Mechanical loading is a central cue in periodontal tissues, where compression of the periodontal ligament guides remodeling and orthodontic tooth movement (OTM). However, most mechanobiology studies have used two-dimensional cultures with poorly defined loading, and the role of autophagy under realistic three-dimensional compression remains unclear. In this study, we constructed a three-dimensional static compression model by encapsulating human periodontal ligament cells in collagen–alginate–CaSO<sub>4</sub> hydrogels, whose swelling, degradation, and viscoelasticity approximate those of native matrix. When exposed to a controlled static compressive stress, the cells exhibited an early autophagic response with increased ATG7, Beclin1, and LC3-II/LC3-I; accumulation of LC3-positive puncta; and reduced p62 expression between 4 and 8 h. Pharmacological modulation showed that activation of AKT-mTOR signaling suppressed this response, whereas its inhibition further augmented autophagy, identifying AKT-mTOR as a negative regulator of compression-induced autophagy. Together, these findings demonstrate that moderate static compression drives AKT-mTOR-dependent autophagy in periodontal ligament cells and establish a simple hydrogel platform for quantitative studies of periodontal remodeling.
The mixing of organic cations represents yet another direction to explore in the field of chiral organic-inorganic hybrid metal halides (OIHMH). Here, we perform structural optimizations, electronic structures, and non-linear optical (NLO) studies using the density functional theory of two recently synthesized chiral OIHMHs, [R-MePEA][C3A]PbBr4, and [R-MePEA][C4A]PbBr4, with mixed chiral arylammonium and achiral alkylammonium cations. We find that the noncovalent weak interactions (e.g. Br...NH interactions) play an important role in the formation of these OIHMHs. Our study further indicates that the two non-centrosymmetric compounds exhibit relative wide bandgaps (~3.5 eV), strong second harmonic generation (SHG) responses (~0.5-1.5*KDP), and moderate birefringence (~0.088), indicating possible applications NLO materials. Atom response theory analysis reveals that the SHG responses are determined mainly by the occupied Br 3p non-bonding orbitals as well as by the unoccupied Pb 5p orbitals which shows the important contribution of the inorganic PbBr4 layer to the nonlinear optical properties.
Abstract This study aimed to explore the use of ornamental stone waste as a support for the α-MoO₃ catalyst to develop efficient and sustainable heterogeneous systems for biodiesel production. The catalysts were characterized using physicochemical techniques including X-ray diffraction with Rietveld refinement, FTIR and Raman spectroscopy, scanning electron microscopy with EDS, transmission electron microscopy, nitrogen adsorption/desorption (BET method), particle size distribution, ammonia temperature-programmed desorption (TPD-NH₃), magnetic measurements, and gas chromatography to analyze simultaneous transesterification/esterification (TES) reactions and quantify methyl and ethyl esters. XRD patterns revealed crystalline phases such as mica-biotite, ferro-tschermakite, albite, quartz, and iron-magnesium silicates—components of the catalytic support—as well as orthorhombic α-MoO₃ phases, also confirmed in the heterogeneous systems. FTIR and Raman analyses showed characteristic vibrational bands, while SEM images displayed irregular agglomerates, corroborated by TEM. Nitrogen adsorption isotherms indicated mesoporous structures with surface areas ranging from 0.615 to 3.87 m²/g. Particle size analysis showed D₅₀ values between 5.02 and 20.00 μm, with total acidity ranging from 77.0 to 245 µmol/g of NH₃. Magnetic tests indicated ferrimagnetic behavior. The catalytic performance confirmed the effectiveness of the waste as a support for α-MoO₃, particularly in the system containing 40% Mo ions (40%σ-MoO₃:Waste), which achieved conversions between 78% and 95%. These findings highlight the environmental and technological potential of the studied catalysts, reinforcing the economic viability and ecological relevance of reusing industrial waste in the sustainable production of biodiesel from waste oils.
Lyubomira Radeva, Aleksandar Belchev, Parsa Karimi Dardashti
et al.
