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Liposomes have been considered promising and versatile drug vesicles. Compared with traditional drug delivery systems, liposomes exhibit better properties, including site-targeting, sustained or controlled release, protection of drugs from degradation and clearance, superior therapeutic effects, and lower toxic side effects. Given these merits, several liposomal drug products have been successfully approved and used in clinics over the last couple of decades. In this review, the liposomal drug products approved by the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) are discussed. Based on the published approval package in the FDA and European public assessment report (EPAR) in EMA, the critical chemistry information and mature pharmaceutical technologies applied in the marketed liposomal products, including the lipid excipient, manufacturing methods, nanosizing technique, drug loading methods, as well as critical quality attributions (CQAs) of products, are introduced. Additionally, the current regulatory guidance and future perspectives related to liposomal products are summarized. This knowledge can be used for research and development of the liposomal drug candidates under various pipelines, including the laboratory bench, pilot plant, and commercial manufacturing.

The accessibility of pediatric medicine is a global challenge. The issuance of the \"Several Opinions on Ensuring the Use of Children's Medicine\" by six ministries in China in 2014 marked the formation of the policy framework. This study aims to systematically review the progress and achievements of incentive policies for China's pediatric medicine. By analyzing policy documents, clinical trial data, review and approval results, medical insurance directories, volume-based procurement data, as well as adverse reaction reports, the implementation effects of incentive policies for China's pediatric medicine were assessed. China has made progress in legislation, research and development, review and approval, production, access, payment, and use of pediatric medicine. The number and variety of pediatric clinical trials have increased year by year. Some medicine on the Encouraged Research and Submission List of Pediatric Medicine have successfully entered the market. Priority review and approval policies have effectively facilitated the rapid approval of pediatric medicine. However, the availability still needs to be improved, especially in the field of medicine for young children (<6 years). Although incentive policies for China's pediatric medicine have achieved favorable effects, the synergy of the policies still needs to be strengthened. It is recommended that the Chinese government place greater emphasis on the introduction of mandatory regulations and incentive policies, enhance the synergy between policies, use a combination of fiscal and medical insurance tools and follow up on the application of new technologies to comprehensively improve the accessibility of pediatric medicine in the future. This might be helpful for guaranteeing the safety, efficacy and economy of pediatric medicine.

Nasal administration is a non-invasive method of drug delivery that offers several advantages, including rapid onset of action, ease of use, no first-pass effect, and fewer side effects. On this basis, nose-to-brain delivery technology offers a new method for drug delivery to the brain and central nervous system, which has attracted widespread attention. In this paper, the development status and trends of nasal drug delivery and nose-to-brain delivery technology are deeply analyzed through multiple dimensions: literature research, questionnaire surveys, and patent analysis. First, FDA-approved nasal formulations for nose-to-brain delivery were combed. Second, we collected a large amount of relevant information about nasal drug delivery through a questionnaire survey of 165 pharmaceutical industry practitioners in 28 provinces and 161 different organizations in China. Third, and most importantly, we conducted a patent analysis of approximately 700+ patents related to nose-to-brain delivery, both domestically and internationally. This analysis was conducted in terms of patent application trends, technology life cycle, technology composition, and technology evolution. The LDA topic model was employed to identify technological topics in each time window (1990–2023), and the five key major evolution paths were extracted. The research results in this paper will provide useful references for relevant researchers and enterprises in the pharmaceutical industry, promoting the further development and application of nasal drug delivery and nose-to-brain delivery technology.

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R2-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection.

