Bioimpacts最新文献

Recently, hydrogels, ionogels, and organogels have emerged as promising 3D hydrophilic networks for biological tissues, but a main challenge remains: balancing mechanical robustness with self-healing materials. The primary objective of this brief perspective is to highlight a few nanoconfined entanglements approaches (i.e., polymer networks under co-planar nanoconfinement) that can lead to stable hydrogels with high modulus and effective self-healing properties. This editorial proposes that this nanoconfinement-based design paradigm marks a groundbreaking advance in soft materials development by basically uncoupling dynamic reconfigurability and stiffness. The broader applications include medical implants, wearable sensors, soft robotics, and adaptive biomimetic materials. In the future, these approaches can aid in designing hybrid materials that integrate colloidal materials, respond to multiple stimuli, and be tailored for real-world devices. The editorial article also discusses current challenges and future perspectives in advancing nanoconfined entanglement constructions as a promising candidate for the next generation of smart materials.

Introduction: Metoprolol is therapeutically formulated as a racemate with stereoselective pharmacokinetics influenced by CYP2D6 polymorphism. Understanding enantioselective disposition is critical for optimizing therapy in hypertensive patients, particularly during long-term treatment, where metabolic and excretory pathways may interact unpredictably.

Methods: This study analyzed plasma samples from 18 hypertensive patients on long-term metoprolol therapy using a validated chiral capillary electrophoresis method. Enantiomer concentrations were quantified, and S/R ratios were evaluated alongside patient demographics, dosing regimens, and co-administered drugs. The study design focused on identifying deviations from expected enantiomeric patterns observed in single-dose or short-term multi-dose administration in healthy individuals studies.

Results: While most patients (70%) exhibited the anticipated S/R ratio≥1, 30% demonstrated inverted plasma S/R ratios (<1), suggesting altered renal excretion or CYP2D6 saturation.

Conclusion: Long-term metoprolol therapy reveals complex enantioselective disposition influenced by metabolic phenotype, renal excretion and drug interactions. The unexpected S/R inversion underscores the need for personalized dosing, particularly in patients with renal impairment or polypharmacy. Enantiomer monitoring may complement pharmacogenomic strategies to optimize therapeutic outcomes.

Bioinformatics algorithms empowered by artificial intelligence (AI), machine learning (ML), and deep learning (DL) are revolutionizing biopharmaceutical design and development. These methods accelerate discovery through rapid in silico prediction of protein structure, function, and immunogenicity, reducing experimental cost and time. Generative and hybrid frameworks, especially those combining AI with physics-informed neural networks (PINNs), enable interpretable, mechanism-aware modeling for enzyme kinetics and protein engineering. Multi-omics integration and graph-based network algorithms support systems-level understanding of therapeutic targets. Despite remarkable progress, challenges persist, including limited data for novel modalities, interpretability gaps, and computational scalability. Recent advances such as AlphaFold 3, OpenFold, and NeuralPlexer, alongside evolving FDA and EMA guidelines for AI-derived therapeutics, are helping bridge innovation and clinical translation. The future of drug discovery will rely on synergistic human-algorithm collaboration to ensure responsible, reproducible, and clinically relevant biopharmaceutical innovation.

As a wide-ranging category of nanostructured materials, metal-organic-frameworks (MOFs) display distinctive characteristics, including uniformly ordered porosity, exceptional stability, and extensive tunability. These attributes enable the strategic design of MOFs in advanced biosensing platforms, including electrochemical and fluorescent biosensors. This editorial discusses the latest developments in MOF-based biosensors, emphasizing structural and surface functionalization strategies, enzyme immobilization, and signal amplification approaches that enhance analytical sensitivity and selectivity. Particular focus is placed on the MOF hybrid nanocomposites and micro/nano-sensing architectures designed to achieve precise control over activity-structure relationships. Moreover, current challenges in accomplishing scalable, biocompatible, and reproducible synthesis as well as in balancing stability with diffusion efficiency are examined. Finally, emerging trends combining computational modeling, advanced characterization, and machine-learning (ML)-guided design are highlighted as pathways toward next-generation analytical and point-of-care sensors with improved performance and broader practical applicability.

