Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Objective To propose a image processing strategy based on salient object detection algorithm for optimizing the presentation of prosthetic visual information at a limited resolution level,aiming to detect and enhance the salient objects in the scene and remove the background information.Methods A salient object detection model combining CNN and Transformer was used to extract salient objects.On this basis,methods such as object magnification,contour enhancement and contrast enhancement were utilized to optimize the image information.Psychophysical experiments were carried out at 5 resolution levels(16×16,24×24,32×32,48×48 and 64×64).Results In the simulated prosthetic vision,this image processing strategy had a remarkable effect on improving the object recognition ability of the subjects.Regardless of the resolutions of 16×16,24×24,32×32,48×48 and 64×64,the proposed strategy achieved the highest recognition accuracies,specifically 34%±6%,56%±9%,72%±9%,89%±4%and 96%±2%.Conclusion Using the salient object detection method and image processing strategy to extract and enhance salient objects can help prosthesis implant recipients effectively improve their object recognition ability.

For middle-aged and elderly patients with conditions such as spinal fractures and degenerative spinal diseases, spinal internal fixation is a core surgical procedure for reconstructing spinal stability, heavily relying on the biomechanical stability provided by pedicle screw systems. Whereas, these patients are often complicated by osteoporosis that can significantly compromise the stability of the bone-pedicle screw interface, leading to a marked increase in pedicle screw loosening and surgical failure rates. The bone cement-augmented pedicle screw technique, which involves injecting bone cement into the vertebral body or screw trajectory to optimize the mechanical properties of the bone-pedicle screw composite, has been proven to significantly enhance fixation strength and effectively prevent screw-related failures, thereby reducing the incidence of internal fixation failure in high-risk populations undergoing spinal fusion. However, the widespread clinical application of this technique has faced challenges such as inaccurate clinical decision-making (indication and contraindication selection), non-standardized operative practices, and insufficient awareness of complication prevention, resulting in considerable variability in clinical outcomes and even severe complications. To address this, Prof. Luo Fei from First Affiliated Hospital of Army Medical University initiated the project and the Chinese Association Orthopaedic Surgeons organized relevant experts to develop the Evidence-based clinical practice guideline for bone cement-augmented pedicle screw technique ( version 2025), based on current evidence. The guidelines put forward 8 recommendations regarding the clinical value, scope of application, and operational standards of the technique, aiming to provide evidence-based medical support and technical standardization for clinical decision-making.

Objective To design a performance testing platform to evaluate the working status and performance characteristics of the ventilator proportional solenoid valve.Methods The performance testing platform had its hardware including a high-pressure gas source,a pressure regulating valve,sensors and etc,and its software designed based on PyQt5 and composed of several modules for data acquisition,parameter setting,image display,indicator computation,result output and etc.Two kinds of proportional solenoid valves(Valve 1、Valve2)were selected for static and dynamic tests to verify the performance of the platform.Results The platform developed facilitated the proportional solenoid valve to carry out accurate computation of static and dynamic indicators at real time and time domain and waveform feature extraction of sensor data by precision control and data acquisition for the proportional solenoid valve.Static tests showed that Valve 1 gained advantages over Valve 2 in static flow characteristics involving in lowered repeatability,return error and offset while enhanced stability;dynamic tests indicated Valve 2 had rapid flow variations and significant flow fluctuation impacts,Valve 1 showed smooth dynamic response changes,and Valve 2 behaved better than Valve 1 in dynamic performance.Conclusion The testing platform developed comprehensively demonstrates the performance characteristics and working performance of the ventilator proportional solenoid valve,which is of great significance to enhance the reliability and safety of the ventilator.[Chinese Medical Equipment Journal,2025,46(1):13-19]

Carbon dioxide(CO2),a colorless,odorless,low-density negative contrast agent with no nephrotoxicity or allergic reactions,has seen increasingly widespread application in the field of vascular imaging in recent years,particularly in patients with iodine allergy or renal insufficiency.When combined with digital subtraction angiography,CO2 angiography has demonstrated high-quality imaging in various arterial and venous sites such as the abdominal aorta,renal arteries,iliac arteries,lower limb arteries,and inferior vena cava.It has also shown safety and efficacy in clinical scenarios such as peripheral arterial disease,dialysis access evaluation,and transjugular intrahepatic portosystemic shunt procedures.This review systematically summarizes domestic and international research progress on CO2 angiography,outlines its physicochemical properties,injection dosages and parameters,clinical indications,and imaging characteristics,and compares its image quality with that of iodine-based contrast agents.Common complications,their mechanisms,and preventive measures are also discussed.Although the image quality of CO2 is slightly inferior to that of iodine agents,it remains sufficient for most diagnostic and therapeutic needs,with a low overall incidence of mainly mild and transient adverse effects.With the development of automated injection systems and digital variance angiography technology,CO2 imaging quality is expected to continue improving,and its application scope is likely to expand further.Future efforts should focus on strengthening multicenter clinical research and establishing standardized operational protocols to promote the broader adoption and regulated use of this technology.

