Search Results (16,467)

Drug-resistant epilepsy remains a therapeutic challenge, requiring new molecular targets beyond conventional antiepileptic drugs. Small-conductance calcium-activated potassium (SK) channels and Na/K-ATPase (NKA) contribute to afterhyperpolarization via distinct mechanisms, offering complementary ways to suppress hyperexcitability. We examined SK activation and NKA modulation in synchronized epileptiform activity in a primary culture of cortical neurons obtained from rat embryos. Epileptiform discharges were induced by magnesium-free solution and assessed by patch-clamp and calcium imaging. The SK2/3 activator CyPPA (10 µM) reduced epileptiform current (EC) amplitude and integral and decreased synchronized calcium transient (CT) frequency but gradually elevated basal calcium. In contrast, ouabain (1 nM), a selective modulator of high-affinity NKA isoforms, attenuated EC amplitude, strongly suppressed CTs, and showed persistent effects after washout, accompanied by asynchronous glial calcium activity. Co-application of CyPPA with ouabain abolished CyPPA-induced calcium elevation while maintaining suppression of neuronal synchrony. The broader SK/IK activator NS309 (10 µM) reduced CT frequency and basal calcium without affecting glia. Thus, SK activation and NKA signaling suppress epileptiform synchronization through distinct yet convergent pathways: SK channels via afterhyperpolarization and NKA via afterhyperpolarization and calcium-dependent signaling. Their combination enhances efficacy and prevents adverse calcium buildup, supporting SK–NKA co-targeting as a strategy against drug-resistant epilepsy. Full article

In large-scale radiological events, there is a need to triage affected individuals based on their biological absorbed dose. Biodosimetry measures biological responses in relation to the received dose. Radiation-responsive protein biomarkers in peripheral blood lymphocytes, especially intracellular proteins, have been validated for biodosimetry with immunochemical-based measurement methods. However, these antibody-based assays can suffer from stability and batch-to-batch variations. Aptamers are single-stranded oligonucleotide alternatives to antibodies that are stable and much smaller in size, making them ideal probes for intracellular targets. However, few aptamers have been developed against intracellular targets, and these efforts are especially hampered due to the time-consuming nature of the conventional aptamer selection method. An efficient method for isolating aptamers against intracellular radiation-responsive proteins is not available yet. Herein, we used a microfluidic aptamer isolation method to develop an aptamer against the intracellular radiation biomarker BAX in blood lymphocytes. The isolated aptamer has a dissociation constant of 6.95 nM against human BAX protein and a bright detail similarity score of 1.9 when colocalizing with anti-BAX aptamer intracellularly. The in situ labeling of the intracellular BAX protein also shows the aptamer can be used to differentiate 2.5 Gy or 3 Gy of radiation in ex vivo human and in vivo mouse peripheral blood samples exposed to X-rays. In conclusion, this proof-of-concept study indicates that the microfluidic-enabled aptamer isolation method could be used for the development of a panel of targeted intracellular proteins for radiation biodosimetry applications. Full article

Background/Objectives: Antimicrobial resistance is a critical global health challenge. Among resistant pathogens, the group of bacteria collectively referred to as ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) is of particular concern due to their ability to evade multiple classes of antimicrobials. This study aimed to investigate the occurrence and resistance patterns of ESKAPE bacteria in wild birds from Northern and Central Italy sites, and to assess the presence of other bacteria of public health relevance. Methods: Cloacal swabs were collected from 141 wild birds. Samples were processed on selective and differential media, and bacterial identification was performed using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. Antimicrobial susceptibility was evaluated through Minimum Inhibitory Concentration assays and interpreted according to international guidelines. Results: Thirty-seven isolates belonging to the ESKAPE group were identified: E. faecium (n = 10), K. pneumoniae (n = 9), P. aeruginosa (n = 8), Enterobacter spp. (n = 7), S. aureus (n = 2), and A. baumannii (n = 1). Multidrug-resistant isolates were observed among K. pneumoniae and Enterobacter hormaechei. Escherichia coli, although not included in the ESKAPE group, was frequently detected and often co-isolated with clinically relevant bacteria, highlighting its potential role as a reservoir of resistance genes. Conclusions: Wild birds can harbor resistant bacteria of clinical importance, including multidrug-resistant ESKAPE species. Their presence in avian populations underscores the role of wildlife in the environmental dissemination of antimicrobial resistance, with implications for both animal and human health. Full article

