Search Results (9,074)

An efficient bacteriophage delivery system needs to be developed to overcome the challenges associated with phage instability, rapid diffusion, and loss of infectivity at the infection site. Hydrogels have been found to be potential carriers. Hydrogels have emerged as promising carriers due to their biocompatibility, tunable physicochemical properties and capacity for controlled release. However, the molecular factors that regulate phage–hydrogel interactions remain poorly understood. In this study, we employed an in silico framework combining molecular docking, molecular dynamics (MD) simulations, MM/PBSA binding energy calculations, machine learning-based adhesion prediction, and diffusion modeling to explore phage–hydrogel interactions at the molecular level. Surface-exposed bacteriophage proteins, such as capsid and tail proteins, were evaluated against eight different hydrogel polymers. Binding site analysis revealed the presence of multiple solvent-accessible pockets that can interact with the polymer. Docking studies showed favorable and stable interactions, with hyaluronic acid showing strong binding affinity to multiple phage proteins (−5.5 to −5.7 kcal/mol) and GelMA showing high affinity to the capsid gp10 protein (−5.6 kcal/mol). The integrity of the structural complexes was further confirmed by 100 ns MD simulations, stable RMSD and RMSF trajectories, compact structural conformations, and favorable MM/PBSA binding energies. Machine learning classification successfully differentiated high- and low-adhesion systems and identified hydrogen bonding and electrostatic interactions as key determinants of sustained yet reversible phage retention. Collectively, our findings suggest that the hydrogels enriched with charged and polar functional groups can facilitate stable but non-destructive phage binding, enabling controlled and sustained release. This study provides mechanistic insights into rational hydrogel design for phage delivery systems and highlights the potential of high-throughput computational strategies to accelerate the development of optimized phage therapeutics. Full article

Bovine leukemia virus (BLV) is an oncogenic retrovirus and the etiological agent of enzootic bovine leukosis (EBL), which is spread worldwide. This study presents data on the genetic diversity of BLV in the Novosibirsk region of Russia. ELISA-positive samples were selected from six districts of the Novosibirsk region (Dovolnoye, Barabinsk, Tatarsk, Toguchin, Bolotnoye, and Kochenyovo districts). To assess the diversity of circulating BLV genotypes, samples were collected from settlements and districts that were geographically distant from each other and had no shared pasture lands. In total, 1410 bp fragments encoding the env gene region were obtained from 417 BLV-positive samples. Phylogenetic analysis classified 325 BLV strains (77.9%) as genotype 4 (G4) and 92 strains (22.1%) as genotype 7 (G7). A pairwise identity matrix was constructed for 268 amino acid residues. Pairwise identity of BLV amino acid sequences in the gp51 region ranged from 96.6% to 100% for G4 and from 97.4% to 100% for G7. Multiple alignment of the amino acid sequences identified 74 mutations found in the Russian BLV variants. Through the addition of 417 novel env BLV sequences to GenBank, this study significantly expands the foundational data and knowledge of BLV molecular epidemiology in Russia. Full article

