Search Results (13,028)

Tuberculosis, caused by Mycobacterium tuberculosis (M. tb), remains a leading global threat, escalated now by the rise of multidrug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) strains. In search of a novel anti-tubercular agent with a distinct mechanism of action, this study explores deep-sea marine metabolites as potential inhibitors of the F₄₂₀-dependent oxidoreductase Rv1155, a redox enzyme essential for M. tb survival. A total of 2773 marine-derived compounds curated from the CMNPD, Reaxys, and MarinLit databases were screened using an integrated CADD workflow combining molecular docking, in-silico ADMET profiling, and molecular dynamics (MD) simulations. Docking identified 68 metabolites with strong affinity (−10.98 to −15.95 kcal/mol) for the Rv1155 binding pocket, and from which three compounds, Upenamide (CMNPD_22964), Aspyronol (Compound_1749), and Fiscpropionate F (Compound_1796), were shortlisted as hit candidates. Among these, Upenamide displayed the strongest binding (ΔG = −28.56 kcal/mol) with stable RMSD and hydrogen bond persistence during 100 ns MD simulation, while Aspyronol demonstrated a promising ADMET profile comparable to the native cofactor F₄₂₀₂. MM-GBSA analysis further confirmed the strong binding strength (ΔG _bind = −24.77 to −34.07 kcal/mol) for all three hit candidates. These findings confirm the strong and stable interaction of selected deep-sea marine metabolites with Rv1155. This validated screening pipeline established here provides a cost-effective framework for future experimental validation and expansion to additional F₄₂₀-related drug targets in M. tb. Full article

Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by complex interactions across molecular, cellular, circuit, and behavioral scales. While experimental and clinical studies have provided critical insights into PD pathology, integrating these heterogeneous data into coherent mechanistic frameworks and translational strategies remains a major challenge. Computational modeling offers a powerful approach to bridge these scales, enabling the systematic investigation of disease mechanisms, candidate biomarkers, and therapeutic strategies. In this review, we survey state-of-the-art computational approaches applied to PD, spanning molecular dynamics and biophysical models, cellular- and circuit-level network models, systems and abstract-level simulations of basal ganglia function, and whole-brain and data-driven models linked to clinical phenotypes. We highlight how multiscale and hybrid modeling strategies connect α-synuclein pathology, mitochondrial dysfunction, oxidative stress, and dopaminergic degeneration to alterations in neural dynamics and motor and non-motor symptoms. We further discuss the role of computational models in biomarker discovery, including imaging, electrophysiological, and digital biomarkers. In particular, eye-movement-based measures are highlighted as quantitative, reproducible behavioral signals that provide principled constraints for individualized computational modeling. We also review the emerging impact of computational approaches on drug discovery, target prioritization, and in silico clinical trials. Finally, we examine future directions toward personalized and precision medicine in PD, emphasizing digital twin frameworks, longitudinal validation, and the integration of patient-specific data with mechanistic and data-driven models. Together, these advances underscore the growing role of computational modeling as an integrative and hypothesis-generating framework, with the long-term goal of supporting data-constrained predictive approaches for biomarker development and translational applications. Full article

Background: Experimental autoimmune thyroiditis is an important animal model for studying Hashimoto’s thyroiditis. Our aim was to develop the model using CBA/J-DR3 mice expressing human HLA-DR3, which is associated with autoimmune thyroiditis in humans, to better simulate human autoimmune thyroiditis. Such a humanized model can be used to test specific antigen therapies for autoimmune thyroiditis. Methods: CBA/J-DR3 mice were produced by back-crossing B6-DR3 mice to the CBA/J background. Female CBA/J-DR3 mice were immunized with human thyroglobulin (Tg) in complete Freund’s adjuvant on days 0 and 7. On day 21, mice were sacrificed, blood collected, spleen and thyroid harvested for analysis. Splenocytes were analyzed for T cell responses to Tg and its major T-cell epitope in human autoimmune thyroiditis, Tg.2098. Serum anti-thyroglobulin antibodies were measured by ELISA, and thyroid-stimulating hormone was measured using the Luminex assay. Thyroid histology and immunohistochemistry were examined. Results: Immunized CBA/J-DR3 mice showed significant T cell proliferation in response to Tg (stimulation index 3.4 ± 4.5) and Tg.2098 (1.5 ± 0.7). Anti-thyroglobulin antibody levels were elevated in immunized mice when compared to control mice (2.05 ± 0.75 vs. 0.15 ± 0.06, p < 0.0001). T cells demonstrated higher reactivity to thyroid antigens by enhanced production of pro-inflammatory cytokines. Thyroid immunohistochemistry revealed mild CD3-positive T-cell infiltration. Conclusions: This novel humanized CBA/J-DR3 mouse model of Hashimoto’s thyroiditis demonstrates key features of human autoimmune thyroiditis. The HLA-DR3 background and the immune response to Tg and Tg.2098 enhance translational relevance, making this a valuable model for studying thyroid disease pathogenesis and testing targeted immune-modifying therapies. Full article

