Biomedical Applications Research Articles

Designing microplastic-binding peptides with a variational quantum circuit-based hybrid quantum-classical approach.

De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics-a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework's effectiveness in generating peptide candidates, evaluate its efficiency for property-oriented design, and validate the candidates with molecular dynamics simulations. This quantum computing-based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies.

Deep representation learning of protein-protein interaction networks for enhanced pattern discovery.

Protein-protein interaction (PPI) networks, where nodes represent proteins and edges depict myriad interactions among them, are fundamental to understanding the dynamics within biological systems. Despite their pivotal role in modern biology, reliably discerning patterns from these intertwined networks remains a substantial challenge. The essence of the challenge lies in holistically characterizing the relationships of each node with others in the network and effectively using this information for accurate pattern discovery. In this work, we introduce a self-supervised network embedding framework termed discriminative network embedding (DNE). Unlike conventional methods that primarily focus on direct or limited-order node proximity, DNE characterizes a node both locally and globally by harnessing the contrast between representations from neighboring and distant nodes. Our experimental results demonstrate DNE's superior performance over existing techniques across various critical network analyses, including PPI inference and the identification of protein functional modules. DNE emerges as a robust strategy for node representation in PPI networks, offering promising avenues for diverse biomedical applications.

Biodegradable Nanobowls with Controlled Dents.

Nanobowls show promising potential in biomedical applications, such as bioimaging, cargo delivery, and disease theranostics, due to their unique concave structure and interior cavities. However, the lack of biodegradable nanobowls with manipulable size (especially the dent size) still exists as an obstacle for their in-depth exploration and application in biomedical fields. Herein, polypeptide-based nanobowls are successfully obtained by the self-assembly of a graft polypeptide [named TPE-P(GAAzo21-stat-GA29)] via a solvent-switch method. Through the synergistic effect between the hydrogen bonding and π-π stacking interactions, the size of nanobowls and the corresponding dents can be facilely controlled by altering either the initial polypeptide concentration or the cosolvents in self-assembly. Furthermore, such polypeptide-based nanobowls are demonstrated to be biocompatible and biodegradable in vitro, which may promote the development of biomedical nanobowls in the future.

Review on Superhydrophobic Polymer Composite Coating Materials and its Coating Technology for Advanced Applications

ABSTRACT Researchers all over the world are working to produce improved polymeric materials suitable for cutting-edge advanced applications that will eventually replace traditional materials. Polymeric materials that display super hydrophobic characteristics are quite valuable and interesting when it comes to the development of things with lotus leaf surface effects. In recent decades, super hydrophobic surfaces (SHS) have been considered as key parameter, and superhydrophobic coatings are vital to achieve materials with hydrophobic surface. The most crucial criteria influencing super hydrophobic surfaces are low surface free energy and higher surface roughness. Explorations on superhydrophobic surfaces (SHS) are very interesting because of their importance in varied nature of industrial, engineering and bio-medical applications. Superhydrophobic membranes have made a lot of curiosity recently, especially for efficient oil-water separation application. A major exploitation of these SHS is to increase the corrosion resistance of surfaces exposed to corrosive environments as well as their long-term chemical stability and maintaining the superhydrophobic characteristics. The other major important sectors utilize these materials to achieve ultra-superhydrophobic behavior are space and aerospace, defense, automotive, marine, water-oil separation, biomedical-engineering, and sensors. Superhydrophobic surfaces repel water primarily due to their surface texture and physicochemical properties. The present article deals with the basic and fundamental principles, importance and applications of superhydrophobic behavior of materials, including different analytical and fabrication techniques used to study and to ascertain the super hydrophobic properties of the materials.

Engineering a Binding Peptide for Oriented Immobilization and Efficient Bioelectrocatalytic Oxygen Reduction of Multicopper Oxidases.