Background: Conventional therapies for moderate-to-severe inflammatory acne include topical agents, systemic antibiotics, hormonal treatments, and oral isotretinoin. However, increasing resistance of Cutibacterium acnes to antibiotics and the potential adverse effects of systemic agents have prompted growing interest in non-pharmacological alternatives such as fractional microneedling radiofrequency (RF-MN), recently introduced in the clinical practice. Objective: This report of five cases aims to document the clinical benefits and safety of RF-MN using the Focus Dual^® device in the treatment of moderate-to-severe inflammatory acne vulgaris. Methods: Five patients (2 male, 3 female; aged 19–28 years; Fitzpatrick skin types II–III) with moderate-to-severe acne were treated with two RF-MN sessions at 4-week intervals using the Focus Dual^® device (Med & Tech, Occhiobello (RO), Italy). Acne severity was assessed using the Face Global Acne Grading System (F-GAGS) and the 5-point Global Improvement Score (GIS), with evaluations performed by two independent blinded raters (G.P and O.R). Standardized photographic documentation and lesion counting were conducted at baseline (T0) and 4 weeks after the second session (T2). All individual F-GAGS scores for each of the five patients showed a reduction from baseline to T2, as consistently assessed by both evaluators. Two patients improved from moderate to mild acne, one improved from severe to moderate, and one remained mild. GISs indicated clinical improvement ranging from Grade 1 to Grade 2 in all cases, with individual improvements between 8.33% and 37.93%. No adverse events were reported during treatment or follow-up. Conclusions: RF-MN appears to be a promising therapeutic option for moderate-to-severe inflammatory acne, providing clinical improvement and reduction in acne severity without adverse effects. Prospective studies with a larger sample are needed to confirm these preliminary results and support the potential role of RF-MN as an adjunctive or standalone treatment in patients with limited tolerance or response to conventional therapies. Full article

Introduction: Inflammatory and immune-based prognostic markers such as the Lung Immune Prognostic Index (LIPI) and the Glasgow Prognostic Score (GPS) have gained increasing attention in ES-SCLC, particularly in patients receiving first-line chemoimmunotherapy. However, no prior study has explored a broader, integrated inflammatory framework that evaluates these parameters collectively. Methods: We retrospectively evaluated 166 patients with ES-SCLC treated with first-line platinum–etoposide plus atezolizumab or durvalumab between 2019 and 2025. LIPI could be calculated in 123 patients based on available dNLR and LDH values, while GPS and the Combined Inflammatory Prognostic Score (CIPS) could be assessed in 120 patients with accessible CRP and albumin data. Results: Median PFS and OS were 8.16 and 15.96 months, respectively. In univariate analyses, poor ECOG PS, liver and bone metastases, poor LIPI, poor GPS, and high-risk CIPS were associated with shorter PFS and OS. In multivariate analysis, only LIPI and GPS remained independent predictors of both PFS and OS, while ECOG PS was independently associated with OS. Although CIPS demonstrated clear prognostic separation in univariate analysis, it did not retain independent significance, likely due to sample size limitations and overlap with LIPI and GPS components. Conclusions: LIPI and GPS are strong independent prognostic markers in ES-SCLC receiving chemoimmunotherapy. While CIPS did not demonstrate independent prognostic value in multivariate analysis, its simplicity, balanced two-tier design, and use of routinely available biomarkers highlight its potential clinical utility. To our knowledge, this is the first study to assess a combined inflammatory prognostic model in this population. Prospective multicenter validation is warranted. Full article

Background: The Global Positioning System (GPS) and wearable monitoring technologies are increasingly applied in sport science to quantify training load; however, data from female cross-country skiers in nations with emerging competitive programs remain scarce. This case series covering the complete national team roster analyzed the complete annual training cycle of the Korean women’s national cross-country skiing team (KCF) using GPS and heart rate-based wearable sensors. Methods: All three national team members were monitored throughout the 2022–2023 season (52 weeks), structured into General Preparation Period 1 (April–July), General Preparation Period 2 (August–November), and Competition Period (December–March). Individualized five-zone intensity thresholds were established through graded exercise testing on a roller ski treadmill with ventilatory threshold and blood lactate determination, independently assessed by two exercise physiologists (PhD level). Results: The total annual training volume was 667.72 h, comprising roller/on-snow skiing (54.0%), running (23.3%), and strength training (22.7%). The endurance-only intensity distribution demonstrated a polarized pattern (Zones 1–2: 91.5%). The total annual training distance reached 4673.30 km. The mean FIS points were 108.46 ± 38.60, and the mean VO₂max was 60.17 ± 6.11 mL·kg⁻¹·min⁻¹. Conclusions: When benchmarked against world-class female (WCF) standards (800–950 h annually), the overall training volume was approximately 18–30% lower. The relative strength training allocation (22.7%) exceeded typical WCF values (10–15%). These observations should be interpreted cautiously given the small sample size and cross-study comparison design, using published literature-based benchmarks. Full article

