The disturbance of the human microbiota influences the occurrence and progression of many diseases. Live therapeutic bacteria, with their genetic manipulability, anaerobic tendencies, and immunomodulatory properties, are emerging as promising therapeutic agents. However, their clinical applications face challenges in maintaining activity and achieving precise spatiotemporal release, particularly in the harsh gastrointestinal environment. This review highlights the innovative bacterial functionalized encapsulation strategies developed through advances in physicochemical and biological techniques. We comprehensively review how bacterial encapsulation strategies can be used to provide physical barriers and enhanced adhesion properties to live microorganisms, while introducing superior material properties to live bacteria. In addition, this review outlines how bacterial surface coating can facilitate targeted delivery and precise spatiotemporal release of live bacteria. Furthermore, it elucidates their potential applications for treating different diseases, along with critical perspectives on challenges in clinical translation. This review comprehensively analyzes the connection between functionalized bacterial encapsulation and innovative biomedical applications, providing a theoretical reference for the development of next-generation bacterial therapies.

Objective:To explore the structural relationship between WMSDs in the upper limbs and various risk factors in the occupational population in China, based on a large sample epidemiological survey and structural equation analysis, and to establish a structural equation model, so as to lay a foundation for the prevention and control of such diseases.Methods:The Chinese version of the Musculoskeletal Disorders Electronic Questionnaire was used to conduct a nationwide survey on the prevalence of WMSDs in the upper extremity. Six factors related to WMSDs in the upper extremity were extracted by the classification standard of adverse ergonomic factors and their source and confirmatory factor analysis, including work organization, work type, upper extremity work posture, individual factors, upper extremity fatigue and upper extremity WMSDs. The structural equation analysis was carried out and the structural equation model was established.Results:The incidence of WMSDs and fatigue in the upper limbs was 24.44% and 43.76%, respectively. The adjusted structural equation model fitting indicators were generally up to the standard (GFI=1.000, AGFI=1.000, RMSEA=0.043, NFI=0.808, TLI=0.784) . The four exogenous latent variables of work organization, work type, upper limb work posture and individual factors were correlated. There was a strong positive correlation between job type and upper limb work posture ( r=0.865) , a moderate positive correlation between work organization and job type and upper limb work posture ( r=0.570, 0.490) , and a weak negative correlation between individual factors and the other three exogenous latent variables. Upper limb work posture and individual factors had direct effects on upper limb WMSDs, and the effect coefficients were 0.10 and 0.06, respectively. Upper limb fatigue played a mediating role between work organization, work type, upper limb work posture and upper limb WMSDs. The effect coefficient was 0.46, and the composition ratios of indirect effects were 100.0%, 100.0%, and 38.3%, respectively. The direct path effect of upper limb work posture, individual factors and upper limb WMSDs was weaker than the mediating path through upper limb fatigue. Conclusion:When carrying out the prevention and control of upper limbWMSDs, it is necessary to comprehensively consider the pathogenesis path of upper limb muscle fatigue and upper limb WMSDs caused by work organization, work type, and upper limb work posture, so as to provide theoretical reference for improving the prevention and control level of such diseases.

