ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

Objective To construct and compare logistic regression and decision tree models for predicting systemic inflammatory response syndrome (SIRS) in patients with type B aortic dissection (TBAD) after interventional surgery. Methods A retrospective analysis was conducted on clinical data of TBAD patients at Peking University Shenzhen Hospital from 2020 to 2024. The patients were divided into a SIRS group and a non SIRS group based on whether SIRS occurred within 24 hours after surgery. Multivariate logistic regression was used to analyze the influencing factors of SIRS occurrence in TBAD intervention patients, and a decision tree model was constructed using SPSS Modeler to compare the predictive performance of the two models. Results A total of 742 patients with TBAD were included, including 579 males and 163 females, aged 27-97 (58.85±10.79) years. Within 24 hours after intervention, a total of 506 patients developed SIRS, with an incidence rate of 68.19%. Logistic regression analysis showed that the extensive involvement of the dissection, the surgical time≥2 hours, PET coated stents implanted, serum creatinine, white blood cell count, C-reactive protein, monocyte (MONO), neutrophil count levels elevated, estimated glomerular filtration rate and decreased albumin levels were independent risk factors for SIRS (P<0.05). The decision tree model selected a total of 10 explanatory variables and 6 layers with 37 nodes, among which MONO was the most important predictor. The area under the decision tree model curve was 0.829 [95%CI (0.800, 0.856)], which was better than the logistic regression model's 0.690 [95%CI (0.655, 0.723)], and the difference was statistically significant (P<0.001). Conclusion The incidence of SIRS after TBAD intervention is high, and the decision tree model has better predictive performance than logistic regression. It can identify high-risk patients with higher accuracy and provide a practical tool for early clinical intervention.

With the holistic concept of traditional Chinese medicine(TCM)as the foundation,Professor ZHOU Yunfeng proposes the three-part Tuina(Chinese therapeutic massage)manipulation to treat insomnia by"taking the head and brain as the essential and simultaneously regulating the abdomen and back".Taking the theories of Ying-nutrient and Wei-defensive Qi,Zang-Fu organs,and meridians as evidence,this method primarily regulates the mind and concurrently modulates Zang-Fu organs.The three-part Tuina manipulation regulates the spirit by grasping the five meridians(Governor Vessel,Bladder Meridian of Foot Taiyang,and Gallbladder Meridian of Foot Shaoyang)and stimulating points such as Baihui(GV20),Yintang(GV29),Taiyang(EX-HN5),and Shenting(GV24);it regulates the abdomen by selecting Zhongwan(CV12),Shenque(CV8),Qihai(CV6),and Guanyuan(CV4),which means to calm the mind by regulating spleen-stomach Qi activities;it treats the back by selecting Jianjing(GB21),Xinshu(BL15),Pishu(BL20),Weishu(BL21),and Shenshu(BL23),which means to calm the mind by regulating Qi activities of the whole body.Mind regulation runs through the whole process of this manipulation,which combines points from three body regions to produce a synergistic effect,concurrently treating the three body parts,i.e.,the head,abdomen,and back,to mitigate the clinical symptoms and improve sleep quality in insomniacs.

Purpose To investigate the value of two-dimensional speckle tracking in evaluating right ventricular function in cardiac amyloidosis(CA).Materials and Methods A total of 25 patients with CA,30 patients with hypertrophic cardiomyopathy(HCM)and 30 healthy controls were retrospectively selected from July 2021 to October 2023 in Wuhan Asia Heart Hospital.The regional difference was represented by the formula apical ratio.The parameters of conventional echocardiography,two-dimensional strain and apical ratio of the three groups were analyzed and compared.Results Only segmental strain was different in right ventricular structure and function between CA group and HCM group,and the differences were statistically significant(all P<0.05).In the CA group,the strain gradient of the right ventricle from the apex of the heart to the base was the same as that of the left ventricle,that was,the"apex preservation"pattern,which was not seen in the HCM group and the control group.Comparison of the right ventricular free wall longitudinal strain-apical ratio,right ventricle four-chamber longitudinal strain-apical ratio,left ventride global longitudinal strain-apical ratio among the three groups,it was found that the levels of CA group were higher than HCM group and control group,and the differences were statistically significant(H=40.40,43.18,55.43,all P<0.05).The right ventricular free wall longitudinal strain-apical ratio had the highest CA recognition value,the area under the curve was 0.935,sensitivity was 96%and specificity was 83%.Conclusion Two-dimensional speckle tracking can accurately evaluate the right ventricular function impairment in CA patients and provide a new choice for clinical evaluation and identification of CA patients.

