Search Results (565)

Background/Objectives: Targeting ABL tyrosine kinase (TK) remains a cornerstone of chronic myeloid leukemia (CML) therapy. Methods: In this study, a series of novel 4-((2-(4-(aryl)thiazol-2-yl)hydrazineylidene)methyl)-N,N-diphenylaniline derivatives (1–12) were synthesized through the reaction of 2-(4-(diphenylamino)benzylidene)hydrazine-1-carbothioamide (intermediate A) with substituted 2-bromo-1-arylethanones. Cytotoxic activity was evaluated in K562 CML cells using the MTT assay. The most active compound was further assessed in HL-60 acute myeloid leukemia (AML) cells and healthy peripheral blood mononuclear cells (PBMCs). Apoptosis induction was analyzed by Annexin V/ethidium homodimer staining, while ABL TK inhibition was determined using the ADP-Glo kinase assay. Molecular docking studies were performed to investigate binding interactions within the ATP-binding site of ABL TK, and pharmacokinetic properties were also predicted. Results: Intermediate A demonstrated superior antiproliferative activity compared to derivatives 1–12 and exhibited cytotoxicity comparable to imatinib in K562 cells (IC₅₀ = 6.15 ± 1.26 µM vs. 5.14 ± 1.44 µM, respectively). In HL-60 cells, intermediate A showed an IC₅₀ of 12.04 ± 1.70 µM, similar to imatinib. Notably, intermediate A displayed enhanced selectivity toward K562 cells over PBMCs (SI = 12.9) relative to imatinib (SI = 6.2). The compound significantly induced apoptosis in K562 cells and inhibited ABL TK activity. Docking studies revealed a distinct binding orientation within the ATP-binding pocket of ABL TK. The compound showed acceptable predicted physicochemical and ADME characteristics based on in silico analysis. Conclusions: Intermediate A emerges as a significant anti-CML candidate exhibiting potent cytotoxic, apoptotic, and moderate ABL TK inhibitory activity, together with a favorable selectivity profile. Full article

Electricity spot prices jointly encode network physics and strategic bidding outcomes. In a well-functioning market, nodal and temporal price patterns tend to remain approximately invariant under mild perturbations-exhibiting symmetry-preserving regularities in distribution shape, spatial gradients, and temporal variation. Conversely, congestion binding, net-load stress, and abnormal bidding can induce symmetry breaking, manifested as heavy tails, mean shifts, and localized price discontinuities. This study develops a symmetry-guided and explainable diagnostic framework to identify price anomalies and attribute their dominant drivers. First, representative anomaly types (spike and mean shift) are defined using statistically and operationally motivated criteria, together with robustness checks across alternative thresholds. Second, principal component analysis is applied to construct compact, anomaly-specific feature sets, filtering weakly related variables while retaining system stress, congestion proxies, and renewable-induced variability indicators. Third, leveraging the optimization structure of market clearing and the associated KKT conditions, we characterize the price–feature linkage as a piecewise mapping and quantify each feature’s contribution via a sampling-based influence scoring procedure, yielding a ranked causal attribution. Case studies on a regional day-ahead spot market dataset demonstrate that the proposed framework achieves high consistency with expert assessments, with traceability accuracy exceeding 85% overall and particularly strong performance for spike-type anomalies. The method reduces reliance on purely manual diagnosis and black-box learning, and provides symmetry-oriented, actionable evidence for market surveillance and renewable-friendly flexibility and congestion management design. The proposed framework enables transparent identification of dominant structural drivers underlying different types of electricity price anomalies, linking observed price signals to market-clearing mechanisms. The results provide actionable diagnostic insights for market monitoring and regulatory assessment in electricity markets with high renewable penetration. Full article

Diabetes mellitus represents a major global health challenge, which has generated ongoing interest in developing enzymatic strategies to modulate carbohydrate digestion. Phlorizin, a dihydrochalcone found predominantly in plants of the genus Malus, has been extensively investigated for its antidiabetic potential; however, its practical application is limited by its low water solubility. Enzymatic fructosylation represents an effective biocatalytic approach to overcome this limitation and modulate the functional properties of phenolic compounds. In this study, the inhibitory activity of an enzymatically fructosylated phlorizin-enriched fraction, containing 4-O-mono-fructosyl phlorizin (4PHF) as its main component, was evaluated against key carbohydrate-digesting enzymes using in vitro assays complemented by in silico molecular docking analyses. The 4PHF-enriched fraction showed potent inhibition of α-amylase in vitro, with an IC₅₀ value of 2.69 µg/mL. However, no significant inhibition of α-glucosidase was observed within the analyzed concentration range, indicating a selective inhibitory profile. Molecular docking analyses supported the experimental findings, revealing favorable binding orientations and predicted affinities of 4PHF for α-amylase and α-glucosidase, stabilized primarily by hydrogen bond interactions. Overall, the combined in vitro and in silico results demonstrate that enzymatic fructosylation effectively reprograms the enzyme interaction profile of phlorizin, highlighting 4PHF as a structurally optimized modulator of carbohydrate-digesting enzymes, with potential relevance for applied research on enzyme inhibition related to metabolic diseases. Full article

