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.

ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

A 42-year-old male with chest tightness and dyspnea was admitted to the hospital. Chest CT indicated diffuse interstitial lung infiltration. Despite receiving anti-infective therapy, glucocorticoid therapy, and immunosuppressive agents, the patient developed refractory hypoxaemia. Endotracheal intubation and invasive mechanical ventilation failed to improve oxygenation. Therefore the patient was diagnosed with rapidly progressive interstitial lung disease (RP-ILD) accompanied by type Ⅰ respiratory failure. Veno-venous (VV) extracorporeal membrane oxygenation (ECMO) was initiated, and oxygenation improved in this patient. The patient subsequently underwent bilateral lung transplantation with veno-arterio-venous (VAV) ECMO support. ECMO machine was withdrawn on day 1, and extubation was achieved on day 9 after surgery. Histopathology revealed fibrotic nonspecific interstitial pneumonia (NSIP) with hyaline membrane formation. The patient developed ICU-acquired myasthenia and received early rehabilitation, with gradual recovery of muscle strength. During follow-up, graft lung function remained stable. This case demonstrates that ECMO can serve as a bridge to lung transplantation in RP-ILD patients.

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.

Background/Aims: Regorafenib is recommended by guidelines and trials as a sequential second-line therapy following progression on first-line sorafenib or lenvatinib in hepatocellular carcinoma (HCC). However, efficacy is limited, highlighting the urgent need to screen suitable patients and develop sensitization strategies. Methods: Acquired sorafenib- or lenvatinib-resistant (SR or LR) HCC cell lines and organoids were established. Genome-wide CRISPR library screen was performed in SR or LR cell strains to identify synthetic lethal targets of regorafenib. RNA-seq and FITC-regorafenib efflux assay were used to elucidate ERBB3-driven downstream signaling. Preclinical mouse models of cell line- and patient-derived xenografts and clinical cohorts of HCC patients were employed to validate the efficacy of ERBB3-guided patient stratification. Results: Screening with CRISPR library, we showed that inhibition of ERBB3 was synthetic lethal with regorafenib in SR or LR cell strains and organoids. Mechanistically, SR or LR triggered feedback activation of ERBB3 signaling and mediated regorafenib efflux via ERBB3-HIF1A-ABCB1 cascade pathway, limiting sensitivity to regorafenib. Moreover, ERBB3-low tumors following SR or LR exhibited significant sensitivity to regorafenib, suggesting its potential as a predictive biomarker to screen optimal candidates for sequential therapy. Seribantumab, an ERBB3-targeting monoclonal antibody, inhibited ERBB3-HIF1A-ABCB1 cascade, and its combination with regorafenib exerted marked synergistic anti-tumor effects on ERBB3-high tumors resistant to sorafenib or lenvatinib both in vitro and in vivo. Conclusions This study revealed that ERBB3 was a key resistance factor driving limited efficacy to sequential regorafenib, but also an effective therapeutic target whose inhibition enhanced regorafenib sensitivity after SR or LR.

Objective To systematically evaluate the efficacy and safety of traditional Chinese medicine (TCM) compound in treating pulmonary nodules, providing evidence-based medical evidence for TCM intervention in pulmonary nodules. Methods Computer search of PubMed, CNKI, Wanfang, VIP, and SinoMed was conducted to select randomized controlled trials (RCTs) of TCM compound intervention in pulmonary nodules, with the retrieval time from the inception to November 29, 2023. The Cochrane bias risk assessment tool was used to evaluate the quality of the included studies, and Review Manager 5.4 was used for Meta-analysis. Results A total of 18 RCTs were included, covering 8 provinces across the country, with a total sample size of 1301 patients. The TCM compounds used in the included studies all incorporated the method of dissolving phlegm and dissipating nodules. There was a high risk of bias uncertainty in the included studies. Meta-analysis results suggested that TCM compound could significantly reduce the diameter of pulmonary nodules [MD=−1.41, 95%CI (−1.70, −1.13), P<0.001], decrease the number of nodules [MD=−0.37, 95%CI (−0.73, −0.01), P=0.05], alleviate clinical symptoms [MD=−4.84, 95%CI (−6.04, −3.64), P<0.001], and improve lung function [forced expiratory volume in one second (FEV1), MD=0.55, 95%CI (0.09, 1.01), P=0.02; FEV1/forced vital capacity, MD=6.12, 95%CI (4.47, 7.78), P<0.001]. However, there was no statistically significant difference in the probability of malignancy between the experimental group and the control group [MD=−0.01, 95%CI (−0.01, 0.00), P=0.09]. Conclusion TCM compound can significantly reduce the diameter of pulmonary nodules, decrease the number of nodules, alleviate clinical symptoms, and improve lung function, but future multicenter, large-sample, high-quality RCTs are still needed to further explore and verify this conclusion.

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.