Curcumin is a widely researched natural molecule due to its abundance of pharmacological effects, such as antioxidant, antitumor, anti-inflammatory, etc. The main limitation of curcumin, however, is its low aqueous solubility, which worsens its biopharmaceutical characteristics. The aim of this study was to encapsulate curcumin in albumin nanoparticles and to subsequently incorporate them into a polyvinyl alcohol patch, resulting in a new drug formulation for skin application. The nanoparticles were characterized by a small mean diameter of approximately 162 nm, a narrow size distribution, and a negative zeta potential. TEM confirmed the small size of the nanoparticles. The ratio between the drug and albumin was optimized, achieving approximately 88% encapsulation efficiency. Protein–ligand docking, utilizing CB-Dock, indicated a strong interaction between curcumin and albumin. The binding between the molecules was proved via diffuse-reflectance UV–vis and XRD analyses. The encapsulated curcumin showed a significantly potentiated scavenging activity against ABTS and DPPH radicals in comparison with the pure drug, as well as a protective effect in H<sub>2</sub>O<sub>2</sub>-induced oxidative stress in fibroblasts. The loaded nanoparticles were further incorporated in a PVA hydrogel patch, which was characterized in terms of mechanical properties and in vitro release. Therefore, the resulting system could provide more effective skin delivery and an improved antioxidant activity of curcumin.
Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.
Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while theoretical simulation and graph neural network-based machine learning methods--especially crystal graph convolutional neural networks (CGCNNs)--have become effective alternatives, achieving remarkable results in predicting material elastic properties. This study trained two CGCNN models using shear modulus and bulk modulus data of 10987 materials from the Matbench v0.1 dataset, which exhibit high accuracy (mean absolute error <13, coefficient of determination R-squared close to 1) and good generalization ability. Materials were screened to retain those with band gaps between 0.1-3.0 eV and exclude radioactive element-containing compounds. The final predicted dataset comprises two parts: 54359 crystal structures from the Materials Project database and 26305 crystal structures discovered by Merchant et al. (2023 Nature 624 80). Ultimately, this study completed the prediction of shear modulus and bulk modulus for 80664 inorganic crystals. This work enriches existing material elastic data resources and provides robust support for material design, with all data openly available at https://doi.org/10.57760/sciencedb.j00213.00104.
Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.
Large Language Model (LLM)-based agents have demonstrated the ability to improve performance in chemistry-related tasks by selecting appropriate tools. However, their effectiveness remains limited by the inherent prediction errors of chemistry tools. In this paper, we take a step further by exploring how LLMbased agents can, in turn, be leveraged to reduce prediction errors of the tools. To this end, we propose ChemHAS (Chemical Hierarchical Agent Stacking), a simple yet effective method that enhances chemistry tools through optimizing agent-stacking structures from limited data. ChemHAS achieves state-of-the-art performance across four fundamental chemistry tasks, demonstrating that our method can effectively compensate for prediction errors of the tools. Furthermore, we identify and characterize four distinct agent-stacking behaviors, potentially improving interpretability and revealing new possibilities for AI agent applications in scientific research. Our code and dataset are publicly available at https: //anonymous.4open.science/r/ChemHAS-01E4/README.md.
Deiby F. Aparicio Acevedo, Marlyn C. Ortiz Villamizar, Vladimir V. Kouznetsov
The quinoline derivative, <i>N</i>-(7-chloro-4-morpholinoquinolin-2-yl)benzamide, was synthesized in a conventional three-step procedure from 4,7-dichloroquinoline using a <i>N</i>-oxidation reaction/C2-amide formation reaction/C4 S<sub>N</sub>Ar reaction sequence. The structure of the compound was fully characterized by FT-IR, <sup>1</sup>H-, <sup>13</sup>C-NMR, DEPT-135°, and ESI-MS techniques. Its physicochemical parameters (Lipinski’s descriptors) were also calculated using the online SwissADME database. Such derivatives are relevant therapeutic agents exhibiting potent anticancer, antibacterial, antifungal, and antiparasitic properties.