Organ transplantation is an effective alternative treatment for patients with end-stage organ failure. However, the shortage of donor organs has limited the widespread application of clinical transplantation. In recent years, breakthroughs in CRISPR-Cas9 gene editing technology have overcome the barrier of hyperacute rejection in xenotransplantation, offering a potential solution to the organ shortage crisis. Rejection remains a critical factor affecting graft survival. Antigen-presenting cells play a vital role in the initiation and progression of rejection and immune regulation in xenotransplantation. Therefore, in-depth investigation into the role of antigen-presenting cells in xenotransplantation is of great significance. This article summarizes the roles and therapeutic strategies of professional antigen-presenting cells, including macrophages, dendritic cells and B cells in xenotransplantation, aiming to provide insights for future research on immune regulation mechanisms in this field.

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.

Objective To explore the association between serum uric acid levels and the cumulative incidence risk of atrial fibrillation, and to evaluate the predictive value of different uric acid levels for the onset of atrial fibrillation. Methods A retrospective selection of 451 879 participants from the large-scale prospective epidemiological cohort UK Biobank, aged 40-69 years, all completed a median follow-up of 13.6 years. Participants were divided into groups based on the interquartile range of serum uric acid levels (Q1–Q4) related to gender and whether they were diagnosed with hyperuricemia. Cox proportional hazards model, sensitivity analysis, and other methods were used to compare baseline data and atrial fibrillation incidence during follow-up among each group of participants. Results Individuals with higher baseline uric acid levels tended to be older, more obese, and had lower education levels and a history of cancer, along with significantly higher levels of triglyceride, low-density lipoprotein cholesterol, and C-reactive protein, but lower high-density lipoprotein cholesterol levels (P<0.001); the highest uric acid group showed the highest diabetes prevalence (6.49%). Participants with higher serum uric acid levels (log-rank P<0.05) or diagnosed with hyperuricemia had significantly higher incidence of atrial fibrillation (P<0.001). After adjusting for potential confounders, compared to Q1 uric acid level group, the Q4 level was associated with a 20% increased risk of atrial fibrillation (HR=1.20, 95%CI 1.16–1.25). Each 74.7 μmol/L increase in uric acid level was associated with a 9% increased incidence risk of atrial fibrillation (HR=1.09, 95%CI 1.08–1.11). Individuals with hyperuricemia had a 20% increased incidence risk of atrial fibrillation (HR=1.20, 95%CI 1.17–1.24). A nonlinear association was observed between uric acid levels and the incidence risk of atrial fibrillation (P for nonlinearity <0.01). Subgroup analysis indicated significant heterogeneity of the risk effect across subgroups, with a higher risk associated with hyperuricemia in females. Conclusions Elevated blood uric acid levels may increase the cumulative risk of atrial fibrillation, and this pathogenic effect is significantly correlated with age, race, cancer history, body mass index, and sex.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo investigate the detection status of scoliosis and its influencing factors among primary and secondary school students in Jiangqiao Town in Jiading District of Shanghai, thereby providing evidence for the prevention and management of scoliosis in children and adolescents. MethodsA stratified cluster random sampling was conducted in March 2025. Three primary schools and two secondary schools in Jiangqiao Town were randomly selected; within each school, 3‒4 entire classes were randomly chosen. Screening was performed in accordance with the criteria specified in GB/T 16133‒2014 Screening for Abnormal Spinal Curvature in Children and Adolescents, and a self-designed structured questionnaire was administered to conduct face-to-face surveys (Cronbach’s α=0.730). ResultsA total of 772 valid questionnaires were collected on-site, representing a 100% response rate. The sample consisted of 413 males (53.50%) and 359 females (46.50%), including 385 primary school students (49.87%) and 387 secondary school students (50.13%). The detection rate of scoliosis in primary and secondary school students was 12.05%. The detection rates were 8.72% for males and 15.88% for females, with the female rate being significantly higher than the male rate (χ²=9.29, P=0.002). For primary and secondary school students, the detection rates were 6.75% and 17.31%, respectively, with the secondary school student rate being significantly higher (χ²=20.31, P<0.001). The results of univariate analysis revealed that students with sufficient sleep and standard sitting postures during reading/writing had a significantly lower scoliosis detection rate compared to those with insufficient sleep and non-standard sitting postures (χ²=5.90, P=0.015 and χ²=5.09, P=0.024). Multivariate logistic regression analyses revealed that the detection rate of scoliosis was significantly higher in females than in males (OR=1.846， 95%CI: 1.156‒2.984), and significantly higher in secondary school students than in primary school students (OR=2.722， 95%CI: 1.656‒4.472). ConclusionThe scoliosis detection rate among primary and secondary school students in Jiangqiao Town in Jiading District is relatively high. Females and secondary school students are key populations requiring targeted prevention and control efforts. It is imperative to strengthen health education on scoliosis, promote early screening and diagnosis, and implement timely targeted interventions.

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Osteoarthritis (OA) causes chronic pain that significantly impairs quality of life, with current treatments often proving insufficient and accompanied by adverse effects. Recent research has identified the dorsal root ganglion (DRG) and its resident macrophages as crucial mediators of chronic OA pain through neuroinflammation driven by macrophage polarization. We present a novel injectable thermo-sensitive hydrogel system, KAF@PLEL, designed to deliver an anti-inflammatory peptide (KAF) specifically to the DRG. This biodegradable hydrogel enables sustained KAF release, promoting the reprogramming of DRG macrophages from pro-inflammatory to anti-inflammatory phenotypes. Through comprehensive in vitro and in vivo studies, we evaluated the hydrogel's biocompatibility, effects on macrophage polarization, and therapeutic efficacy in chronic OA pain management. The system demonstrated significant capabilities in preserving macrophage mitochondrial function, suppressing neuroinflammation, alleviating chronic OA pain, reducing cartilage degradation, and improving motor function in OA rat models. The sustained-release properties of KAF@PLEL enabled prolonged therapeutic effects while minimizing systemic exposure and side effects. These findings suggest that KAF@PLEL represents a promising therapeutic approach for improving outcomes in OA patients through targeted, sustained treatment.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis