Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design. In this study, we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-based design to target the PB1-binding interface on PA. Biological evaluation of these compounds through a viral yield reduction assay revealed that compounds 27 and 31 both had a low micromolar range of the half maximal effective concentration (EC₅₀) values against A/WSN/33 (H1N1) (8.03 μmol/L for 27; 14.6 μmol/L for 31), while the most potent candidate 24 had an EC₅₀ value of 690 nM. Compound 24 was effective against different influenza strains including a pandemic H1N1 strain and an influenza B strain. Mechanistic studies confirmed that compound 24 bound PA with a K _d which equals to 1.88 μmol/L and disrupted the binding of PB1 to PA. The compound also decreased the lung viral titre in mice. In summary, we have identified a potent anti-influenza candidate with potency comparable to existing drugs and is effective against different viral strains. The therapeutic options for influenza infection have been limited by the occurrence of antiviral resistance, owing to the high mutation rate of viral proteins targeted by available drugs. To alleviate the public health burden of this issue, novel anti-influenza drugs are desired. In this study, we present our discovery of a novel class of indazole-containing compounds which exhibited favourable potency against both influenza A and B viruses. The EC₅₀ of the most potent compounds were within low micromolar to nanomolar concentrations. Furthermore, we show that the mouse lung viral titre decreased due to treatment with compound 24. Thus our findings identify promising candidates for further development of anti-influenza drugs suitable for clinical use.

Gene therapy, harnessing the power of CRISPR-Cas9 and/or DNAzyme systems, stands as a pivotal approach in cancer therapy, enabling the meticulous manipulation of genes pivotal to tumorigenesis and immunity. However, the pursuit of precise gene therapy encounters formidable hurdles. Herein, a near-infrared upconversion theranostic nanomachine is devised and tailors for CRISPR-Cas9/DNAzyme systems mediate precise gene therapy. An ingenious logic DNAzyme system consists of Chain 1 (C1)/Chain 2 (C2) and endogenous lncRNA is designed. We employ manganese modified upconversion nanoparticles for carrying ultraviolet-responsive C1-PC linker-C2 (C₂P) chain and Cas9 ribonucleoprotein (RNP), with outermost coats with hyaluronic acid. Upon reaching tumor microenvironment (TME), the released Mn²⁺ ions orchestrate a trifecta: facilitating endosomal escape, activating cGAS-STING signaling, and enabling T1-magnetic resonance imaging. Under near-infrared irradiation, Cas9 RNP/C₂P complex dissociates, releasing Cas9 RNP into the nucleus to perform gene editing of Ptpn2, while C1/C2 chains self-assemble with endogenous lncRNA to form a functional DNAzyme system, targeting PD-L1 mRNA for gene silencing. This strategy remodels the TME by activating cGAS-STING signaling and dual immune checkpoints blockade, thus realizing tumor elimination. Our theranostic nanomachine armed with the CRISPR-Cas9/DNAzyme logic systems, represents a resourceful and promising strategy for advancing cancer systemic immunotherapy and precise gene therapy.

Structural optimization of lead compounds is a crucial step in drug discovery. One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules, thereby accelerating the molecular optimization process. Deep molecular diffusion generative models simulate a gradual process that creates novel, chemically feasible molecules from noise. However, the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules, leading to challenges in modifying the scaffold-based molecular structures, and creates limitations in the stability and diversity of the generated molecules. To address these challenges, we propose a deep molecular diffusion generative model, the three-dimensional (3D) equivariant diffusion-driven molecular generation (3D-EDiffMG) model. The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder (dual-SWLEE) is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions. Additionally, a gate multilayer perceptron (gMLP) block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies. The experimental results show that 3D-EDiffMG effectively generates unique, novel, stable, and diverse drug-like molecules, highlighting its potential for lead optimization and accelerating drug discovery.