Background: Microvascular invasion (MVI) is a critical prognostic factor in hepatocellular carcinoma (HCC), but preoperative three-class prediction remains challenging. Radiomics and clinical biomarkers may enable more accurate and individualized assessment. Aim: The aim of this study was to develop and validate a Transformer-based deep learning framework that integrates radiomic and clinical features for direct three-class MVI classification in HCC patients. Methods: This retrospective study included 437 patients with pathologically confirmed hepatocellular carcinoma (HCC) and microvascular invasion (MVI) status from two campuses of a single institution. Patients from Hospital A (n = 305) were randomly divided into training and internal test cohorts, while patients from Hospital B (n = 132) were used as an independent external validation cohort. Radiomic features were extracted from preoperative Gd-BOPTA-enhanced MRI, and clinical laboratory data were collected. A two-stage feature selection strategy, combining univariate statistical testing and recursive feature elimination, was applied. A Transformer-based model was built to classify three MVI categories (M0, M1, M2), and its performance was evaluated in both the internal test cohort and the external validation cohort. Results were compared with those from traditional machine learning models, including Random Forest, Logistic Regression, XGBoost, and LightGBM. Results: On the internal test set (n = 76, Hospital A), the model achieved an accuracy of 0.733 (95% CI: 0.64–0.83), a weighted F1-score of 0.733, and a macro-average AUC of 0.880 (95% CI: 0.807–0.953). The sensitivity and specificity for M1 were 0.56 (95% CI: 0.31–0.78) and 0.86 (95% CI: 0.74–0.94), respectively; for high-risk M2 cases, the sensitivity was 0.73 (95% CI: 0.64–0.81) and the specificity was 0.91 (95% CI: 0.85–0.96). On the external validation set (n = 132, Hospital B), performance remained stable with an accuracy of 0.758, a weighted F1-score of 0.768, and a macro-average AUC of 0.886 (95% CI: 0.833–0.940). Conclusions: This Transformer-based model enables accurate and objective three-class MVI prediction using multi-modal features, supporting individualized surgical planning and improved clinical outcomes. In particular, the ability to preoperatively identify high-risk M2 patients may inform surgical margin design, guide adjuvant therapy strategies, and influence liver transplantation eligibility. Full article

The structural behavior of tile vaults remains challenging to evaluate accurately through numerical models, due to their geometry, the heterogeneity of its mechanical properties, and its boundary conditions. This study presents an experimental investigation carried out as part of a teaching innovation project aimed at deepening the understanding of masonry behavior through hands-on construction and collapse testing. Scaled vaults were built using traditional methods, employing thin bricks and fast-setting gypsum, materials typically selected for their accessibility and compatibility with heritage-inspired craftsmanship. The models were incrementally loaded until failure, enabling direct observation of collapse mechanisms. Plastic limit analysis was used to estimate structural capacity, with a focus on verifying the compatibility conditions of hinge formation. The vaults were documented using photogrammetric reconstruction (Structure-from-Motion) to generate accurate 3D models, and the evolution of collapse mechanisms was analyzed through digital motion tracking of observed hinges. Experimental loading reached values up to 4 kN/m² without collapse, confirming that even thin-tile vaults exhibit considerable reserve capacity. While these values should be understood as conservative lower-bound estimates due to the workshop conditions, results also highlight the significant influence of construction imperfections and boundary conditions. This work reinforces the educational value of physical experimentation and offers empirical insights into tile masonry behavior that cannot be captured through purely digital or parametric models. Full article

