Through network pharmacology and molecular docking technology, combined with in vitro experiment verification, we explored the mechanism of action of Porana racemosa Roxb. (PRA) in the treatment of rheumatoid arthritis (RA), and provided a modern pharmacological basis for the treatment of RA by PRA. The potential target of chemical components in the analyzed moth rattan was predicted by Swiss Target Prediction database; OMIM, GeneCards, TTD and Disgenet databases were used to search the disease targets of RA; the protein interaction (PPI) network and medicine-composition-target network were constructed using STRING database and Cytoscape software; GO (gene ontology) functional enrichment and KEGG (kyoto encyclopedia of genes and genomes) pathway analysis were carried out using DAVID database, and molecular docking software was used to dock the potentially active ingredients of PRA and core targets; finally, MH7A cells were selected for cell viability, scratch healing and mRNA expression level analysis of key genes to explore the effects of PRA and their potentially active ingredients on the proliferation, migration and apoptosis of MH7A cells. In this study, a total of 628 potentially active ingredient targets, 1 890 RA targets and 235 intersection targets were identified. It was screened that the potentially active ingredients of RA treatment by PRA were ethylcaffeate, N-p-coumaroyltyramine, 9,12,15-octadecatrienoic acid, methyl ester and so on, and the core targets involved tumor necrosis factor (TNF), matrix metalloproteinase 9 (MMP9), prostaglandin-endoperoxide synthase 2 (PTGS2) and so on. 1 200 GO entries and 166 KEGG pathway entries were obtained from the enrichment analysis; molecular docking results showed that N-p-coumaroyltyramine and ethylcaffeate had good binding activity with TNF, MMP9, cysteine-aspartate protease 3 (CASP3), PTGS2, B-cell lymphoma 2 (BCL2) proteins. In vitro experiments showed that PRA, ethylcaffeate and N-p-coumaroyltyramine could inhibit the proliferation, migration and invasion of MH7A cells, up-regulate the expression of apoptosis-related gene CASP3 mRNA, and down-regulate the expression of MMP9, PTGS2 and BCL2 mRNA, and it can also down-regulate the expression of phosphatidylinositol 3-kinase (PI3K) and protein kinase B (AKT) mRNA and PI3K and p-AKT proteins. This study preliminarily revealed that the treatment of RA by PRA may be related to proliferation, migration, invasion, apoptosis and regulation of PI3K/AKT signaling pathway.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

A liquid chromatography-tandem mass spectrometry(LC-MS/MS)method was developed for determination of three kinds of β-agonists(Clenbuterol(CL),Ractopamine(RAC)and Salbutamol(SAL))residues in animal liver samples.The liver sample homogenates were extracted with organic solvent,followed by clean-up using the automatic magnetic solid-phase extraction(MSPE),and then analyzed using LC-MS/MS.The results showed that the magnetic mixed-mode cation exchange adsorbent(M-MCX)exhibited 34%higher adsorption capacity than the conventional mixed-mode cation exchange(MCX)column.Furthermore,the clean-up was conducted by using an automatic MSPE device,and 8 samples could be simultaneously treated within 30 min.The limits of detection(LOD)were 0.01-0.1 μg/kg,the average recoveries ranged from 88.2%to 110.5%,and the relative standard deviations(RSDs)were in range of 2.9%-10.3%at three spiked levels for the three kinds of β-agonists.Compared with the traditional SPE technique,the present method had many advantages such as simple operation,rapidity and high efficiency,which was suitable for high-throughput and automatic detection of residues in routine analysis.

Objective To summarize the prescription and medication rules for the treatment of diabetes mellitus by contemporary Lingnan famous Chinese medical practitioners through data mining analysis.Methods The literature on treating diabetes mellitus by contemporary Lingnan famous Chinese medical practitioners were retrieved from the databases of CNKI,Wanfang and VIP,and the collections of clinical experience and medical cases of contemporary Lingnan famous practitioners were also reviewed to screen the relevant medical records of diabetes mellitus treated by Lingnan famous practitioners.The information of Chinese medicine prescriptions for diabetes mellitus in the medical records was input to Excel to establish a database,and then the frequency analysis was performed in terms of the nature,flavors and meridian tropism,therapeutic-action classification of the Chinese herbal medicines and traditional Chinese medicine(TCM)syndrome types.Moreover,the association rule analysis and cluster analysis were carried out using IBM SPSS Modeler 18.0 and SPSS Statistics 26.0.Results A total of 62 medical records of diabetes mellitus treated by the 24 contemporary Lingnan famous Chinese medical practitioners such as DENG Tie-Tao,LIU Shi-Chang,XIONG Man-Qi,LI Sai-Mei,ZHU Zhang-Zhi,LIU Min,and FAN Guan-Ji were screened out for the analysis.And a total of 101 prescriptions involving 210 Chinese herbal medicines were included.There were 29 commonly-used Chinese medicines with a frequency being or more than 15 times,and they were Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Astragali Radix,Bupleuri Radix,Puerariae Lobatae Radix,Poria,Atractylodis Macrocephalae Rhizoma,Codonopsis Radix,Scutellariae Radix,etc.According to the therapeutic action,the Chinese medicines were mainly classified into the categories of drugs for supplementing the deficiency,drugs for clearing heat,and drugs for dispelling wind-damp.The primary syndrome types were syndrome of deficiency of both spleen and kidney and syndrome of liver stagnation and spleen deficiency,and the syndromes were usually complicated with the syndrome elements of qi deficiency,yin deficiency,yang deficiency,cold-damp,phlegm-damp,and damp-heat.The association rule analysis yielded 28 core drug combinations consisting of 2-4 herbs,and the cluster analysis yielded 5 new candidate prescriptions,which were mainly derived from the modification of classical formulas such as Xiao Chaihu Decoction,Fuzi Lizhong Decoction,Yuquan Pills plus Zengye Decoction,and Zuogui Pills.Conclusion For the treatment of diabetes mellitus,contemporary Lingnan famous Chinese medical practitioners primarily adopt the methods of invigorating spleen and warming kidney,and soothing liver,draining dampness and resolving turbidity,which has more distinctive regional characteristics of Lingnan and can provide a reference for syndrome differentiation and treatment of diabetes mellitus in the Lingnan area.