Cyclin-dependent kinase 2 (CDK2) plays a central role in cell cycle regulation and represents an important molecular target in anticancer drug development. In this study, a series of novel isatin derivatives substituted with a trifluoromethoxy group at the C6 position were designed and evaluated as potential CDK2 inhibitors using a comprehensive in silico approach. Density functional theory calculations were applied to analyze the electronic properties of the proposed compounds. Molecular docking and molecular dynamics simulations were used to investigate binding modes, conformational stability, and key interactions within the CDK2 active site. Binding free energies were estimated using the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) method, while QSAR-based (Quantitative Structure–Activity Relationship) ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses were performed to assess drug-likeness and pharmacokinetic profiles. The results indicate that the investigated derivatives form stable complexes with CDK2, supported by persistent hydrogen bonds in the hinge region and favorable hydrophobic interactions. The trifluoromethoxy substituent significantly affects ligand orientation and promotes deeper insertion into the hydrophobic pocket compared with previously studied isatin analogues. ADMET predictions suggest generally favorable absorption and toxicity profiles, with moderate solubility limitations. Overall, these findings support the potential of 6-trifluoromethoxy-isatin derivatives as promising CDK2 inhibitors and provide a basis for further experimental studies. Full article

Background/Objectives: Fragment-based approaches in the field of drug discovery and design have been widely developed and employed in both academia and industry. We present here an innovative in silico fragment-based drug design approach aimed at designing new inhibitors in the ATP-binding site of protein kinases. Methods: This tool, named Frags2Drugs (F2D), relies on a three-dimensional fragment library obtained from co-crystallized ligands. This library is stored in a graph-oriented database containing the required information to link fragments together. F2D builds every possible molecule that fits into the given cavity on a minute scale. Molecules are then filtered to keep those presenting the best predicted affinity. Several specific molecular filters can be applied, including protein kinase inhibitor-like filters. Results: We validated our method by reconstructing existing co-crystallized ligands and known kinase inhibitors. In this study, we provide several examples of its use to retrieve known or design new type I, type I1/2, type II, and macrocyclic inhibitors on several protein kinases. Conclusions: We have developed an in silico fragment-based ligand design tool able to identify novel kinase inhibitors by growing any scaffolds positioned in the ATP-binding site.. Full article

Background/Objectives: γ-Aminobutyric acid aminotransferase (GABA-AT) is a key enzyme responsible for GABA catabolism and represents a validated therapeutic target for epilepsy. Although existing GABA-AT inhibitors such as vigabatrin are clinically effective, their long-term use is limited by safety concerns, highlighting the need for alternative inhibitors with improved profiles. In this study, we employed an integrated natural product-oriented discovery strategy to identify novel GABA-AT inhibitors from plant-derived compounds. Methods: A library of 1006 plant-derived compounds collected from seven medicinal plants traditionally associated with sedative or anxiolytic effects was subjected to primary virtual screening using GNINA. Top-ranked candidates were further refined through secondary precision docking using aglycone forms to account for biologically relevant metabolic conversion. Detailed interaction analyses and molecular dynamics simulations were performed to assess binding stability and energetic favorability. Results: Based on computational prioritization, quercetin, salvianolic acid A, and scutellarein were selected for experimental validation. Cell-based GABA-AT activity assays in HepG2 cells demonstrated that quercetin and salvianolic acid A significantly inhibited intracellular GABA-AT activity, exhibiting comparable or greater efficacy than vigabatrin, while scutellarein showed moderate inhibition. The observed cellular inhibitory effects were consistent with predicted binding modes and dynamic stability observed in in silico analyses. Conclusions: Collectively, this study highlights the utility of an aglycone-focused, structure-based screening strategy for natural product drug discovery and identifies plant-derived aglycones as promising GABA-AT inhibitor candidates for further pharmacological development. Full article