Objective To design an intelligent airborne soldier physical training system based on human body composition analysis to solve the problems in diversity of training mode,targeted training plan and high incidence of military training-related injuries.Methods The intelligent airborne military physical training system was designed with B/S architecture and developed with Python language,which was composed of four functional modules for airborne soldier information acquisition,trainee physical fitness state assessment,physical fitness training program recommendation and airborne soldier physical fitness training program evaluation.The airborne soldier information acquisition module collected and analyzed the trainee physiological parameter information with a human body composition analyzer,clarified the parameter characteristics related to physical training with considerations on military physical training requirements and constructed a trainee physical fitness assessment parameter model;the trainee physical fitness state assessment module established an evaluation model based on machine learning to realize stage-by-stage physical fitness evaluation for airborne soldiers;the physical fitness training program recommendation module was constructed based on the physical training feature similarity algorithm and graph embedding theory to provide decision making assistance for program development of airborne military physical training;the airborne soldier physical fitness training program evaluation module compared the physical fitness and evaluation results before and after training by means of list and chart,and updated the training program based on the evaluation results by calling the physical training program recommendation module.Results The intelligent airborne soldier physical training system contributed to forming an individualized physical fitness training recommendation mechanism after trainee body evaluation,modifying training program based on comparison and feedback for stage-by-stage training evaluation,so as to decrease the incidence of military training-related injuries while increasing the training efficiency.Conclusion The system developed improves airborne soldier physical training in rationality and reliability,and provides references for intelligent military training of the PLA.[Chinese Medical Equipment Journal,2025,46(2):16-23]

Objective To investigate the effects of short-term and long-term high-fructose diets on hippocampal neurometabolites and anxiety and depression-like behaviors in mice,revealing the potential mechanisms of high-fructose diets in mood disorders and providing a experimental basis for the prevention and treatment of related diseases.Methods C57BL/6J male mice were randomly divided into two groups,control group(standard diet,n=10)and experimental group(high-fructose diet,n=10).Four weeks(short-term)and eight weeks(long-term)later,each group of mice was examined for body weight and fasting blood glucose,and neurometabolites levels in the hippocampus were detected by hydrogen proton magnetic resonance spectroscopy(1H-MRS),followed by the open-field test,the forced-swimming test,and the tail-suspension test to evaluate anxiety-like and depression-like behaviors.Results High fructose diet for 4 weeks elevated glutamate levels and reduced glutathione and myo-inositol levels in mice,accompanied by shortened immobility time in the forced swim test.High fructose diet for 8 weeks not only led to abnormalities in body weight and glucose metabolism but also caused a reversal decrease in hippocampal glutamate levels and induced significant anxiety-like behaviors,and the decrease in hippocampal glutamate levels showed a significant negative correlation with the enhancement of anxiety-like behaviors.Conclusion Altered hippocampal glutamate levels may be a key contributing factor to the anxiety-like behaviors induced by long-term high-fructose diet.

To review the clinical characteristics, short-term outcomes, and risk factors of patients with infective endocarditis(IE) who underwent surgical treatment at a single center, and to summarize treatment experience.

Background: The genus Atractylodes, native to East Asia, encompasses several species that serve as sources for the widely used traditional Chinese medicines Atractylodis Macrocephalae Rhizoma and Atractylodis Rhizoma. However, the international trade arouses concern regarding potential confusion and misidentification of Atractylodes species. Objective: A comprehensive understanding of the chemical diversity is crucial for ensuring the quality and exploring the potential variations in medicinal efficacy of Atractylodes. Methods: The GC-MS/MS-based metabolomics and multivariate statistical analysis identified 589 differentially accumulated metabolites across 5 Atractylodes species. Results: A total of 150 metabolites were predicted as potential chemical markers for species differentiation and quality assessment of Atractylodes. According to the metabolic profiles, the species of Atractylodes can be roughly classified into three categories: A. lancea and A. coreana with the volatile oil components being mainly atractylodin and β-eudesmol; A. macrocephala with the biomarker being atractylon; and A. japonica and A. carlinoides lying between the two categories above. Conclusions: Metabolomic results indicated that the metabolic profiles of A. carlinoides and A. macrocephala were similar and distinct from those of the other three species. Sesquiterpenoids were the main chemical components in the rhizome of A. carlinoides, which indicated the potential medicinal value of this plant.