University students face sustained academic, employment, and social pressures. Campus pathways, as central linear spaces in daily routines, hold significant potential to influence well-being, yet existing research has largely overlooked how their environmental characteristics affect multidimensional health. Using Shenyang Jianzhu University as a case, this study identified frequently used pathways through GPS tracking and surveys, and quantitatively analyzed how environmental features affect walking willingness, emotional experience, and social interaction. By comparing high- and low-benefit groups, the key environmental thresholds were identified to inform health-oriented design. Beyond verifying some established understandings (e.g., daily commuting paths prioritize efficiency, while leisure paths focus on experiential quality), the study further revealed several mechanisms through quantitative analysis. For example, “road accessibility”—an indicator of convenience—showed a significant negative correlation with emotional experience. The study established quantifiable prediction models and identified design thresholds for campus pathways. A high aesthetic greenery was key to achieving high overall benefits, while low building enclosure and vegetation complexity promoted social interaction. This achievement transforms health-oriented campus pathway design from qualitative principles into a measurable and optimizable scientific practice, thus providing an empirical basis and practical guidance for the construction of health-supportive campus environments. Full article

This study employed a metakaolin-based geopolymer (GP) to solidify potassium copper hexacyanoferrate after its saturation with adsorbed Cs⁺. The experiment was designed using response surface methodology (RSM) in the Design–Expert 13 software, targeting the compressive strength and cumulative leaching fraction of the solidified form. A regression model was developed to achieve the multi-objective optimization of the comprehensive performance of the GP solidified product. Regression analysis identified the optimal mix proportion as Na₂O/Al₂O₃ = 0.84, SiO₂/Al₂O₃ = 2.8, and H₂O/Na₂O = 10.23. Under these conditions, the experimentally measured compressive strength was 23.41 MPa. The 42-day cumulative leaching fractions at 25 °C and 40 °C were 7.906 × 10⁻⁴ cm and 1.5923 × 10⁻³ cm, respectively, both significantly below the national standard threshold (Standard Code GB7023-2011) of 2.6 × 10⁻¹ cm. The percentage error remained within 10%, indicating strong agreement with predicted values. These results suggest that metakaolin-based GP exhibits promising potential for the immobilization of radionuclides. Full article

Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling and estimation, particularly in the presence of uncertainty in structural systems. Flexible structures in aerospace and robotics require advanced control to mitigate vibrations under model uncertainty. This paper proposes a data-driven strategy leveraging a Gaussian Process (GP) integrated within a Nonlinear Model Predictive Control (NMPC) framework. The core innovation lies in using a Gaussian Process Nonlinear AutoRegressive model with eXogenous input (GP-NARX) as a probabilistic predictor to capture structural dynamics while quantifying uncertainty. The operational mechanism involves a tight coupling where the GP provides multi-step-ahead forecasts that the NMPC optimizer uses to minimize a cost function subject to constraints. Validated through simulations on Duffing oscillators, linear oscillators, and cantilever beams, the GP-NMPC achieved an 88.2% reduction in displacement amplitude compared to uncontrolled systems. Quantitative analysis shows high predictive accuracy, with a Root Mean Square Error (RMSE) of 0.0031 and a Standardized Mean-Squared Error (SMSE) below 0.05. Furthermore, Mean Standardized Log Loss (MSLL) evaluations confirm the reliability of the predictive uncertainty within the control loop. These results demonstrate strong performance in both regulation and tracking tasks, justifying this Bayesian-predictive coupling as a powerful approach for high-performance structural vibration control and a potential foundation for bio-inspired mechanical design. Full article

