Cedrus atlantica wood tar (CAWT) is traditionally used as a medicinal product, especially in low- and middle-income countries. Despite its traditional use, scientific support for its efficacy remains limited. This study evaluated the biological properties of CAWT using an integrated approach that combined qualitative and quantitative phytochemical analysis, disc diffusion and microdilution tests for antimicrobial assays (disc diffusion and microdilution), antioxidant activity (DPPH and ferric-reducing power assays), in silico ADMET/toxicity, docking, and MD/MMGBSA and provided a balanced comparison with reference antioxidants. This study demonstrated that CAWT is rich in secondary metabolites linked to biological activity, including polyphenols (307.39 ± 58.45 mg GAE/g), tannins (124.42 ± 6.14 mg TAE/g), and flavonoids (15.62 ± 2.53 mg QE/g). For free radical scavenging, CAWT inhibited DPPH with an IC50 of 19.781 ± 2.51 µg/mL and showed ferric-reducing activity with an IC50 of 83.7 ± 2.88 µg/mL for its antimicrobial activity against Pseudomonas aeruginosa; inhibition zones reached 35.66 ± 0.58 mm. In silico analysis, Swiss ADMET and pkCSM predicted ≥ 94% intestinal absorption, no cytochrome P450 liabilities, and low acute toxicity for six dominant terpenoids. Docking pinpointed trans-cadina-1(6),4-diene and α/β-himachalene as high-affinity ligands of LasR and gyrase B (ΔG ≈ −8 kcal mol−1). A 100 ns GROMACS run confirmed stable hydrophobic locking of the lead LasR complex (RMSD 0.22 nm), while MM/GBSA calculated a dispersion-dominated binding free energy of −37 kcal mol−1. Overall, CAWT showed in vitro antioxidant activity (DPPH and ferric-reducing assays) and inhibitory effects in disc diffusion assays, while in silico predictions for major terpenoids suggested favorable oral absorption and low acute toxicity. However, chemical composition analysis and bio-guided fractionation are necessary to confirm the antimicrobial activity and to validate the compounds responsible for the observed effects.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and oxidative stress-driven neuronal damage. Catalase, a key antioxidant enzyme, plays a vital role in decomposing hydrogen peroxide (H2O2) into water and oxygen, thereby protecting neurons from reactive oxygen species (ROS)-mediated toxicity. In AD, the catalase function is compromised due to reduced enzymatic activity and aggregation, which not only diminishes its protective role but also contributes to amyloid plaque formation through catalase-Aβ co-oligomers. Hence, therapeutic strategies aimed at simultaneously preventing catalase aggregation and enhancing its enzymatic function are of great interest. In this study, we screened twelve naturally occurring metabolites for their ability to modulate catalase aggregation and activity. Among these, dimethylglycine (DMG) emerged as the most potent candidate. DMG significantly inhibited thermally induced aggregation of catalase and markedly enhanced its enzymatic activity in a concentration-dependent manner. Biophysical analyses revealed that DMG stabilizes catalase by promoting its native folded conformation, as evidenced by increased melting temperature (Tm), higher Gibbs free energy of unfolding (ΔG°), and reduced exposure of hydrophobic residues. TEM imaging and Thioflavin T assays further confirmed that DMG prevented amyloid-like fibril formation. Molecular docking and dynamics simulations indicated that DMG binds to an allosteric site on catalase, providing a structural basis for its dual role in stabilization and activation. These findings highlight DMG as a promising therapeutic molecule for restoring catalase function and mitigating oxidative stress in AD. By maintaining catalase stability and activity, DMG offers potential for slowing AD progression.