The rapid emergence and evolution of avian viral pathogens present a major challenge to global poultry health and food security. Traditional vaccine development is often slow, costly, and limited by antigenic diversity. In this study, we present a comprehensive artificial intelligence (AI)-driven pipeline for the rational design, modeling, and optimization of multi-epitope vaccines targeting economically important RNA and DNA viruses affecting poultry, including H5N1, NDV, IBV, IBDV, CAV, and FPV. We utilized advanced machine learning and deep learning tools for epitope prediction, antigenicity assessment, and structural modeling (via AlphaFold2), and codon optimization. B-cell and T-cell epitopes were selected based on binding affinity, conservation, and immunogenicity, while adjuvants and linker sequences enhanced construct stability and immune response. In silico immune simulations forecasted robust humoral and cellular responses, including cytokine production and memory cell activation. The study also highlights challenges such as data quality, model interpretability, and ethical considerations. Our work demonstrates the transformative potential of AI in veterinary vaccinology and offers a scalable model for rapid, data-driven vaccine development against avian diseases. Full article

Background: Immunotherapy has emerged as a breakthrough for the treatment of advanced non-small-cell lung cancer (NSCLC), significantly improving patients’ progression-free survival (PFS) and overall survival (OS). However, the medical burden of response assessment has worsened for long-term maintenance therapy. It remains unclear whether a specific response assessment interval could provide both survival benefits and cost savings. Methods: We retrospectively included patients with advanced NSCLC who underwent immunotherapy and achieved PFS > 12 months. We utilized propensity score matching (PSM) to reduce the selection bias. The survival outcomes were evaluated using the log-rank test and Cox proportional hazard models, while the economic impact was assessed through the performance of a cost minimization analysis (CMA). A medical expenditure extrapolation model was developed based on epidemiological statistics and data from clinical trials. Results: After PSM, a total of 376 patients were included. The survival difference was not significant [hazard ratio (HR) = 0.78, 95% confidence intervals (CIs) = 0.53–1.14; p = 0.200] between the 2-month response assessment group (n = 188) and the 3-month response assessment group (n = 188). Patients receiving immunotherapy alone and those with a positive PD-L1 expression experienced a significant survival benefit. Our extrapolation model projects that, annually, there will be approximately 7026 new long-term responders to immunotherapy in the United States. Adopting a 3-month assessment strategy could reduce annual healthcare expenditure by nearly USD 6 million. Conclusions: This study presented the first statistical evidence supporting a refined response assessment strategy for long-term responders to immunotherapy with advanced NSCLC. These findings support the adoption of a less frequent, yet equally effective, monitoring approach to make tumor surveillance more precise and cost-effective. Full article

Because they account for realistic effects in opportunistic maintenance modeling, dependency hypotheses are extremely diverse in the literature. Despite recent reviews, a clear view of the dependency hypotheses is currently missing in the literature, especially regarding component interactions, resource constraints and human factors. In this paper, we provide a conceptual background on dependence modeling and the notion of maintenance opportunity. Then, a critical systematic literature review, following the PRISMA guidelines, is carried out, focusing on the current hypotheses in opportunistic maintenance, including component interactions, workers’ skills and resource constraints, economic dependence and optimization objectives. The different dependence types are identified and defined, and their presence in the literature is quantified. The included papers in this review ($n=91$) were selected on the basis of relevance to the research questions from the Web of Science, Scopus and Google Scholar databases. Exclusion criteria were set, related to the year of publication (from 2000) and language (limited to French or English), and inclusion criteria required the paper to cover modeling, simulating or reviewing literature related to opportunistic maintenance with dependencies. The results show that economic dependence is mostly modeled by sharing downtime or set-up costs. The objective function for optimization is mostly found to be the economic cost of maintenance, with concerningly little consideration for environmental indicators. These results are finally discussed in light of advances in predictive analytics and current challenges in the sustainability of industrial processes. Further developments should consider including the social and environmental aspects of sustainability in the dependencies, but also look into the benefits that predictive analytics can bring to opportunistic maintenance. The variety of modeling assumptions and dependences presented in the literature does not always allow comparing the results of the models. Full article

