ACS Applied Nano Materials最新文献

Transdermal drug delivery systems (TDDS) for antibiotics have seen significant advances in recent years that aimed to improve the efficacy and safety of these drugs. TDDS offer many advantages over other conventional delivery systems such as non-invasiveness, controlled-release pattern, avoidance of first-pass metabolism. The objective of this review is to provide an overview on the recent advances in the TDDS of different groups of antibiotics including β-lactams, tetracyclines, macrolides, and lincosamides, utilized for their effective delivery through the skin and to explore the challenges associated with this field. The majority of antibiotics do not have favorable properties for passive transdermal delivery. Thus, novel strategies have been employed to improve the delivery of antibiotics through the skin, such as the use of nanotechnology (nanoparticles, solid-lipid nanoparticles, nanoemulsions, vesicular carriers, and liposomes) or the physical enhancement techniques like microneedles and ultrasound. In conclusion, the transdermal delivery systems could be a promising method for delivering antibiotics that have the potential to improve patient outcomes and enhance the efficacy of drugs. Further research and development are still needed to explore the potential of delivering more antibiotic drugs by using various transdermal drug delivery approaches.

This study introduces an innovative neural network framework named spectral integrated neural networks (SINNs) to address both forward and inverse dynamic problems in three-dimensional space. In the SINNs, the spectral integration technique is utilized for temporal discretization, followed by the application of a fully connected neural network to solve the resulting partial differential equations in the spatial domain. Furthermore, the polynomial basis functions are employed to expand the unknown function, with the goal of improving the performance of SINNs in tackling inverse problems. The performance of the developed framework is evaluated through several dynamic benchmark examples encompassing linear and nonlinear heat conduction problems, linear and nonlinear wave propagation problems, inverse problem of heat conduction, and long-time heat conduction problem. The numerical results demonstrate that the SINNs can effectively and accurately solve forward and inverse problems involving heat conduction and wave propagation. Additionally, the SINNs provide precise and stable solutions for dynamic problems with extended time durations. Compared to commonly used physics-informed neural networks, the SINNs exhibit superior performance with enhanced convergence speed, computational accuracy, and efficiency.

Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.

The gastrointestinal tract (GIT) is an important and complex system by which humans to digest food and absorb nutrients. The GIT is vulnerable to diseases, which may led to discomfort or even death in humans. Therapeutics for GIT disease treatment face multiple biological barriers, which significantly decrease the efficacy of therapeutics. Recognizing the biological barriers and pathophysiological characteristics of GIT may be helpful to design innovative therapeutics. Nanotherapeutics, which have special targeting and controlled therapeutic release profiles, have been widely used for the treatment of GIT diseases. Herein, we provide a comprehensive review of the biological barrier and pathophysiological characteristics of GIT, which may aid in the design of promising nanotherapeutics for GIT disease treatment. Furthermore, several typical diseases of the upper and lower digestive tracts, such as Helicobacter pylori infection and inflammatory bowel disease, were selected to investigate the application of nanotherapeutics for GIT disease treatment.

With the rapid development of drug delivery systems, extracellular vesicles (EVs) have emerged as promising stars for improving targeting abilities and realizing effective delivery. Numerous studies have shown when compared to conventional strategies in targeted drug delivery (TDD), EVs-based strategies have several distinguished advantages besides targeting, such as participating in cell-to-cell communications and immune response, showing high biocompatibility and stability, penetrating through biological barriers, etc. In this review, we mainly focus on the mass production of EVs including the challenges and strategies for scaling up EVs production in a cost-effective and reproducible manner, the loading and active targeting methods, and examples of EVs as vehicles for TDD in consideration of potential safety and regulatory issues associated. We also conclude and discuss the rigor and reproducibility of EVs production, the current research status of the application of EVs-based strategies to targeted drug delivery, clinical conversion prospects, and existing chances and challenges.

Glaucoma is the leading cause of blindness worldwide. However, its surgical treatment, in particular via trabeculectomy, can be complicated by fibrosis. In current clinical practice, application of the drug, Mitomycin C, prevents or delays fibrosis, but can lead to additional side effects, such as bleb leakage and hypotony. Previous in silico drug screening and in vitro testing has identified the known antibiotic, josamycin, as a possible alternative antifibrotic medication with potentially fewer side effects. However, a suitable ocular delivery mechanism for the hydrophobic drug to the surgical site does not yet exist. Therefore, the focus of this paper is the development of an implantable drug delivery system for sustained delivery of josamycin after glaucoma surgery based on crosslinked γ-cyclodextrin. γ-Cyclodextrin is a commonly used solubilizer which was shown to complex with josamycin, drastically increasing the drug's solubility in aqueous solutions. A simple γ-cyclodextrin crosslinking method produced biocompatible hydrogels well-suited for implantation. The crosslinked γ - cyclodextrin retained the ability to form complexes with josamycin, resulting in a 4-fold higher drug loading efficiency when compared to linear dextran hydrogels, and prolonged drug release over 4 days.

Leptomeningeal disease (LMD) refers to the infiltration of cancer cells into the leptomeningeal compartment. Leptomeninges are the two membranous layers, called the arachnoid membrane and pia mater. The diffuse nature of LMD poses a challenge to its effective diagnosis and successful management. Furthermore, the predominant phenotype; solid masses or freely floating cells, has altering implications on the effectiveness of drug delivery systems. The standard of care is the intrathecal delivery of chemotherapy drugs but it is associated with increased instances of treatment-related complications, low patient compliance, and suboptimal drug distribution. An alternative involves administering the drugs systemically, after which they must traverse fluid barriers to arrive at their destination within the leptomeningeal space. However, this route is known to cause off-target effects as well as produce subtherapeutic drug concentrations at the target site within the central nervous system. The development of new drug delivery systems such as liposomal cytarabine has improved drug delivery in leptomeningeal metastatic disease, but much still needs to be done to effectively target this challenging condition. In this review, we discuss about the anatomy of leptomeninges relevant for drug penetration, the conventional and advanced drug delivery methods for LMD. We also discuss the future directions being set by different clinical trials.