ObjectiveTo investigate the liver-protecting and anti-liver fibrosis effects of astragaloside Ⅳ （AS-Ⅳ） in vitro and in vivo， as well as its mechanism of action in intervention against liver fibrosis. MethodsIn the animal experiment， C57BL/6J mice were divided into control group， model group， low-dose AS-Ⅳ （20 mg/kg） group， and high-dose AS-Ⅳ （80 mg/kg） group. The mice were given intraperitoneal injection of carbon tetrachloride for 6 weeks to induce liver fibrosis， and since week 3 of injection， the mice in the low-dose AS-Ⅳ group and the high-dose AS-Ⅳ group were given AS-Ⅳ by gavage at a dose of 20 mg/kg and 80 mg/kg， respectively. The serum levels of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） were measured after 4 weeks of administration， as well as the serum levels of hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ N-terminal peptide （PⅢNP）， and collagen type Ⅳ （Col-Ⅳ）. HE staining， picrosirius red staining， and Masson staining were used to observe liver histopathology and collagen deposition； RT-qPCR was used to measure the mRNA expression levels of Acta2， Col1a1， and Col3a1 in liver tissue， and Western blot was used to measure the protein expression levels of α-smooth muscle actin （α-SMA）， collagen type Ⅲ （Col-Ⅲ）， phosphatidylinositol 3-kinase （PI3K）， phosphorylated PI3K （pPI3K）， protein kinase B （Akt）， and phosphorylated AKT （p-Akt） in liver tissue； transcriptome sequencing was performed for liver tissue to identify differentially expressed genes and perform a bioinformatics analysis. In the cell experiment， transforming growth factor-β （TGF-β） was used to induce the activation of LX-2 cells， and the PI3K inhibitor LY294002 and the PI3K activator 740 Y-P were used for intervention. The cells were divided into control group， model group， AS-Ⅳ group， LY294002 group， and AS-Ⅳ+740 Y-P group， and the cells were harvested after 36 hours of intervention. Changes in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K/PI3K， and pAkt/Akt in LX-2 cells were measured， as well as changes in the relative mRNA expression levels of Acta2， Col1a1， and Col3a1. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsIn the animal experiment， compared with the model group， the AS-Ⅳ treatment group had significant reductions in the serum levels of ALT， AST， HA， LN， PⅢNP， and Col-Ⅳ （all P<0.01）， the mRNA expression levels of Acta2， Col1a1， and Col3a1 in liver tissue （all P<0.05）， and the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） in liver tissue （all P<0.05）. In the cell experiment， compared with the control group， the model group had significant increases in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） after TGF-β induction （all P<0.05）； compared with the model group， the AS-Ⅳ group had significant reductions in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） （all P<0.05）， and both the AS-Ⅳ group and the LY294002 group had significant reductions in the protein expression level of pPI3K and the relative mRNA expression levels of Acta2， Col1a1， and Col3a1 （all P<0.05）. Compared with the AS-Ⅳ group， there were significant increases in the protein expression level of pPI3K and the relative mRNA expression levels of Acta2， col1a1， and Col3a1 after 740 Y-P intervention （all P<0.05）. ConclusionAS-Ⅳ can inhibit hepatic stellate cell activation and improve liver fibrosis， possibly by inhibiting the PI3K/Akt signaling pathway.

Objective To analyze the main causes and risk factors of unplanned reoperation after liver transplantation. Methods The clinical data of 242 liver transplant recipients in the First Affiliated Hospital of Anhui Medical University from January 2015 to December 2024 were retrospectively analyzed. According to whether unplanned reoperation was performed during the same hospitalization after surgery, the recipients were divided into the reoperation group (n=36) and the non-reoperation group (n=206). The preoperative, intraoperative and postoperative data of the two groups, as well as donor and graft-related data, were compared to analyze the risk factors of unplanned reoperation after liver transplantation and the survival status of the two groups. Results Among the 242 liver transplant recipients, 36 underwent unplanned reoperations, with a total of 54 procedures including various laparotomies, endoscopic and interventional surgeries, among which there were 20 laparotomies, 18 endoscopic surgeries and 16 interventional surgeries. The most common cause of unplanned reoperation was biliary complications (20 times), followed by vascular complications (17 times). Compared with the non-reoperation group, the reoperation group had longer graft cold ischemia time, higher postoperative fatality rate of recipients, longer length of stay in the intensive care unit and postoperative hospital stay, and higher total hospitalization costs (all P<0.05). The incidence of unplanned reoperation was higher in recipients who underwent split liver transplantation (P<0.05). Multivariate analysis showed that intraoperative blood loss ≥1 000 mL, positive culture of graft perfusate and split liver transplantation were independent risk factors for unplanned reoperation (all P<0.05). The postoperative 7-day, 1-month, 3-month and 6-month survival rates of recipients in the reoperation group and the non-reoperation group were 100% vs. 98.1%, 88.9% vs. 94.2%, 69.4% vs. 90.8% and 66.7% vs. 90.8%, respectively, and the postoperative survival rate of recipients in the reoperation group was lower than that in the non-reoperation group (P<0.05). Conclusions The main causes of unplanned reoperation after liver transplantation are biliary complications, vascular complications, abdominal incision infection and intra-abdominal hemorrhage. Intraoperative massive blood loss, positive culture of graft perfusate and split liver transplantation are the risk factors associated with unplanned reoperation after liver transplantation.