The application of preservatives and ethylene stimulants is critical for improving latex yield and extending the lifespan of rubber trees; however, traditional manual spraying methods are inefficient and unsuitable for large-scale plantation management. Moreover, existing segmentation models are challenged by complex bark textures and varying illumination conditions, resulting in blurred spraying boundaries and reduced recognition accuracy. To address these issues, this study proposes an improved segmentation model based on the YOLOv12n-Seg framework, termed YOLO-RSTS (YOLO for Rubber Spraying Target Segmentation), for accurately distinguishing preservative and stimulant spraying regions on rubber trees. The proposed model introduces three novel modules: CrossScaleDSC, CG-Attention, and C2f-DSC, which enhance long-range dependency modeling, suppress background noise through combined spatial–channel attention, and enable fine-grained multi-scale feature extraction with low computational complexity. In addition, RFCAConv and DWConv are incorporated into the backbone and head to strengthen spatial diversity and contextual representation. Experiments conducted on a self-constructed dataset demonstrate that YOLO-RSTS significantly outperforms the baseline YOLOv12n, achieving improvements of 6.3% in Precision (from 0.819 to 0.882), 6.3% in mAP0.50 (from 0.788 to 0.851), and 8.1% in Recall (from 0.740 to 0.821), while reducing the parameter count by 14.5% (from 2.72M to 2.33M). Meanwhile, compared with the latest YOLOv13n, YOLO-RSTS also achieves superior performance, with increases of 7.5% in mAP0.50 and 9.2% in F1 score. These results indicate that the proposed method provides an effective and efficient solution for vision-based autonomous spraying and holds significant potential for advancing intelligent rubber plantation management.

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees.

Purpose In this study, a new parameter, volume under response surface (VURS), based on the multiple integrals response surface (MIRS) was applied to establish in vitro-in vivo correlations (IVIVC) refer to in vitro dissolution data and in vivo pharmacokinetic data. Materials and methods The in vivo predictive capacity of f 2 factor, dissolution efficiency (DE), and VURS were compared by investigating the multi-sourced diclofenac sodium extended-release tablets. In vitro dissolution tests were investigated under various conditions. Beagle dogs were used for in vivo pharmacokinetic study as a preliminary investigation of the new approach. In vivo pharmacokinetic experiments were conducted based on the crossed-over design principle, and the blood concentration was determined by LC-MS/MS method. Results Data indicated both DE value and f 2 factor were unable to discriminate the significant difference in relative bioavailability among the test formulations, although they could suggest in vivo bio-inequivalent risk to some extent. VURS is successfully explored to establish an IVIVC in beagle dogs with diclofenac sodium extended-release formulations with similar release mechanism. Conclusions Compared with DE value and f 2 factor, the advantage of VURS was demonstrated to predict in vivo parameters of test formulation with a similar or dissimilar release mechanism.

An analysis strategy that can simultaneously determine the amino acid site information and configuration for polymyxin (PM) components is reported in this study. The main components of PMB1, PMB2, and PMB1-I (the Leucine at position 7 changed to be Isoleucine) were purified from the mixture of PMB. The PMB component was hydrolyzed by hydrochloric acid to be a mixture of amino acids, which were derivatized with Marfey’s reagent of Nα-(5-fluoro-2,4-dinitrophenyl)-l-alanine amide (l-FDAA), and then, the configurations of amino acids were easily determined by HPLC–MS. However, the aforementioned method could not offer the information of amino acid at each site, a specific enzymatic hydrolysis method combined Edman degradation was developed. The fatty acyl-diaminobutyric acid (position 1) of PMB component was removed by ficin, and the PMB components were transformed to be PMB nonapeptide. The amino acids at each site of the nonapeptide were determined by N-terminal sequencing. Combining the results of the above methods, the amino acid information and configuration of each site in PM components can be determined, especially the l-Leucine and l-Isoleucine in PMB1 and PMB1-I. The workflow was useful for other PM analogues to identify the sequence and configuration for quality control.

A kind of serial-parallel hybrid polishing machine tool based on the elastic polishing theory is developed and applied to finish mould surface with using bound abrasives. It mainly consists of parallel mechanism of three dimensional moving platform, serial rotational mechanism of two degrees of freedom and the elastic polishing tool system. The active compliant control and passive conformity of polishing tool are provided by a pneumatic servo system and a spring, respectively. Considering the contradiction between the machining quality and efficiency, the optimization model of process parameters is found according to different machining requirements, namely single objective optimization and multi-objective optimization, which provide a choice of parameters as a basis for the operators in practice. Many polishing experiments are conducted to collect the data samples. The genetic algorithm integrated with artificial neural network is used for researching for the optimal process parameters in term of the various optimization objectives. This research also lays the foundation for further establishing polishing expert system.