Search Results (1,582)

Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in human fertility, no clinically approved WEE2 modulator is available. In this study, we employed an integrated in silico approach that combines structure-based virtual screening, molecular dynamics (MD) simulations, and MM-PBSA free-energy calculations to identify repurposed drug candidates with potential WEE2 inhibitory activity. Screening of ~3800 DrugBank compounds against the WEE2 catalytic domain yielded ten high-affinity hits, from which Midostaurin and Nilotinib emerged as the most mechanistically relevant based on kinase-targeting properties and pharmacological profiles. Docking analyses revealed strong binding affinities (−11.5 and −11.3 kcal/mol) and interaction fingerprints highly similar to the reference inhibitor MK1775, including key contacts with hinge-region residues Val220, Tyr291, and Cys292. All-atom MD simulations for 300 ns demonstrated that both compounds induce stable protein–ligand complexes with minimal conformational drift, decreased residual flexibility, preserved compactness, and stable intramolecular hydrogen-bond networks. Principal component and free-energy landscape analyses further indicate restricted conformational sampling of WEE2 upon ligand binding, supporting ligand-induced stabilization of the catalytic domain. MM-PBSA calculations confirmed favorable binding free energies for Midostaurin (−18.78 ± 2.23 kJ/mol) and Nilotinib (−17.47 ± 2.95 kJ/mol), exceeding that of MK1775. To increase the translational prioritization of candidate hits, we place our structure-based pipeline in the context of modern machine learning (ML) and deep learning (DL)-enabled virtual screening workflows. ML/DL rescoring and graph-based molecular property predictors can rapidly re-rank docking hits and estimate absorption, distribution, metabolism, excretion, and toxicity (ADMET) liabilities before in vitro evaluation. Full article

Background/Objectives: The underlying molecular mechanisms of deep vein thrombosis (DVT), which continues to be a major global public health concern, remain unclear. A key component of anticoagulant therapy, heparin (HP) interacts with heparin-binding growth factors including pleiotrophin (PTN) and midkine (MK), both of which have basic amino acid-rich domains that have a strong affinity for HP. The purpose of this study was to determine if changes in the levels of circulating HP, MK, and PTN are linked to the onset of acute DVT. Methods: Thirty patients diagnosed with acute DVT by venous Doppler ultrasonography (VDU) and 28 healthy controls with normal VDU findings were enrolled. Serum HP, MK, and PTN concentrations were measured using ELISA. In DVT patients, blood samples were obtained before and after routine subcutaneous low-molecular-weight heparin treatment; controls provided a single blood sample. ROC curve analysis was used to assess diagnostic performance. Results: Prior to treatment, patients with acute DVT exhibited significantly lower serum HP levels (p < 0.05) and significantly higher MK and PTN levels compared with healthy controls (both p < 0.05). Following heparin administration, serum HP levels increased significantly (p < 0.05), while MK and PTN levels showed a decreasing trend that did not reach statistical significance (p > 0.05). ROC curve analysis demonstrated limited diagnostic performance for HP (sensitivity 10.3%, specificity 68.8%), PTN (62.1%, 54.2%), and MK (82.8%, 35.4%). Conclusions: Decreased circulating HP and increased MK and PTN levels are characteristics of acute DVT that may indicate endogenous HP sequestration through binding to these growth factors. This imbalance could lead to less free HP being available, which would encourage the formation of thrombus. Therapeutic approaches that target MK- and PTN-mediated HP interactions may constitute a unique approach for the therapy of acute DVT, as evidenced by the partial normalization seen after exogenous heparin delivery. Full article