Background: Endocrine complications remain a major cause of long-term morbidity in patients with transfusion-dependent β-thalassemia (TDT), with hypogonadism being the most frequently reported abnormality. Although iron overload is central to disease pathophysiology, its relationship with reproductive endocrine function in well-chelated adult women remains unclear. Methods: This retrospective longitudinal study evaluated endocrine function in 15 adult women with transfusion-dependent β-thalassemia major over a two-year follow-up period at a tertiary care center. Age, hormonal profiles, ovarian reserve markers, and clinical reproductive characteristics were assessed at baseline and follow-up. An age-matched control group of 22 healthy women was included. Endocrine and biochemical evaluation comprised gonadotropins (follicle-stimulating hormone and luteinizing hormone), estradiol, thyroid-stimulating hormone, prolactin, anti-Müllerian hormone, hemoglobin, serum iron, total iron-binding capacity, vitamin B12, folate, 25-hydroxyvitamin D, and cardiac and hepatic MRI T2* assessment of iron burden. Results: Hypogonadism was clinically prevalent, while other endocrine axes largely remained within reference ranges during follow-up. No newly emerging overt endocrine disorders were identified. Reproductive hormone levels showed no significant temporal changes and were comparable to those of healthy controls. AMH levels demonstrated marked interindividual variability and did not consistently correlate with systemic or imaging-based iron indices. Conclusions: In well-chelated adult women with transfusion-dependent β-thalassemia, reproductive endocrine parameters appear biochemically stable over short-term follow-up, yet clinically relevant hypogonadism persists. AMH variability may reflect subtle ovarian reserve impairment not captured by conventional gonadotropin measurements, supporting the need for longitudinal, phenotype-oriented endocrine surveillance. Full article

Analyzing the evolution of cultivated land quality protection policy in China is crucial for refining its frameworks and constructing a “trinity” system integrating quantity, quality, and ecological sustainability. This study employs content analysis to systematically trace the evolutionary patterns of such policies, based on a review of 200 national and local policy documents issued between 1986 and 2014. The results reveal the following: (1) Policy development has occurred in five distinct stages: embryonic, practical exploration, system construction, in-depth transformation, and comprehensive upgrading. The policy system is now maturing toward an integrated “trinity” protection mechanism. Accordingly, governmental priorities have shifted from emphasizing subsistence benefits to prioritizing ecological benefits. (2) Despite a multifaceted policy framework, effectiveness is hindered by the absence of binding national legislation, which remains in the drafting phase. This gap has resulted in fragmented implementation, inconsistent regional standards, and limited policy efficacy. (3) To strengthen the system, we propose three optimization pathways: elevating the legislative hierarchy for robust legal safeguards, implementing zoning-based control mechanisms for targeted governance, and refining interest linkage policies to enhance stakeholder coordination. Furthermore, by constructing a policy orientation index, we quantify the distinct shift from quantity control towards quality and ecological priorities. The study links this discursive evolution to land governance challenges, arguing that policy fragmentation and weak legal binding may undermine land value stability and long-term investment. Our findings extend beyond descriptive policy history, offering a framework for assessing how policy discourse translates into tangible land system outcomes. Full article

