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.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

As a key treatment method for end-stage liver diseases, long-term efficacy of liver transplantation is limited by rejection and serious complications such as liver and kidney toxicity, infection risks and tumor recurrence caused by immunosuppressive drugs. In recent years, nanotechnology, with its targeted delivery capabilities and spatio-temporal controlled release characteristics, has demonstrated great potential in precisely regulating immune responses. This article systematically reviews the multi-level application strategies and mechanisms of nanomedicine in the immunoreprogramming of liver transplantation, covering innate immunoreprogramming (phenotypic transformation of Kupffer cells or macrophages, inhibition of inflammasomes and mitochondrial repair), adaptive immune remodeling (targeted regulation of dendritic cells, gene silencing vectors and reprogramming of T cell functions), and elaborates on the strategies for coordinated regulation of the microenvironment. The article also focuses on the targeted application of liver sinusoidal endothelial cells. In response to clinical translation bottlenecks, innovative solutions such as the design of degradable polymer carriers, the construction of intelligent response systems and the optimization of large-scale production pathways are proposed, providing theoretical support and practical directions for achieving the paradigm shift from "global immunosuppression" to "precise immune tolerance".

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.