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 construct the inflammation score(IS)and nutrition-immunity score(NIS)for patients with multiple myeloma(MM),and to verify their prognostic stratification effects and significance.Methods:The clinical data of 129 newly diagnosed MM patients admitted to our hospital from August 2011 to September 2022 were retrospectively analyzed.Univariate and multivariate Cox regression analysis of overall survival(OS)were comducted on clinical parameters,including inflammatory indicators such as red blood cell volume distribution width(RDW)and platelet count(PLT),nutritional-immune indicators such as albumin(ALB),absolute lymphocyte count(ALC),and suppressed immunoglobulin count(S-Ig count).To construct IS and NIS for prognosis,X-tile software and multivariate Cox regression analysis were used to verify the prognostic stratification role and significance of IS and NIS.The time-dependent receiver operating characteristic(ROC)curve,C-index curve,calibration curve,and decision curve analysis(DCA)were used to evaluate the discrimination,accuracy,and clinical net benefit of IS and NIS in predicting overall survival(OS),and compared to the international staging system(ISS).Results:IS was constructed based on the scores of RDW and PLT,and NIS was constructed based on the scores of ALB,ALC,and S-Ig count.According to X-tile analysis and multivariate Cox regression analysis,IS and NIS can divide the patients into three risk strata respectively:low,medium and high IS and NIS groups.The differences in OS and hazard ratio(HR)between the low,medium,and high strata were statistically significant(P＜0.05).IS and NIS are both independent prognostic predictors for MM.The area under the ROC curve(AUC)and C index of IS and NIS for predicting 1-to 7-year OS were greater than those of ISS,and both were greater than 0.7.The prediction results of IS and NIS for 1-,3-,and 5-year OS rates were well consistent with the actual observed results.The DC A curves of IS and NIS for predicting 1-,3-,and 5-year OS were higher than that of ISS in a wide range of threshold probability intervals.Conclusion:IS and NIS have independent predictive significance for OS in MM patients.Their predictive discrimination,accuracy,and clinical net benefit are higher and better than ISS,and they may have potential application value in MM prognosis.

Relevant China's literature on the application of brain-computer interface technology in the field of rehabilita-tion of stroke patients was retrieved in the China Knowledge Network database from its establishment to December 31,2024,and CiteSpace visual analysis software was used to analyze the selected literature in terms of trend of annual publica-tion number,author collaboration network,keyword co-occurrences and emergences and to generate a corresponding knowledge map.It's pointed out brain-computer interface technology showed significant application potential for motor function recovery and neurorehabilitation,which had the research hotspots of the cross technologies covering motor imagina-tion,rehabilitation training and virtual reality and the research frontiers of the fusion application of intelligent algorithms of deep learning and pattern recognition.The challenges and future development directions of the field were investigated,and references were provided for promoting the application of brain-computer interface technology to rehabilitation of sroke patients in China.[Chinese Medical Equipment Journal,2025,46(9):65-69]

Relevant China's literature on the application of brain-computer interface technology in the field of rehabilita-tion of stroke patients was retrieved in the China Knowledge Network database from its establishment to December 31,2024,and CiteSpace visual analysis software was used to analyze the selected literature in terms of trend of annual publica-tion number,author collaboration network,keyword co-occurrences and emergences and to generate a corresponding knowledge map.It's pointed out brain-computer interface technology showed significant application potential for motor function recovery and neurorehabilitation,which had the research hotspots of the cross technologies covering motor imagina-tion,rehabilitation training and virtual reality and the research frontiers of the fusion application of intelligent algorithms of deep learning and pattern recognition.The challenges and future development directions of the field were investigated,and references were provided for promoting the application of brain-computer interface technology to rehabilitation of sroke patients in China.[Chinese Medical Equipment Journal,2025,46(9):65-69]

Aim To explore the key components and mechanisms of Lizhong decoction in treating rats with cold-damp diarrhea based on network pharmacology,molecular docking technology and animal experiments.Methods By literature review and database collec-tion,the components of Lizhong decoction,therapeutic targets,and the mapping with diarrhea disease targets were conducted to construct an intersection target pro-tein-protein interaction network for screening core tar-gets,and GO and KEGG pathway enrichment analysis was performed to build an"active component-target-pathway"network,followed by molecular docking vali-dation.Forty-eight rats were randomly divided into the normal control group(K),model group(DG),Lizhong decoction group(LZDG),and Pulsatilla decoction group(BTDG).Subsequently,a rat cold-damp diar-rhea model was established using Senna combined with low-temperature high-humidity environment,and the rats were intervened with Lizhong decoction and Pul-satilla decoction.HE staining was used to detect path-ological changes in intestinal tissue,ELISA was em-ployed to measure the levels of peripheral blood IL-6,IL-10,IL-1 β,and TNF-α,and western blot was used to determine the expression of colon tight junction pro-teins.Results Network pharmacology initially identi-fied 125 compounds in Lizhong decoction,5 186 drug target components,438 disease targets,and 60"drug-disease"shared targets.GO and KEGG enrichment a-nalysis showed that signaling pathways such as IL-17 and TNF were highly enriched.Molecular docking in-dicated that the core components of the drug had good binding activity with corresponding key targets.Liz-hong decoction could effectively improve the clinical symptoms of rats with cold-damp diarrhea,and com-pared with the DG group,the diarrhea rate,diarrhea in-dex,and other related indicators also gradually de-creased to normal levels.Compared with the DG group,the LZDG group showed reduced inflammation levels and a recovery in energy metabolism levels.Conclusion It can regulate targets such as MMP9 and IL-17 signaling pathways through multi-components like Calycosin and formononetin to exert its therapeutic effect on cold-damp diarrhea.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.