Umami taste in lager beer not only determined body fullness and the backbone of aftertaste, but also affected the controllability and interpretability of flavor expression across the entire brewing process. Based on stage-wise sampling, peptidomic profiles were established on wort fermentation day 0, day 1, day 3, and day 9. A total of 25,592 peptides were identified by reversed-phase liquid chromatography–quadrupole time-of-flight mass spectrometry (RPLC-QTOF-MS). Molecular informatics screening was performed using UMPred-FRL (a feature representation learning-based meta-predictor for umami peptides) and TastePeptides-Meta (a one-stop platform for taste peptides and prediction models), yielding 7255 potential umami peptides. From these, 145 peptides were further selected for molecular docking. In addition, 6 representative umami peptides were selected for receptor-level validation and structural analysis. Mechanistically, the umami receptor taste receptor type 1 member 1/taste receptor type 1 member 3 (T1R1/T1R3) belonged to class C G protein-coupled receptor (GPCR) and relied on the extracellular Venus flytrap (VFT) domain for ligand capture. Ligand-induced VFT conformational convergence transmitted changes to the transmembrane region and triggered signal transduction. Docking and energy decomposition indicated that the ionic group primarily contributed to orientation and anchoring. Salt-bridge or hydrogen-bond networks were formed around Lys228, Arg240, Glu206, Asp210, Asn141, and Gln138, thereby reducing conformational freedom. Meanwhile, hydrophobic side chains obtained major binding gains within a hydrophobic microenvironment formed by Val135, Ile137, Leu165, Tyr166, Trp78, and His79. These results reflected a synergistic mode in which charge pairing enabled positioning and hydro-phobic complementarity promoted VFT closure. To experimentally confirm sensory relevance, 6 representative peptides were individually spiked into 4 brewing-stage beer samples, which produced a clear stratification pattern across stages. Notably, peptides with favorable docking-derived binding propensity did not necessarily enhance umami perception, and several longer peptides showed persistent negative sensory shifts, supporting that binding affinity alone could not be treated as a proxy for perceived umami in the beer matrix. At the node level, the cumulative abundance of umami peptides showed a significant positive correlation with umami scores, with a Pearson correlation coefficient of r = 0.963 and p = 0.037. This result indicated good linear consistency between umami peptide content and the upward shift in umami taste in lager beer. Umami peptide clusters were further proposed as a more appropriate functional unit, and an umami peptide cluster database spanning the full process was constructed. This database provided a reusable resource for process control and flavor prediction. Full article

Modern advances in artificial intelligence have accelerated the development of computational tools for protein–ligand structure prediction, yet their real-world performance remains uneven across receptor classes and ligand chemotypes. Recently published cryo-EM structures of several different psychedelics bound to the serotonin 5HT_2A receptor provide a unique opportunity to explore how modern AI-based modeling performs in a pharmacologically important GPCR system. Here, we compare three major approaches: AI-based protein–ligand cofolding (Boltz-2), a leading AI-driven docking module (Uni-Mol Docking v2), and a widely used classical physics-based docking pipeline (AutoDock Vina) across a series of tryptamine and phenethylamine psychedelics. Predicted binding poses were comparatively assessed through structural alignment with these newly available cryo-EM complexes. Additionally, calcium-mobilization assays were performed to provide a coarse functional readout for comparison with computationally predicted binding affinities. This study integrates methodological review with exploratory benchmarking to illustrate how different modeling paradigms behave on a shared receptor–ligand test set. Our results highlight substantial variation between modeling strategies, with AI-based cofolding often producing global binding orientations more closely resembling experimental structures, and classical docking showing greater variability across ligands, while still outperforming AI-driven docking on average. These observations underscore both the growing utility and current limitations of AI-assisted structure prediction in serotonergic drug discovery, and emphasize the importance of careful, experimentally anchored evaluation as such tools continue to advance. Full article

Contemporary artificial intelligence policies systematically externalize environmental costs. Despite divergent governance models, the European Union, the United States, and China converge on the same outcome: none impose binding restrictions on the energy intensity, carbon footprint, or infrastructural expansion of AI systems. This article demonstrates that sustainability is treated as an externality, rather than as a mandatory regulatory constraint, in all major jurisdictions. Focusing on energy consumption, computational infrastructure, and carbon budgets, the analysis shows that current AI policy choices generate predictable patterns of environmental omission and cost externalization. Policy measures aimed at strengthening rights protection and technological autonomy—such as tightening compliance requirements, developing large-scale models, and duplicating infrastructure—are adopted without corresponding limits on energy use or emissions, generating growing tensions with planetary constraints. This article makes three contributions to the literature on AI governance and sustainability. First, it conceptualizes sustainability as a binding material constraint, rather than as a normative objective or efficiency-based goal. Second, through a comparative policy analysis, it shows that despite divergent regulatory styles, the European Union, the United States, and China converge in the absence of enforceable environmental limits applicable to AI systems. Third, it identifies the policy mechanisms—compliance-driven computational expansion, infrastructure duplication, and scale-oriented incentives—that systematically generate environmental externalization across jurisdictions. The article concludes that effective AI policy requires recognizing sustainability as a hard material limit, translated into binding environmental restrictions that condition regulatory design, infrastructure planning, and the permissible scale of computational systems. Full article

