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 Exploring genetic differences among geographic strains of Anopheles sinensis in China. Methods Based on the mitochondrial cytochrome oxidase subunit I(COⅠ )and ribosomal the second internal transcribed spacer(ITS2), genetic distance analyses were conducted on An. sinensis samples collected nationwide. Results Genetic differentiation was observed within An. sinensis geographic strains, primarily reflected in the genetic distances of COⅠ sequences, with some distances between samples exceeding those between closely related species. However, ITS2 sequences showed relatively low levels of differentiation within geographic strains. No significant separation was observed, and gene exchange exhibited a random pattern among geographic strains. Conclusions Significant differences in genetic distance were observed among An. sinensis samples, along with evidence of interspecific continuity among sibling species. For the identification of closely related species using wild-collected Anopheles mosquitoes samples, reliance solely on commonly used barcodes such as COⅠ and ITS2 may be insufficient. Additional molecular markers, including COⅡ and D3 gene fragments, should also be considered. Caution should be exercised when using COⅠ genes to identify cryptic species, especially within Anopheles mosquitoes.

Objective To elucidate the application scope and delineate the criteria of dobutamine stress echocardiography (DSE) in assessing surgical risk associated with valvular heart diseases in patients presenting with reduced left ventricular ejection fraction (LVEF). Methods This retrospective analysis encompassed data from patients with reduced LVEF (LVEF<40%) who underwent DSE prior to valvular heart surgery at the Valvular Surgery Center of Beijing Anzhen Hospital between May 2016 and September 2024. Patients were stratified based on the terminal dose of dobutamine into two cohorts: a limited dose group [receiving a maximum dobutamine dose of <5 µg/(kg·min) due to concomitant conditions such as suspected atrial thrombus, aortic dilation, or previous myocardial infarction] and a non-limited dose group (where dosage was adjusted to either achieve the maximum positive standard or the maximal dose tolerable by the patient). Within the non-limited dose group, patients were further classified into positive and negative response groups. The positive response post DSE was defined based on the following criteria: LVEF≥55% (the maximum positive standard), LVEF<55% but with a 5% improvement (the minimum positive standard) from resting situation, aortic maximum velocity (AVmax)≥400 cm/s, or mean pressure gradient (meanPG)≥40 mm Hg, latter two applicable only to patients with aortic stenosis. Clinical indicators were compared in the three groups. Results A total of 99 patients were enrolled, aged 35 to 87 years, with an average age of (61.7±10.1) years. The number of males was significantly higher than that of females (3:1). There were 61 (61.6%) patients undergoing aortic valve surgery, 25 (25.3%) undergoing mitral valve surgery, and 13 (13.1%) undergoing combined aortic and mitral valve surgery. Nineteen (19.2%) patients experienced adverse symptoms such as palpitations, head and face numbness, dizziness, chest tightness post DSE. Additionally, new-onset or aggravated ventricular premature ventricular contractions were in 40 (40.4%) patients. All above symptoms and signs resolved minutes after cessation of the test. No severe adverse events necessitating oxygen therapy or emergency intervention occurred. Among the 93 patients in the non-limited dose group, 86 were classified in the positive response group and 7 in the negative response group. The left ventricular end diastolic diameter, left ventricular end systolic diameter, and LVEF in the positive group all showed significant improvement compared to preoperative values, and were also significantly better than those in the negative group (P<0.05). All 7 patients with negative DSE results underwent interventional surgery, which was significantly different from the positive group (36 patients, 41.9%). In the limited dose group (n=6), 4 patients with a positive result or a positive trend on DSE underwent median sternotomy, 2 received interventional treatment due to a negative response. No significant differences in mortality were observed among the groups (P>0.05). Conclusion Dobutamine stress echocardiography significantly contributes to risk stratification in surgical interventions for patients with valvular heart disease complicated by reduced LVEF. It aids in determining optimal timing and methods for surgery. With comprehensive patient evaluation and controlled dobutamine dosing, the application of DSE can be safely expanded.

Messenger ribonucleic acid (mRNA) is a promising therapeutic drug with great potential in the fields of immunology, oncology, vaccines and inborn metabolic diseases. However, due to its instability and susceptibility to nuclease degradation, efficient delivery vectors are required. Lipid nanoparticles (LNPs) are recognized as the most mature delivery vectors due to their advantages of easy formulation, high stability, efficient cell uptake and endosomal escape. However, the accumulation of LNPs in the liver severely limits the targeting and treatment of mRNA-LNP technology beyond the liver. To overcome this obstacle, researchers have been focusing on various means to achieve precise delivery of extrahepatic tissues and organs. This article mainly expounds the research progress of LNP-specific delivery mRNA from three aspects: endogenous targeting, active targeting and selection of administration route, in order to provide ideas and directions for the design of new mRNA-LNP delivery systems in the future.

Due to patient compliance and convenience, oral medication is likely the most common and acceptable method of drug administration. However, traditional dosage forms such as tablets or capsules may lead to low drug bioavailability and poor therapeutic efficiency. Therefore, with advancements in material science and micro/nano manufacturing technology, various carriers have been developed to enhance drug absorption in the gastrointestinal tract. In this context, we initially discuss the key biological factors that hinder drug transport and absorption (including anatomical, physical, and biological factors). Building on this foundation, recent progress in both conventional and innovative oral drug delivery routes aimed at improving drug bioavailability and targeting is reviewed. Finally, we explore future prospects for oral drug delivery systems as well as potential challenges in clinical translation.