Antimicrobial peptides are potential alternatives to conventional antibiotics, primarily due to broad-spectrum activity and low propensity for inducing bacterial resistance. However, their clinical translation faces challenges, including peptide stability and potential mammalian cell toxicity. This study centers on DGL13K, an all D-amino acid peptide, which overcomes proteolytic susceptibility and demonstrates notable stability and broad-spectrum bactericidal activity without inducing de novo bacterial resistance. This work aimed to enhance the therapeutic properties of DGL13K by using targeted modifications to increase antimicrobial potency and decrease toxicity, as determined by hemolysis. DGL13K derivatives were synthesized and tested, involving amino acid substitutions, stereochemical alterations, and N-terminal functionalization with polyethylene glycol (PEG) or myristoylate. While some modifications altered bacterial specificity and reduced hemolytic activity, none of the tested alterations resulted in a substantial overall improvement compared to the parent DGL13K sequence. Furthermore, the antibacterial efficacy of DGL13K and its variants was significantly inhibited in the presence of 50% serum, suggesting limitations for systemic applications. The findings suggest that the DGL13K sequence, derived from an evolutionarily selected protein, is already highly optimized. Given its stability, broad-spectrum efficacy, in vivo activity, low resistance profile, and high safety margin, DGL13K is a promising therapeutic candidate for topical/localized infections. Full article

Background/Objectives: Porcine reproductive and respiratory syndrome virus (PRRSV) remains one of the most economically significant pathogens in the global swine industry. Despite the availability of commercial vaccines for over three decades, they fail to induce sterile immunity and often provide inconsistent protection against heterologous PRRSV strains. This study aimed to predict vaccine immunogenicity by detecting strain-specific immune responses that related to an immune correlate of protection (CoP) against different PRRSV-2 strains. Methods: Post-weaning pigs were vaccinated with five commercially available PRRSV-2 vaccines or received sterile PBS injection as a control. At 28 days post-vaccination (dpv), all pigs were humanely euthanized for large-volume blood collection to isolate peripheral blood mononuclear cells (PBMCs) and plasma, establishing the immune bank. PBMCs and plasma from each group were then tested against six PRRSV-2 strains to evaluate immune responses. In addition, T cell epitope coverage between vaccine and field PRRSV-2 strains was assessed using the EpiCC (in silico) tool to enhance predictive capacity. Results: While neutralizing antibodies were undetectable in all vaccinated pigs at 28 dpv, PRRSV-specific IFNγ–producing cells were detected at various levels in each vaccinated group following restimulation with different PRRSV-2 strains. Additionally, a positive correlation was observed for the EpiCC coverage of the N gene and mean IFNγ responses to VR2332 (SLA class I and II) and NC24-6 (SLA class II). Conclusions: The PRRSV immune bank demonstrated potential as a tool for predicting vaccine immunogenicity against different PRRSV-2 strains and EpiCC provides additional information on T cell epitope cross conservation. The combined approach may provide a valuable framework for selecting PRRSV vaccines for more effective prevention and control in endemic areas. Full article

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement. Full article

To develop new hybrid anticancer agents, 3,5-bis(benzylidene)-4-piperidone scaffolds (compounds 1–6) were functionalized with (1R)-borneoyl chloroacetate (8) or (1S)-camphorsulfonyl chloride (10). Covalent attachment of the camphorsulfonyl moiety via N-sulfonylation yielded hybrid molecules (16–21) that exhibited selective cytotoxic and cytostatic activity against cancer cells, with submicromolar IC₅₀ values. In silico ADME analysis indicated that these camphorsulfonyl-conjugated piperidones have improved drug-like properties (enhanced absorption, metabolism, and bioavailability) compared to curcumin. The most potent analogs were halogen-substituted and trimethoxy-substituted analogs, which showed the strongest tumor cell growth inhibition while sparing normal cells. Overall, this terpene-functionalization strategy addresses curcumin’s pharmacokinetic limitations and improves its anticancer profile. These hybrid molecules hold promise as potential anticancer agents. Full article

