Search Results (145)

The development of thermostable and pH-robust endo-polygalacturonases (endo-PGases) is crucial for industrial applications such as food processing. This study aimed to engineer the thermostability of an acidic, thermophilic endo-PGase (PoxaEnPG28B) by rigidifying its flexible regions. We employed an integrated computational strategy combining molecular dynamics (MD) simulations at elevated temperatures with in silico analyses of unfolding free-energy changes to identify and design stabilizing mutations. This approach successfully yielded the mutant D249K, which exhibited a 5 °C higher optimal temperature (70 °C) and a 68.8% longer half-life at 55 °C, and it retained over 76.8% activity at 75 °C. Notably, D249K maintained the wild-type’s optimal pH (5.0) and broad pH stability (3.0–8.0). Although it is not the absolute top performer in every single metric, D249K achieves the best overall balance between thermostability and pH robustness among all reported thermophilic endo-PGases. MD simulations revealed that its enhanced stability sems from reduced global and local flexibility and a more compact structure. In juice extraction applications, D249K increased yields by up to 98.5%, significantly surpassing the wild-type. This study demonstrates the efficacy of MD-guided flexible region engineering for the GH28 family and presents D249K as a highly promising industrial biocatalyst. Full article

Reactive structural adhesives—epoxies, polyurethanes, and acrylics—are essential in high-performance applications, yet their development remains complex due to multiscale adhesion mechanisms, combinatorial formulation spaces, and stringent performance requirements. Traditional trial-and-error approaches are time- and resource-intensive. Machine learning (ML) provides a powerful framework to accelerate adhesive design by capturing nonlinear relationships between formulation, processing, and performance, while enabling predictive modeling, optimization, and experiment prioritization. This review presents a process-oriented guide for ML-assisted adhesive development, covering component selection, feature engineering, initial dataset design, model choice, and iterative workflows integrating classical design-of-experiments, active learning, and Bayesian optimization. Emphasis is placed on interpreting ML outputs through the lens of polymer chemistry, reaction kinetics, and fracture mechanics to extract mechanistic insights and guide rational formulation design. Key challenges—including small, noisy datasets, multi-component interactions, and multi-objective trade-offs—are discussed, along with emerging directions such as collaborative databases, automated knowledge extraction, and hybrid ML–chemistry approaches to further enhance structural adhesive development. The review underscores the potential of integrating ML into adhesive R&D to reduce experimental burden, improve formulation efficiency, and enable data-driven exploration of complex chemistries. Full article

Metal–organic frameworks (MOFs) are among the most structurally diverse classes of crystalline nanomaterials, offering exceptional tunability, porosity, and chemical modularity. These characteristics have positioned MOFs as promising platforms for nanomedicine, bioimaging, and integrated nanotheranostic applications. However, the rational design of MOFs that satisfy stringent biomedical requirements, including high drug loading capacity, controlled and stimuli responsive release, selective targeting, physiological stability, biodegradability, and multimodal imaging capability, remains challenging due to the vast combinatorial design space and the complex interplay between physicochemical properties and biological responses. The objective of this review is to critically examine recent advances in artificial intelligence approaches based on Transformer architectures for the design and optimization of MOFs aimed at next-generation nanotheranostics. In contrast to prior reviews that broadly survey machine learning methods for MOF research, this article focuses specifically on Transformer-based models and their ability to capture long-range, hierarchical, and multiscale relationships governing MOF structure, chemistry, and functional behavior. We review state-of-the-art models, including MOFormer, MOFNet, MOFTransformer, and Uni MOF, and discuss graph-based and sequence-based representations used to encode MOF topology and composition. This review highlights how Transformer-based models enable predictive assessment of properties directly relevant to nanotheranostic performance, such as adsorption energetics, framework stability, diffusion pathways, pore accessibility, and surface functionality. By explicitly linking these predictive capabilities to drug delivery efficiency, imaging performance, targeted therapeutic action, and combined diagnostic and therapeutic applications, this work delineates the specific contribution of Transformer-based artificial intelligence to biomedical translation. Finally, we discuss emerging opportunities and remaining challenges, including generative Transformer models for inverse MOF design, self-supervised learning on hybrid experimental and computational datasets, and integration with autonomous synthesis and screening workflows. By defining the scope, novelty, and contribution of Transformer-based design strategies, this review provides a focused roadmap for accelerating the development of MOF-based platforms for next-generation nanotheranostics. Full article

