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.

Based on copper death, this study investigates the effect and mechanism of Shenmai Injection on isoproterenol(ISO)-induced myocardial fibrosis(MF) in rats. SPF-grade male SD rats were randomly divided into a normal group, model group, captopril(5 mg·kg~(-1)) positive control group, and Shenmai Injection low(6 mL·kg~(-1)), medium(9 mL·kg~(-1)), and high(12 mL·kg~(-1)) dose groups. Except for the normal group, the rats in the other groups were subcutaneously injected with ISO(5 mg·kg~(-1)) once a day for 10 consecutive days to establish an MF model. Starting from the second day after successful modeling, intraperitoneal injections of the respective treatments were administered for 28 consecutive days. Hematoxylin-eosin(HE) and Masson staining were used to observe pathological changes and fibrosis levels in the myocardial tissue. Colorimetry was employed to detect serum Cu~(2+) concentration in rats. The levels of inflammatory cytokines interleukin-6(IL-6), interleukin-1β(IL-1β), interleukin-18(IL-18), tumor necrosis factor-α(TNF-α), as well as mitochondrial energy metabolites adenosine triphosphate(ATP), adenosine diphosphate(ADP), and adenosine monophosphate(AMP) in serum were measured using enzyme-linked immunosorbent assay(ELISA). Western blot was performed to detect the expression of collagen Ⅰ(Col-Ⅰ), collagen Ⅲ(Col-Ⅲ), and copper death-related proteins dihydrolipoamide acetyltransferase(DLAT), ferredoxin 1(FDX1), lipoic acid synthetase(LIAS), and heat shock protein 70(HSP70) in myocardial tissue. Immunofluorescence was used to detect the expression of DLAT, FDX1, and HSP70, while immunohistochemistry was conducted to examine the expressions of DLAT, FDX1, LIAS, and HSP70. The results showed that, compared to the model group, the myocardial structure disorder and collagen fiber deposition in the drug treatment groups were significantly improved, the cardiac index level was reduced, serum Cu~(2+), IL-6, IL-1β, IL-18, TNF-α, ADP, and AMP levels were significantly decreased, ATP levels were significantly increased, and the expressions of Col-Ⅰ, Col-Ⅲ, and HSP70 proteins in myocardial tissue were significantly reduced, while the expressions of DLAT, FDX1, and LIAS proteins were significantly elevated. In conclusion, Shenmai Injection effectively alleviates myocardial structure disorder and interstitial collagen fiber deposition in ISO-induced MF rats, promotes copper excretion, and reduces copper death in the ISO-induced rat MF model.
Animals ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Myocardium/metabolism* ; Drug Combinations ; Fibrosis/metabolism* ; Copper/blood* ; Cardiomyopathies/genetics* ; Humans

This study aims to investigate the optimal ratio of Astragali Radix-Curcumae Rhizoma(AC) for inhibiting the proliferation of 143B osteosarcoma cells, and to investigate the mechanism by which AC inhibits osteosarcoma growth and metastasis through angiogenesis and cell adhesion mediated by the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt)/hypoxia inducible factor-1α(HIF-1α) pathway. A subcutaneous 143B tumor-bearing nude mouse model was successfully established and randomly divided into the model group, and the AC 1∶1, 2∶1, and 4∶1 groups. Body weight, tumor volume, and tumor weight were recorded. Real-time quantitative polymerase chain reaction(RT-qPCR) and Western blot were used to detect the mRNA and protein expression levels of PI3K, Akt, phosphorylated Akt(p-Akt), HIF-1α, vascular endothelial growth factor A(VEGFA), transforming growth factor-β1(TGF-β1), epithelial cadherin(E-cadherin), neural cadherin(N-cadherin), vimentin, matrix metalloproteinase 2(MMP2), matrix metalloproteinase 9(MMP9), B-cell lymphoma-2(Bcl-2), Bcl-2-associated X protein(Bax), and caspase-3 in the hypoxic core region of the tumor tissue. A cell hypoxia model was established, and the effects of AC-medicated serum(model group, AC 1∶1, 2∶1, and 4∶1 groups) on angiogenesis, proliferation, adhesion, invasion, and migration of 143B osteosarcoma cells were examined through CCK-8, flow cytometry, Transwell assay, cell adhesion assay, and HUVEC tube formation assay. The results showed that compared with the model group, the tumor weight and volume were smallest in the 2∶1 group. The expression levels of PI3K, Akt, p-Akt, HIF-1α, VEGFA, and TGF-β1 were significantly decreased, and the protein expression of E-cadherin was significantly increased, while the protein expression of N-cadherin, vimentin, MMP2, and MMP9 was significantly decreased. Additionally, the protein expression of Bax and caspase-3 was significantly increased, and Bcl-2 protein expression was significantly decreased. In vitro experiments showed that after intervention with AC-medicated serum at a 2∶1 ratio, the cell activity, adhesion, invasion, and migration of 143B cells were significantly reduced, apoptosis was significantly increased, and HUVEC tube formation was significantly decreased. In conclusion, the 2∶1 ratio of AC showed the most effective inhibition of 143B cell growth. AC can inhibit the growth and metastasis of osteosarcoma 143B cells by regulating the PI3K/Akt/HIF-1α signaling pathway, inhibiting angiogenesis and reducing cell adhesion, invasion, and migration.
Osteosarcoma/pathology* ; Animals ; Proto-Oncogene Proteins c-akt/genetics* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Humans ; Mice ; Cell Adhesion/drug effects* ; Cell Proliferation/drug effects* ; Neovascularization, Pathologic/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Phosphatidylinositol 3-Kinases/genetics* ; Cell Line, Tumor ; Mice, Nude ; Signal Transduction/drug effects* ; Astragalus Plant/chemistry* ; Bone Neoplasms/physiopathology* ; Male ; Rhizome/chemistry* ; Mice, Inbred BALB C ; Angiogenesis