The widespread integration of artificial intelligence into university academic activity requires responsibly addressing the ethical challenges it poses. This study critically analyses these challenges, identifying opportunities and risks in various academic disciplines and practices. A systematic review was conducted using the PRISMA method of publications from January 2024 to January 2025. Based on the selected works (n = 60), through a systematic and rigorous examination, this study identifies ethical challenges in teaching and research; opportunities and risks of its integration into academic practice; specific artificial intelligence tools categorised according to study approach; and a contribution to the current debate, providing criteria and practical guidelines for academics. In conclusion, it can be stated that the integration of AI offers significant opportunities, such as the optimisation of research and personalised learning, as well as notable human and ethical risks, including the loss of critical thinking, technological dependence, and the homogenisation of ideas. It is essential to adopt a conscious approach, with clear guidelines that promote human supervision, ensuring that AI acts as a tool for improvement rather than for the replacement of intelligent human performance, and that it supports human action and discernment in the creation of knowledge. Full article

The purpose of this study was to assess the effect of dietary Saponaria officinalis inclusion in the diet of dairy cows during the dry period on colostrum and milk quality, focusing specifically on the somatic cell count (SCC), the physicochemical composition and fatty acid (FA) profile. The effect of the addition on various biochemical parameters was evaluated in the serum of cows and their calves. This study was conducted on Ukrainian Black and White cattle. Twenty-four cows were selected from the herd twenty-one days prior to their expected calving date. They were allocated into three groups of eight cows each (n = 8) and began to be fed soapwort, as outlined below. Group C was fed a conventional TMR diet (0 g/cow per day); Group E1—TMR and 20 g of dry soapwort root powder per cow per day; and Group E2—50 g of dry soapwort root powder per cow per day. Blood samples were collected from the cows 21 d before labour and on the day of calving, and from calves on day 3, in week 2, and in week 6. Biochemical profiles of the liver, lipids, and kidneys were evaluated in the collected samples. Colostrum was collected from the cows two hours after calving during the initial complete milking, and milk was collected in the eighth week of lactation. The fundamental composition, SCC, and FA profiles were assessed in colostrum and milk. Higher protein levels were found in the serum of calves in the second week of life in groups E1 (p < 0.01) and E2 (p < 0.05) compared to group C. In calves, serum ALT levels were higher in group C than in groups E1 and E2 at 2 and 6 weeks of age (p < 0.05). The addition did not affect the biochemical profiles of lipids, liver, or kidneys in cows. The SCC in the colostrum was higher in group E2 than in group C (p < 0.05). A decrease (p < 0.01) in SCC was noted in the milk of groups E2 and E3 compared to the control group. In the colostrum of cows in group E2, an increase in UFA levels was observed, including beneficial acids, such as C18:2n6c, C22:6n3, and conjugated linoleic acid (CLA), while trans isomers decreased. The results suggest that Saponaria officinalis can be used as a supplement in the feeding of cows during the dry period to improve colostrum quality and also support the physiological development of calves and the health of the cows’ mammary glands. Full article

Oxidative stress (OxS) is a central factor in neurodegenerative diseases (NDs), including Parkinson’s disease (PD). Phenolic compounds, including flavonoids and coumarins, counteract reactive species and modulate key intracellular survival pathways, highlighting their therapeutic potential. Parastrephia quadrangularis (Pq), a plant from the Atacama Desert traditionally used by Andean communities, contains phenolic compounds with antioxidant, antifungal, and anti-inflammatory activities. However, its neuroprotective potential remains unexplored. Here, a hydroalcoholic extract (HAE) of Pq and four subfractions (MeOH, EtOAc, DCM, and n-hex) were obtained and assessed for in vitro antioxidant activity, with HAE selected for its consistent activity. In SH-SY5Y cells, HAE-Pq lowered basal reactive oxygen species and attenuated hydrogen peroxide-induced OxS. The UHPLC-MS analysis of HAE-Pq unveiled a high abundance of flavonoids, followed by coumarins and phenolic acids, and identified 16 additional metabolites, including jaceidin as the most abundant. In vivo assays using a Drosophila genetic PD model induced by overexpression of human α-synuclein, showed that HAE-Pq was non-toxic and non-aversive and that it delayed the onset of motor defects by one week in female flies. This study provides the first evidence of the neuroprotective potential of Pq, supporting its value as a source of bioactive metabolites relevant to NDs and reinforcing its ethnopharmacological validation. Full article