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 examine the potential causal associations between different dietary taste preferences and the risk of prostatitis, so as to provide reference for the primary prevention of this disease. Methods Summary-level genome-wide association study(GWAS)data on five taste preferences-sweet, sour, salty, bitter and spicy—were obtained from the GWAS, and GWAS data for prostatitis were sourced from the FinnGen database. The potential causal associations between the dietary taste preferences and risk of prostatitis were analyzed with MR. The inverse variance weighted(IVW)method served as the primary analytical approach. Heterogeneity was assessed using Cochran's Q test. Horizontal pleiotropy was evaluated via the MR-Egger intercept and Mendelian randomization pleiotropy residual sum and outlier(MR-PRESSO)tests. Leave-one-out analysis was performed to evaluate the influence of individual single nucleotide polymorphisms(SNPs)on the causal estimates. Results Preferences for sweet(OR=1.338, 95%CI:1.024-1.748, P=0.033)and salty(OR=1.356, 95%CI:1.092-1.684, P=0.006)tastes showed positive causal associations with the risk of prostatitis, whereas a preference for bitter taste was negatively associated(OR=0.638, 95%CI:0.416-0.978, P=0.039). No significant causal links were observed for sour or spicy taste preferences(P＞0.05). Sensitivity analyses and reverse MR analyses further supported the robustness of the findings.Conclusion Sweet and salty taste preferences may be potential dietary risk factors for prostatitis, while bitter taste preference could play a protective role. Further research is needed to elucidate these causal relationships, which may contribute to developing tailored dietary intervention strategies for prostatitis prevention.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

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.

With the accelerating aging of the population, the proportion of elderly patients with lung cancer continues to rise, presenting multiple challenges to perioperative management. This paper systematically reviews the clinical characteristics of elderly lung cancer patients. Based on the comprehensive geriatric assessment, it proposes incorporating seven major geriatric syndromes—frailty, delirium, sarcopenia, cognitive impairment, malnutrition, dysphagia, and mood disorders—into the core evaluation system. By integrating multimorbidity management with complication prevention and control, an integrated "geriatric syndrome-multimorbidity-complication" perioperative management model is constructed. Furthermore, this paper outlines stratified intervention strategies for geriatric syndromes, a "five-step" workflow for comorbidity management, and a comprehensive intervention pathway for complications across the preoperative, intraoperative, and postoperative phases. Multidisciplinary team (MDT) collaboration serves as the core mechanism to achieve individualized comprehensive treatment. This paper aims to provide a novel perioperative comprehensive treatment model for elderly lung cancer patients, which is centered on geriatrics, supported by multidisciplinary collaboration, and guided by precision medicine.

Objective : To investigate the effects of cinnamaldehyde(CA) on the growth, metastasis and apoptosis of human endometriosis(EMs) cells and to explore whether the mechanism is related to ribosomal protein S7(RPS7) expression. Methods : Endometriosis cells were divided into control group, CA group, sh-NC group, CA+sh-RPS7 group. Effects of CA on cell growth in human endometriotic cells were determined using Cell Counting Kit-8(CCK-8) and colony formation assay. Effects of CA on cell metastasis were performed by motility assay and Transwell assay. Effects of CA on cell apoptosis were evaluated by Hoechst 33258 staining and flow cytometry. Meanwhile, the levels of PCNA, E-cadherin, Vimentin, Bax and Bcl-2 were evaluated using Western blot in human endometriotic cells with treatment CA. The expression of RPS7 was detected by qRT-PCR and Western blot assay. The RPS7 overexpression of human endometriotic cells was established by cell transfection. CA-mediated effects on cell proliferation and apoptosis were determined by CCK-8 assay and flow cytometry in human endometriotic cells with RPS7 overexpression. Results : CA repressed cell growth as well as down-regulated PCNA. The half inhibitory concentration(IC50) value was 53.60 μmol/L after 24 h treatment, and colony formation rate was 25.32%. Additionally, CA inhibited metastasis which was associated with downregulated Vimentin and upregulated E-cadherin. The relative migration rates were 35% and 29% as well as invasion rate was 40%. Further, CA induced apoptosis by cell cycle G2/M phase arrest and cell apoptosis rate was 25.1%, which related to the up-regulation of of Bax and the down-regulation of Bcl-2. CA inhibited the expression of RPS7 and overexpression of RPS7 promoted cell proliferation and suppressed apoptosis in CA-mediated cells. Conclusion CA inhibits cell growth, metastasis, and induces cell apoptosis by downregulating the expression of RPS7.

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Plutonium isotopes(238Pu,239Pu,240Pu and 241Pu)in the environment are important"fingerprint"nuclides in the study of nuclear activity traceability.The content of plutonium isotopes in the environmental metrics is usually very low,and the measurement of these isotopes,especially 238Pu,using mass spectrometry is seriously interfered with by the coexisting 238U.The analysis of several plutonium isotopes in soil usually requires combination of multiple measurement techniques,which leads to a long analysis time and large uncertainty in the isotope ratio.In this work,the hydrous titanium oxide(HTiO)precipitated by the hydrolysis of titanium oxydichloride(TiOCl2)under near-neutral condition was used to preconcentrate plutonium from the soil digestion solution,and the highly efficient decontamination of 238U in the sample was achieved by TK200 resin column chromatography with a decontamination factor of 108.Simulation resuts of density functional theory(DFT)showed that NH3 was considered as a promising reaction gas to eliminate the interference of 238U from 238Pu measurement using mass spectrometry due to the significant discrepancy of the chemical reactivity of U+and Pu+with the reactive gas NH3.Experiments confirmed that by optimizing the flow rates of collision gas(He)and reaction gas(NH3),the interference of 238U could be effectively suppressed,and the decontamination factor of 238U was 104.Combined with chemical separation,the overall decontamination factor of 238U could reach 1012 by using the developed method.By combining chemical separation and tandem quadrupole inductively coupled plasmon-mass spectrometry(ICP-MS/MS)measurement,the simultaneous determination of four ultra-trace plutonium isotopes in soil was realized,and the detection limit of plutonium isotopes was at the femtogram level.Analysis of the international standard reference materials(NIST-SRM-4357 and IAEA-384)showed that the established method could be successfully used for the accurate analysis of ultra-trace four plutonium isotopes(238Pu,239Pu,240Pu and 241Pu)in soil samples.

The electrocatalytic nitrate reduction reaction(NO3RR)presents a sustainable pathway for large-scale ammonia production,yet it faces significant challenges due to proton supply limitations caused by the high energy barrier for water dissociation,which slows ammonia(NH3)generation.Herein,a palladium(Pd)-modified copper-cobalt(CuCo)hollow fiber penetration electrode that enabled H2 injection through its hollow structures,thereby enhancing proton availability for NO3RR was developed.The active Pd component efficiently dissociated H2,facilitating active hydrogen(*H)spillover and speeding up the cascade NO3RR process on Cu and Co sites.As a result,a half-cell energy efficiency of 39.53%and an NH3 Faradaic efficiency(FE)of 97.11%±1.17%at-0.1 V(vs RHE)were achieved,comparable to state-of-the-art systems.Importantly,the H2-assisted approach prevented the oxidation of active Cu and Co phases,demonstrating exceptional stability with less than 5.6%decay in current density(267 mA/cm2)and retention of NH3 FE at 94.8%after over 70 h of electrolysis.These findings offered valuable insights into proton supply pathways and design of NO3RR electrodes.