Introduction: In recent decades, the development of simple and reliable techniques for detecting chemical and biological molecules has gained considerable importance. These methods have found extensive applications in different fields, including medicine, biotechnology, food safety, and environmental monitoring. Among these, aptasensors have attracted significant attention owing to their exceptional selectivity, high sensitivity, and versatility in targeting a wide range of analytes. Integrating aptasensors with nanotechnology, particularly gold nanoparticles (Au NPs), has revolutionized the efficiency and performance of these devices. Au NPs, with their unique features like as high surface-to-volume ratio, chemical stability, and extraordinary optical properties, serve as powerful tools for enhancing the aptasensor's capabilities. These features enable signal amplification, reduction of nonspecific interference, and enhancement of accuracy and sensitivity.

Methods: This review focuses on recent advances in fluorescent aptasensors amplified by Au NPs. It analyzes the various experiments undertaken to develop and apply these sensors and concludes with a discussion of the technology's future prospects.

Results: The findings establish the capability of aptamer-Au NP hybrids to detect a broad spectrum of analytes-including mycotoxins, antibiotics, pesticides, heavy metals, and disease biomarkers-with exceptional sensitivity.

Conclusion: This review emphasizes the powerful potential of aptamer-Au NP hybrids for biosensing applications and suggests a path for future work to move this technology from the lab into practical use.

Introduction: Bacterial keratitis refers to a prevalent sight-threatening ophthalmologic infection. Owing to the challenge of antimicrobial resistance in treating bacterial keratitis, novel therapeutic strategies are needed. Resolvin D1 (RvD1), an endogenous lipid mediator, exhibits anti-inflammatory and immune-regulatory effects. The present study was aimed at investigating whether RvD1 alleviates lipopolysaccharide-induced inflammation to protect corneal fibroblasts and explore its potential mechanisms.

Methods: In this study, Raw264.7 cells were polarized towards M1 or M2 macrophages by the addition of lipopolysaccharides LPS or interleukin (IL)-4, respectively, and were treated with or without RvD1. Flow cytometry and Western blot were used to determine the expression of M1 and M2-related markers. EdU assay and trans-well assay were performed to detect the proliferation and migration ability of corneal fibroblasts. Bioinformatics analysis (GO and KEGG) of RNA-seq was applied to explore the RvD1-related signaling pathways. siRNA-c-Fos was further used to confirm the role of Fos expression in RvD1-mediated macrophage polarization. Flow cytometry and Western blotting analysis were performed to demonstrate that RvD1 alleviated LPS-induced inflammation by suppressing M1 macrophage polarization, facilitating M2 macrophage polarization, and increasing corneal fibroblast proliferation. Bioinformatics analysis identified PI3K-AKT, IL-17, and MAPK signaling pathways as potential targets of RvD1 in corneal inflammation.

Results: Enrichment analysis indicated that the RvD1 target gene showed a strong relationship to the regulation of macrophage polarization. RvD1 highly upregulated M2 macrophages by promoting c-Fos expression and enhanced the proliferation and migration of mouse corneal fibroblasts through modulating c-Fos expression.

Conclusion: Our findings reveal that RvD1 conferred protective effects against LPS-induced inflammation by enhancing M2 macrophage polarization through the promotion of c-Fos expression. Thus, RvD1 may be a potential therapeutic compound for enhancing corneal fibroblast proliferation and migration while attenuating inflammation.

Despite the revolutionized diagnostic effect of fluorine-18 -fludeoxyglucose ([¹⁸F]-FDG) as a positron emission tomography (PET) radiotracer in oncological divisions, lack of specificity and sensitivity in discovery of some tumor subtypes was inevitable. Fibroblast activation protein (FAP) is overexpressed in a vast majority of neoplasms, particularly in more than 90% of epithelial tumors which could be an appropriate target for evaluation of tumor's molecular and metabolic functions by FAP inhibitor (FAPI) ligands. Considerably extensive radiolabeled FAPIs have been investigated during clinical trials for diagnostic as well as theranostic applications with encouraging outcomes. In the same cancers, PET/CT imaging by FAPIs are demonstrating to be valuable alternative to [¹⁸F]-FDG in assessment of cancers in which [¹⁸F]-FDG PET performance is suboptimal due to [¹⁸F]-FDG high background uptake or relatively low avidity. Furthermore, the propensity to specifically target FAP expression through FAP-targeted medications or radiotracer therapy creates prospects for image-guided treatment in both cancer and non-cancer indications. FAPI PET will remain a fascinating field of study in the future years.

