Browse Articles

Dimensional Evolution from a Giant Molybdenum-Red Cage-like {Mo ₂₀₀ } to 1D Chains Enabling Ultrahigh Proton Conduction

Oral health knowledge, attitude, and practices of parents of autistic children

Adaptive Protein Corona Nanoassemblies Couple Cytokine Signaling with Endogenous Antigen Transport for Systemic Cancer Immunity

Mindfulness and artificial intelligence self-efficacy among university students: the mediating role of artificial intelligence literacy

Exploring the Structure and Chemistry of One-Dimensional and Two-Dimensional Lepidocrocite TiO ₂ at Atomic Resolution

Discovery of indolylchalcone benzenesulfonamides as selective inhibitors of tumor-associated carbonic anhydrase IX and XII

Abstract Indole, characterized by its favorable bioavailability, unique structural attributes, and a broad pharmacological profile, represents a privileged scaffold in anticancer drug discovery. Chalcones constitute another important scaffold in medicinal chemistry and numerous chalcone derivatives have been reported to exhibit diverse biological activities, including anticancer and enzyme inhibitory effects. Several chalcone-based compounds have also been investigated as carbonic anhydrase inhibitors. Sulfonamides are a classical class of carbonic anhydrase (CA, EC 4.2.1.1) inhibitors with diverse therapeutic relevance, being employed as diuretics, anticonvulsants, topical antiglaucoma agents and in the management of obesity and cancer. Herein, we designed and synthesized a novel series of indolylchalcone–benzenesulfonamide hybrids ( 15a − m and 16a − d ) and evaluated their inhibitory activities against a panel of four human carbonic anhydrases (hCA isoforms I, II, IX and XII). Interestingly, most of the tested compounds inhibited the tumor-associated hCA IX and XII isoforms with single- to double-digit nanomolar inhibition constants ( K i s). In particular, compound 15h (( E )-4-(3-(5-cyano-1 H -indol-3-yl)acryloyl)benzenesulfonamide) showed potent inhibition of hCA IX ( K i = 8.9 nM) with marked selectivity over hCA I, II, and XII (SI = 61.9, 7.2, and 5.7, respectively), whereas compound 15j (( E )-4-(3-(6-bromo-1 H -indol-3-yl)acryloyl)benzenesulfonamide) exhibited strong inhibition of hCA XII ( K i = 4.9 nM) with pronounced selectivity over hCA I, II, and IX (SI = 628.4, 12.8, and 11.9, respectively), compared with the reference inhibitor acetazolamide (AAZ). Structure–activity relationship analysis revealed that electron withdrawing groups, particularly cyano and bromo substituents on the indole scaffold, conferred enhanced selectivity toward tumor-associated carbonic anhydrase isoforms. Molecular docking demonstrated that both compounds adopted the canonical sulfonamide-binding mode, coordinating with Zn²⁺ and engaging in stable hydrogen-bond interactions with key active-site residues. Complementary molecular dynamics simulations confirmed the persistence of these interactions, as reflected by lower RMSD values and the maintenance of critical contacts throughout the simulation period. In addition, in silico ADME prediction using the SwissADME server suggested that compound 15j possesses a more favorable balance of lipophilicity and polarity, indicating improved membrane permeability and oral absorption potential compared to 15h and acetazolamide (AAZ), whereas 15h exhibited relatively higher polarity that may limit permeability despite comparable lipophilicity. Accordingly, these findings suggest that the novel indolylchalcone-benzenesulfonamide hybrids 15h and 15j represent promising leads for the development of potent and selective inhibitors of tumor-associated carbonic anhydrases.