Objective:To explore the effects of mechanical ventilation（MV）at birth transition on lung pathophysiology and pathobiology in a very preterm animal model.Methods:Based on the model of respiratory distress syndrome（RDS）in very preterm rabbits at gestational age 26（term 31）days well established by the research group using perinatal medication and lung-protective ventilation strategies（very low tidal volume 1-3 mL/kg），we conducted a secondary data analysis. The studied rabbits were re-grouped according to the MV length（≤3 h，3-6 h，6-9 h，9-12 h，and >12 h）. The changes in lung mechanics，histopathology，phospholipid biochemistry，and mRNA relative expression of inflammatory/growth factor in lung tissue were evaluated over the time course of MV. The trend of each variable was tested by ANOVA trend test（ F trend）and Jonckheere-Terpstra trend test（ J-T value）with corresponding P value. Results:With the prolonged MV length，there was improved mean dynamic compliance of respiratory system（ F trend=16.722， P trend<0.001），along with decreased mean peak inspiratory pressure（ F trend =42.226， P trend<0.001）. The content of total phospholipids，disaturated phosphatidylcholine（ J-T=1 222，1 197， P trend=0.018，0.034，respectively）and total protein（ J-T=1 247 ，P trend= 0.009）in bronchoalveolar lavage fluid gradually increased. The wet lung weight in the ≤3 h group was significantly higher than in the other groups（ F=6.819， P<0.001）. The lung injury score（total，or hemorrhage，or inflammation）had progressive exacerbation in the ≤3 h，3-6 h and 6-9 h groups. The lung tissue mRNA expression of major proinflammatory cytokines increased modestly over the time groups in contrast to decreased expression of growth factors，of which the change of keratinocyte growth factor reached statistical significance（ J-T=531， P trend =0.034）. Conclusion:In the 26-day very preterm rabbits，with prolonged MV time，the content of surfactant phospholipid in the alveolar increased gradually，the lung compliance and lung fluid clearance gradually improved. Nevertheless，ventilator-induced lung injury remained evolving. The study warrants further study on the pathogenesis and protective strategies of early postnatal acute lung injury and chronic lung disease.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Purpose To evaluate the static structural changes of mitral annulus and leaflets in mitral valve prolapse via three-dimensional transesophageal echocardiography(3D-TEE),and their continuous dynamic changes during the cardiac cycle.Materials and Methods Twenty-six patients with mitral valve prolapse diagnosed by transesophageal echocardiography in Wuhan Asia Heart Hospital from June 2022 to June 2023 were retrospectively included,and 30 patients who were examined by transesophageal echocardiography in our hospital during the same period due to monitoring of patent foramen ovale or non-cardiac surgery were selected as the control group.The related parameters of the valve annulus were measured.In addition,the meaningful parameters were measured at late diastole(LD),early systole(ES),middle systole(MS)and late systole(LS),and the change rate was calculated.The static structure of mitral valve device was compared between the two groups,and the differences of various parameters at different times were analyzed.Results Compared with the control group,the annulus height(AH)and the ratio of annulus height and combined width(AHCWR)in the case group decreased,and the other parameters increased(t=-7.447-12.472,all P<0.05),but there was no statistical difference in the maximum displacement distance of the annulus between the two groups(P>0.05).Comparing the related parameters of LD,ES,MS and LS at four different time points,compared with the control group,all the parameters in the case group increased except AH and AHCWR(t=-16.224-15.683,all P<0.05).Comparing the continuous changes at four different times,the continuous changes of all parameters in the control group were statistically significant(F=7.632-323.837,all P<0.05),and there were significant differences in the parameters of the control group from LD to ES(all P<0.05).Except anterolateral to posteromedial diameter,intertrigonal diameter and annulus area,the continuous changes of other parameters in the case group were statistically significant(F=3.041-71.082,all P<0.05),and only non-planar angle,AH and AHCWR in the case group had statistical differences in LD to ES(all P<0.05).Compared with the control group,the change rate of parameters in the case group decreased,and the difference was statistically significant(t/U=-7.069-780.000,all P<0.05),and the change rate of C-ring was greater than that of intertrigonal diameter.Conclusion 3D-TEE can quantitatively analyze the dynamic changes of mitral annulus and leaflets,and analyze the continuous changes of mitral valve in cardiac cycle in detail,which can provide more accurate preoperative evaluation for patients and guide treatment.