Making advancements in Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting biological activities in new compounds. Traditional 2D-QSAR and 3D-QSAR methods often face challenges in terms of computational efficiency and predictive accuracy. This study introduces a machine learning approach using 3D Discrete Tchebichef Moments (3D-DTM) to address these issues. The 3D-DTM method offers efficient computation, robust descriptor generation, and improved interpretability, making it a promising alternative to conventional QSAR techniques. By capturing global 3D shape information, this method provides better representation of molecular interactions essential for biological activities. We applied the 3D-DTM model to a dataset of 46 molecules derived from the Dihydrophenanthrene scaffold, screened against the enzymatic activity of 3-chymotrypsin-like protease, a key antiviral target. Principal Component Analysis and k-means clustering refined descriptors, followed by stepwise Multiple Linear Regression (step-MLR), Partial Least Squares Regression (PLS-R), and Feed-Forward Neural Network (FFNN) techniques for 3DTMs-QSAR model development. The results showed high correlation and predictive accuracy, with significant validation from internal and external tests. The step-MLR model emerged as the optimal method due to its balance of predictive power and simplicity. Validation through y-Randomization and applicability domain analysis confirmed the model’s robustness. Virtual screening of 100 novel compounds identified 32 with improved pIC50 values. This study highlights the potential of 3D-DTMs in QSAR modeling, providing a scalable and reliable tool for computational chemistry and drug discovery. A user-friendly software tool was also developed to facilitate 3D-DTM extraction from input 3D molecular images.

We investigated the roles and regulation of contractile and sodium ion transporter proteins in the pathogenesis of diarrhea in the acute ulcerative colitis. Acute ulcerative colitis was induced in male Sprague-Dawley rats using dextran sulfate sodium (DSS) in drinking water for seven days. The effects of nobiletin, a citrus flavonoid, were also examined. Increased myeloperoxidase activity, colon mass, and inflammatory cell infiltration were associated with damage to goblet cells and the epithelial cell lining indicating the development of acute ulcerative colitis. SERCA-2 calcium pump expression remained unchanged, whereas the phospholamban (PLN) regulatory peptide was reduced and its phosphorylated form (PLN-P) increased, suggesting a post-translational increase in SERCA-2 activity in the inflamed colon. Higher levels of IP3 were associated with a decrease in the Gαq protein levels without altering phospholipase C expression, suggesting that IP3 regulation is independent of Gαq protein signaling. In addition, the expression of sodium/hydrogen exchanger isoforms NHE-1, NHE-3 and carbonic anhydrase-1 and sodium pump activity were decreased in the inflamed colon. Nobiletin treatment of colitis selectively reversed the inflammatory and oxidative stress markers, including superoxide dismutase and catalase without restoring the expression of ion transporters. This study highlights alterations in the expression of ion transporters and their regulatory proteins in acute ulcerative colitis. These changes in the ion transporters are likely to reduce NaCl absorption and alter contractility, thereby contributing to the pathogenesis of diarrhea in the present model of acute ulcerative colitis. Nobiletin selectively ameliorates acute colitis in this model.

Mechanobiology explores how physical forces and cellular mechanics influence biological processes. This field has experienced rapid growth, driven by advances in high-resolution imaging, micromechanical testing, and computational modeling. At the same time, the increasing complexity and volume of mechanobiological imaging and measurement data have made traditional analysis methods difficult to scale. Artificial intelligence (AI) has emerged as a practical tool to address these challenges by providing new methods for interpreting and predicting biological behavior. Recent studies have demonstrated potential in several areas, including image-based analysis of cell and nuclear morphology, traction force microscopy (TFM), cell segmentation, motility analysis, and the detection of cancer biomarkers. Within this context, we review AI applications that either incorporate mechanical inputs/outputs directly or infer mechanobiologically relevant information from cellular and nuclear structure. This study summarizes progress in four key domains: AI/ML-based cell morphology studies, cancer biomarker identification, cell segmentation, and prediction of traction forces and motility. We also discuss the advantages and limitations of integrating AI/ML into mechanobiological research. Finally, we highlight future directions, including physics-informed and hybrid AI approaches, multimodal data integration, generative strategies, and opportunities for computational biophysics-aligned applications.