AIM To establish an HPLC method for the simultaneous content determination of gallic acid,protocatechuic acid,morroniside,loganin,sweroside,paeoniflorin,hypericin,astragalin,salvianolic acid B,salvianolic acid A,epimedin C and icariin in Bushen Huoxue Sanjie Capsules.METHODS The analysis was performed on a 30℃thermostatic Agilent 5 TC-C18 column(250 mm×4.6 mm,5 μm),with the mobile phase comprising of acetonitrile-0.1%phosphoric acid flowing at 1.0 mL/min in a gradient elution manner,and the detection wavelength was set at 240 nm.RESULTS Twelve constituents showed good linear relationships within their own ranges(r≥0.999 8),whose average recoveries were 97.11%-101.14%with the RSDs of 0.60%-2.65%.CONCLUSION This simple,accurate and reproducible method can be used for the quality control of Bushen Huoxue Sanjie Capsules.

The n-butanol fraction of Alpinia oxyphylla Fructus 70% ethanol extract was separated and purified using column chromatography with MCI Gel CHP-20, Sephadex LH-20, ODS, and silica gel, combined with semi preparative liquid phase and TLC separation methods. One new halogenated 4,5-seco-eudesmane sesquiterpenoid and two new eremophilane sesquiterpenoids were isolated and purified from the n-butanol fraction of Alpinia oxyphylla Fructus. The structures of the isolated compounds were identified using modern spectroscopic methods (1D, 2D NMR, UV, IR, MS, etc.), and the absolute configuration of the new compounds were determined using the methods of calculated ECD and induced ECD.

ObjectiveGlutathione (γ-glutamyl-L-cysteinylglycine, GSH) is the most abundant non-protein compound containing sulfhydryl (―SH) groups in cells. It serves as a source of reducing equivalents, effectively neutralizing harmful reactive substances, and playing a crucial role in maintaining cellular redox balance. Therefore, sensitive detection and accurate measurement of GSH levels in tissues are of great importance. In this work, we presents a novel method for GSH detection utilizing electron paramagnetic resonance (EPR) spectroscopy. MethodsInitially, ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate acid)) solution was mixed with K₂S₂O₈ solution and reacted in the dark for 12 to 16 h to prepare ABTS·⁺ solution, which was then quantified using UV-Vis spectroscopy. Subsequently, the concentration of glutathione (GSH) was determined based on the changes in the EPR signal of ABTS·⁺. On this basis, the optimal reaction time and temperature were explored to establish a standard equation correlating the EPR signal intensity of ABTS·⁺ with GSH concentration. Finally, the derived standard curve was employed to quantitatively analyze the GSH concentration in whole blood from C57BL/6J mice, and the results were compared with those reported in the literature to verify the accuracy of the method. ResultsThe experimental results demonstrate that this method has a linear detection range from50 nmol/L to 15 μmol/L for GSH, spanning two orders of magnitude, with a limit of detection (LOD) at0.50 nmol/L. The measured GSH content in mouse whole blood is (10 660±706) nmol/g Hb, which agrees with the value of (11 200±237) nmol/g Hb as previously reported. Furthermore, a similar method was developed for detection of glutathione disulfide (GSSG) at higher reaction temperature. ConclusionThis article presents a novel assay for the rapid detection of GSH using the intensity of EPR signal from ABTS·⁺ as indicator. This method demonstrates enhanced detection sensitivity and a broader linear range compared to conventional colorimetric methods. Furthermore, we have extended the application of this method to detect GSH content in blood samples efficiently and accurately, offering valuable information for assessing tissue redox balance, thus holding significant potentials.