Major depressive disorder (MDD) is a prevalent mental health condition that inflicts a high burden on individuals and healthcare systems. There is a clinical need to detect MDD relapse practically and effectively to improve treatment outcomes for patients. To address this, we developed a smart monitoring system using an Artificial Intelligence (AI) approach to estimate MDD severity and relapse risk from patients’ smartphone behavioral data (i.e., digital phenotyping). Thirty-five MDD patients were recruited from the Institute of Mental Health in Singapore, who installed the smartphone study app Sallie. Their symptoms were quantified using the Hamilton Depression Rating Scale (HAMD-17) at the start of the trial, and every 30 days after over 3 months. The app collected behavioral data such as activity, activity type, and GPS location used to train AI models such as logistic regression, decision trees, and random forest classifiers. We found that passive data collection continued for most participants (up to 79% retention rate) after 3 months. We also used five-fold cross-validation to predict HAMD-17 severity ranging from two to four classes and the relapse status, achieving 91%, 88%, and 78% accuracies for two to four classes, respectively, and a relapse prediction accuracy of 86% whereby four patients relapsed during the study. Additionally, anxiety factors within the HAMD-17 were significantly predicted (Pearson correlation coefficient = 0.78, p = 1.67 × 10⁻¹⁴). These results demonstrate the promise of using smartphone behavioral data to estimate depressive symptoms and identify early indicators of relapse. Full article

Interleukin-10 (IL-10) is a key immunoregulatory cytokine that suppresses inflammatory gene transcription in myeloid cells through signal transducer and activator of transcription 3 (STAT3). In Alzheimer’s disease and neuroinflammation, microglia express IL10ra and exhibit STAT3 Tyr705 phosphorylation following IL-10 stimulation, indicating IL-10 receptor-dependent STAT3 activation. Recent studies demonstrate that IL-10 induces promoter-selective STAT3-dependent transcriptional regulation in microglia through chromatin-associated mechanisms, whereas gp130-dependent cytokines activate STAT3 to induce transcription of defined target genes, including Socs3 and Ccl5. Following IL-10 receptor activation, STAT3 binds regulatory regions of inflammatory genes, including Il1b, Tnf, Il6, and Nlrp3, with reduced RNA polymerase II and NF-κB binding. IL-10-dependent transcriptional repression involves formation of a nuclear SHIP1–STAT3 complex, localization of histone deacetylase (HDAC)1 and HDAC2 to H3K4me1-enriched enhancer regions, reduced H3K27ac, and decreased chromatin accessibility at regulatory regions of inflammatory genes. IL-10-activated STAT3 induces Socs3, which regulates JAK1 and TYK2 activity and STAT3 phosphorylation. Impairment of IL-10 receptor signaling in microglia is associated with increased inflammatory gene expression, enhanced inflammasome-related transcription, demyelination, and amyloid accumulation. This review focuses on IL-10–STAT3-dependent transcriptional regulation in microglia, including receptor signaling, chromatin-associated mechanisms, and disease-associated gene expression in Alzheimer’s disease and neuroinflammation. Full article

Powdery mildew (PM) is a major fungal disease in cucumber (Cucumis sativus L.) cultivation. Grafting serves as an important agricultural practice for improving disease resistance and stress tolerance in scions. This study aimed to determine the effects of different pumpkin rootstocks on PM resistance in grafted cucumber plants. Susceptible ‘Xintai Mici’ cucumber scions were grafted onto 10 different pumpkin rootstock varieties, with self-grafted plants serving as the experimental control. Grafting significantly promoted plant biomass accumulation compared to the self-grafted control, and this enhancement was positively correlated with the rootstock’s root system size. However, grafted plant growth was still negatively affected by PM infection. Among the 10 rootstocks, seedlings grafted onto rootstock GP8 exhibited the lowest disease index, the slowest spore development, and the strongest PM resistance. While some resistant pumpkin rootstocks failed to confer significant PM resistance to their grafted cucumber scions, rootstock GP8 provided consistent PM resistance to its grafted plants. Furthermore, cucumber grafted onto rootstock GP8 showed a significantly enhanced net photosynthetic rate and increased antioxidant enzyme activities (superoxide dismutase, ascorbate peroxidase, and glutathione reductase). Concurrently, these plants accumulated lower levels of superoxide anions and exhibited the smallest increases in malondialdehyde content among all the grafted combinations. Additionally, during PM infection, the expression levels of salicylic acid biosynthesis-related genes (CsICS1 and CsPAL) and downstream disease resistance genes (CsPR1, CsPR5, and CsNPR1) were significantly higher in scions grafted onto rootstock GP8 compared to self-grafted cucumbers. These results suggest that the enhanced PM resistance in grafted cucumber is significantly influenced by the rootstock, potentially through the regulation of photosynthetic performance, reactive oxygen species metabolism, and the expression of genes associated with the salicylic acid signaling pathway in the scion. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