Groundwater in Mediterranean regions is facing increasing threats from climate change and intensive agriculture, necessitating robust vulnerability assessment tools. This study evaluates nitrate pollution vulnerability of the Almyros aquifer (Thessaly, Greece) using the DRASTIC index under the high-emission scenario RCP8.5. Bias-corrected Med-CORDEX climate projections were integrated into a coupled hydrological–hydrogeological modeling framework to simulate recharge, groundwater levels, and nitrate transport. DRASTIC results for the baseline (1991–2018) showed strong agreement with observed nitrate concentrations, while future projections (2031–2060, 2071–2100) revealed shifting vulnerability patterns, particularly in low-lying agricultural areas. The findings highlight climate-driven changes in groundwater vulnerability and support targeted adaptive management strategies. Full article

Exposure to microgravity results in cardiovascular deconditioning, with endothelial cell apoptosis recognized as a pivotal initiating event. However, the mechanosensitive mechanisms underlying this process remain poorly understood. Here, we demonstrate that the expression of mechanosensitive ion channel protein PIEZO1 is upregulated in human umbilical vein endothelial cells (HUVECs) under simulated microgravity. Functional studies revealed that PIEZO1 activation promotes endothelial apoptosis under simulated microgravity conditions. Proteomic analysis following PIEZO1 knockdown revealed extensive alterations in biological processes associated with apoptosis. Furthermore, we found that PIEZO1 activation triggers calcium influx, leading to elevated expression of the HMGA2. Moreover, we identify that PIEZO1 activation induces calcium influx, which subsequently elevates the expression of HMGA2. The knockdown of HMGA2 significantly mitigated microgravity-induced endothelial apoptosis, indicating its role in PIEZO1-mediated apoptosis. These findings reveal a novel PIEZO1–Ca²⁺–HMGA2 axis critical for microgravity-induced endothelial apoptosis, providing mechanistic insight into cardiovascular adaptation to spaceflight and potential therapeutic targets for countermeasure development. Full article

The Z.D. granite weathering crust rare earth deposit in Ganzhou, China is a world-class resource. In situ leaching extraction may mobilize potentially toxic elements (PTEs) into surrounding soils. This study analyzed nine PTEs (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn) in top soils within and around the mining area. A multi-method approach was employed, integrating geochemical assessment, pollution and ecological risk indices, and probabilistic health risk evaluation via Monte Carlo simulation and source apportionment using Positive Matrix Factorization (PMF) combined with Geographic Information System (GIS) analysis. Results indicated generally low background levels, though with localized Pb enrichment, and an overall low level of pollution and ecological risk. However, for children in nearby areas with prolonged exposure, there was a 9.11% probability of non-carcinogenic risk and a 13.82% probability of carcinogenic risk. PMF-GIS analysis traced PTEs to four sources: natural parent material, industrial emissions, mining/riverine transport, and agriculture. In conclusion, while current soil environmental risks in the Z.D. mining area remain manageable, the study underscores the need to monitor progressive PTE accumulation and children’s health risks. This work provides a scientific basis for targeted soil management and ecological restoration in rare earth mining regions. Full article