Objective To analyze the value of abdominal CT images combined with serological indicators in predicting the ureteral involvement in idiopathic retroperitoneal fibrosis(IRF). Methods The CT images of 79 IRF patients were analyzed retrospectively,including the involved sites and enhancement characteristics of the lesions.According to the inclusion and exclusion criteria,43 patients with complete serological data were selected and assigned into a ureteral involvement group(n=29)and a non-ureteral involvement group(n=14) according to whether ureters were involved in IRF.Logistic regression analysis was performed to select independent risk factors for ureteral involvement in IRF.The receiver operating characteristic(ROC)curve was plotted to evaluate the predictive value of the CT arterial phase enhancement magnitude and serum cystatin C(CysC)for ureteral involvement in IRF. Results The CT images of IRF usually showed a soft tissue density lesion encompassing the abdominal aorta,iliac arteries,ureters,and retroperitoneal tissue,with a wide range of distribution.The ureteral involvement group and the non-ureteral involvement group showed differences in gender(P=0.031),CT arterial phase enhancement amplitude(P=0.014),CT venous phase enhancement amplitude(P=0.032),and serum CysC(P=0.036).Logistic regression analysis showed that gender(P=0.034),CT arterial phase enhancement amplitude(P=0.046),and serum CysC(P=0.041)were independent risk factors for ureteral involvement in IRF.The area under the curve for CT arterial phase enhancement combined with serum CysC to predict ureteral involvement in IRF was 0.776.Ten patients had lower levels of erythrocyte sedimentation rate(P<0.001),C-reactive protein(P=0.021),and IgG4(P<0.001)in the follow-up period than before treatment. Conclusion The combination of abdominal CT images with serological indicators demonstrates high accuracy in predicting the ureteral involvement in IRF,providing reference for early clinical diagnosis.
Humans ; Male ; Female ; Retroperitoneal Fibrosis/pathology* ; Middle Aged ; Retrospective Studies ; Tomography, X-Ray Computed ; Aged ; Adult ; Ureter/diagnostic imaging* ; Predictive Value of Tests ; Cystatin C/blood*

The gut-brain axis is a bidirectional communication pathway connecting the central nervous system and gastrointestinal tract,playing a key role in the occurrence and development of diseases related to this axis.The vermiform appendix,as a part of the gut that is connected to the cecum,has a unique anatomical location,a rich microbiome,and abundant immune cells.Appendicitis and appendectomy have been found to be associated with the development of diseases related to the gut-brain axis.This review first introduces the anatomy and functions of the vermiform appendix and then expounds the associations of appendicitis and appendectomy with diseases related to the gut-brain axis.Furthermore,this review summarizes and prospects the mechanisms of the vermiform appendix in affecting the occurrence and development of diseases related to the gut-brain axis.
Humans ; Appendix/anatomy & histology* ; Brain ; Appendicitis ; Appendectomy/adverse effects* ; Gastrointestinal Microbiome ; Brain-Gut Axis

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

The aim of this updated guideline is to provide comprehensive recommendations for the management of gout in patients with common comorbidities, such as chronic kidney disease(CKD), cardiovascular disease(CVD), diabetes, osteoarthritis(OA), and gastrointestinal disorders. This guideline was developed by a multidisciplinary expert panel consisting of specialists in endocrinology, rheumatology, nephrology, cardiology, gastroenterology, and methodology. The development process adhered to standard methodologies, including PICO(population, intervention, comparator, and outcomes) question deconstruction, systematic literature review, the Grading of Recommendations Assessment, Development and Evaluation(GRADE) for evidence and recommendation evaluation, Delphi voting, and expert consensus. The guideline presents 26 evidence-based recommendations addressing 7 clinical questions for patients with hyperuricemia and gout in the context of comorbidities. Key recommendations include the maintenance of strict serum urate targets, particularly for patients with CKD stage≥3, chronic gouty arthritis, and OA, in order to prevent disease progression. In patients with CVD or diabetes, intra-articular triamcinolone is preferred over systemic glucocorticoids. Prioritized anti-inflammatory treatments for patients with CKD, gastrointestinal diseases and OA are recommended. The guideline also introduces emerging therapies, such as interleukin-1 inhibitors and selective urate transport inhibitors, as potential treatment options for refractory cases. The update offers a comprehensive, patient-centered approach to managing gout, particularly in individuals with associated comorbidities. Multidisciplinary collaboration and emerging new treatments and evidence ensure the optimization of the recommendations.

Objective To screen out the indexes of medical insurance fund under China Healthcare Security Diagno-sis Related Groups(CHS-DRG)payment based on Delphi method and improve the efficiency of medical insurance fund supervision.Methods Using the Delphi method,32 experts in the medical insurance field were selected and 2 rounds of expert questionnaires were conducted to design an evaluation scale for the monitoring indicators of medi-cal insurance funds under CHS-DRG payment.The consultation results were evaluated from three aspects of motiva-tion,authority,and coordination.Results A medical insurance fund supervision index system based on three dimen-sions of medical insurance fund use quality,efficiency and safety supervision is constructed,including 3 first-level indicators,7 second-level indicators and 44 third-level indicators.Results The construction ofmedical insurance fund supervision index system under CHS-DRG payment should respect the actual clinical diagnosis and treatment,strengthen the intelligent supervision method,and timely intervene in medical insurance fund management risks.

Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.