A synthetically accessible library of N-acyl sulfonamides was constructed using a combination of copper(I)-catalyzed azide–alkyne cycloaddition (CuAAC) and N-acylation of primary sulfonamides. The proposed two-step reaction sequence had a high experimentally confirmed synthetic success rate (up to 85%) and gave reasonable product yields (up to 61%). As a result of the validation process, a 262-member compound library (out of >70K accessible combinations) was prepared. Biological profiling of the synthesized library by differential scanning fluorimetry and enzymatic assays identified several low micromolar inhibitors of human carbonic anhydrase. The interaction of the discovered hits with the biological target was studied by docking and molecular dynamics. Full article

Cancer immunotherapy has transformed oncology, but lasting responses are still limited due to resistance mechanisms within the tumor microenvironment. MicroRNAs (miRNAs) have emerged as critical regulators of immune checkpoint pathways, antigen presentation, T-cell activity, and macrophage polarization. By modulating both tumor-intrinsic and immune cell–intrinsic processes, miRNAs influence the efficacy of immune checkpoint inhibitors, therapeutic vaccines, and adoptive cell therapies. Additionally, circulating and exosomal miRNAs are being investigated as minimally invasive biomarkers to predict patient response and resistance to immunotherapy. Clinical trials of miRNA-based treatments, including mimics and inhibitors, have highlighted both the promise and challenges of translating these molecules into clinical use. Advances in delivery systems, RNA chemistry, and combinatorial strategies are paving the way for their integration into precision immuno-oncology. This review offers a comprehensive overview of the mechanistic, biomarker, and therapeutic roles of miRNAs in cancer immunotherapy, highlighting ongoing clinical progress and prospects. Full article

Cancer continues to pose a major global health burden, with conventional therapeutic modalities such as surgical resection, chemotherapy, radiotherapy, and immunotherapy often hindered by limited tumor specificity, substantial systemic toxicity, and the emergence of multidrug resistance. The rapid advancement of nanotechnology has introduced functionalized nanomaterials as innovative tools in the realm of precision oncology. These nanoplatforms possess desirable physicochemical properties, including tunable particle size, favorable biocompatibility, and programmable surface chemistry, which collectively enable enhanced tumor targeting and reduced off-target effects. This review systematically examines recent developments in the application of nanomaterials for cancer therapy, with a focus on several representative nanocarrier systems. These include lipid-based formulations, synthetic polymeric nanoparticles, inorganic nanostructures composed of metallic or non-metallic elements, and carbon-based nanomaterials. In addition, the article outlines key strategies for functionalization, such as ligand-mediated targeting, stimulus-responsive drug release mechanisms, and biomimetic surface engineering to improve in vivo stability and immune evasion. These multifunctional nanocarriers have demonstrated significant potential across a range of therapeutic applications, including targeted drug delivery, photothermal therapy, photodynamic therapy, and cancer immunotherapy. When integrated into combinatorial treatment regimens, they have exhibited synergistic therapeutic effects, contributing to improved efficacy by overcoming tumor heterogeneity and resistance mechanisms. A growing body of preclinical evidence supports their ability to suppress tumor progression, minimize systemic toxicity, and enhance antitumor immune responses. This review further explores the design principles of multifunctional nanoplatforms and their comprehensive application in combination therapies, highlighting their preclinical efficacy. In addition, it critically examines major challenges impeding the clinical translation of nanomedicine. By identifying these obstacles, the review provides a valuable roadmap to guide future research and development. Overall, this work serves as an important reference for researchers, clinicians, and regulatory bodies aiming to advance the safe, effective, and personalized application of nanotechnology in cancer treatment. Full article

Compound 221s (2,9) is a novel antihypertensive drug candidate synthesized utilizing danshensu, borneol, and proline by using the strategy of combinatorial molecular chemistry. This study aimed to systematically identify the safety of danshensu-derived compound 221s (2,9) by conducting an acute toxicity test and long-term toxicity study and to elucidate the in vivo metabolic pathways of 221s (2,9) in order to provide critical insights into the observed toxicity. In the acute toxicity study, a single oral dose of 221s (2,9) at 3000 mg/kg in mice produced no clinical signs of toxicity or mortality, indicating an MTD of 3000 mg/kg. In a subsequent 12-week repeated-dose toxicity study in rats, doses of 20, 40, and 80 mg/kg were well tolerated, with no adverse clinical observations or deaths. Notably, organ coefficient analysis revealed transient lung injury, which resolved following a 4-week recovery period. The metabolite identification study indicated that metabolism in rats is predominated by Phase II metabolites, potentially contributing to the low toxicity of 221s (2,9). Further investigation into the impact of the drug metabolic enzyme–transporter interplay on the in vivo disposition of 221s (2,9) is warranted. Full article