Enzymatic fuel cells (EFCs) are emerging as promising technologies in renewable energy and biomedical applications, utilizing enzyme catalysts to convert the chemical energy of renewable biomass into electrical energy, known for their high energy conversion efficiency and excellent biocompatibility. Currently, EFCs face challenges of poor stability and catalytic efficiency at the cathodes, necessitating solutions to enhance the oriented immobilization of multicopper oxidases for improved heterogeneous electron transfer efficiency. This study successfully identified a surface-binding peptide (SBP, 13 amino acids) derived from a methionine-rich fragment (MetRich, 53 amino acids) in E. coli CueO through semirational design. The first phase of engineering focused on the structural characteristics of MetRich, pinpointing fragment N394-H406 (SBP 1.0, corresponding to variant CueO-M12) as the key region dominating the binding. Subsequent site-saturation mutagenesis, combined with electrochemical screening, yielded three variants, and among them, the variant CueO-M12-1 (CueO-M12 H398I) exhibited a more uniform favorable orientation with a 1.38-fold increase in current density. Further electrocatalytic kinetics analysis revealed a significant 21.2-fold improvement in kinetics current density (Jk) compared with that of CueO-WT, leading to the development of SBP 2.0. When SBPs were fused to laccase from Bacillus pumilus (BpL) and fungal bilirubin oxidase from Myrothecium verrucaria (MvBOD), respectively, they transformed a sluggish adsorption process into a rapid and oriented one. In addition, compared with SBP 1.0, SBP 2.0 endows BpL and MvBOD with enhanced electrocatalytic capabilities for oxygen reduction and glucose/O2 EFC performance. The engineered SBPs are promising for serving as a versatile "glue" to enable the immobilization of oxidoreductases in an oriented manner, which leads to a breakthrough in bioelectrocatalysis and thereby overcoming the current bottleneck of EFCs.

Unlocking the Power of Antioxidant Nanoparticles: Insights into Classification, Formulation, Characterization, and Biomedical Applications

Unlocking the Power of Antioxidant Nanoparticles: Insights into Classification, Formulation, Characterization, and Biomedical Applications

Covalent integration of polymers and porous organic frameworks

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research.

Towards Accurate Biocompatibility: Rethinking Cytotoxicity Evaluation for Biodegradable Magnesium Alloys in Biomedical Applications

Magnesium and its alloys represent promising candidates for biomedical implants due to their biodegradability and mechanical properties, which are similar to natural bone. However, their rapid degradation process characterized by dynamic pH fluctuations and significant hydrogen gas evolution during biocorrosion adversely affects both in vitro and in vivo assessments. While the ISO 10993-5 and 12 standards provide guidelines for evaluating the in vitro biocompatibility of biodegradable materials, they also introduce testing variability conditions that yield inconsistent results. To address these inherent characteristics of Mg alloys, developing improved methods that accurately simulate the physiological environment for in vitro biocompatibility testing is essential. This study introduces two novel extraction approaches for evaluating Mg alloys: a buffered solution utilizing PBS/DMEM with quaternary dilutions and a modified ISO standard protocol employing decuple dilution of conventional unbuffered extracts. The present findings establish that controlled optimization of extraction conditions, specifically buffer composition and dilution parameters, enables reliable in vitro cytotoxicity assessment of Mg alloys, providing a robust methodology that advances the preclinical evaluation of these promising biodegradable materials.

Recent Advances of Strategies and Applications in Aptamer-Combined Metal Nanocluster Biosensing Systems

Metal nanoclusters (NCs) are promising alternatives to organic dyes and quantum dots. These NCs exhibit unique physical and chemical properties, such as fluorescence, chirality, magnetism and catalysis, which contribute to significant advancements in biosensing, biomedical diagnostics and therapy. Through adjustments in composition, size, chemical environments and surface ligands, it is possible to create NCs with tunable optoelectronic and catalytic activity. This review focuses on the integration of aptamers with metal NCs, detailing molecular detection strategies that utilise the effect of aptamers on optical signal emission of metal NC-based biosensing systems. This review also highlights recent advancements in biosensing and biomedical applications, as well as illustrative case studies. To conclude, the strengths, limitations, current challenges and prospects for metal NC-based systems were examined.

Harnessing Nanomaterials for Enhanced DNA-Based Biosensing and Therapeutic Performance.

The integration of nanomaterials with DNA-based systems has emerged as a transformative approach in biosensing and therapeutic applications. Unique features of DNA, like its programmability and specificity, complement the diverse functions of nanomaterials, leading to the creation of advanced systems for detecting biomarkers and delivering treatments. Here, we review the developments in DNA-nanomaterial conjugates, emphasizing their enhanced functionalities and potential across various biomedical applications. We first discuss the methodologies for synthesizing these conjugates, distinguishing between covalent and non-covalent interactions. We then categorize DNA-nanomaterials conjugates based on the properties of the DNA and nanomaterials involved, respectively. DNA probes are classified by their application into biosensing or therapeutic uses, and, several nanomaterials are highlighted by their recent progress in living biological. Finally, we discuss the current challenges and future prospects in this field, anticipating that significant progress in DNA-nanomaterial conjugates will greatly enhance precision medicine.