Forces exerted by cells due to their internal contractility play fundamental roles in a host of processes, including adhesion, migration, survival and differentiation. Traction force microscopy (TFM) enables the determination of forces exerted by cells or cell collectives on their environment, which is typically taken to be an extra-cellular matrix (ECM)-coated substrate. Sample preparation for TFM involves the plating of cells onto an environment embedded with fiducial markers. The imaging of these fiducial markers in the presence and absence of the cells then enables calculation of the displacement of localized regions of the environment, and, consequently, the spatial distribution of forces exerted by the cells on their environment. Here, we consider the most widely used implementation of TFM (two-dimensional or 2D TFM) which enables the determination of in-plane forces exerted by cells plated on top of an elastic soft substrate. We present streamlined methods for preparing TFM substrates, with special consideration towards experimental steps involved in implementing it using an epifluorescence microscope. We highlight considerations involved in substrate choice between polyacrylamide (PAA) gels and soft silicones, fiducial marker (microbead) choice and distribution as well as microbead and ECM coupling to the substrate. We also point out caveats related to sub-optimal choices in the methodology which can affect the resultant traction force distribution, as well as further derived quantities such as inter-cellular forces in cell pairs computed using the traction force imbalance method (TFIM).

Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.

A topological analysis was performed by taking ESEEM measurements of site-specifically labeled E protein from SARS-CoV-2. The intensity of deuterium modulation arising from either deuterated solvent or deuterated lipid acyl chains revealed exposure to solvent or the bilayer hydrophobic region. Spin-labeled lipids and soluble spin labels were used as points of comparison. The data indicate that spin labels placed along the transmembrane helix of the E protein showed close contact with lipid acyl chains, but also substantial contact with solvent, while those placed on the C-terminal domain showed substantial but lower exposure to lipid acyl chains, with comparable solvent exposure. The results support the view that the C-terminal domain is in contact with the bilayer surface.

Protein droplets exhibit complex self-assembly and deposition behaviors driven by evaporation, which has attracted increasing attention in recent years. Under evaporation, limited volume and locally concentrated protein solutions can undergo liquid–liquid phase separation (LLPS) and liquid–liquid crystalline phase separation (LLCPS), inducing the formation of concentrated droplets and anisotropic structures. The combined effects of interfacial tension and internal flow field induce a variety of deposition patterns on the substrate, providing great significance for the development of functional biomaterials. This paper reviews the physical processes experienced by protein/fibril droplets during evaporation, focusing on the formation mechanism of evaporation and their phase separation behaviors. At the same time, the review systematically summarized the key factors affecting the deposition patterns, and a variety of methods were introduced to pattern deposition, such as external electric field and micro-structured substrates. Furthermore, the potential applications of proteins/fibrils droplet deposition were discussed in multiple fields. This review aims to provide systematic theoretical support and experimental reference for understanding and controlling the deposition behavior of proteins/fibrils droplets, and to promote their further application in functional materials and biomedical engineering.

The combination of antimicrobial agents with different mechanisms of action is an important step in the fight against drug-resistant microorganisms. In this study, the interaction of the lysozyme enzyme with ampicillin and colistin was investigated. These antibiotics are highly effective against Gram-positive (ampicillin) and Gram-negative (colistin) pathogenic microorganisms. Spectroscopic and kinetic methods and molecular docking were used in the research. The results of the spectroscopic analysis confirmed the intermolecular interaction of lysozyme with ampicillin or colistin. The formation of the lysozyme complex with ampicillin was accompanied by mixed quenching of the enzyme fluorescence and changes in its secondary structure (a slight decrease in the content of α-helices). The interaction of lysozyme with colistin was complemented by dynamic quenching of the enzyme fluorescence. The method of molecular docking established that the interactions of lysozyme with colistin were predominantly van der Waals, while hydrogen bonds predominated in the lysozyme complex with ampicillin. Despite the presence of interactions of ampicillin and colistin with amino acid residues from the active site of lysozyme, this did not affect its ability to cause destruction of bacterial cell walls. The results obtained can be used in the development of antibacterial drugs.