The prevalence of food allergies has been steadily increasing in recent years. β-lactoglobulin (β-LG), the main allergenic protein of milk and dairy allergies, is more commonly observed in infants and children. In this study, a β-LG-specific aptamer was selected using the combinatorial chemistry process known as systematic evolution of ligands by exponential enrichment (SELEX), and a quartz crystal microbalance with dissipation monitoring (QCM-D)-based aptasensor was developed using a novel surface functionalization technique, which mimics an artificial cell membrane on the QCM-D sensor surface, creating a physiologically relevant environment for the binding of the target to the sensor. Through SELEX combined with next-generation sequencing (NGS), the aptamer Apt 356 was identified. Its binding to β-LG was confirmed via dot blot analysis. The selected Apt 356 was then used for the development of a QCM-D-based sensor. To fabricate the sensor, the quartz surface was functionalized with a supported lipid bilayer (SLB). The β-LG-specific aptamer was immobilized onto this SLB. The results demonstrated that the QCM-D system allows real-time observation and evaluation of the binding of β-LG. While there have been some studies on aptasensors for the β-LG protein, to the best of our knowledge, this is the first QCM-D-based aptasensor developed specifically for β-LG protein detection. Full article

Fluorescent chemosensors are increasingly becoming relevant in recognition chemistry due to their sensitivity, selectivity, fast response time, real-time detection capability, and low cost. Boronic acids have been reported for the recognition of mycolactone, the cytotoxin responsible for tissue damage in Buruli ulcer disease. A library of fluorescent arylboronic acid chemosensors with various signaling moieties with certain beneficial photophysical characteristics (i.e., aminoacridine, aminoquinoline, azo, BODIPY, coumarin, fluorescein, and rhodamine variants) and a recognition moiety (i.e., boronic acid unit) were rationally designed and synthesised using combinatorial approaches, purified, and fully characterised using a set of complementary spectrometric and spectroscopic techniques such as NMR, LC-MS, FT-IR, and X-ray crystallography. In addition, a complete set of basic photophysical quantities such as absorption maxima (λ_abs^max), emission maxima (λ_em^max), Stokes shift (∆λ), molar extinction coefficient (ε), fluorescence quantum yield (Φ_F), and brightness were determined using UV-vis absorption and fluorescence emission spectroscopy techniques. The synthesised arylboronic acid chemosensors were investigated as chemosensors for mycolactone detection using the fluorescent-thin layer chromatography (f-TLC) method. Compound 7 (with a coumarin core) emerged the best (λ_abs^max = 456 nm, λ_em^max = 590 nm, ∆λ = 134 nm, ε = 52816 M⁻¹cm⁻¹, Φ_F = 0.78, and brightness = 41,197 M⁻¹cm⁻¹). Full article

Lithium-ion battery lifetime prediction traditionally relies on extensive full-cycle testing, which is costly and time-consuming. This paper proposes a novel framework for strategic sample optimization that significantly reduces testing requirements while preserving the ability to capture key degradation patterns. The approach combines combinatorial analysis with model-informed selection to identify a minimal yet representative subset of test conditions that span the primary stress axes, temperature, depth of discharge, and charge rate. A recent predictive modeling technique is then used to validate that the selected samples enable accurate lifetime estimation. Results demonstrate that as few as 3 samples (11% of the original 27-sample dataset) can achieve prediction errors below 2%, reducing cycling costs by approximately 90%. This framework offers a scalable solution for battery developers seeking to streamline accelerated aging protocols. Future work will extend the methodology to additional publicly available datasets to assess its generalizability across chemistries and use cases. Full article