Exploring Metal Cluster Interactions with Functionalized Graphene via Molecular Dynamics Simulation

Abstract: This study investigates the interaction between copper (Cu) and silver (Ag) clusters and graphene-based materials using molecular dynamics simulations. It focuses on how graphene oxidation and aminated polyethylene glycol (PEG-NH2) functionalization influence interaction strength and cluster dynamics. The analysis includes pristine graphene (PG), low-oxidized graphene oxide (GOL), and PEGylated graphene oxide (GO-PEG-NH2). The results reveal that clusters on PG exhibit high mobility, while GO-PEG-NH2 significantly restricts mobility due to strong interactions, as evidenced by highly negative interaction energies. GO-PEG-NH2 systems also display pronounced subdiffusive behavior (α < 1), indicating strong binding and constrained motion. These findings underscore the critical role of PEG-NH2 functionalization in controlling cluster diffusion, paving the way for innovative designs in biomedical and catalytic nanocarrier applications.

Closing Editorial: Research Progress on Chitosan Applications

Chitosan has attracted significant attention due to its versatile properties, which make it an ideal candidate for varied biomedical and industrial applications [...]

Stable conductive PANI-based hydrogels with antibacterial activity

Hydrogels have been utilized in various medical applications, including drug delivery, tissue repair, biosensors, wound dressing, and antimicrobial activity. Electrically conducting hydrogels are particularly promising due to their unique features, such as high-water content, biocompatibility, and adjustable mechanical and electrical properties. In this study, we developed novel conductive polyaniline-based hydrogel systems with enhanced antibacterial and mechanical properties. We specifically investigated the contributions of polyacrylamide, chitosan, phytic acid, and polyaniline to the hydrogel’s electrical sensitivity and stability under strain. Phytic acid and polyaniline were found to significantly improve the hydrogel’s electrical sensitivity and mechanical stability. Phytic acid, in the presence of calcium ions, further enhanced the mechanical properties, while polyaniline increased the electrical conductivity of the hydrogel by approximately sevenfold and also improved its mechanical properties. The newly developed conductive hydrogel system shows great potential for biomedical applications, including wearable sensors.

CryptoBench: Cryptic protein-ligand binding sites dataset and benchmark.

Structure-based methods for detecting protein-ligand binding sites play a crucial role in various domains, from fundamental research to biomedical applications. However, current prediction methodologies often rely on holo (ligand-bound) protein conformations for training and evaluation, overlooking the significance of the apo (ligand-free) states. This oversight is particularly problematic in the case of cryptic binding sites (CBSs) where holo-based assessment yields unrealistic performance expectations. To advance the development in this domain, we introduce CryptoBench, a benchmark dataset tailored for training and evaluating novel CBS prediction methodologies. CryptoBench is constructed upon a large collection of apo-holo protein pairs, grouped by UniProtID, clustered by sequence identity, and filtered to contain only structures with substantial structural change in the binding site. CryptoBench comprises 1,107 structures with predefined cross-validation splits, making it the most extensive CBS dataset to date. To establish a performance baseline, we measured the predictive power of sequence- and structure-based CBS residue prediction methods using the benchmark. We selected PocketMiner as the state-of-the-art representative of the structure-based methods for CBS detection, and P2Rank, a widely-used structure-based method for general binding site prediction that is not specifically tailored for cryptic sites. For sequence-based approaches, we trained a neural network to classify binding residues using protein language model embeddings. Our sequence-based approach outperformed PocketMiner and P2Rank across key metrics, including AUC, AUPRC, MCC, and F1 scores. These results provide baseline benchmark results for future CBS and potentially also non-CBS prediction endeavors, leveraging CryptoBench as the foundational platform for further advancements in the field. The CryptoBench dataset, including the benchmark model, is available on Open Science Framework - https://osf.io/pz4a9/. The code and tutorial are available at the GitHub repository - https://github.com/skrhakv/CryptoBench/. Supplementary data are available at Bioinformatics online.

Light-guided dynamic phantom to mimic microvasculature for biomedical applications: an exploration for pulse oximeter.