The gut microbiome produces thousands of metabolites with potential to modulate central nervous system function through peripheral or direct neural mechanisms. Tourette syndrome, attention-deficit/hyperactivity disorder, and autism spectrum disorder exhibit shared neurotransmitter dysregulation and microbiome alterations, yet mechanistic links between microbial metabolites and receptor-mediated neuromodulation remain unclear. We screened 27,642 microbiome SMILES metabolites for blood–brain barrier permeability using rule-based SwissADME classification and a PyTorch 2.0 neural network trained on 7807 experimental compounds (test accuracy 86.2%, AUC 0.912). SwissADME identified 1696 BBB-crossing metabolites following Lipinski’s criteria, while PyTorch classified 2484 metabolites with expanded physicochemical diversity. Following 3D conformational optimization (from SMILES) and curation based on ≤32 rotatable bonds, molecular docking was performed against five neurotransmitter receptors representing ionotropic (GABRA2, GRIA2, GRIN2B) and metabotropic (DRD4, HTR1A) receptor classes. The top 50 ligands across five receptors demonstrated method-specific BBB classification (44% SwissADME-only, 44% PyTorch-only, 12% overlap), validating complementary prediction approaches. Fungal metabolites from Ascomycota dominated high-affinity top ligands (66%) and menaquinone MK-7 showed broad phylogenetic conservation (71.4% of phylum). Our results establish detailed receptor–metabolite interaction maps, with fungal metabolites dominating high-affinity ligands, challenging the prevailing bacterial focus of the microbiome and providing a foundation for precision medicine and a framework for developing microbiome-targeted therapeutics to address clinical needs in neurodevelopmental disorders. Full article

A novel bacterial strain, designated as 4-30^T, was isolated from a soil sample collected from the Kubuqi Desert in Inner Mongolia, northern China. The isolate was a Gram-stain-positive, aerobic, motile, and coccus-shaped bacterium, and its colonies were circular, opaque, convex, smooth, and orange-pigmented on Luria–Bertani agar. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain 4-30^T belonged to the genus Planococcus. Growth occurred at 4–38 °C (optimum, 25–28 °C), pH 6.0–11.0 (optimum, pH 9.0), and in 0–10% (w/v) NaCl (optimum, 1%). Strain 4-30^T contained iso-C_14:0, anteiso-C_15:0, C_16:1 ω7c alcohol, and iso-C_16:0 as major cellular fatty acids (>10%) and MK-7 and MK-8 as predominant menaquinones. Its polar lipid profile consisted of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, and two unidentified polar lipids. The genomic DNA G+C content was 45.9%. The average nucleotide identity (ANI) values between strain 4-30^T and the closely related species were relatively low (ANIm < 85.6%, ANIb < 82.9% and OrthoANIu < 83.3%), and the digital DNA–DNA hybridization (dDDH) between strain 4-30^T and type strains of the genus Planococcus were 20.0–26.7%. Based on phylogenetic, genotypic, chemotaxonomic, and phenotypic analyses, strain 4-30^T is considered to represent a novel species of the genus Planococcus, for which the name Planococcus circulans sp. nov. is proposed. The type strain is 4-30^T (=CDMCC 1.2409^T = KCTC 43405^T). Full article

This study focuses on the development of sustainable construction materials via geopolymers synthesized from metakaolin and slag, aiming to identify environmentally friendly alternatives for construction material systems. A metakaolin–slag geopolymer mortar (MK–slag) was prepared using metakaolin and slag as fully solid waste raw materials, with sodium silicate solution and sodium hydroxide acting as composite activators. Initially, single-factor experiments were conducted to determine the optimal ranges for metakaolin–slag content, water/binder ratio, and water glass modulus. Subsequently, response surface methodology was employed to develop regression equations that analyze the main and interaction effects of these variables on the 7-day and 28-day compressive strength and water absorption of the mortar. The optimal mix ratio was then identified. The microstructure and formation mechanisms of MK–slag mortar were studied using scanning electron microscopy (SEM), X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). The results indicate that all factors follow quadratic polynomial relationships with the response variables, showing a regression coefficient (R²) greater than 0.98, indicating an excellent model fit and prediction accuracy. According to model predictions, the optimal mix parameters under multi-objective optimization were found to be a metakaolin-to-slag ratio of 45%: 55%, a water/binder ratio of 0.45, and a water glass modulus of 1.3. After 28 days of curing, the primary hydration products were gel-like substances such as N-A-S-H and C-A-S-H. These gels interweave and overlap to form a high-density, structurally robust binary solid waste geopolymer mortar. This approach expands the application of solid waste materials, such as metakaolin and slag, while enhancing the recycling and utilization efficiency of these waste products. Full article

