggered LPO levels in the PI, and RPL-fed fish showed the highest levels of LPO in the PC due to lower antioxidant activities, while RL-fed fish presented the best oxidative status. The results suggest that the dietary amount of n-6 and the unsaturated/saturated fatty acids ratio are factors to be considered in aquafeed formulation, including VOs, to improve the intestinal oxidative status in fish.

Implantoplasty is a technique increasingly used to remove the biofilm that causes peri-implantitis on dental implants. This technique of mechanization of the titanium surface makes it possible to eliminate bacterial colonies, but it can generate variations in the properties of the implant. These variations, especially those in fatigue resistance and electrochemical corrosion behavior, have not been studied much. In this work, fatigue tests were performed on 60 dental implants without implantoplasty, namely 30 in air and 30 in Hank’s solution at 37 °C, and 60 with implatoplasty, namely 30 in air and 30 in Hank’s solution at 37 °C, using triaxial tension–compression and torsion stresses simulating human chewing. Mechanical tests were performed with a Bionix servo-hydraulic testing machine and fracture surfaces were studied by scanning electron microcopyElectrochemical corrosion tests were performed on 20 dental implants to determine the corrosion potentials and corrosion intensity for control implants and implantoplasty implants. Studies of titanium ion release to the physiological medium were carried out for each type of dental implants by Inductively Coupled-Plasma Mass Spectrometry at different immersion times at 37 °C. The results show a loss of fatigue caused by the implantoplasty of 30%, observing that the nucleation points of the cracks are in the areas of high deformation in the areas of the implant neck where the mechanization produced in the treatment of the implantoplasty causes an exaltation of fatigue cracks. It has been observed that tests performed in Hank’s solution reduce the fatigue life due to the incorporation of hydrogen in the titanium causing the formation of hydrides that embrittle the dental implant. Likewise, the implantoplasty causes a reduction of the corrosion resistance with some pitting on the machined surface. Ion release analyses are slightly higher in the implantoplasted samples but do not show statistically significant differences. It has been observed that the physiological environment reduces the fatigue life of the implants due to the penetration of hydrogen into the titanium forming titanium hydrides which embrittle the implant. These results should be taken into account by clinicians to determine the convenience of performing a treatment such as implantoplasty that reduces the mechanical behavior and increases the chemical degradation of the titanium dental implant.

Background: This work proposes the development of new vesicular systems based on anesthetic compounds (lidocaine (LID) and capsaicin (CA)) and antimicrobial agents (amino acid-based surfactants from phenylalanine), with a focus on physicochemical characterization and the evaluation of antimicrobial and cytotoxic properties. Method: Phenylalanine surfactants were characterized via high-performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR). Different niosomal systems based on capsaicin, lidocaine, cationic phenylalanine surfactants, and dipalmitoyl phosphatidylcholine (DPPC) were characterized in terms of size, polydispersion index (PI), zeta potential, and encapsulation efficiency using dynamic light scattering (DLS), transmitted light microscopy (TEM), and small-angle X-ray scattering (SAXS). Furthermore, the interaction of the pure compounds used to prepare the niosomal formulations with DPPC monolayers was determined using a Langmuir balance. The antibacterial activity of the vesicular systems and their biocompatibility were evaluated, and molecular docking studies were carried out to obtain information about the mechanism by which these compounds interact with bacteria. Results: The stability and reduced size of the analyzed niosomal formulations demonstrate their potential in pharmaceutical applications. The nanosystems exhibit promising antimicrobial activity, marking a significant advancement in pharmaceutical delivery systems with dual therapeutic properties. The biocompatibility of some formulations underscores their viability. Conclusions: The proposed niosomal formulations could constitute an important advance in the pharmaceutical field, offering delivery systems for combined therapies thanks to the pharmacological properties of the individual components.