BACKGROUND:The patient underwent multiple hypoproteinemia after total hip arthroplasty,which affected postoperative healing and rehabilitation.OBJECTIVE:To investigate and screen the risk factors for hypoproteinemia after total hip arthroplasty,and to establish a nomogram prediction model so as to provide guidance for judging whether hypoproteinemia occurs after total hip arthroplasty.METHODS:A total of 355 patients who underwent total hip arthroplasty were included,and according to whether hypoproteinemia occurred on the first day after surgery,they were divided into 238 cases in the hypoproteinemia group and 117 cases in the normal group,with a hypoproteinemia rate of 67％.Data were collected,including age,gender,diabetes mellitus,hypertension,hyperuricemia,hyperlipidemia,anesthesia method,preoperative leukocytes,preoperative erythrocytes,preoperative hemoglobin,preoperative platelets,preoperative plasma prothrombin time,preoperative activated partial prothrombin time,preoperative international normalized ratio,preoperative thrombin time,preoperative fibrinogen,preoperative erythrocyte sedimentation rate,preoperative C-reactive protein,preoperative D-dimer,preoperative mean corpuscular hemoglobin content,preoperative mean corpuscular volume,operation time,body mass index,preoperative procalcitonin,and preoperative hematocrit.SPSS 27.0 software was used for univariate analysis,followed by R language(4.3.1)to perform least absolute shrinkage and selection operator regression and 10-fold cross-validation of the observation indicators to obtain the intersection of the two risk factors.SPSS 27.0 software was used to perform multivariate binary logistic regression to obtain the final risk factors.The prediction model of hypoproteinemia after total hip arthroplasty was constructed by R language.The receiver operating characteristic curve,calibration curve,and clinical decision curve were constructed to assess the predictive model predictive ability.RESULTS AND CONCLUSION:(1)Univariate analysis,least absolute shrinkage and selection operator regression,and multivariate logistic regression were used to screen out significant differences in age(OR=1.024,P=0.023),preoperative platelets(OR=0.995,P=0.028),and preoperative erythrocyte sedimentation rate(OR=1.031,P=0.045)in judging whether hypoproteinemia would occur after surgery(P＜0.05).(2)The nomogram prediction model was constructed based on the final risk factors screened by multivariate Logistic regression,and the prediction ability of the model was evaluated by constructing the receiver operating characteristic curve,and the area under the calculated receiver operating characteristic curve reached 0.835(95％CI=0.779-0.891),C-index=0.835.A threshold of 0-0.83 could bring better clinical efficacy calculated by the decision curve analysis.The model has good sensitivity and accuracy,which can better identify the risk of postoperative hypoproteinemia for medical staff and patients before total hip arthroplasty.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

BACKGROUND:Knee finite element modelling can provide insight into knee mechanics,but its complex image segmentation is more difficult for researchers.With the development of deep learning techniques,deep learning techniques have been widely used in knee joint finite element modelling.OBJECTIVE:To replace the manual segmentation step in finite element modelling of the knee joint by using 3D Swin UNETR in combination with a semi-automatic segmentation technique for statistical shape models.METHODS:Manual(artificial)knee joint finite element model was developed based on MR and semi-automatic knee joint finite element model was developed based on 3D Swin UNETR+statistical shape model segmentation.The same loads and boundary conditions were applied to both models.Validation was performed by calculating the Dice similarity coefficient,mean distance,and comparing the peak equivalent stresses,maximum principal stresses,and maximum shear stresses of the two models.RESULTS AND CONCLUSION:(1)The Dice similarity coefficients of the manual and semi-automatic segmented femur and tibia were more than 98％,and the average distances were less than or equal to(0.35±0.08)mm.(2)With the longitudinal load of 750 N and 10 Nm internal overturning moment applied to the femur tip of both manual and semi-automatic finite element models,the peak equivalent stress,maximum principal stress,and maximum shear stresses of meniscus in manual finite element model were 14.12,18.54,and 7.35 MPa;peak equivalent force,maximum principal stress,and maximum shear stress of femoral cartilage were 2.22,2.15,and 1.18 MPa;peak equivalent force,maximum principal stress,and maximum shear stress of tibial cartilage were 2.50,1.91,and 1.41 MPa;semi-automatic finite element model of meniscus:peak equivalent force,maximum principal stress,and maximum shear stress were 14.93,18.53,and 7.75 MPa.The peak equivalent force,maximum principal stress,and maximum shear stress of femoral cartilage were 2.26,2.18,and 1.20 MPa;the peak equivalent stress,maximum principal stress,and maximum shear stress of tibial cartilage were 2.60,1.91,and 1.46 MPa.The peak equivalent stress,maximum principal stress,and maximum shear stress of manual and semi-automatic finite element models were basically consistent,with no significant difference(P＞0.05).(3)The semi-automatic segmentation technique proposed in this study can replace manual segmentation in creating accurate finite element models of the knee joint.