Objective A cross-sectional survey on the postoperative home care status and barriers was conducted among elderly patients with osteoporotic fractures nationwide,in order to provide a basis for promoting the improvement of standardized home care for elderly patients with osteoporotic fractures.Methods From October to November 2023,a survey on the current situation and barriers of home environment protection was conducted among elderly patients with osteoporotic fractures in the orthopedic wards of 594 hospitals across 31 provinces(autonomous regions/municipalities directly under the central government)using a convenience sampling method.Results A total of 14,349 questionnaires were distributed,and 12,496 valid questionnaires were collected,resulting in an effective response rate of 87.09％.Among the patients,5,502 cases(44.03％)had implemented home-based prevention and treatment of osteoporosis before the fracture.2 095(16.77％)of the patients experienced a subsequent fracture,of which 65.11％of the patients who experienced a subsequent fracture received medication intervention after the initial fracture,while 19.86％of the patients who experienced a subsequent fracture did not comply with the treatment for osteoporosis after the initial fracture.Additionally,77.66％(n=1 627/2 095)of the patients received community medical services after the initial fracture.Barriers to care factors in the home environment after fracture from the patient's perspective presented the complexity of the social-ecological system model in 6 dimensions at 2 levels:micro(basic personal situation,physiological factors,psychological factors,and behavioural factors),and meso(social support factors,and healthcare worker factors).Conclusion In the vast majority of elderly patients in China,before osteoporotic fracture,home-based measures to prevent osteoporosis have not been adequately implemented;after the initial osteoporotic fracture,the pathway of re-fracture prevention and management in the patient's home environment is not yet complete and its popularity needs to be improved;the barriers to home care faced by elderly patients with osteoporotic fracture are complex.It is recommended to promote effective linkages among hospitals,community health centres and families to strengthen the closed-loop management of re-fracture prevention and management.

Diabetic retinopathy(DR),as one of the most common microvascular complications in patients with diabe-tes mellitus(DM),has a complex and multifaceted etiology that involves multiple factors.The unique characterization of DR in genomics,epigenomics,transcriptomics,proteomics,and metabolomics has opened new avenues for the exploration and development of novel therapeutic strategies for this disease.However,the inherent limitation of single-omics approa-ches is that they can only analyze the biological and molecular mechanisms underlying the disease from a single dimension,which makes it difficult to comprehensively capture the complex nature of DR.With the rapid advancement of high-throughput sequencing technology,multi-omics joint analysis combines the advantages of different histological techniques and can explore the pathogenesis of DR from multiple dimensions and levels.This paper aims to comprehensively review the latest progress of multi-omics technologies in revealing the pathogenesis of DR and its complex molecular mechanisms and thoroughly explore the new opportunities,current challenges,and future development prospects of multi-omics tech-nologies for DR research.