Cats are among the most beloved and affectionate companion animals to humans. Historically, they have been utilised to manage pests or offer comfort and companionship, a practice that continues today. Due to human malpractice, unowned free-roaming cats (as stray pets or feral cats) are now considered amongst the 100 worst invasive species, and are responsible for the decline and even the disappearance of many wild species worldwide. Free-roaming cats maintain their hunting instincts, causing problems for native species, which is recognised as a major issue in island biodiversity. Despite their impact, limited studies have been conducted to understand the spatial activity of free-roaming cats in the Mediterranean when they are away from their caregivers (owners who feed and care for their cats while allowing unrestricted outdoor roaming). To investigate this, we used GPS tracking collars to monitor 15 free-roaming cats on the island of Cyprus, during spring–autumn 2022. The monitored cats were active in a spectrum of different habitats, from forests and farmland to shrublands and the suburbs. We monitored cats for 5.6 days, on average, to investigate their home range sizes (KDE 95%; median: males = 55,678 m²; females = 11,377 m²), daily distance travelled (median: males = 1233 m; females = 538 m), and daily/nocturnal activity, and the factors that influence these patterns. The animals’ sex, shelter availability, and the type of coverage in an area show statistically significant differences in relation to their home range, while activity peaked during the afternoon hours, a finding that is also statistically confirmed. Although the sample size of the study is relatively small, the influence of environmental and anthropogenic factors on the home range of free-roaming cats in Cyprus is revealed. These findings offer quantitative evidence and can contribute to wildlife conservation and free-roaming cat management. Full article

Background/Objectives: Although prescribing physical activity (PA) is a well-established preventive strategy in primary health care (PHC), its ethical implications remain under-researched. This study examines how general practitioners (GPs) and nurses experience, interpret, and manage ethical tensions in PAP. Methods: A qualitative study was conducted with 28 PHC professionals (13 GPs, 15 nurses) from rural and urban centers in Toledo, Spain (M = 18.4 years of experience). Data were collected through semi-structured interviews and analyzed using reflexive thematic analysis. Beauchamp and Childress’ four-principles framework was applied abductively to synthesize ethical conflicts and coping strategies. Results: Two main themes emerged: (1) Ethical conflicts in PAP, characterized by tensions between autonomy and paternalism, and the challenge of balancing beneficence with non-maleficence under institutional pressures; and (2) Professional coping strategies, where clinicians used relational care, individualized tailoring, and interprofessional collaboration to mitigate moral distress. Results indicated that clinical codes, such as “unrealistic goals” or “institutional pressure,” often overlapped across multiple ethical principles, necessitating a nuanced, multi-dimensional approach to counseling. Conclusions: PAP is not a neutral clinical task but an ethically grounded practice constrained by structural and organizational factors. To move toward safe and equitable health promotion, PAP must be conceptualized as a relational intervention. We propose an Ethical Reflective Tool and a conceptual framework to support clinical reflection, enhance professional accountability, and guide policy-level support for preventive care in PHC. Full article

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article