Dynamic phantoms capable of changing optical properties by control are essential for standardizing and calibrating spectroscopy systems such as the pulse oximeter. However, current liquid dynamic phantoms containing human blood have a short shelf life and require complex experimental setups. Some solid dynamic phantoms are influenced by the angular-dependent performance of the liquid crystal display (LCD), some have a low spatial resolution, and some have slow control of optical properties. We aimed to develop a solid dynamic phantom, which can overcome these obstacles by changing the optical properties rapidly and generating dynamic biological signals. The absorption properties of the phantom can be controlled in real time by modulating an LCD. A light guide was employed to avoid the angular-dependent performance of the LCD by isolating the scattering top-layer tissue-mimicking silicone phantom from the LCD. The dynamic phantom was characterized at 940, 660, 530, and 455nm to create a lookup table. Photoplethysmography signals of different heart rates from 80 to 120 beats per minute were synthesized, and oxygen saturation levels at 86%, 90%, 95%, and 100% were generated at multiple wavelengths. The design, characterization, and potential applications of the dynamic phantom have been presented. This dynamic phantom can simulate various biological signals by applying corresponding modulation signals and has the potential to calibrate and validate pulse oximeter, imaging, and spectroscopy systems.

Achieving 3-D Structural Uniformity in Cellulose Gel Beads via Salt Screening

Cellulose microgel beads fabricated using the dropping technique suffer from structural irregularity and mechanical variability. This limits their translation to biomedical applications that are sensitive to variations in material properties. Ionic salts are often uncontrolled by-products of this technique, despite the known effects of ionic salts on cellulose assembly. In this study, the coagulation behavior of cellulose/salt solutions was explored as a way to combat these challenges. An ionic salt (NaCl) was added to a cellulose solution (cellulose/NaOH/urea) prior to coagulation in a hydrochloric acid bath. Quantification of the bead geometry and characterization of the pore architecture revealed that balancing the introduction of salt with the resultant solution viscosity is more effective at reducing structural variability and diffusion limitations than other pre-gelling techniques like thermal gelation. Three-dimensional visualization of the internal pore structure of neat cellulose, thermo-gel, and salt-gel beads revealed that adding salt to the solution is the most effective way to achieve 3-D structural uniformity throughout the bead. Coupled with nanoindentation, we confirmed that the salt produced during coagulation plays a critical role in mechanical variability, and that adding salt to the solution before dropping into the coagulation bath completely screens this effect, producing uniform microgel beads with reproducible mechanical properties.

Structural and some Mechanical Properties of Parquetina Nigrescens Pod Nanoparticles Reinforced Mixed Matrix Membrane for Dental Applications

Polylactic acid (PLA) is a biocompatible, and biodegradable thermoplastic. It is renewable being produced by bacterial fermentation of potatoes, sugarcane, or corn starch. PLA has been attempted in many applications to replace petroleum-based polymers because some of its mechanical properties are excellent and it can be processed easily but low toughness and thermal stability are setbacks from one application to another. To improve on mechanical properties of the PLA to be suitable as biomaterials for dental and other applications, this study focuses on synthesis of Parquetinanigrescens pod nanoparticles (PNPNP) as reinforcements in the PLA to engineer new nanocomposites for biomedical applications. Chemical, structural and some mechanical properties were investigated. TEM result indicates an average size of 14.14 nm of the PNPNP used for reinforcing the PLA. Fourier Transform Infrared Spectrograph establishes a reduction in the amount of light transmitted by the PLA. This is due to PNPNP additions with changes in the wavenumbers of the observed peaks affirming formation of new compounds due to interactions of the PLA molecules and PNPNP. X-ray diffractograms reaffirms new compound formations. Moreover, there are enhancements in mechanical properties up to 3% by weight of PNPNP addition to the PLA with about 92% improvement in the impact toughness.

Selection of Best Suitable Titanium Alloy for Biomedical Applications Using Analytical Hierarchical Process

Selection of Best Suitable Titanium Alloy for Biomedical Applications Using Analytical Hierarchical Process

Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.

Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications.

Harnessing the Potential of Spider Silk Proteins for Biomedical Applications: from Native Silk Fibers to Designed Bioactive Materials

AbstractSpider silk has only been used anecdotally throughout human history, owing to limited supply from natural sources and recalcitrance toward biochemical functionalization. Recent advances in materials synthetic biology have ameliorated the challenges of mass‐producing spider silk and have thus paved the way for reengineering and redesigning these materials for real‐world applications. Here, an update is presented on the development of biomaterials comprising spider silk proteins within the past five years and the respective chemical and genetic approaches behind these developments. Potential applications are further highlighted in areas such as 3‐dimension (3D) cell culturing, drug delivery, theranostics, wound healing, tissue engineering, anti‐infection, and so on. By providing some glimpses into the latest innovations centered around spider silk proteins, as well as the challenges facing their biomedical applications, it is hoped that this will inspire more translational studies of these materials for real‐world impact.