Industrial symbiosis and carbon capture are increasingly recognized as critical strategies for reducing emissions and resource consumption in industrial parks. However, existing research remains fragmented across tools, methods, and case-specific applications, providing limited guidance for effective real-world deployment of data-driven approaches. This study addresses this gap through a PRISMA-guided scoping review of 116 publications, complemented by a targeted practitioner survey conducted within the IEA IETS Task 21 initiative to assess practical relevance and adoption challenges. The review identifies a broad landscape of data-driven tools, ranging from high-technology-readiness simulation and optimization platforms to emerging visualization and matchmaking solutions. While the literature demonstrates substantial methodological maturity, the combined evidence reveals a persistent gap between tool availability and effective implementation. Key barriers include fragmented and non-standardized data infrastructures, confidentiality constraints, limited stakeholder coordination, and weak policy and market incentives. Based on the integrated analysis of literature and practitioner insights, the paper proposes a conceptual framework that links tools and methods with data infrastructure, stakeholder governance, policy, and market enablers, and implementation contexts. The findings highlight that improving data governance, interoperability, and collaborative implementation pathways is as critical as advancing analytical capabilities. The study concludes by outlining focused directions for future research, including AI-enabled optimization, standardized data-sharing frameworks, and coordinated pilot projects to support scalable low-carbon industrial transformation. Full article

Harmful algal blooms pose significant risks to water resource management and aquatic ecosystem health, rendering early detection of algal bloom proliferation essential. In this study, we present an electrochemical strategy for the real-time detection of individual Chlorella cells using the single-particle collision method at an ultramicroelectrode (UME). The detection principle relies on monitoring changes in the redox probe flux at the UME induced by attachment of the target. Both diffusional and migrational transport were considered to promote particle collision at the UME. Detection sensitivity for negatively charged microalgae was enhanced by exploiting migration effects. To control migration strength, neutral and charged redox probes were selected, and the ionic strength was adjusted to tune electrostatic attraction, yielding microalgae capture on the UME with a collision frequency that depended on the solution composition. Conversely, migration was suppressed by increasing the ionic strength, and inverse migration was implemented, and resulting collision responses were compared. Furthermore, COMSOL Multiphysics simulations were used to estimate the size of detected Chlorella cells. The collision frequencies expected from diffusion and migration were compared with the experimental values, and a calibration curve relating collision frequency to Chlorella concentration was established. Consequently, this methodology provides a promising platform for the early monitoring of algal blooms by simultaneously determining microalgal size and concentration. Full article

A target state estimation method based on multiple quadrotors is proposed for unknown maneuvering targets, and a distributed formation control method Image-Based Visual Servoing (IBVS) is also proposed to achieve encirclement tracking of unknown maneuvering targets. In the tracking control, collision avoidance constraints for nodes within the formation are also introduced, and based on the shared position information within the formation, the positions of other nodes within the Field of View (FOV) of each node are predicted for detecting unknown targets. Firstly, an Interacting Multiple Model (IMM) was designed based on multiple motion modes to estimate the position and velocity of the target. A virtual camera coordinate system containing translational and yaw rotations was established between the formation and the target based on the estimated values. Then, a distributed control method based on IBVS was further designed by combining image deviation. At the same time, a safe distance between nodes within the formation was introduced, and collision avoidance constraints of the Control Barrier Function (CBF) were designed. Finally, the position of the formation nodes within the FOV was predicted. The simulation results demonstrate that, utilizing the proposed estimation method, the estimation accuracy for target velocity improves by 26.5% in terms of Root Mean Square Error (RMSE) compared to existing methods. Furthermore, the proposed control method enables quadrotor formations to successfully achieve encirclement tracking of unknown maneuvering targets, significantly reducing tracking errors in comparison to conventional approaches. Full article