Numerical simulations of the forming limit diagram (FLD) for SUS430/Al1050/TA1 laminated metal composites (LMCs) are conducted through the crystal plasticity finite element (CPFE) model integrated with the Marciniak–Kuczyński (M–K) theory. Representative volume elements (RVEs) that reconstruct the measured crystallographic texture, as characterized by electron backscatter diffraction (EBSD), are developed. The optimal grain number and mesh density for the RVE are calibrated through convergence analysis by curve-fitting simulated stress–strain responses to the uniaxial tensile data. The established multi-scale model successfully predicts the FLDs of the SUS430/Al1050/TA1 laminated sheet under two stacking sequences, namely, the SUS layer or the TA1 layer in contact with the die. The Nakazima test results validate the effectiveness of the proposed model as an efficient and accurate predictive tool. This study extends the CPFE–MK framework to multi-layer LMCs, overcoming the limitations of conventional single-layer models, which incorporate FCC, BCC, and HCP crystalline structures. Furthermore, the deformation-induced texture evolution under different loading paths is analyzed, establishing the relationship between micro-scale deformation mechanisms and the macro-scale forming behavior. Full article

Metakaolin (MK) obtained from calcined kaolinitic clay is a highly reactive pozzolanic ingredient for use as an emerging supplementary cementitious material (SCM) in modern sustainable binder productions. It provides elevated alumina to promote formations of Alumina Ferrite Monosulfate (AFm) and Calcium-Aluminium-Silicate-Hydrate (C-A-S-H) phases, enhancing the chloride binding capacity. However, due to inherent material uncertainty and lack of approach in quantifying hydration kinetics and chloride binding capacity across varied mixes, robustly assessing the chloride resistance of metakaolin-blended concrete remains challenging. In light of this, a machine learning-aided framework that encompasses physics-based material characterisation and ageing modelling is developed to bridge the knowledge gap. Through applying to laboratory experiments, the impacts of uncertainty on the phase assemblage of hydrated system and chloride penetration are quantified. Moreover, the novel Extended Support Vector Regression (XSVR) method is incorporated and verified against a crude Monte Carlo Simulation (MCS) to demonstrate the capability of achieving effective and efficient uncertainty-aware chloride resistance analyses. With the surrogate model established using XSVR, quality control of metakaolin towards durable design optimisation against chloride-laden environments is discussed. It is found that the fineness and purity of adopted metakaolin play important roles. Full article

Striping noise is a common problem in multi-detector scanning radiometers on remote sensing satellites, typically caused by response inconsistency among detector elements. For payloads with a multi-column redundant architecture, this paper proposes a detector-selection framework that jointly considers sensitivity and uniformity from the perspective of detector-element selection to mitigate striping noise. First, the degree of detector consistency is quantified using the Inter-Row Brightness Temperature Difference (IRBTD). Then, a dynamic programming approach based on the Viterbi algorithm is employed to select detector elements row by row with linear time complexity, optimizing the process through a weighted cost function that integrates sensitivity and consistency. Experiments on raw data from the FY-4B Geostationary High-speed Imager (GHI) show that the method reduces inconsistency by 10–40% while increasing the noise-equivalent temperature difference (NEdT) by only 1–4% (≤4 mK). The average IRBTD decreases by approximately 20–100 mK, and high-frequency striping energy is significantly suppressed (reduction of 50–90%). The algorithm exhibits linear time complexity and low computational overhead, making it suitable for real-time on-board processing. Its weighting parameter enables flexible trade-offs between sensitivity and uniformity. By suppressing striping noise directly during the detector-selection stage without introducing data distortion or requiring calibration adjustments, the proposed method can be widely applied to scanning radiometers that employ multi-column long-linear-arrays. Full article

Background and Objectives: The integrated stress response (ISR) is a convergent node in neurodegeneration. We systematically mapped open-access mammalian in vivo evidence for synthetic ISR modulators, comparing efficacy signals, biomarker engagement, and safety across mechanisms and disease classes. Methods: Following PRISMA 2020, we searched PubMed (MEDLINE), Embase, and Scopus from inception to 22 September 2025. Inclusion required mammalian neurodegeneration models; synthetic ISR modulators (eIF2B activators, PERK inhibitors or activators, GADD34–PP1 ISR prolongers); prespecified outcomes; and full open access. Extracted data included model, dose and route, outcomes, translational biomarkers (ATF4, phosphorylated eIF2α), and safety. Results: Twelve studies met the criteria across tauopathies and Alzheimer’s disease (n = 5), prion disease (n = 1), amyotrophic lateral sclerosis and Huntington’s disease (n = 3), hereditary neuropathies (n = 2), demyelination (n = 1), and aging (n = 1). Among interpretable in vivo entries, 10 of 11 reported benefit in at least one domain. By class, eIF2B activation with ISRIB was positive in three of four studies, with one null Alzheimer’s hAPP-J20 study; PERK inhibition was positive in all three studies; ISR prolongation with Sephin1 or IFB-088 was positive in both studies; and PERK activation was positive in both studies. Typical regimens included ISRIB 0.1–2.5 mg per kg given intraperitoneally (often two to three doses) with reduced ATF4 and phosphorylated eIF2α; oral GSK2606414 50 mg per kg twice daily for six to seven weeks, achieving brain-level exposures; continuous MK-28 delivery at approximately 1 mg per kg; and oral IFB-088 or Sephin1 given over several weeks. Safety was mechanism-linked: systemic PERK inhibition produced pancreatic and other exocrine toxicities at higher exposures, whereas ISRIB and ISR-prolonging agents were generally well-tolerated in the included reports. Conclusions: Directional ISR control yields consistent, context-dependent improvements in behavior, structure, or survival, with biomarker evidence of target engagement. Mechanism matching (down-tuning versus prolonging the ISR) and exposure-driven safety management are central for translation. Full article