[Purpose]To analyze the incidence and mortality of pancreatic cancer in 2020 and the change trend from 2010 to 2020 in cancer registration areas of Gansu Province.[Methods]The data of pancreatic cancer from 2010 to 2020 were collected from cancer registries in Gansu Province.The crude incidence/mortality rate,age-standardized incidence/mortality rate by Chinese standard population(ASIRC/ASMRC)and world standard population(ASIRW/ASMRW),0～74 years old cumulative rate and proportion of pancreatic cancer were calculated.Joinpoint 4.7.0 software was used to calculate the average annual percentage change(AAPC)of ASIRC/ASMRC of pancreatic cancer in cancer registration areas of Gansu Province from 2010 to 2020.[Results]In 2020,a total of 838 new cases of pancreatic cancer were reported in the cancer registration areas of Gansu Province,with a crude incidence rate of 6.52/105,ASIRC and ASIRW of 4.03/105 and 4.49/105 respectively,accounting for 2.50％of all malignant tumor incidence.In 2020,702 cases of pan-creatic cancer deaths were reported in the cancer registration areas of Gansu Province,with a crude mortality rate of 5.46/105,ASMRC and ASMRW of 3.25/105 and 3.73/105,respectively,ac-counting for 3.98％of all malignant tumor deaths.From 2010 to 2020,a total of 2 413 cases of pancreatic cancer were reported in cancer registration areas in Gansu Province,accounting for 1.90％of all malignant tumors in the province.The crude incidence rate of pancreatic cancer was 5.28/105,the ASIRC was 4.18/105,the ASIRW was 4.63/105,and the cumulative rate of 0～74 years old was 0.49％.From 2010 to 2020,a total of 1 871 pancreatic cancer deaths were reported in cancer registration areas of Gansu Province,accounting for 2.38％of all malignant tumor deaths in the province.The crude mortality rate was 3.92/105,the ASMRC was 3.09/105,the ASMRW was 3.50/105,and the cumulative rate of 0～74 years old was 0.36％.In terms of sex and region,the incidence and mortality of pancreatic cancer from 2010 to 2020 in men were higher than those in women,and higher in rural areas than those in urban areas.From 2010 to 2020,the incidence and mortality were at a low level under the age of 44 years old,and increased significantly after 45 years old,reaching a peak in the age group of 80～84 years old.ASIRC showed no significant change from 2010 to 2020 with an AAPC of 0.41 1％(P＞0.05).From 2010 to 2020,the ASMRC showed an significantly increasing trend with an AAPC of 6.515％(P=0.014).[Conclusion]From 2010 to 2020,the ASRIC of pancreatic cancer in Gansu Province showed no significant change,while the ASMRC showed a significantly in-creasing trend.The incidence and mortality rates were higher in men than those in women and higher in rural areas than those in urban areas.Middle-aged and elderly men in rural areas are the key groups of prevention and treatment of pancreatic cancer,so targeted prevention and control measures should be carried out.

Objective This study aims to analyze the factors influencing per capita current health expenditure in Guizhou Province from 2016 to 2022 using the gray correlation analysis method.Methods Based on the"SHA2011"accounting results of current health expenditure in Guizhou Province,as well as data from the"Guizhou Statistical Yearbook"and"Guizhou Health Statistical Yearbook",the gray correlation analysis method was used to analyze the factors influencing per capita current health expenditure in Guizhou Province from 2016 to 2022.Results The factors with the highest correlation to per capita current health expenditure in Guizhou Province were health expenditure(0.829),followed by the number of health technical personnel per thousand people(0.715),the number of practicing(assistant)physicians per thousand people(0.705),and per capita GDP(0.704).The factor with the lowest correlation was the proportion of the tertiary industry to GDP(0.543).Conclusion Health expenditure investment has the strongest correlation with per capita current health expenditure in Guizhou Province.Health re-source investment and health service capacity are the main influencing factors of per capita current health expenditure in Guizhou Province.At the same time,the impact of economic and social factors on current health expenditure should be fully recognized.