The pressing need for sustainable, plant-based alternatives is highlighted by the growing resistance of agricultural pests to synthetic pesticides. This study examined the pesticidal potential of phytocompounds from C. tora discovered by GC–MS analysis against important tomato insect (T. absoluta) and fungal pathogen (A. solani). The binding stability and interaction dynamics of specific metabolites with fungal virulence (polygalacturonase, MAP kinase HOG1, and effector AsCEP50) and insect neuromuscular (ryanodine receptor and sodium channel protein) targets were assessed using molecular docking and 100 ns molecular dynamics simulations. Among the screened compounds, squalene and 4,7,10,13,16,19-docosahexaenoic acid, methyl ester (DHAME) exhibited the strongest binding affinities and conformational stability, with MM-GBSA binding free energies of −38.09 kcal·mol⁻¹ and −52.81 kcal·mol⁻¹ for squalene complexes in T. absoluta and A. solani, respectively. Persistent hydrophobic and mixed hydrophobic–polar contacts that stabilised active-site residues and limited protein flexibility were found by ProLIF analysis. These lively and dynamic profiles imply that DHAME and squalene may interfere with calcium signalling and stress-response pathways, which are essential for the survival and pathogenicity of pests. Hydrophobic interactions were further confirmed as the primary stabilising force by the preponderance of van der Waals and nonpolar solvation energies. The findings show that C. tora metabolites, especially squalene and DHAME, are promising environmentally friendly biopesticide candidates that have both insecticidal and antifungal properties. Their development as sustainable substitutes in integrated pest management systems are supported by their stability, binding efficacy and predicted biosafety. Full article

In the production of clad rolled plates from asymmetric sandwich-type slab for pipeline applications, achieving both target mechanical properties and high geometric flatness remains a critical challenge due to differential thermal stresses between the dissimilar steel layers during accelerated cooling. This study aims to develop an optimal cooling schedule for a 25 mm thick clad plate, comprising a X70-grade steel base layer and an AISI 316L cladding, to ensure required strength and minimal bending. A comprehensive approach was employed, integrating a 3D finite element model (Ansys) for simulating thermoelastic stresses with a CatBoost machine learning model trained on industrial data to predict heat transfer coefficients accurately. A parametric analysis of cooling strategies was conducted. Results showed that a standard cooling strategy caused unacceptable bending of plate after cooling exceeding 130 mm. An optimized strategy featuring delayed activation of the lower cooling headers (on the cladding side) created a compensating thermoelastic moment, successfully reducing bending to approximately 20 mm while maintaining the base layer’s requisite mechanical properties. The findings validate the efficacy of the combined FEM-machine learning methodology and propose a viable, industrially implementable cooling strategy for high-quality clad plate production. Full article

The development of effective vaccines is a critical step in effective disease management in aquaculture. This study introduces a novel Multiepitope Chimeric Vaccine (MCV) designed to enhance immunity in lumpfish against Vibrio anguillarum, Aeromonas salmonicida, Yersinia ruckeri, Moritella viscosa and Piscirickettsia salmonis. Epitopes from major toxins and virulence factors were selected to construct the MCV in silico. Structural validation showed 96.7% of residues in favored regions, confirming stability. Codon optimization yielded a G+C content of 54.61% and a Codon Adaptation Index (CAI) of 1, indicating strong expression potential in Escherichia coli. Immune simulations predicted robust B- and T-cell responses, suggesting induction of both humoral and cell-mediated immunity. Experimental vaccination of lumpfish (n = 35/group) with E. coli-expressed MCV led to significantly elevated IgM levels at four- and six-weeks post-vaccination (p ≤ 0.05, p ≤ 0.01, respectively). Upon pathogen challenge, vaccinated groups showed delayed mortality against V. anguillarum, A. salmonicida, and P. salmonis, though survival differences were not statistically significant across treatments. These results highlight the immunogenicity potential of the MCV and its capacity to elicit targeted immune responses. However, further optimization is necessary to improve protective efficacy and survival outcomes. This study lays a foundation for the application of multiepitope vaccines in lumpfish aquaculture and supports ongoing efforts toward sustainable disease control strategies. Full article

