As population aging becomes an increasingly critical global issue, the incidence of central nervous system (CNS) diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and stroke, has risen sharply. However, the blood–brain barrier (BBB) presents a significant obstacle to the effective treatment of these CNS disorders, limiting the ability of therapeutic agents to reach the brain. In this context, intranasal drug delivery, which bypasses the BBB, has attracted considerable attention in recent years. By utilizing pathways such as the olfactory and trigeminal nerves, intranasal drug delivery facilitates the rapid transport of drugs to the brain, thereby enhancing both the bioavailability and targeting efficiency of the drugs. This review provides an overview of the molecular mechanisms underlying intranasal drug delivery, its advancements in the treatment of CNS diseases, strategies to improve delivery efficiency, and a discussion of the challenges and potential future directions in this field. The aim of this paper is to offer valuable insights and guidance for researchers and clinicians working in the area of CNS disease treatment.

Psoriasis is an immune disease caused by rapid and incomplete differentiation of skin basal cells. Natural products such as indirubin have historically served as excellent sources for the treatments of psoriasis. However, the poor solubility and bioavailability due to its plane and rigid crystal structure, which limits its efficacy. Herein, to improve the efficacy of indirubin, a hydrogel-based microemulsion drug delivery system was developed for transdermal delivery. The mean droplet size of the optimized microemulsion was 84.37 nm, with a polydispersity index (PDI) less than 0.2 and zeta potential value of 0~−20 mV. The transdermal flux and skin retention of indirubin at 24 h were 47.34 ± 3.59 μg/cm² and 8.77 ± 1.26 μg/cm², respectively. The optimized microemulsion was dispersed in carbomer 934 hydrogel to increase the consistency. The indirubin-loaded microemulsion gel was tested on an imiquimod-induced psoriasis mouse model. Results showed that this preparation can improve psoriasis symptoms by down-regulating the expression of IL-17A, Ki67, and CD4⁺T. This experiment provides great scalability for researchers to treat psoriasis, avoid first-pass effects, and increase the concentration of targeted drugs.

Carrier-free self-assembly has gradually shifted the focus of research on natural products, which effectively improve the bioavailability and the drug-loading rate. However, in spite of the existing studies, the development of self-assembled natural phytochemicals that possess pharmacological effects still has scope for further exploration and enhancement. Herein, a nano-delivery system was fabricated through the direct self-assembly of Rhein and Matrine and was identified as a self-assembled Rhein-Matrine nanoparticles (RM NPs). The morphology of RM NPs was characterized by TEM. The molecular mechanisms of self-assembly were explored using FT-IR, ¹H NMR, and molecular dynamics simulation analysis. Gelatin methacryloyl (GelMA) hydrogel was used as a drug carrier for controlled release and targeted delivery of RM NPs. The potential wound repair properties of RM NPs were evaluated on a skin wound-healing model. TEM and dynamic light scattering study demonstrated that the RM NPs were close to spherical, and the average size was approximately 75 nm. ¹H NMR of RM NPs demonstrated strong and weak changes in the interaction energies during self-assembly. Further molecular dynamics simulation analysis predicted the self-assembly behavior. An in vivo skin wound-healing model demonstrated that RM NPs present better protection effect against skin damages. Taken together, RM NPs are a new self-assembly system; this may provide new directions for natural product applications.

Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products.

Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0.

Gentian is a traditional Chinese herb with heat-clearing, damp-drying, inflammation-alleviating and digestion-promoting effects, which is widely used in clinical practice. However, there are many species of gentian. According to the pharmacopoeia, Gentiana manshurica Kitag, Gentiana scabra Bge, Gentiana triflora Pall and Gentianarigescens Franch are included. Therefore, accurately identifying the species of gentian is important in clinical use. In recent years, with the advantages of low cost, convenience, fast analysis and high sensitivity, infrared spectroscopy (IR) has been extensively used in herbal identification. Unlike one-dimensional spectroscopy, a two-dimensional correlation spectrum (2D-COS) can improve the resolution of the spectrum and better highlight the details that are difficult to detect. In addition, the residual neural network (ResNet) is an important breakthrough in convolutional neural networks (CNNs) for significant advantages related to image recognition. Herein, we propose a new method for identifying gentian-related species using 2D-COS combined with ResNet. A total of 173 gentian samples from seven different species are collected in this study. In order to eliminate a large amount of redundant information and improve the efficiency of machine learning, the extracted feature band method was used to optimize the model. Four feature bands were selected from the infrared spectrum, namely 3500–3000 cm⁻¹, 3000–2750 cm⁻¹, 1750–1100 cm⁻¹ and 1100–400 cm⁻¹, respectively. The one-dimensional spectral data were converted into synchronous 2D-COS images, asynchronous 2D-COS images, and integrative 2D-COS images using Matlab (R2022a). The identification strategy for these three 2D-COS images was based on ResNet, which analyzes 2D-COS images based on single feature bands and full bands as well as fused feature bands. According to the results, (1) compared with the other two 2D-COS images, synchronous 2D-COS images are more suitable for the ResNet model, and (2) after extracting a single feature band 1750–1100 cm⁻¹ to optimize ResNet, the model has the best convergence performance, the accuracy of training, test and external validation is 1 and the loss value is only 0.155. In summary, 2D-COS combined with ResNet is an effective and accurate method to identify gentian-related species.