ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

Antibody-drug conjugates （ADCs） are a novel class of anti-tumor agents composed of a targeted monoclonal antibody， a cytotoxic drug， and a linker connecting the two. They combine the high specificity of antibodies with the potent cytotoxicity of chemotherapeutic agents. Triple-negative breast cancer （TNBC） is characterized by high aggressiveness， elevated risks of recurrence and metastasis， and poor prognosis， largely due to the lack of effective therapeutic targets. This review summarizes the research progress of ADCs in the treatment of TNBC. It has been found that ADCs targeting human epidermal growth factor receptor 2 （such as trastuzumab deruxtecan）， trophoblast cell surface antigen 2 （such as sacituzumab govitecan and datopotamab deruxtecan）， zinc transporter LIV-1 （such as ladiratuzumab vedotin）， HER-3 （such as patritumab deruxtecan）， epidermal growth factor receptor （such as AVID100）， and glycoprotein non-metastatic melanoma protein B （such as glembatumumab vedotin） have all demonstrated promising therapeutic effects against TNBC. Despite challenges including acquired resistance and treatment-related toxicities， ADCs are undoubtedly reshaping the therapeutic landscape for TNBC and are expected to occupy a more central position in TNBC treatment in the future.

Hepatitis B virus （HBV） exclusively infects liver parenchymal cells and forms covalently closed circular DNA （cccDNA） within their nuclei. HBV cccDNA serves as the essential template for viral gene transcription， the sole source of progeny virus production， and the key driver of viral antigen expression， and it is the molecular basis for the persistence of HBV infection. Therefore， elimination and/or functional silencing of cccDNA is the key to eradicate chronic HBV infection. This article discusses the critical scientific issues that need to be solved during elimination of the harm of HBV infection from the perspectives of the synthesis， transcription， and clearance of cccDNA， as well as the impact of nonparenchymal cells on cccDNA， in order to provide a reference for eradicating HBV infection in the future.

As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

ObjectiveTo investigate the inhibitory effect of Huoluo Xiaolingdan on triple negative breast cancer （TNBC） in mice through its regulation of TCF1⁺CD8⁺ stem cell-like T cells infiltration. MethodsA mouse model of TNBC was established and the mice were randomly divided into the model group， low-dose （3.9 g·kg^-1）， medium-dose （7.8 g·kg^-1） and high-dose （15.6 g·kg^-1） Huoluo Xiaolingdan groups， and anti-PD-1 antibody treatment group. Each group was given a dose of 0.01 mL·g^-1， while the model group and the anti-PD-1 treatment group were also given an equivalent volume of normal saline. The drug was administered for 21 days. In the anti-PD-1 antibody group， mice were intraperitoneally injected with 100 μg of mouse anti-PD-1 antibody twice a week， for a total of five injections. The tumor volume， survival time and tumor mass were measured at different time points. Hematoxylin-eosin （HE） staining was used to observe the histological changes of the tumor. The expression of CD8⁺T cells and TCF1⁺CD8⁺ stem-like T cells in tumor tissues was detected by immunohistochemistry and immunofluorescence staining. Flow cytometry was used to detect the difference of immune cell subsets in tumors and the expression difference of TCF1⁺CD8⁺ stem cell-like T cells in tumors and peripheral blood. The expression level of PD-L1 in tumor tissues was detected by Western blot. ResultsCompared with model group， the tumor volume and mass of in low-， medium- and high-dose Huoluo Xiaolingdan groups and anti-PD-1 group were decreased （P<0.05， P<0.01）. The median survival time of mice in low-， medium- and high-dose Huoluo Xiaolingdan groups and anti-PD-1 group was as follows： 27.00 days （95%CI， 0.45-2.65）， 31.00 days （95%CI， 0.32-1.89）， 34.00 days （95%CI， 0.40-2.33）， and 35.00 days （95%CI， 0.42-2.47）. All of them were higher than that of the model group ［24.50 days （95%CI， 0.37-10.5）］. Flow cytometry showed that compared with the model group， the proportion and number of infiltrating CD8⁺ T cells in tumor were increased in low-， medium- and high-dose Huoluo Xiaolingdan groups and anti-PD-1 group （P<0.05， P<0.01）， while the proportion of tumor regulatory T cells （Treg） and M2 macrophages decreased （P<0.05）. Compared with the model group， the proportion of IFN-γ⁺CD8⁺ T and GrzB⁺CD8⁺ T cells in tumors in low-， medium- and high-dose Huoluo Xiaolingdan groups and anti-PD-1 group was increased （P<0.01）， and the proportion of TCF1⁺CD8⁺ T cells in tumor and peripheral blood was also increased. Immunofluorescence staining further showed that the number of TCF1⁺CD8⁺ T cells in tumor tissues increased in low-， medium- and high-dose Huoluo Xiaolingdan groups. Western blot analysis showed no significant decrease in the PD-L1 protein expression in tumor tissues between the Huoluo Xiaolingdan groups and the model group. ConclusionHuoluo Xiaolingdan can inhibit TNBC in mice by increasing tumor infiltration of TCF1⁺CD8⁺ stem-like T cells， enhancing CD8⁺ T cell activity， and regulating immune cell subgroups such as M2 macrophages and Treg cells to enhance anti-tumor immunity. This study provides a theoretical basis for the clinical application of Huoluo Xiaolingdan in breast cancer treatment and combination therapy.