Chagas disease remains a significant neglected tropical disease that predominantly affects vulnerable populations in rural, low-income areas of Latin America. The management of this condition is severely hindered by the limitations of current therapies, which are characterized by substantial toxicity, diminished efficacy during the chronic phase, and the emergence of parasitic resistance. Given the promising activity of SB-83 (a 2-aminothiophenic derivative) against Leishmania spp., the present study sought to evaluate its trypanocidal activity against Trypanosoma cruzi. The results showed that SB-83 exhibited potent inhibitory effects on the epimastigote forms of T. cruzi (IC₅₀ = 6.23 ± 0.84 μM), trypomastigotes (EC₅₀ = 7.31 ± 0.52 μM) and intracellular amastigotes (EC₅₀ = 5.12 ± 0.49 μM). Furthermore, the cellular proliferation assay results indicated CC₅₀ values of 77.80 ± 2.05 µM for LLC-MK2 CCL-7 and 24.21 ± 1.2 µM for Vero CCL-87, with a selectivity index above 10 for LLC-MK2 cells. In addition, the compound increased TNF-α, IL-12, nitric oxide, and ROS while decreasing IL-10. Moreover, in silico and in vitro assays confirmed its binding to trypanothione reductase, disrupting redox balance. Flow cytometry further revealed apoptosis induction in trypomastigotes, whereas electron microscopy showed cellular disruption and organelle disorganization. Therefore, SB-83 demonstrated potent activity against the TcI-resistant strain linked to Chagas cardiomyopathy at non-toxic concentrations for host cells, supporting its potential as a therapeutic candidate. Full article

This study presents a graph-based framework for improving predictive maintenance in nuclear power plants (NPPs), integrating data balancing techniques with a proposed Graph Attention Network (GAT) with a Mutual k-Nearest Neighbor (Mk-NN) strategy, named GAT-Mk-NN. To enhance the system’s ability to discriminate between genuine faults and sensor anomalies, we introduce a novel procedure for generating synthetic false positives that simulate realistic sensor failures. To mitigate class imbalance, we employ structured oversampling and multiple synthetic data generation strategies. Our results demonstrate that our GAT-Mk-NN model achieves the best trade-off between accuracy and computational efficiency, reaching an F1-score of 0.882 and an accuracy of 0.884. Performance analysis reveals that low to moderate graph connectivity enhances both robustness and model generalization. Our GAT-Mk-NN model structure outperformed other state-of-the-art graph architectures (enhanced GCN, GraphSAGE, GIN, graph transformer, ChebNet, TAG, ARMA graph, simple GCN, GATv2, and hybrid GNN). The findings highlight the potential of graph-based learning for fault detection in sensor-dense industrial environments, offering actionable insights for deploying fault-tolerant diagnostics in critical systems. Full article

