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.

Tuberculosis is a zoonotic disease posing a substantial public health threat.Immunological diagnosis and vaccine im-munization are both necessary to control tuberculosis prevalence.However,the identical antigenic components in diagnostic reagents and vaccines hinder the use of animal vaccines and limit the specificity of clinical diagnosis in humans.Differentiating infected from vaccinated animals can overcome these problems.This article reviews the progress in differential diagnosis research from three as-pects:the diagnostic effects of antigens,methods for discovering new antigens,and screening of new host immune markers,to provide a theoretical basis for future research.

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 To observe the effect of acupuncture synchronized with speech training on speech function of patients with post-stroke motor aphasia.Methods Sixty inpatients with post-stroke motor aphasia were selected from the Affiliated Hospital of Shandong Univer-sity of Traditional Chinese Medicine,from January to August,2023.They were randomly divided into control group(n=30)and synchronous group(n=30).Both groups received acupuncture and speech training;the con-trol group received acupuncture in the morning and speech training in the afternoon,while the synchronous group received acupuncture and speech training synchronously,for three weeks.They were assessed with Chi-nese Rehabilitation Research Center Standard Aphasia Examination(CRRCAE),Non-Language-Based Cognitive Assessment(NLCA)and Communication Activities of Daily Living(CADL)before and after treatment.Results The subscores of CRRCAE and NLCA,and score of CADL increased in both groups(|t|>2.081,P<0.05)after treatment,and they were better in the synchronous group than in the control group(|t|>2.680,P<0.05).Conclusion Synchronous mode of acupuncture and speech training is more effective on post-stroke motor aphasia than time-sequence mode.

Objective To observe the effect of synchronous acupuncture and articulation training on spastic dysarthria after stroke.Methods From January to August,2023,64 stroke patients in the Affiliated Hospital of Shandong University of Tradi-tional Chinese Medicine were selected,and randomly divided into control group(n=32)and synchronous group(n=32).Both groups received routine neurological treatment and basic articulation training.The control group added asynchronous acupuncture,while the synchronous group added synchronous acupuncture,for four weeks.Before and after treatment,the modified Frenchay Dysarthria Assessment(m-FDA),Speech Intelligibility Test(SIT),maximum phonation time(MPT)and maximum counting ability(MCA)were used to evaluate the curative effects.Results After treatment,the scores of m-FDA in all the dimensions decreased in both groups(t>2.882,P<0.01);ex-cept for the jaw dimension,the scores of m-FDA in reflexes,respiration,lips,soft palate,larynx,tongue and speech dimensions were significantly lower in the synchronous group than in the control group(t>2.050,P<0.05).After treatment,the results of SIT,and MPT and MCA significantly increased in both groups(t>21.061,P<0.001),and they were better in the synchronous group than in the control group(t>11.412,P<0.001).Conclusion Synchronous acupuncture and articulation training can effectively alleviate the severity of spastic dysarthria after stroke,which was superior to asynchronous acupuncture and articulation training.

The number of patients with eye diseases in China is enormous,and the negative effects of these conditions,such as impaired visual function,psychological burdens,and restricted social participation,are becoming increasingly severe.Due to the limited and unevenly distributed ophthalmic resources,and the significant limitations of traditional diagnostic and therapeutic approaches in terms of accuracy and efficiency,there is an urgent need for more sensitive and efficient modalities.With the rapid advancement of artificial intelligence technology,ophthalmic diagnosis has entered a new stage of intelligent transformation.Facial photos,as a noninvasive and convenient medium,show unique advantages in eye disease diagnosis.Artificial intelligence systems based on facial photo analysis have been applied to the screening and diagnosis of conditions such as myopia,strabismus,ptosis,and thyroid eye disease,showing promising results.This review introduces the workflow of intelligent diagnosis for ocular diseases based on facial photographs,with a focus on recapitulating relevant research findings both domestically and internationally in recent years.It summarizes the innovative features and application advantages of intelligent diagnosis systems for eye diseases based on facial photos,analyzes the current technical bottlenecks and challenges in application,proposes corresponding countermeasures,and discusses future development directions,aiming to provide references and new insights for the intelligent screening and diagnosis of eye diseases.

Objective:To explore the differences of metabolomic profiles in day 3 (D3) culture medium of embryos with varying developmental potentials, in order to provide a theoretical foundation for the establishment of embryo selection technology platform using metabolomics.Methods:Eight patients who received in vitro fertilization and embryo transfer (IVF-ET) treatment at Reproductive Medicine Center of Guangxi Zhuang Autonomous Region Maternal and Child Health Hospital between November 13 and December 5, 2023 were selected as the study subjects. The D3 culture medium from patient embryos was collected and divided into high-quality blastocysts ( n=42), non-high-quality blastocysts ( n=33), and embryos that failed to form blastocysts (non-formation group, n=43) according to the formation of day 5 blastocysts. High-performance liquid chromatography-mass spectrometry was employed to perform non-targeted metabolomic analysis in the D3 culture medium from three distinct groups. Results:1) The metabolites in D3 culture medium of embryos with varying developmental potentials exhibit significant differences. Specifically, 79 differential metabolites were identified between the blastocyst formation group and the non-blastocyst formation group (all P<0.05); additionally, 73 differential metabolites were found between the high-quality blastocyst group and the non-high-quality blastocyst group (all P<0.05). 2) The area under the receiver operating characteristic curve of significantly differential metabolites for predicting potential of D3 embryo blastocyst formation and high-quality blastocyst formation were both greater than 0.9, demonstrating excellent predictive performance. 3) KEGG pathway enrichment analysis revealed that differential metabolites associated with blastocyst formation potential were primarily enriched in pathways including D-amino acid metabolism, glycine-serine-threonine metabolism, arginine biosynthesis, and histidine metabolism ( P<0.05). For high-quality blastocyst formation, the differential metabolites were predominantly enriched in pathways related to tryptophan metabolism, D-amino acid metabolism, serotonergic synapses, and protein digestion and absorption ( P<0.05). Conclusion:Embryos with different developmental potentials have significantly different metabolic profiles, and it is feasible to predict the developmental potential of D3 embryos by metabolomics analysis.

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.