OBJECTIVE To construct and evaluate nomogram prediction model for refractory chemotherapy-induced nausea and vomiting （CINV）. METHODS The data of malignant tumor patients who received chemotherapy at the Third People’s Hospital of Zhengzhou from January 2017 to December 2023 were collected. These patients were categorized into the occurrence group and the non-occurrence group according to the occurrence of refractory CINV. Multivariate Logistic regression analysis was employed to screen predictive factors for refractory CINV and constructing a nomogram prediction model. Model performance was assessed via receiver operating characteristic curve analysis. Model calibration was evaluated using Bootstrap resampling. Decision curve analysis （DCA） was used to determine the clinical net benefit of three strategies under different risk thresholds. Clinical impact curves were utilized to assess the clinical value of the model at different risk thresholds. Shapley additive explanations （SHAP） analysis was performed to evaluate individual factor contributions to the predictive model. RESULTS A total of 388 patients were included， with 219 experiencing refractory CINV. Multivariate Logistic regression identified 11 predictive factors for refractory CINV， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， etc. The model’s area under the curve was 0.80 [95% confidence interval （0.76， 0.84）]， with a mean error of 0.036. DCA demonstrated the prediction model had higher clinical net benefit when the risk threshold was between 0.05 and 0.85. SHAP analysis revealed the top three predictive factors as gastrointestinal disease history （0.924）， chemotherapy- induced emetic risk classification （0.866）， and electrolyte levels （0.581）. CONCLUSIONS Eleven factors， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， are identified as predictors of refractory CINV. The model based on these factors has good predictive ability， which can be used to predict the risk of refractory CINV.

Reproductive system diseases are among the primary contributors to the decline in social fertility rates and the intensification of aging, posing significant threats to both physical and mental health, as well as quality of life. Recent research has revealed the substantial potential of the gut microbiota in improving reproductive system diseases. Under healthy conditions, the gut microbiota maintains a dynamic balance, whereas dysfunction can trigger immune-inflammatory responses, metabolic disorders, and other issues, subsequently leading to reproductive system diseases through the gut-gonadal axis. Reproductive diseases, in turn, can exacerbate gut microbiota imbalance. This article reviews the impact of the gut microbiota and its metabolites on both male and female reproductive systems, analyzing changes in typical gut microorganisms and their metabolites related to reproductive function. The composition, diversity, and metabolites of gut bacteria, such as Bacteroides, Prevotella, and Firmicutes, including short-chain fatty acids, 5-hydroxytryptamine, γ-aminobutyric acid, and bile acids, are closely linked to reproductive function. As reproductive diseases develop, intestinal immune function typically undergoes changes, and the expression levels of immune-related factors, such as Toll-like receptors and inflammatory cytokines (including IL-6, TNF-α, and TGF-β), also vary. The gut microbiota and its metabolites influence reproductive hormones such as estrogen, luteinizing hormone, and testosterone, thereby affecting folliculogenesis and spermatogenesis. Additionally, the metabolism and absorption of vitamins can also impact spermatogenesis through the gut-testis axis. As the relationship between the gut microbiota and reproductive diseases becomes clearer, targeted regulation of the gut microbiota can be employed to address reproductive system issues in both humans and animals. This article discusses the regulation of the gut microbiota and intestinal immune function through microecological preparations, fecal microbiota transplantation, and drug therapy to treat reproductive diseases. Microbial preparations and drug therapy can help maintain the intestinal barrier and reduce chronic inflammation. Fecal microbiota transplantation involves transferring feces from healthy individuals into the recipient’s intestine, enhancing mucosal integrity and increasing microbial diversity. This article also delves into the underlying mechanisms by which the gut microbiota influences reproductive capacity through the gut-gonadal axis and explores the latest research in diagnosing and treating reproductive diseases using gut microbiota. The goal is to restore reproductive capacity by targeting the regulation of the gut microbiota. While the gut microbiota holds promise as a therapeutic target for reproductive diseases, several challenges remain. First, research on the association between gut microbiota and reproductive diseases is insufficient to establish a clear causal relationship, which is essential for proposing effective therapeutic methods targeting the gut microbiota. Second, although gut microbiota metabolites can influence lipid, glucose, and hormone synthesis and metabolism via various signaling pathways—thereby indirectly affecting ovarian and testicular function—more in-depth research is required to understand the direct effects of these metabolites on germ cells or granulosa cells. Lastly, the specific efficacy of gut microbiota in treating reproductive diseases is influenced by multiple factors, necessitating further mechanistic research and clinical studies to validate and optimize treatment regimens.

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.

Objective To evaluate the effectiveness of thermal tomography in breast cancer (BC) screening. Methods We conducted a general population-based BC screening in three regions of Hubei Province (Xiantao, Hongan, and Yangxin Districts). Participants underwent a questionnaire-based interview for baseline data collection. They then received a physical examination, thermal tomography, and ultrasound from doctors and technicians. We compared the efficacies, including sensitivity, specificity, and false-positive rates, of ultrasound and thermal tomography in BC screening. Results A total of 59 712 eligible women were included in this screening program. The BI-RADS 1, 2, 3, 4, and 5 accordance rates between the two screening methods were 0.9549, 0.8047, 0.9037, 0.3352, and 0, respectively. The overall accordance rate was 0.933 1. The kappa consistency results showed that the Cicchetti-Allison and Fleiss-Cohen kappa values were 0.7970 and 0.8583, respectively. Although the two methods had equal sensitivities, specificity was higher in thermal tomography than in ultrasound. The false-positive rate was lower in thermal tomography than in ultrasound. The area under the receiver operating characteristic (ROC) curve of thermal tomography was significantly larger than that of ultrasound. Conclusion The general consistency between thermal tomography and ultrasound in BC screening was high. Thermal tomography outperformed ultrasound in terms of specificity and diagnostic efficiency. Therefore, thermal tomography has a high application value for general population-based BC screening.

To investigate body composition and associated factors in adolescents undergoing long-term regular sports training.