Synergy of Dual-Atomic Sites and Interfacial Dynamics for 83.7% Selective Hydroxylamine Electrosynthesis

Comparative evaluation of domain-specific and general-purpose transformer models for Arabic poet classification

Abstract Arabic poet classification presents distinctive challenges stemming from the morphological richness and stylistic diversity inherent in both classical and modern Arabic verse. This study conducts an extensive comparative evaluation of several neural language models to assess their ability to represent poetic expression and capture authorial characteristics. Two carefully curated datasets are utilised: FrequentPoets , representing prolific authors with extensive verse collections, and CrossEraPoets , encompassing poets from distinct historical periods to examine temporal stylistic variation. A comparative evaluation framework is introduced to contrast domain-specific and general-purpose language models across prolific authorship and cross-era stylistic variation. The domain-adapted AraPoemBERT consistently achieves superior performance, attaining 73.11% accuracy (73.00% F1) on FrequentPoets and 77.06% accuracy (77.04% F1) on CrossEraPoets , whereas the general-purpose GPT-4o demonstrates considerably lower performance under zero-shot and few-shot evaluation settings. The results highlight the significance of domain-adapted pretraining for morphologically complex languages like Arabic and suggest the potential advantage of transformer-based architectures in modelling stylistic and linguistic nuances unique to Arabic verse. These findings also suggest that temporal diversity may play an important role in model generalisation across different poetic styles. The study contributes to Arabic Natural Language Processing (NLP) and digital humanities by enabling computational authorship attribution, stylistic analysis, and cross-historical exploration of Arabic literary heritage. Overall, the proposed framework provides a robust foundation for future research in Arabic poetry analytics and domain-specific language modelling.

Free-Anionic Duality of Interstitial Electrons Drives Superconductivity in Electride La ₃ In

Retraction Note: An enzymatic BioBattery based on multicopper oxidases with reduced substrate diffusion constraints for sustainable energy harvesting

Photocatalytic Hydrogen Cycling via Benzotrithiophene-Based Mesoporous Covalent Organic Frameworks for Efficient Fine Chemical Coproduction

An optimized ensemble learning model for interpretable and efficient obesity classification

Discovery and Biosynthesis of Nitrilobacillins by Post-Translational Introduction of C-Terminal Nitrile Groups

The PROTECT databank a population based linked administrative resource on child maltreatment and intellectual disability with early findings

Correction to “The Role of Quantum Tunneling in Metal–Ligand Proton Tautomerism”

Circadian-oriented intermittent versus continuous enteral feeding is associated with differential circadian clock gene expression in critically ill patients: a randomized controlled study

Chemoenzymatic Synthesis of 6-Sulfo Lewis X-Related Glycans for Probing Their Ligand-Binding Proteins

IPGMVL: based on interactive progressive graph convolution with multi-view learning traffic flow forecasting

Renal NR4A1 predicts ischemic AKI severity and recovery after partial nephrectomy

MOD2SAD: enhancing malicious office document detection through semantic-aware deobfuscation

Abstract Advanced Persistent Threat campaigns have increasingly adopted semantic obfuscation techniques in malicious Office macros, rendering the code logic opaque to traditional scrutiny. Despite the decline in volumetric attacks following Microsoft’s default blocking policy, these sophisticated vectors can bypass traditional static syntactic pattern matching and evade dynamic analysis through environment awareness guardrails. To recover logic hidden by such obfuscation, this paper proposes a static analysis framework centered on semantic-aware code reconstruction. Unlike conventional methods, our approach reconstructs the underlying execution logic from obfuscated scripts to extract hidden Indicators of Compromise (IoCs). A notable feature of this framework is the Obfuscation Awareness and Splitting Approach. This algorithmic mechanism addresses the challenges of context window limitations and logical fragmentation by utilizing quantitative metrics to identify high-density obfuscation zones and optimally partition scripts, ensuring the preservation of semantic continuity during reconstruction. We then employ a generative semantic engine to process these partitions, feeding a hybrid feature extraction pipeline for multidimensional threat characterization. We systematically evaluate the framework on a contemporary dataset of obfuscated malicious macros. Experimental results demonstrate that our semantic reconstruction approach achieves an average precision of 74.57% in IoC extraction, outperforming conventional static analysis in our evaluation. When integrated with machine learning classifiers, the framework attains a maximum detection accuracy of 98.89%. The experimental results indicate the effectiveness and robustness of our semantic deobfuscation-based framework in real-world malware detection, offering enterprises a scalable solution for defensive deployment.