The development of targeted therapies against epidermal growth factor receptor (EGFR) has transformed the clinical management of EGFR-driven malignancies, especially non-small cell lung cancer (NSCLC). However, the therapeutic benefit of ATP-competitive tyrosine kinase inhibitors (TKIs) is often undermined by acquired resistance mutations such as T790M and C797S, which either enhance ATP affinity or preclude covalent drug binding. Allosteric inhibition of EGFR has emerged as a promising alternative, leveraging cryptic, mutation-specific binding pockets to achieve superior selectivity and reduced off-target toxicity. Allosteric ligands, particularly those targeting the αC-helix adjacent clefts, have shown potent activity against drug-resistant EGFR isoforms but suffer from suboptimal pharmacokinetics and systemic stability. To overcome these limitations, smart nanoconjugates functionalized with allosteric inhibitors have been developed to enhance targeted delivery, improve intracellular trafficking, and facilitate stimuli-responsive drug release. These nanosystems are capable of co-delivering synergistic agents such as siRNA or CRISPR-Cas9 payloads, amplifying pathway suppression and delaying resistance onset. Surface modification strategies, including PEGylation and bioorthogonal ligand conjugation, further improve circulation half-life and tumor accumulation via active and passive targeting. This review systematically discusses the molecular basis of EGFR allosteric inhibition, engineering principles of nanocarrier platforms, including immunogenicity, scale-up feasibility, and regulatory complexities.

The field of mesenchymal stem cell (MSC) therapy has grown rapidly over the last ten years. MSCs' regenerative, and immunomodulatory properties have led to extensive research into them as therapeutic agents for the cell-based management of chronic ocular diseases. However, poor biocompatibility, penetration, and transport to the target ocular tissues restrict the use of MSC-based treatment. The involvement of exosomes in MSC biological functions has been clarified by a growing body of research, which also indicates that MSC-derived extracellular vesicles (EVs) have similar anti-inflammatory, anti-apoptotic, tissue-repairing, neuroprotective, and immunomodulatory qualities as MSCs. Recent developments in exosomes produced from MSCs may help address the difficulties MSC therapy faces. MSC-derived exosomes' nanoscale size enables them to quickly cross biological barriers and enter immune-privileged organs. This enables the effective delivery of therapeutic factors, like trophic and immunomodulatory agents, to ocular tissues that are normally difficult for both conventional therapy and MSC transplantation to target. Furthermore, mesenchymal stem cell transplantation hazards are reduced when EVs are used. The properties of EVs produced by MSCs and their biological roles in corneal regeneration are the main topics of this review of the literature.

Introduction: Accurate and automated assessment of anterior cruciate ligament (ACL) injuries in MR images is essential for athlete healthcare and rapid diagnosis of knee injuries. However, challenges such as the small size of the ligament, variations in MR image quality, and complex anatomical structures complicate the classification process.

Methods: In this study, we propose a hierarchical deep learning model for the detection and classification of ACL injuries. The model consists of two main phases: ACL segmentation and injury classification. In the first phase, we employ an encoder-decoder architecture with attention mechanisms to accurately identify the ACL region in MR images, while suppressing background noise. Skip connections are used to preserve spatial details and improve segmentation accuracy. In the second phase, the segmented ACL region is input into a hierarchical convolutional neural network (CNN) for classification. Dense blocks are incorporated to maximize feature reuse, while max-pooling and global average pooling (GAP) layers help to reduce overfitting and improve feature extraction.

Results: The proposed method was evaluated on a knee MRI dataset and compared with other state-of-the-art approaches. Our model demonstrated high accuracy in both segmentation and classification tasks, owing to the integration of attention mechanisms and hierarchical feature extraction.

Conclusion: This approach offers a robust solution for the automated assessment of ACL injuries, providing clinicians and sports medicine specialists with a reliable tool for more efficient and accurate diagnosis.