Triplexes (TRX) are a class of flipons that can form due to the interaction of RNA with B-DNA. While many proteins have been proposed to bind triplexes, structural models of these interactions do not exist. Here, I present AlphaFold V3 (AF3) models that reveal interactions between the high-mobility group protein B1 (HMGB1), HNRNPU (SAF-A), TP53, ARGONAUTE (AGO), and REL domain proteins. The TRXs result from the sequence-specific docking of RNAs to DNA via Hoogsteen base pairing. The RNA and DNA strands in apolar TRX are oriented in the opposite 5′ to 3′ direction, while copolar TRX have RNA and DNA strands pointing in the same 5′ to 3′ direction. TRXs can incorporate different RNA classes, including long noncoding RNAs (lncRNAs), short RNAs, such as miRNAs, piRNAs, and tRNAs, nascent RNA fragments, and non-canonical base triplets. Many pathways regulated by TRX formation have evolved to constrain retroelements (EREs), which are both an existential threat to the host and a source of genotypic variation. TRXs help set the boundaries of active chromatin, repressing the expression of most EREs, while depending on other flipons to modulate cellular programs. The TRXs help nucleate folding of intrinsically disordered proteins. Full article

In this study, a phase change material (PCM) in the form of technical-grade octadecane and oriented strand boards (OSBs), which are boards made from wood strands, are used to develop a latent heat storage board with the aim of utilizing this material in building construction while lowering energy consumption. The incorporation of PCM into buildings is difficult for several reasons, including the organic phase material’s flammability and leakage during phase change. These obstacles have been overcome to a significant extent in the engineered OSB material. To avoid PCM from leaking throughout the phase change regime, PCM was hosted in the oriented strand board (OSB) using high-density polyethylene to develop a shape-stabilized phase change wood-based board (SSPCM-OSB). To improve the binding between PCM and OSBs and reduce the flammability, additional additives were added. Extensive testing was conducted to determine the physical and thermal properties and heat transfer characteristics of the developed SSPCM-OSB. The newly developed oriented strand board with SSPCM integration has a lower heat flux than a conventional OSB and comparable flammability characteristics. Full article

Coordinating ecotourism development with economic growth is central to achieving sustainability in regions where natural assets are both a comparative advantage and a binding constraint. This study assesses the ecotourism economy coupling coordination in Hainan Free Trade Port (China) during 2017–2023. Building on sustainable development theory, systems theory, and the tourism-led growth hypothesis, we conceptualize three coordination pathways, industrial structure upgrading, clustering effects, and urban–rural linkages, and operationalize them through an 18-indicator evaluation system covering ecotourism and economic subsystems. Indicator weights are determined using the entropy weight method, and the coupling coordination degree model is applied to quantify the interaction intensity and coordination level. Gray Relational Analysis is further used as a robustness-oriented complement to identify the factors most associated with coordination changes. Results show that both subsystems improved overall with noticeable fluctuations: the ecotourism index rose from 0.239 to 0.719, while the economic development index increased from 0.370 to 0.610. The coupling coordination degree advanced from moderate dysregulation (0.230 in 2017) to near quality coordination (0.995 in 2023), while shock-sensitive years highlight the vulnerability of tourism-related performance. The findings suggest that improving industrial structure and strengthening tourism-related productive capacity and external connectivity are key levers for sustaining coordination without compromising ecological efficiency. Full article

Cholesterol acts as a metabolic cue that reshapes diverse signaling networks, including hedgehog and several sterol-regulated pathways orchestrated by key proteins, including sterol regulatory element-binding protein 2 (SREBP2), sterol O-acyltransferase 1 (SOAT1), Niemann–Pick type C1 (NPC1), and proprotein convertase subtilisin/kexin type 9 (PCSK9). Research over the past decade has highlighted cholesterol metabolism as a key modulator of cancer development and a promising therapeutic target. By integrating mechanistic and translational evidence, this review seeks to clarify how cholesterol metabolism interfaces with oncogenic signaling and set directions for future investigation. Accumulating preclinical and clinical data suggest that dysregulated cholesterol levels, often associated with high-fat diets, may contribute to tumorigenesis and malignant transformation. Implicated pathways, such as SREBP, NPC1, PCSK9, and SOAT1, orchestrate various processes of lipid metabolism, including cholesterol synthesis, esterification, receptor degradation, and transport, that harbor a tumorigenic environment and promote oncogenic processes. Additionally, these enzymes and corresponding pathways provide a promising direction for developing metabolism-oriented anticancer strategies. Cholesterol metabolism dysregulation serves as a major avenue for cancer signaling and growth, but studies also highlight key molecular mechanisms and targets for future treatments. Future studies should focus on expanding studies into further cancer types, investigating combination therapies, and developing novel inhibitors of key molecular targets. Full article