Concentration gradient generation plays a pivotal role in advancing applications across drug screening, chemical synthesis, and biomolecular studies, yet conventional methods remain constrained by labor-intensive workflows, limited throughput, and inflexible gradient control. This study presents a novel multilayer microfluidic chip leveraging shear flow-driven partitioning–recombination mechanisms to enable the flexible and high-throughput generation of concentration gradient droplets. The chip integrates interactive upper and lower polydimethylsiloxane (PDMS) layers, where sequential fluid distribution and recombination are achieved through circular and radial channels while shear forces from the oil phase induce droplet formation. Numerical simulations validated the dynamic pressure-driven concentration gradient formation, demonstrating linear gradient profiles across multiple outlets under varied flow conditions. The experimental results revealed that the shear flow mode significantly enhances mixing uniformity and droplet generation efficiency compared to continuous flow operations, attributed to intensified interfacial interactions within contraction–expansion serpentine channels. By modulating hydrodynamic parameters such as aqueous- and oil-phase flow rates, this system achieved tunable gradient slopes and droplet sizes, underscoring the intrinsic relationship between flow dynamics and gradient formation. The proposed device eliminates reliance on complex channel networks, offering a compact and scalable platform for parallelized gradient generation. This work provides a robust framework for optimizing microfluidic-based concentration gradient systems, with broad implications for high-throughput screening, combinatorial chemistry, and precision biomolecular assays. Full article

Parasitic diseases pose a serious threat to the health of humans and the steady development of livestock husbandry. Although there are certain drug-based treatment methods, with the widespread application of drugs, various parasites are gradually developing drug resistance. Natural products are highly favored by researchers due to their characteristics such as low toxicity, multi-target effects, and low risk of drug resistance. The ocean, as the largest treasure trove of biological resources on Earth, has a special ecosystem (high pressure, high salt, and low oxygen). This enables marine organisms to develop a large number of unique structures during their survival competition. So far, a variety of compounds, such as terpenoids, have been isolated from the algae. As potential drugs, these compounds have certain curative effects on various diseases, including tumors, parasitic diseases, Alzheimer’s disease, and tuberculosis. This paper systematically reviews and analyzes the current advances in research on the antiparasite effects of seaweed extracts. The primary objective of this research is to formulate a conceptual foundation for marine pharmaceutical exploration, focusing on the creation of innovative marine-based medicinal compounds to overcome the emerging problem of parasite resistance to conventional treatments. Full article

Microplastics have emerged as a pervasive marine contaminant, with extreme concentrations reported in deep-sea sediments (e.g., 1.9 million particles/m²) and localized accumulations near Antarctic research stations. Particular concern has been raised regarding their synergistic effects with co-occurring antibiotics, which may potentiate toxicity and facilitate antibiotic resistance gene dissemination through microbial colonization of plastic surfaces. To investigate these interactions, a 185-day controlled exposure experiment was conducted using Aurelia aurita polyps. Factorial combinations of microplastics (0, 0.1, 1 mg/L) and tetracycline (0, 0.5, 5 mg/L) were employed to simulate environmentally relevant pollution scenarios. Microbiome alterations were characterized using metagenomic approaches. Analysis revealed that while alpha and beta diversity measures remained unaffected at environmental concentrations, significant shifts occurred in the relative abundance of dominant bacterial taxa, including Pseudomonadota, Actinomycetota, and Mycoplasmatota. Metabolic pathway analysis demonstrated perturbations in key functional categories including cellular processes and environmental signal transduction. Furthermore, microplastic exposure was associated with modifications in polyp life-stage characteristics, suggesting potential implications for benthic–pelagic population dynamics. These findings provide evidence for the impacts of microplastic–antibiotic interactions on cnidarian holobionts, with ramifications for predicting jellyfish population responses in contaminated ecosystems. Full article

In this study, we introduced a novel graph product derived from the standard Cartesian product and investigated its structural properties, with a particular emphasis on its independence number and spectral characteristics in relation to identical neighbor structures. A key finding is that the spectrum of this newly defined product graph consists entirely of integral eigenvalues, a significant property with applications in chemistry, network theory, and combinatorial optimization. We defined $C{N}_s$ vertices as the vertices having an identical set of neighbors and classified graphs containing such vertices as $C{N}_s$ graphs. Furthermore, we introduced the $C{N}_s$ Cartesian product for these graphs. To formally characterize the relationships between $C{N}_s$ vertices, we constructed an $n\times n$$C{N}_s$ matrix, where an entry is 1 if the corresponding pair of vertices are $C{N}_s$ vertices and 0 otherwise. Utilizing this matrix, we established that the spectrum of the $C{N}_s$ Cartesian product consists exclusively of integral eigenvalues. This finding enhances our understanding of graph spectra and their relation to structural properties. Full article