Faced with the challenge of reducing food waste, transforming discarded fruit into functional ingredients useful for the food industry is a valuable solution. Ingredients from fruit such as persimmons, which are rich in indigestible carbohydrates and bioactive compounds with antiradical capacity, could positively impact on the health of certain population groups due to their potential prebiotic effect. This study aimed to select the most suitable drying conditions and milling intensity for obtaining powdered persimmon ingredients with a prebiotic-like effects observed in vitro for postmenopausal women, and to evaluate this effect by considering the stimulation of health-promoting bacterial growth and short-chain fatty acids (SCFAs) production. First, the effect of the drying method (hot air drying at 60 and 70 °C, and freeze-drying) and grinding intensity on antiradical capacity, particle size, and the release of bioactive antiradical components into the intestinal lumen after an in vitro gastrointestinal digestion was determined. Next, the effect of these conditions on the microbiota composition of postmenopausal women was preliminary assessed in a batch colonic fermentation experiment for 24 h. The results showed that the ingredient dried with air at 70 °C had the highest phenol and flavonoid content, suffered the least degradation during in vitro gastrointestinal digestion and promoted the differential growth of fiber-degrader genera. Consequently, this was the ingredient selected as the most suitable. Lastly, the impact of this ingredient on the microbiota composition of 4 postmenopausal women has been evaluated in a long-term study using the Simulator of the Human Intestinal Microbial Ecosystem (SHIME^®) coupled to high throughput sequencing. The growth stimulation of health-associated bacteria, such as Akkermansia muciniphila, Faecalibacterium prausnitzii or Phascolarctobacterium faecium, and the promotion of beneficial metabolic pathways, such as the sugar uptake-specific phosphotransferase system, sugar metabolism and propionate and isobutyrate production, were detected along 14 days of persimmon powder supplementation. A holistic framework for promoting human health while advancing environmental sustainability is represented by the combination of sustainable by-product valorization and microbiota-targeted functional food development. Full article

The detection of low-altitude slow-small (LSS) targets, such as drones, is challenged by their small radar cross-section (RCS) and low signal-to-clutter ratio (SCR), resulting in short effective range and susceptibility to background clutter in complex environments. To overcome the limitations of conventional radar and electro-optical methods, this paper proposes a novel detection theory based on broadband spectral modulation imaging (BSMI). We analyze the recognition accuracy for drone targets across different zenith angles and detection ranges through numerical simulations. A snapshot-based BSMI detection system was designed and implemented, with experiments conducted under consistent conditions for validation. Results demonstrate that the system achieves over 90% classification accuracy, confirming the theory’s effectiveness. This study significantly enhances detection probability and suppresses false alarms for low-altitude drones, providing a viable technical solution for monitoring unauthorized aerial activities. Full article

Background/Objectives: Premium intraocular lenses (IOLs) are increasingly being selected for cataract and refractive lens surgery, but their functional performance depends critically on pupil size and corneal spherical aberration (SA). This study evaluates how these factors modulate the optical behavior of the RayOne EMV and Tecnis Synergy using a profilometry-based Through Object modulation transfer function (TO MTF) analysis. Methods: The surface profiles of the RayOne EMV and Tecnis Synergy were measured with a confocal optical profilometer and implemented in pseudophakic eye models via ray tracing. TO MTF at 50 cycles/mm was computed for object vergences from −4.0 D to +2.0 D over entrance pupil diameters from 2.0 to 5.5 mm in three corneal configurations derived from the Liou–Brennan model and ISO recommendations: mean population SA, aberration-free, and a myopic LASIK-like oblate cornea. Simulated optotype images were generated to relate TO MTF values to the expected distant, intermediate, and near visual performances. Results: RayOne EMV delivered high-quality distant image performance in all models. Its depth of focus increased only modestly and showed a strong dependence on pupil size. Intermediate and near vision rarely reached clinically acceptable levels. The Tecnis Synergy produced a broad depth-of-field plateau in distant to near visual performance for mean population spherical aberration at a 3.5 mm pupil. However, image quality at 90 cm remained limited. Optical performance worsened with increasing pupil size and positive spherical aberration, particularly under post-myopic LASIK conditions. Conclusions: The RayOne EMV behaves predominantly as a distance-oriented design with minimal true presbyopic benefit; the Tecnis Synergy provides a wider range of vision but is highly sensitive to corneal spherical aberration and pupil size, so thorough preoperative evaluation of corneal asphericity and functional pupil diameter is essential for IOL selection and power targeting. Full article