[Purpose]To analyze the incidence and mortality of pancreatic cancer in 2020 and the change trend from 2010 to 2020 in cancer registration areas of Gansu Province.[Methods]The data of pancreatic cancer from 2010 to 2020 were collected from cancer registries in Gansu Province.The crude incidence/mortality rate,age-standardized incidence/mortality rate by Chinese standard population(ASIRC/ASMRC)and world standard population(ASIRW/ASMRW),0～74 years old cumulative rate and proportion of pancreatic cancer were calculated.Joinpoint 4.7.0 software was used to calculate the average annual percentage change(AAPC)of ASIRC/ASMRC of pancreatic cancer in cancer registration areas of Gansu Province from 2010 to 2020.[Results]In 2020,a total of 838 new cases of pancreatic cancer were reported in the cancer registration areas of Gansu Province,with a crude incidence rate of 6.52/105,ASIRC and ASIRW of 4.03/105 and 4.49/105 respectively,accounting for 2.50％of all malignant tumor incidence.In 2020,702 cases of pan-creatic cancer deaths were reported in the cancer registration areas of Gansu Province,with a crude mortality rate of 5.46/105,ASMRC and ASMRW of 3.25/105 and 3.73/105,respectively,ac-counting for 3.98％of all malignant tumor deaths.From 2010 to 2020,a total of 2 413 cases of pancreatic cancer were reported in cancer registration areas in Gansu Province,accounting for 1.90％of all malignant tumors in the province.The crude incidence rate of pancreatic cancer was 5.28/105,the ASIRC was 4.18/105,the ASIRW was 4.63/105,and the cumulative rate of 0～74 years old was 0.49％.From 2010 to 2020,a total of 1 871 pancreatic cancer deaths were reported in cancer registration areas of Gansu Province,accounting for 2.38％of all malignant tumor deaths in the province.The crude mortality rate was 3.92/105,the ASMRC was 3.09/105,the ASMRW was 3.50/105,and the cumulative rate of 0～74 years old was 0.36％.In terms of sex and region,the incidence and mortality of pancreatic cancer from 2010 to 2020 in men were higher than those in women,and higher in rural areas than those in urban areas.From 2010 to 2020,the incidence and mortality were at a low level under the age of 44 years old,and increased significantly after 45 years old,reaching a peak in the age group of 80～84 years old.ASIRC showed no significant change from 2010 to 2020 with an AAPC of 0.41 1％(P＞0.05).From 2010 to 2020,the ASMRC showed an significantly increasing trend with an AAPC of 6.515％(P=0.014).[Conclusion]From 2010 to 2020,the ASRIC of pancreatic cancer in Gansu Province showed no significant change,while the ASMRC showed a significantly in-creasing trend.The incidence and mortality rates were higher in men than those in women and higher in rural areas than those in urban areas.Middle-aged and elderly men in rural areas are the key groups of prevention and treatment of pancreatic cancer,so targeted prevention and control measures should be carried out.

BACKGROUND:Knee finite element modelling can provide insight into knee mechanics,but its complex image segmentation is more difficult for researchers.With the development of deep learning techniques,deep learning techniques have been widely used in knee joint finite element modelling.OBJECTIVE:To replace the manual segmentation step in finite element modelling of the knee joint by using 3D Swin UNETR in combination with a semi-automatic segmentation technique for statistical shape models.METHODS:Manual(artificial)knee joint finite element model was developed based on MR and semi-automatic knee joint finite element model was developed based on 3D Swin UNETR+statistical shape model segmentation.The same loads and boundary conditions were applied to both models.Validation was performed by calculating the Dice similarity coefficient,mean distance,and comparing the peak equivalent stresses,maximum principal stresses,and maximum shear stresses of the two models.RESULTS AND CONCLUSION:(1)The Dice similarity coefficients of the manual and semi-automatic segmented femur and tibia were more than 98％,and the average distances were less than or equal to(0.35±0.08)mm.(2)With the longitudinal load of 750 N and 10 Nm internal overturning moment applied to the femur tip of both manual and semi-automatic finite element models,the peak equivalent stress,maximum principal stress,and maximum shear stresses of meniscus in manual finite element model were 14.12,18.54,and 7.35 MPa;peak equivalent force,maximum principal stress,and maximum shear stress of femoral cartilage were 2.22,2.15,and 1.18 MPa;peak equivalent force,maximum principal stress,and maximum shear stress of tibial cartilage were 2.50,1.91,and 1.41 MPa;semi-automatic finite element model of meniscus:peak equivalent force,maximum principal stress,and maximum shear stress were 14.93,18.53,and 7.75 MPa.The peak equivalent force,maximum principal stress,and maximum shear stress of femoral cartilage were 2.26,2.18,and 1.20 MPa;the peak equivalent stress,maximum principal stress,and maximum shear stress of tibial cartilage were 2.60,1.91,and 1.46 MPa.The peak equivalent stress,maximum principal stress,and maximum shear stress of manual and semi-automatic finite element models were basically consistent,with no significant difference(P＞0.05).(3)The semi-automatic segmentation technique proposed in this study can replace manual segmentation in creating accurate finite element models of the knee joint.