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.

BACKGROUND:With the continuous progress of science and technology,the introduction of new technologies and methods will bring more possibilities and new breakthroughs for the application of shape memory alloys in the fields of assistive and rehabilitation. OBJECTIVE:To review the application status of shape memory alloys in assistive and rehabilitation equipment,discuss their main methods,techniques and results,summarize and put forward suggestions,hoping that shape memory alloys can be continuously optimized and bring more new changes for the development of assistive and rehabilitation equipment. METHODS:WanFang,PubMed,and Web of Science databases were searched by computer."Shape memory alloys,application progress,orthodontics,orthopedic,prosthesis,rehabilitation,properties,implantation,mechanical properties,nickel-titanium memory alloys,actuation"were used as Chinese search terms."Shape memory alloys,application,orthodontics,orthopedic,prosthetics,rehabilitation,properties,implant,drive,progress,prostheses"were used as English search terms.Finally,91 articles were included for review. RESULTS AND CONCLUSION:(1)Shape memory alloy has the characteristics of corrosion resistance,wear resistance,biocompatibility,fatigue resistance,kink resistance and other properties.Compared with other traditional materials(stainless steel,titanium alloy,cobalt-chromium alloy,etc.),shape memory alloy has lower elastic modulus and no biological toxicity,which is suitable for long-term implantation as an implant prosthesis.Due to its shape memory effect and excellent mechanical properties,it is mainly used as a driving element or as a bridge connecting the device and the human body in artificial limbs,orthoses and rehabilitation equipment.(2)The use of shape memory alloy drive elements can reduce the weight of the device,eliminate noise,easy to operate,easy to carry,better assist joint movement;compared with the use of pneumatic,hydraulic,and electrical drive methods of the device,it has obvious advantages.(3)In addition,shape memory alloy can produce permanent and stable stress during deformation.Compared with stainless steel,titanium alloy and aluminum alloy,shape memory alloy has a higher material recovery rate and does not need to be replaced and adjusted frequently,so it is more practical in the correction of deformity.(4)At present,shape memory alloy is most commonly used in orthosis,and the best clinical application effect is in stapes prosthesis.However,due to the limitations of technology and cost,shape memory alloys are rarely used in artificial limbs and rehabilitation equipment,and there is a lack of large sample size studies on the application effect.(5)Although shape memory alloys have been developed in the field of auxiliary and rehabilitation,there are still many problems:it is difficult to accurately control the shape memory alloys;the cooling speed of shape memory alloy is slow;the deformation speed of shape memory alloy cannot be controlled;there is a lack of comparative research and expert consensus on shape memory alloys with different properties;shape memory alloys are costly and expensive.(6)In the future,attention should be paid to the development of new shape memory alloys,increase comparative research,and use new technologies and methods(such as 4D printing)to solve the existing problems,so as to develop high-performance assistive devices and rehabilitation equipment.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

OBJECTIVE: To evaluate the anti-hepatocellular carcinoma (HCC) activity of total alkaloids from Gelsemium elegans Benth. (TAG) in vivo and in vitro and to elucidate their potential mechanisms of action through transcriptomic analysis. METHODS: TAG extraction was conducted, and the primary components were quantified using high-performance liquid chromatography (HPLC). The effects of TAG (100, 150, and 200 µg/mL) on various tumor cells, including SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116, were assessed. Effects of TAG on HCC proliferation and apoptosis were detected by colony formation assays and cell stainings. Caspase-3, Bcl-2, and Bax protein levels were detected by Western blotting. In vivo, a tumor xenograft model was developed using H22 cells. Totally 40 Kunming mice were randomly assigned to model, cyclophosphamide (20 mg/kg), TAG low-dose (TAG-L, 0.5 mg/kg), and TAG high-dose (TAG-H, 1 mg/kg) groups, with 10 mice in each group. Tumor volume, body weight, and tumor weight were recorded and compared during 14-day treatment. Immune organ index were calculated. Tissue changes were oberseved by hematoxylin and eosin staining and immunohistochemistry. Additionally, transcriptomic and metabolomic analyses, as well as quatitative real-time polymerase chain reaction (RT-qPCR), were performed to detect mRNA and metabolite expressions. RESULTS: HPLC successfully identified the components of TAG extraction. Live cell imaging and analysis, along with cell viability assays, demonstrated that TAG inhibited the proliferation of SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116 cells. Colony formation assays, Hoechst 33258 staining, Rhodamine 123 staining, and Western blotting revealed that TAG not only inhibited HCC proliferation but also promoted apoptosis (P<0.05). In vivo experiments showed that TAG inhibited the growth of solid tumors in HCC in mice (P<0.05). Transcriptomic analysis and RT-qPCR indicated that the inhibition of HCC by TAG was associated with the regulation of the key gene CXCL13. CONCLUSION TAG inhibits HCC both in vivo and in vitro, with its inhibitory effect linked to the regulation of the key gene CXCL13.
Animals ; Apoptosis/drug effects* ; Liver Neoplasms/genetics* ; Carcinoma, Hepatocellular/genetics* ; Humans ; Alkaloids/therapeutic use* ; Gelsemium/chemistry* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Mice ; Xenograft Model Antitumor Assays

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.