The prescription of Qidong Yixin oral liquid is derived from the experience of national medical master Ren Jixue in treating viral myocarditis （VMC）. It has the functions of tonifying Qi， nourishing the heart，calming the mind， and relieving palpitations. It is used to treat VMC and angina pectoris of coronary heart disease caused by deficiency of both Qi and Yin. However，the understanding of its efficacy evidence， advantageous aspects， dosage and administration， and medication safety remains insufficient in clinical practice. Therefore，the development of the Expert Consensus on the Clinical Application of Qidong Yixin Oral Liquid （hereinafter referred to as consensus） was initiated. Consensus strictly followed the process and methods of the expert consensus on the clinical application of Chinese patent medicines of the China Association of Chinese Medicine，successively completing multiple tasks such as the consensus project initiation，determination of clinical problems，evidence search and evaluation，formation of recommendation opinions and consensus suggestions，solicitation of opinions，peer review， submission for review and release， and so on. Consensus formed a total of 10 recommendation opinions and 12 consensus suggestions，clarifying the clinical positioning，efficacy advantages，syndrome differentiation，dosage and administration，combination therapy，timing of medication，adverse reactions，contraindications， and precautions of Qidong Yixin oral liquid，indicating that it has good clinical advantages and safety in the treatment of VMC and angina pectoris of coronary heart disease，providing norms and references for physicians to safely and rationally apply Qidong Yixin oral liquid. Consensus was reviewed and approved for release by the Standardization Office of the China Association of Chinese Medicine on December 23， 2024. Standard number：GSCACM-376-2024.

ObjectiveTo investigate the therapeutic mechanism of Xuefu Zhuyutang （XFZYT） for metabolic-associated fatty liver disease （MAFLD） through integrated network pharmacology and animal experiments. MethodsNetwork pharmacology was utilized to predict the core components， key therapeutic targets， and signaling pathways of XFZYT in the treatment of MAFLD. For animal experiments， a rat model of MAFLD was established by feeding a high-cholesterol diet for 4 weeks. Intervention was then administered with low-dose （2 g·kg^-1） and high-dose （4 g·kg^-1） XFZYT for 2 weeks. Biochemical assays were performed to measure the serum levels of aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein （HDL）， and low-density lipoprotein （LDL）. In addition， the activities of superoxide dismutase （SOD） and catalase （CAT） and levels of malondialdehyde （MDA） and glutathione （GSH） in the serum were measured. The same way was adopted to measure the levels of TC and TG in the liver tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to quantify the serum levels of interleukin （IL）-6， IL-1β， and tumor necrosis factor-alpha （TNF-α）. Histopathological evaluations included hematoxylin and eosin （HE） staining for liver tissue morphology， Oil Red O staining for lipid deposition， and dihydroethidium （DHE） probe staining for reactive oxygen species （ROS） levels. Western blot analysis was conducted to assess the protein levels of AMP-activated protein kinase （AMPK）， phosphorylated （p）-AMPK， nuclear factor erythroid 2-related factor 2 （Nrf2）， heme oxygenase-1 （HO-1）， nuclear factor-kappa B （NF-κB）， and p-NF-κB in the liver tissue. Untargeted metabolomics analysis of the serum was performed by liquid chromatography-tandem mass spectrometry （LC-MS/MS）. ResultsNetwork pharmacology analysis predicted 155 potential targets of XFZYT for MAFLD treatment， with core targets including signal transducer and activator of transcription 3 （STAT3）， protein kinase B1 （Akt1）， TNF， and IL-6. Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment primarily implicated the AMPK signaling pathway. Animal experiments demonstrated that compared with the normal group， the model group exhibited dyslipidemia， hepatic function impairment， pronounced hepatic lipid deposition， and inflammatory manifestations， with elevated serum levels of AST， ALT， TC， TG， LDL， and MDA （P<0.05）， reduced HDL and GSH levels plus decreased SOD and CAT activities （P<0.05）， downregulated protein levels of Nrf2， HO-1， and p-AMPK （P<0.05）， and upregulated protein level of p-NF-κB （P<0.05） in the liver tissue. Compared with the model group， XFZYT intervention groups showed significant amelioration of dyslipidemia and hepatic function impairment， markedly reduced hepatic lipid deposition and inflammatory cell infiltration， decreased serum levels of AST， ALT， TC， TG， LDL， and MDA （P<0.05）， increased HDL and GSH levels plus enhanced SOD and CAT activities （P<0.05）， upregulated protein levels of Nrf2， HO-1， and p-AMPK （P<0.05）， and downregulated protein level of p-NF-κB （P<0.05）. Serum metabolomics revealed 511 differentially expressed metabolites （231 upregulated and 280 downregulated） between normal and model groups， while XFZYT groups versus model group showed 94 differential metabolites （51 upregulated and 43 downregulated）. Among them， 11 metabolites displayed the most significant alterations， with enriched pathways including glycerolipid metabolism， cholesterol metabolism， and insulin resistance， multiple of which demonstrated AMPK association. ConclusionXFZYT alleviates MAFLD by regulating the AMPK signaling pathway and associated metabolic networks.