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 investigate the epidemiological characteristics of malaria and provide a basis for developing improved prevention and control measures. Methods Data were obtained from the Chinese Disease Prevention and Control Information System. Malaria surveillance data for Nanning City from January 1,2023, to December 31,2024, were exported from the Infectious Disease Reporting Information Management Subsystem. The characteristics of the two groups of malaria cases were compared. Results A total of 103 imported malaria cases were reported in Nanning City in 2024, representing a 38.32% decrease compared with the same period of the previous year. No statistically significant difference were observed between cases reported in 2023 and 2024 in terms of average age, gender ratio, proportion of parasite species, and monthly reporting distribution;however, statistically significant differences were found in the proportion of reporting areas and current residence areas(χ2= 13.572 and 10.355, respectively; P = 0.001 and 0.035, respectively). The proportion of cases reported in Shanglin County and the proportion of cases residing in Shanglin County were both lower than those during the same period of the previous year. Conclusions The high aggregation of imported malaria cases in Nanning City has decreased. Medical institutions in areas other than Shanglin County should strengthen their vigilance against malaria.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

OBJECTIVE: The occurrence and development of atrial fibrillation (AF) are influenced by the autonomic nervous system and inflammation. Acupuncture is an effective treatment for AF. This study explored the protective effects of acupuncture in a rat model of paroxysmal AF and investigated its mechanisms. METHODS: Male Sprague-Dawley rats (n = 130) were randomly divided into blank control (Con), sham operation (Sham), AF, and acupuncture treatment (Acu) groups. A paroxysmal AF model was established by rapid atrial pacing through the jugular vein. Rats in the Acu group were immobilized to receive acupuncture treatment at Neiguan acupoint (PC6) for 20 min daily for seven days. The other groups were immobilized for the same duration over the treatment period but did not receive acupuncture. The AF induction rate, AF duration, cardiac electrophysiological parameters, and heart rate variability were evaluated by monitoring surface electrocardiogram and vagus nerve discharge signals. After the intervention, the rats were euthanized, and atrial morphology was assessed using haematoxylin and eosin staining. The expression of macrophage F4/80 antigen (F4/80) and cluster of differentiation (CD) 86 in atrial myocardial tissue was detected using immunohistochemistry, immunofluorescence and flow cytometry. The expression levels or contents of interleukin (IL)-1β, IL-6, tumor necrosis factor-α (TNF-α), α7 nicotinic acetylcholine receptor (α7nAChR), phosphorylated Janus kinase 2 (p-JAK2), and phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in atrial myocardial tissue were detected using Western blotting, reverse transcription-quantitative polymerase chain reaction, or enzyme-linked immunosorbent assay. The role of α7nAChR in acupuncture treatment was verified by intraperitoneal injection of the α7nAChR antagonist methyllycaconitine (MLA). RESULTS: Compared with the AF group, acupuncture significantly reduced AF duration and induction rate, improved cardiac electrophysiology by enhancing vagus nerve activity and regulating autonomic balance. It also decreased the pro-inflammatory M1 macrophage proportion, alleviating myocardial injury and infiltration. MLA weakened acupuncture's electrophysiological improvement and anti-inflammatory effect. Results suggest that acupuncture triggers the α7nAChR-JAK2/STAT3 pathway and exerts cardioprotection via neuroimmune regulation. CONCLUSION Acupuncture significantly reduced the AF induction rate, shortened AF duration, improved cardiac electrophysiological parameters, enhanced vagus nerve activity, and decreased the expression of pro-inflammatory M1 macrophages and inflammatory factors in rats with paroxysmal AF. Its positive effects are related to the activation of the α7nAChR-mediated JAK2/STAT3 signalling pathway, indicating that the interaction between cardiac vagus nerve and macrophages may be a potential target for acupuncture in the prevention and treatment of AF. Please cite this article as: Li ZH, Yang WM, Huang Q, Shi GX, Liu CZ, Zhang YQ. Acupuncture activates vagus nerve-macrophage axis and improves cardiac electrophysiology and inflammatory response in rats with atrial fibrillation via α7nAChR-JAK2/STAT3 pathway. J Integr Med. 2025; 23(4): 398-414.
Animals ; Male ; Rats, Sprague-Dawley ; STAT3 Transcription Factor/metabolism* ; alpha7 Nicotinic Acetylcholine Receptor/metabolism* ; Janus Kinase 2/metabolism* ; Atrial Fibrillation/metabolism* ; Vagus Nerve/physiopathology* ; Rats ; Acupuncture Therapy ; Signal Transduction ; Macrophages/metabolism* ; Inflammation/therapy*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

