ObjectiveTraditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays. MethodsThis study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics (MD) simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper. Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. MD simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control. ResultsThe CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels. ConclusionIt was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/AKT signaling pathway. These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.

ObjectiveThis study proposes a novel single-cell protein localization method based on a class perception graph convolutional network (CP-GCN) to overcome several critical challenges in protein microscopic image analysis, including the scarcity of cell-level annotations, inadequate feature extraction, and the difficulty in achieving precise protein localization within individual cells. The methodology involves multiple innovative components designed to enhance both feature extraction and localization accuracy. MethodsFirst, a class perception module (CPM) is developed to effectively capture and distinguish semantic features across different subcellular categories, enabling more discriminative feature representation. Building upon this, the CP-GCN network is designed to explore global features of subcellular proteins in multicellular environments. This network incorporates a category feature-aware module to extract protein semantic features aligned with label dimensions and establishes a subcellular relationship mining module to model correlations between different subcellular structures. By doing so, it generates co-occurrence embedding features that encode spatial and contextual relationships among subcellular locations, thereby improving feature representation. To further refine localization, a multi-scale feature analysis approach is employed using the K-means clustering algorithm, which classifies multi-scale features within each subcellular category and generates multi-cell class activation maps (CAMs). These CAMs highlight discriminative regions associated with specific subcellular locations, facilitating more accurate protein localization. Additionally, a pseudo-label generation strategy is introduced to address the lack of annotated single-cell data. This strategy segments multicellular images into single-cell images and assigns reliable pseudo-labels based on the CAM-predicted regions, ensuring high-quality training data for single-cell analysis. Under a transfer learning framework, the model is trained to achieve precise single-cell-level protein localization, leveraging both the extracted features and pseudo-labels for robust performance. ResultsExperimental validation on multiple single-cell test datasets demonstrates that the proposed method significantly outperforms existing approaches in terms of robustness and localization accuracy. Specifically, on the Kaggle 2021 dataset, the method achieves superior mean average precision (mAP) metrics across 18 subcellular categories, highlighting its effectiveness in diverse protein localization tasks. Visualization of the generated CAM results further confirms the model’s capability to accurately localize subcellular proteins within individual cells, even in complex multicellular environments. ConclusionThe integration of the CP-GCN network with a pseudo-labeling strategy enables the proposed method to effectively capture heterogeneous cellular features in protein images and achieve precise single-cell protein localization. This advancement not only addresses key limitations in current protein image analysis but also provides a scalable and accurate solution for subcellular protein studies, with potential applications in biomedical research and diagnostic imaging. The success of this method underscores the importance of combining advanced deep learning architectures with innovative training strategies to overcome data scarcity and improve localization performance in biological image analysis. Future work could explore the extension of this framework to other types of microscopic imaging and its application in large-scale protein interaction studies.

OBJECTIVE: To investigate the effects of Danmu Extract Syrup (DMS) on lipopolysaccharide (LPS)-induced acute lung injury (ALI) in mice and explore the mechanism. METHODS: Seventy-two male Balb/C mice were randomly divided into 6 groups according to a random number table (n=12), including control (normal saline), LPS (5 mg/kg), LPS+DMS 2.5 mL/kg, LPS+DMS 5 mL/kg, LPS+DMS 10 mL/kg, and LPS+Dexamethasone (DXM, 5 mg/kg) groups. After pretreatment with DMS and DXM, the ALI mice model was induced by LPS, and the bronchoalveolar lavage fluid (BALF) were collected to determine protein concentration, cell counts and inflammatory cytokines. The lung tissues of mice were stained with hematoxylin-eosin, and the wet/dry weight ratio (W/D) of lung tissue was calculated. The levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and IL-1 β in BALF of mice were detected by enzyme linked immunosorbent assay. The expression levels of Claudin-5, vascular endothelial (VE)-cadherin, vascular endothelial growth factor (VEGF), phospho-protein kinase B (p-Akt) and Akt were detected by Western blot analysis. RESULTS: DMS pre-treatment significantly ameliorated lung histopathological changes. Compared with the LPS group, the W/D ratio and protein contents in BALF were obviously reduced after DMS pretreatment (P<0.05 or P<0.01). The number of cells in BALF and myeloperoxidase (MPO) activity decreased significantly after DMS pretreatment (P<0.05 or P<0.01). DMS pre-treatment decreased the levels of TNF-α, IL-6 and IL-1 β (P<0.01). Meanwhile, DMS activated the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathway and reversed the expressions of Claudin-5, VE-cadherin and VEGF (P<0.01). CONCLUSIONS DMS attenuated LPS-induced ALI in mice through repairing endothelial barrier. It might be a potential therapeutic drug for LPS-induced lung injury.
Mice ; Male ; Animals ; Proto-Oncogene Proteins c-akt/metabolism* ; Lipopolysaccharides ; Phosphatidylinositol 3-Kinases/metabolism* ; Interleukin-1beta/metabolism* ; Vascular Endothelial Growth Factor A/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Claudin-5/metabolism* ; Acute Lung Injury/chemically induced* ; Lung/pathology* ; Interleukin-6/metabolism* ; Drugs, Chinese Herbal