Staphylococcus aureus is a Gram-positive bacteria with the greatest impact in the clinical area, due to the high rate of infections and deaths reaching every year. A previous scenario is associated with the bacteria’s ability to develop resistance against conventional antibiotic therapies as well as biofilm formation. The above situation exhibits the necessity to reach new effective strategies against this pathogen. Flourensia retinophylla is a medicinal plant commonly used for bacterial infections treatments and has demonstrated antimicrobial effect, although its effect against S. aureus and bacterial biofilms has not been investigated. The purpose of this work was to analyze the antimicrobial and antibiofilm potential of F. retinophylla against S. aureus. The antimicrobial effect was determined using an ethanolic extract of F. retinophylla. The surface charge of the bacterial membrane, the K+ leakage and the effect on motility were determined. The ability to prevent and remove bacterial biofilms was analyzed in terms of bacterial biomass, metabolic activity and viability. The results showed that F. retinophylla presents inhibitory (MIC: 250 µg/mL) and bactericidal (MBC: 500 µg/mL) activity against S. aureus. The MIC extract increased the bacterial surface charge by 1.4 times and the K+ concentration in the extracellular medium by 60%. The MIC extract inhibited the motility process by 100%, 61% and 40% after 24, 48 and 72 h, respectively. The MIC extract prevented the formation of biofilms by more than 80% in terms of biomass production and metabolic activity. An extract at 10 × MIC reduced the metabolic activity by 82% and the viability by ≈50% in preformed biofilms. The results suggest that F. retinophylla affects S. areus membrane and the process of biofilm formation and removal. This effect could set a precedent to use this plant as alternative for antimicrobial and disinfectant therapies to control infections caused by this pathogen. In addition, this shrub could be considered for carrying out a purification process in order to identify the compounds responsible for the antimicrobial and antibiofilm effect.

Poor long-term survival in localized high-risk soft tissue sarcomas (STSs) of the extremities and trunk highlights the need to identify new prognostic factors. CXCR4 is a chemokine receptor involved in tumor progression, angiogenesis, and metastasis. The aim of this study was to evaluate the association between CXCR4 expression in tumor tissue and survival in STSs patients treated with neoadjuvant therapy. CXCR4 expression was retrospectively determined by immunohistochemical analysis in serial specimens including initial biopsies, tumors post-neoadjuvant treatment, and tumors after relapse. We found that a positive cytoplasmatic expression of CXCR4 in tumors after neoadjuvant treatment was a predictor of poor recurrence-free survival (RFS) (p = 0.003) and overall survival (p = 0.019) in synovial sarcomas. We also found that positive nuclear CXCR4 expression in the initial biopsies was associated with poor RFS (p = 0.022) in undifferentiated pleomorphic sarcomas. In conclusion, our study adds to the evidence that CXCR4 expression in tumor tissue is a promising prognostic factor for STSs.

Non-equilibrium evolution at absolute temperature T and approach to equilibrium of statistical systems in long-time (t) approximations, using both hierarchies and functional integrals, are reviewed. A classical non-relativistic particle in one spatial dimension, subject to a potential and a heat bath (hb), is described by the non-equilibrium reversible Liouville distribution (W) and equation, with a suitable initial condition. The Boltzmann equilibrium distribution Weq generates orthogonal (Hermite) polynomials Hn in momenta. Suitable moments Wn of W (using the Hn’s) yield a non-equilibrium three-term hierarchy (different from the standard Bogoliubov–Born–Green–Kirkwood–Yvon one), solved through operator continued fractions. After a long-t approximation, the Wn’s yield irreversibly approach to equilibrium. The approach is extended (without hb) to: (i) a non-equilibrium system of N classical non-relativistic particles interacting through repulsive short range potentials and (ii) a classical ϕ4 field theory (without hb). The extension to one non-relativistic quantum particle (with hb) employs the non-equilibrium Wigner function (WQ): difficulties related to non-positivity of WQ are bypassed so as to formulate approximately approach to equilibrium. A non-equilibrium quantum anharmonic oscillator is analyzed differently, through functional integral methods. The latter allows an extension to relativistic quantum ϕ4 field theory (a meson gas off-equilibrium, without hb), facing ultraviolet divergences and renormalization. Genuine simplifications of quantum ϕ4 theory at high T and large distances and long t occur; then, through a new argument for the field-theoretic case, the theory can be approximated by a classical ϕ4 one, yielding an approach to equilibrium.