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: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To investigate the possible association between cross-domain associative memory(AM)impairment and hippocampal subfield volumes in patients with schizophrenia(SCZ).Methods We enrolled 28 SCZ patients from Shanghai Mental Health Center,Shanghai Jiao Tong University School of Medicine,and 28 healthy controls(HCs)between 2019 and 2021.Based on an innovative AM paradigm and automated segmentation,3D-T1 weighted data of the objects were processed with PhiPipe and FreeSurfer.Differences in subfield volums between the two groups were analyzed using ANCOVA,while their relationship with AM scores was assessed using Pearson correlation.Results SCZ patients exhibited significantly poorer AM performance across three conditions compared with HCs.Marginally significant reductions were observed in the total volume of bilateral hippocampus,encompassing both the hippocampal head and body.Significant volume reductions were identified in the bilateral presubiculum and parasubiculum.The volumes of bilateral presubiculum head(r=0.273,P=0.042),parasubiculum(r=0.397,P=0.002),and CA1 head(r=0.382,P=0.004)exhibited positive correlations with cross-domain AM performance.Conclusion The bilateral presubiculum and parasubiculum,as hippocampal subregions significantly associated with cross-modal AM deficits in SCZ,may play a crucial role in the pathology of AM.

Objective:To evaluate the cost-effectiveness and impact on mortality of health management services for the elderly aged 65 years and older in national essential public health service project.Methods:Based on the data of county-level medical institutions in Henan Province from 2019 to 2024,the Random Forest Method was used to construct a counterfactual framework to predict the hospitalization expenses under the unmanaged scenario,and then the cost-benefit ratio(BCR)and net income were calculated.Time-dependent Cox proportional hazards model was used to evaluate the effect of health management on all-cause mortality and cardiovascular and cerebrovascular disease mortality in the elderly.Results:A total of 962 955 elderly patients were included,451 119(46.85%)were included in the management group.The average hospitalization cost of the management group was significantly lower than that of the non-management group(P<0.05).Except for 2020-2021,BCRS in 2019 and 2022-2024 were 6.34,2.05,4.45 and 6.60,respectively.The risk of all-cause death was reduced by 76.96%,and the risk of cardiovascular and cerebrovascular death was reduced by 75.57%in the elderly patients included in the management group compared with those not included in the management group.Suggestions:It is necessary to establish a health outcomes-based evaluation system and promote the transformation and upgrading of the service model from single chronic disease management to"integrated health services with multi-disease management".

The advantages of wearable devices were introduced when applied in the field of maternal prenatal care.The current application situation of wearable devices was reviewed for monitoring maternal cardiovascular parameters,physiological and psychosocially perceived stress,physical activity,contraction and placental oxygenation and fetal movement and heart rate monitoring.The deficiencies of wearable devices involved in the field of maternal prenatal care were analyzed,and the future development directions were envisioned.[Chinese Medical Equipment Journal,2025,46(10):106-113]

Objective:To investigate the clinical value of low molecular weight heparin bridging therapy for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents.Methods:The propensity score matching and retrospective cohort study was conducted. The clinical data of 126 patients undergoing tension-free inguinal hernia repair who with long-term oral antiplatelet agents and admitted to Sichuan Academy of Medical Sciences & Sichuan Provincial People′s Hospital (Affiliated Hospital of University of Electronic Science and Technology of China) from January 2017 to January 2025 were collected. There were 120 males and 6 females, aged (74±9)years. Of the 126 patients, 77 patients who discontinued antiplatelet agents alone before inguinal hernia repair were set as the drug withdrawal group, and 49 patients who discontinued antiplatelet agents with low molecular weight heparin bridging therapy before inguinal hernia repair were set as the bridging group. Observation indicators: (1) propensity score matching and comparison of general data of patients between the two groups after matching; (2) intraoperative and postopera-tive conditions; (3) follow-up. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Propensity score matching was performed using the 1∶1 nearest neighbor matching method. The caliper value was set as 0.1. Results:(1) Propensity score matching and comparison of general data of patients between the two groups after matching. Of the 126 patients, 90 patients were success-fully matched, with 45 cases in each of the drug withdrawal group and the bridging group. After propensity score matching, the elimination of hernia ring size, activated partial thromboplasmin time and surgical method factors confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, patients using plasma drainage tubes during the operation in the drug withdrawal group and the bridging group were 8 and 1, respec-tively, showing a significant difference between the two groups ( P<0.05). The visual analogue scale scores of patients in the drug withdrawal group and the bridging group at 48 hours after surgery were 2(range, 1-2) and 2(range, 2-3), respectively, showing a significant difference between the two groups ( Z=-2.57, P<0.05). (3) Follow-up. After propensity score matching, all 90 patients were followed up after surgery for 16.5(range, 9.0-30.0)days. During the follow-up period, there was no significant difference in pain, seroma, incisional infection, readmission within 30 days after surgery getween two groups (P>0.05). No serious thrombotic events occurred in either group of patients, and no patient died. Conclusion:Compared with patients who discontinued antiplatelet agents alone before surgery, preoperative low molecular weight heparin bridging therapy after discontinua-tion of medication is safe and feasible for patients undergoing inguinal hernia repair who with long-term oral antiplatelet agents, in additon to less plasma drainage tubes using during the operation and without more risk of bleeding, but more postoperative pain.

Artificial intelligence(AI)has emerged as a cutting-edge technology leading the future and is a key engine for China's development.In the innovation and research of medical devices,AI has provided critical support in the areas of intelligent diagnostic assistance,intelligent therapeutic assis-tance,intelligent monitoring,life support,et al.Ma-chine learning-enabled device software functions(ML-DSFs)have become an essential component of many medical devices.Recently,the United States Food and Drug Administration(FDA)released a draft guidance titled"Marketing Submission Rec-ommendations for a Predetermined Change Con-trol Plan for Artificial Intelligence/Machine Learn-ing(AI/ML)-Enabled Device Software Functions(Draft)."that aimed to provide a forward-looking approach to foster the development of ML medical devices.By supporting iterative updates through modifications,this approach ensures the continu-ous safety and effectiveness of the devices.This guidance represents the latest in regulatory direc-tion and is especially beneficial for enhancing the quality and efficiency of clinical trials for AI prod-ucts.Therefore,we plan to provide a detailed intro-duction and interpretation of the guidance,with the aim of learning from international advanced regulatory concepts and experiences to promote the development of ML-DSFs with more profound international influence.