Objective To analyze the factors associated with pain after arthroscopic rotator cuff bridge suture.Methods According to the inclusion and exclusion criteria,the data of 112 patients with unilateral rotator cuff injury who received arthroscopic bridge suture in our department were collected and were investigated in the form of telephone follow-up.In this study,SPSS 23.0 was used to input data and conduct statistical analysis.Logistic regression analysis was used to analyze the correlation between the above influencing factors and postoperative pain.Results A total of 112 patients were included for statistical analysis,single factor analysis revealed,including course of disease,smoking history,preoperative University of California,Los Angeles(UCLA)score,Constant score,numeric rating scale(NRS),size of rotator cuff tear,whether it was full-thickness tear and degree of tendon retraction might be related to postoperative pain(P<0.05).The age,gender,body mass index(BMI),drinking history,diabetes and hypertension were not related to postoperative pain(P>0.05).Multiple linear regression analysis concluded that there were four factors related to postoperative pain,and the correlation degree was preoperative NRS,preoperative UCLA score,tear size and smoking history.Conclusion The causes of postoperative pain after arthroscopic rotator cauff repair are complex and diverse.Analyzing the cause of postoperative pain can effectively reduce the pain of patients and promote the recovery of shoulder joint function.

Orodispersible films (oral dispersible films), a novel form of oral solid dosage forms, are widely used for patients with dysphagia and those with uncontrollable autonomic behavior. In this study, suvorexant orodispersible film was prepared by hot melt extrusion technology, and the disintegration time, mechanical properties, in vitro dissolution and pharmacokinetics were evaluated, compared with other orodispersible films and commercially available tablets Belsomra. All experiments were approved by the Committee on the Management and Use of Laboratory Animals of Academy of Military Medical Sciences (IACUC-DWZX-2024-504). The results showed that the dissolution rate of suvorexant orodispersible film was faster and the mechanical strength was better than that of other commercially available orodispersible film, which could meet the needs of storage and transportation. Differential scanning calorimetry and X-ray diffraction results showed that suvorexant was dispersed within the orodispersible film in an amorphous state. The in vitro dissolution of the film was observed to be four times faster than that of the commercial tablets, achieving complete dissolution within five minutes. Pharmacokinetic evaluations in Beagle dogs revealed that the self-formulated orodispersible film exhibited no significant differences in the area under the blood concentration-time curve when compared with Belsomra. However, the film showed a faster onset of action, with a peak time that was twice as rapid, and a maximum blood concentration that was twice as high as that of Belsomra. Leveraging hot melt extrusion technology, the suvorexant orodispersible film offers a straightforward, continuous production process with consistent quality. It serves as an excellent platform for the development of solvent-free film preparations tailored for patients with special needs.

The rheological properties of drug and carrier materials have a wide range of guiding significance for the formulation and process development of solid dispersions. In this study, the rheological properties of materials with different drug carrier ratios were systematically studied with suvorexant as the model drug and copovidone as the carrier material, which provided a sufficient basis for determining the formulation and process of solid dispersions. The optimal suvorexant-copovidone ratio obtained by oscillating temperature scanning was 1∶4. If the ratio is greater than 1∶ 4, the glass transformation temperature of the material will increase significantly, and the solubilization effect of the solid dispersion will show a downward trend. The results of oscillation temperature scanning and oscillation temperature sweep can show that when the extrusion temperature is greater than 150 ℃, the viscosity of the material is less than 10 000 Pa·s, and the melt can be extruded smoothly, and the best extrusion temperature of 160-180 ℃ can be obtained by combining the dissolution results. Finally, the dissolution of suvorexant tablets guided by rheological property studies in multiple media is similar to that of the commercially available tablets Belsomra. Therefore, rheological studies can screen and optimize the formulation and process of suvorexant solid dispersions at the mechanism level, which is of great significance to improve the success rate of R&D and shorten the R&D cycle of solid dispersions prepared by hot melt extrusion.