The pathogenesis of chronic wound healing is complex.It is often difficult to heal due to a long course of disease,difficulty in treatment,and it seriously affects the quality of life in patients.The active ingredients,couplet medicinals,and compound formulas of traditional Chinese medicine(TCM)possess unique advantages in the treatment of chronic wound healing.The phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt)signaling pathway is extremely critical in the treatment of chronic wound healing by regulating a series of biological processes,including cell apoptosis,angiogenesis,and inflammatory responses.This article reviews the relevant research on the regulation of the PI3K/Akt signaling pathway by TCM to promote chronic wound healing.It has been found that the active ingredients of TCM(such as geniposide,astragaloside,and ginsenosides,etc.),and compound formulas(such as Chonghe ointment,Huanglian ointment,Shirun shaoshang ointment,etc.)mainly reduce inflammatory responses,promote angiogenesis,regulate cell autophagy,and accelerate wound healing by activating the PI3K/Akt signaling pathway;at the same time,there are also a few couplet medicinals(such as Huangqi-Honghua)and compound formulas(such as Xiangpi Shengji ointment)that exert anti-inflammatory effects by inhibiting this signaling pathway,to promote wound healing.

Objective:To evaluate the efficacy and feasibility of a domestically developed robotic surgical system based on fiber-optic dedicated line communication in cross-regional urological telesurgery.Methods:This was multicenter，single-arm，retrospective case series study. The data of patients who underwent urological telesurgeries using the telesurgical system between January 2023 and December 2024 were analyzed. The cohort included 59 patients from seven hospitals across China. Among the patients，47 were male（79.7%）and 12 were female（20.3%），with a median age of 63.0（56.0，68.0）years and a body mass index of（24.7 ± 3.0）kg/m 2. Surgical procedures included 32 radical prostatectomies，24 partial nephrectomies，one radical nephrectomy，one adrenalectomy，and one ureteral reconstruction. The perioperative indicators，pathological results and postoperative complications were analyzed. The network monitoring data were collected，and the perioperative data of patients，remote system monitoring data and costs were compared between the two communication modes of optical transport network（OTN）and cloud-connect network（CCN）. Results:All 59 remote surgeries were successfully completed，with a mean operative time of（138.0 ± 54.0）minutes，median intraoperative blood loss of 50.0（30.0，100.0）ml and a postoperative hospital stay of 5.0（4.0，6.0）days. No cases required reoperation，Clavien-Dindo grade ≥3 complications，or readmission. The geographical distance between the primary and remote surgical sites ranged from 450 to 2 800 km. Network monitoring revealed increased bidirectional latency with distance increasing：the shortest latency time（Hefei-Hangzhou，450 km）was（16.59 ± 0.80）ms，while the longest（Harbin-Hangzhou，2 200 km）latency time was（53.31 ± 0.31）ms. Average frame loss per procedure was 0?1.27 frames. The results of subgroup analysis comparing OTN and CCN communication modes showed no significant differences in operative time［（130.7 ± 70.5）minutes vs.（142.1 ± 42.9）minutes， P = 0.442］，postoperative hospitalization［6.0（4.0，8.0）d vs. 5.0（4.0，6.0）d， P = 0.581］，or readmission rates（0 vs. 0）. However，CCN demonstrated significant cost advantages with 500 RMB per operation vs. 3 000 RMB per operation for OTN. Conclusions:Urological telesurgery using fiber-optic communication is feasible. The CCN mode，with its cost-effectiveness，excellent usability，and multi-point interconnection flexibility，is currently the preferred communication model for telesurgical applications.