Potassium (K) is a vital macronutrient for plant growth and yield, yet most soil K occurs in insoluble mineral forms, limiting availability to crops. Reliance on chemical K fertilizers is unsustainable due to cost and environmental concerns. Microbial solubilization of mineral K, particularly by purple nonsulfur bacteria (PNSB), offers an eco-friendly alternative. This study focused on isolating mica-potassium-solubilizing purple nonsulfur bacteria (MK-PNSB) from in-dyke alluvial soil and assessing their effects on hybrid maize germination and seedling growth. Among the isolates, the results showed that strain M-Wa-19 released the highest amount of soluble K under microaerobic light conditions (27.4 mg∙L⁻¹). Under aerobic dark conditions, M-Wa-24 and M-Wa-26 released 20.1–21.0 mg∙L⁻¹ of soluble K. Strains M-Wa-21, M-Wa-25, and M-Sl-13 solubilized K in the range of 14.3–25.1 mg∙L⁻¹ and 12.9–24.4 mg∙L⁻¹ under both incubation conditions. The selected strains were identified by 16S rRNA as Rhodopseudomonas palustris strain M-Sl-13 (PX588604), Rhodoplanes pokkaliisoli strain M-Wa-19 (PX588605), Afifella marina strain M-Wa-21 (PX588606), Rhodocista pekingensis strain M-Wa-24 (PX588607), Rhodocista pekingensis strain M-Wa-25 (PX588608), and Rhodocista pekingensis strain M-Wa-26 (PX588609). None exhibited toxicity to maize seeds; instead, all enhanced seed vigor indices by up to 99.7% and improved plant height and root biomass by 19.0–26.2% and 14.4–22.9%, respectively, under static hydroponic conditions. At a 1:1000 (bacteria and distilled water) dilution rate, strains M-Wa-26, M-Wa-25, M-Sl-13, M-Wa-24, M-Wa-19, and M-Wa-21, along with the six-strain mixture, improved seed vigor index by 3.96–7.91%. These findings suggest that MK-PNSB, individually or in mixtures, hold promise as biofertilizer candidates for sustainable K management in crop production. Full article

With the depletion of shallow coal resources, deep extra-thick coal seam mining has become vital for energy security, yet fully mechanized top-coal caving (FMTC) goaf-side roadways face severe challenges of excessive advanced deformation and intense strata behavior. To address this gap, this study took the 4301 tailgate of a coal mine in Shaanxi province as the engineering background, integrating field investigation, theoretical analysis, FLAC^3D numerical simulation, and industrial tests. Guided by the key stratum theory, we systematically analyzed the influence of overlying key strata fracture on strata pressure. The results show three key strata: near-field secondary key strata (KS1, KS2) with “vertical O-X” fracturing and far-field main key stratum (MKS) with “horizontal O-X” fracturing. The radial extrusion force from MKS rotational blocks is the core cause of 200 m range advanced deformation. A collaborative control scheme of near-field key strata directional fracturing roof-cutting pressure relief and high-strength bolt-cable support was proposed. Industrial verification indicates roadway deformation was significantly reduced, with roof subsidence, floor heave, and rib convergence controlled within safe engineering limits. This study fills the gap of insufficient research on far-field key strata’s impact, providing a reliable technical solution for similar extra-thick coal seam FMTC goaf-side roadway surrounding rock control. Full article

Adiabatic demagnetization refrigeration (ADR) is regaining relevance for refrigeration to temperatures below 1 K as global helium-3 supply is increasingly strained. While ADR at these temperatures is long established with paramagnetic hydrated salts, more recently, frustrated rare-earth oxides were found to offer higher entropy densities and practical advantages, since they do not degrade under heating or evacuation. We report structural, magnetic, and thermodynamic properties of the rare-earth borates Ba₃XB₉O₁₈ and Ba₃XB₃O₉ with X = (Yb, Gd). Except for Ba₃GdB₉O₁₈, which orders at 108 mK, the three other materials remain paramagnetic down to their lowest measured temperatures. ADR performance starting at 2 K in a field of 5 T is analyzed and compared to literature. Full article

This study proposes a data-driven framework for predicting forming limit diagrams (FLDs) of AA7075-T6 aluminum sheets under various temperatures and strain rates. To overcome the limitations of costly and time-consuming experiments, a hybrid dataset combining experimental results and virtual data from rate-dependent crystal plasticity finite element (CPFE) simulations coupled with the Marciniak–Kuczyński (M–K) model was developed. Several machine learning (ML) models—including linear regression (LR), random forest regression (RFR), support vector regression (SVR), Gaussian process regression (GPR), and multilayer perceptron (MLP)—were trained to predict FLDs. The nonlinear dependence of the FLD on temperature and strain rate was accurately captured by the ML models, with nonlinear algorithms demonstrating notably improved predictive performance. The proposed approach offers an efficient, accurate, and cost-effective method for FLD prediction and supports data-driven process design in lightweight alloy forming. Full article