OBJECTIVES: To study new biomarkers for the early diagnosis of retinopathy of prematurity (ROP) by analyzing the differences in blood metabolites based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) and metabolomics. METHODS: Dried blood spots were collected from 21 infants with ROP (ROP group) and 21 infants without ROP (non-ROP group) who were hospitalized in the Sixth Affiliated Hospital of Sun Yat-sen University from January 2013 to December 2016. LC-MS/MS was used to measure the metabolites, and orthogonal partial least squares-discriminant analysis was used to search for differentially expressed metabolites and biomarkers. RESULTS: There was a significant difference in blood metabolic profiles between the ROP and non-ROP groups. The pattern recognition analysis, Score-plot, and weight analysis obtained 10 amino acids with a relatively large difference. Further statistical analysis showed that the ROP group had significant increases in blood levels of glutamic acid, leucine, aspartic acid, ornithine, and glycine compared with the non-ROP group (P<0.05). The receiver operating characteristic curve analysis showed that glutamic acid and ornithine had the highest value in diagnosing ROP. CONCLUSIONS Blood metabolites in preterm infants with ROP are different from those without ROP. Glutamic acid and ornithine are the metabolic markers for diagnosing ROP. LC-MS/MS combined with metabolomics analysis has a potential application value in the early identification and diagnosis of ROP.
Infant, Newborn ; Infant ; Humans ; Tandem Mass Spectrometry ; Infant, Premature ; Chromatography, Liquid ; Retinopathy of Prematurity/diagnosis* ; Glutamic Acid ; Ornithine

Autophagic flux refers to a series of dynamic process of autophagic bilayer membrane formation, autophagosome formation, autophagolysosomes formation and degradation. The etiology of cataract is complex, including congenital abnormalities in lens development due to genetic mutations, oxidative damage due to aging, abnormalities in glucose metabolism due to diabetes, and proliferation of lens epithelial cells(LECs)stimulated by postoperative inflammatory factor, all of which are associated with the development of cataracts. A growing number of research in recent years have discovered that altering the status of LECs can contribute to the pathophysiological process of cataract by regulating autophagic flux. This review summarized the impacts of autophagic flux regulation on cataract.

Stapled peptides with significantly enhanced pharmacological profiles have emerged as promising therapeutic molecules due to their remarkable resistance to proteolysis and performance to penetrate cells. The all-hydrocarbon peptide stapling technique has already widely adopted with great success, yielding numerous potent peptide-based molecules. Based on our prior efforts, we conceived and prepared a double-stapled peptide in this study, termed FRNC-1, which effectively attenuated the bone resorption capacity of mature osteoclasts in vitro through specific inhibition of phosphorylated GSK-3β. The double-stapled peptide FRNC-1 displayed notably improved helical contents and resistance to proteolysis than its linear form. Additionally, FRNC-1 effectively prevented osteoclast activation and improved bone density for ovariectomized (OVX) mice after intravenous injection and importantly, after oral (intragastric) administration. The double-stapled peptide FRNC-1 is the first orally effective peptide that has been validated to date as a therapeutic candidate for postmenopausal osteoporosis (PMOP).

Objective:To analyze the relevant influence factors and the practical effects of improvement strategies of ultrasonic cleaning effect on medical apparatuses of central sterile supply department(CSSD)of hospital.Methods:The medical apparatuses were cleaned by ultrasound in hospital between March and June 2022 were selected.The influencing factors of cleaning effect of medical apparatuses were analyzed by Logistic regression model,and the corresponding improvement strategies were formulated according to the above factors.The medical apparatuses during the period from July to September 2022 after the improvement strategies were implemented were selected to compare the cleaning effects between before and after implemented improvement strategies.Results:Before the improvement strategies were implemented,the cleaning qualities of 41 times were not quality in the cleaning of 465 times for medical apparatuses,and the cleaning qualities of 424 times were quality,which qualified rate of cleaning was 91.18%.The results of Logistic regression analysis showed that whether the normalization of pre-washing,water quality,cleaning medium and cleaning conditions were qualified were independent influence factors of ultrasonic cleaning effect of CCSD medical apparatuses(OR=6.673,OR=5.140,OR=3.983,OR=5.023,P<0.05).After the improvement strategy was implemented,there was not unqualified cleaning in the cleanings of 465 times of medical apparatuses,and the qualified rate of cleaning was 100%,which was higher than that before improvement strategy was implemented(x2=42.891,P<0.001).Conclusion:The independent influence factors of the ultrasonic cleaning effect of CCSD medical apparatuses were respectively the normalization of pre-washing,water quality,cleaning medium and cleaning conditions.The formulated improvement measures based on the above factors can significant improve the cleaning effect.