The AP2/ERF transcription factor family is a class of transcription factors widely present in plants, playing a crucial role in regulating flowering, flower development, flower opening, and flower senescence. Based on transcriptome data from flower, leaf, and stem samples of two Lonicera macranthoides varieties, 117 L. macranthoides AP2/ERF family members were identified, including 14 AP2 subfamily members, 61 ERF subfamily members, 40 DREB subfamily members, and 2 RAV subfamily members. Bioinformatics and differential gene expression analyses were performed using NCBI, ExPASy, SOMPA, and other platforms, and the expression patterns of L. macranthoides AP2/ERF transcription factors were validated via qRT-PCR. The results indicated that the 117 LmAP2/ERF members exhibited both similarities and variations in protein physicochemical properties, AP2 domains, family evolution, and protein functions. Differential gene expression analysis revealed that AP2/ERF transcription factors were primarily differentially expressed in the flowers of the two L. macranthoides varieties, with the differentially expressed genes mainly belonging to the ERF and DREB subfamilies. Further analysis identified three AP2 subfamily genes and two ERF subfamily genes as potential regulators of flower development, two ERF subfamily genes involved in flower opening, and two ERF subfamily genes along with one DREB subfamily gene involved in flower senescence. Based on family evolution and expression analyses, it is speculated that AP2/ERF transcription factors can regulate flower development, opening, and senescence in L. macranthoides, with ERF subfamily genes potentially serving as key regulators of flowering duration. These findings provide a theoretical foundation for further research into the specific functions of the AP2/ERF transcription factor family in L. macranthoides and offer important theoretical insights into the molecular mechanisms underlying floral phenotypic differences among its varieties.
Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Transcription Factors/chemistry* ; Lonicera/classification* ; Flowers/metabolism* ; Phylogeny ; Gene Expression Profiling ; Multigene Family

This study compared the differences in intestinal absorption characteristics of eleven active components in Rubus multibracteatus(RM) extract(protocatechuic acid, tiliroside, scutellarin, luteoloside, astragalin, epicatechin, catechin, xanthotoxin, p-coumaric acid, caffeic acid, and apigenin-7-O-glucuronide) between normal rats and inflammatory pain model rats using the in-vitro everted intestinal sac model. The RM extract was administered at absorption concentrations of 25.0, 50.0, and 100.0 mg·mL~(-1). The contents of the eleven components in intestinal absorption solution samples were quantified by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS), and their cumulative absorption(Q) and absorption rate constant(K_a) were calculated to evaluate the absorption characteristics of these components in normal rats and inflammatory pain model rats. The results show that except for catechin, epicatechin, and caffeic acid, the cumulative absorption-time curves of the other eight components(protocatechuic acid, tiliroside, scutellarin, luteoloside, astragalin, xanthotoxin, p-coumaric acid, and apigenin-7-O-glucuronide) exhibit an upward trend without saturation, with correlation coefficients(R~2) all > 0.9, indicating linear absorption. However, the overall absorption of all components is not dose-dependent with increasing concentration, suggesting that their absorption mechanisms are not solely passive diffusion. In both normal and model rats, the jejunum shows the highest absorption for all components except xanthotoxin. The overall absorption of seven components(excluding protocatechuic acid, caffeic acid, apigenin-7-O-glucuronide, and luteoloside) in normal rats is better than that in model rats across all intestinal segments. These findings indicate that the pathological state of inflammatory pain alters the intestinal absorption of RM extract, and its mechanism needs further investigation.
Animals ; Rats ; Intestinal Absorption/drug effects* ; Male ; Rats, Sprague-Dawley ; Drugs, Chinese Herbal/metabolism* ; Disease Models, Animal ; Pain/metabolism* ; Intestines/drug effects* ; Intestinal Mucosa/metabolism*

PURPOSE: This study aims to identify the prevalence and risk factors of military training-related abdominal injuries and help plan and conduct training properly. METHODS: This questionnaire survey study was conducted from October 2021 to May 2022 among military personnel from 6 military units and 8 military medical centers and participants' medical records were consulted to identify the training-related abdominal injuries. All the military personnel who ever participated in military training were included. Those who refused to participate in this study or provided an incomplete questionnaire were excluded. The questionnaire collected demographic information, type of abdominal injury, frequency, training subjects, triggers, treatment, and training disturbance. Chi-square test and t-test were used to compare baseline information. Univariate and multivariate regression analyses were used to explore the risk factors associated with military training-related abdominal injuries. RESULTS: A total of 3058 participants were involved in this study, among which 1797 (58.8%) had suffered training-related abdominal injuries (the mean age was 24.3 years and the service time was 5.6 years), while 1261 (41.2%) had no training-related abdominal injuries (the mean age was 23.1 years and the service time was 4.3 years). There were 546 injured patients (30.4%) suspended the training and 84 (4.6%) needed to be referred to higher-level hospitals. The most common triggers included inadequate warm-up, fatigue, and intense training. The training subjects with the most abdominal injuries were long-distance running (589, 32.8%). Civil servants had the highest rate of abdominal trauma (17.1%). Age ≥ 25 years, military service ≥ 3 years, poor sleep status, and previous abdominal history were independent risk factors for training-related abdominal injury. CONCLUSION More than half of the military personnel have suffered military training-related abdominal injuries. Inadequate warm-up, fatigue, and high training intensity are the most common inducing factors. Scientific and proper training should be conducted according to the factors causing abdominal injuries.
Humans ; Military Personnel ; Risk Factors ; Prevalence ; Male ; Abdominal Injuries/etiology* ; Female ; Adult ; Surveys and Questionnaires ; Young Adult