The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies.However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semi-annual ultrasonography and the measurement of α-fetoprotein levels, offer suboptimal sensitivityfor early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies.In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.

Hepatocellular carcinoma (HCC) imposes a major health and economic burden worldwide, with disproportionate effects in low- and middle-income countries (LMICs). Surveillance in high-risk populations, typically using semiannual ultrasound and alpha-fetoprotein testing, has been shown to be cost-effective by enabling earlier detection and improving survival. Yet, its overall value is reduced by poor adherence and the limited sensitivity of ultrasound, particularly in patients with metabolic-associated steatotic liver disease. Emerging approaches—including abbreviated magnetic resonance imaging, multi-biomarker models (e.g., gender, age, AFP, AFP-L3, and DCP), and liquid biopsy assays such as methylated DNA markers—demonstrate greater diagnostic accuracy and potential economic advantages compared with conventional methods. Integration of artificial intelligence into imaging may further enhance efficiency and reduce downstream costs. Moving toward precision surveillance, guided by individualized risk stratification that incorporates etiology, fibrosis stage, and molecular profiles, can optimize allocation of resources and maximize cost-effectiveness at the population level. Interventions to improve adherence, including mailed outreach and behavioral economic incentives, have shown both clinical benefit and cost savings, underscoring the role of implementation science. Because socioeconomic disparities influence both access and outcomes, economic models must explicitly address equity to achieve sustainable impact. Future research should prioritize prospective trials that evaluate not only clinical performance but also the real-world cost-effectiveness of novel technologies and stratified surveillance strategies. For LMICs, adapting proven models into affordable, context-appropriate programs is essential. By combining prevention, precision risk assessment, innovative technologies, and equitable implementation, HCC surveillance can deliver both clinical and economic value, reducing the global burden of disease.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

In March 2024, three male and two female specimens of the genus Lispe were intercepted on cargo ships inbound for Wan Zai Port under Gongbei Customs. Based on morphological characteristics and molecular analysis, the specimens were identified as Lispe pumila. In this study, we describe the morphological features, diagnostic characteristics, and geographical distribution of L. pumila, with the aim of providing a reference for the identification of this species when intercepted at ports.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Xanthine oxidase (XO) is an important therapeutic target for the treatment of hyperuricemia and gout. Based on the previously identified potent XO inhibitor 1, seventeen oxadiazoles and their ring-opening analogues were designed and synthesized via the bioisostere replacement strategy. Among them, compounds 2l, 2n, and 3b showed obvious XO inhibitory activity at the concentration of 10 μmol·L^-1, and compound 3b exhibited an IC₅₀ value of 1.45 μmol·L^-1.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.