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.

ObjectiveTo analyze the epidemiological and etiological characteristics of hand-foot-and-mouth disease (HFMD) in Jinjiang District of Chengdu City from 2014 to 2024, and to provide a reference basis for HFMD prevention and control. MethodsA retrospective study was conducted to collect confirmed and clinically diagnosed cases of HFMD in Jinjiang District from January 1, 2014 to December 31, 2024. Descriptive epidemiological methods were used to analyze the distribution of cases by person, time, and place. Joinpoint regression models were applied to examine trends for incidence and severe case rates, while seasonal patterns were analyzed using the seasonal index method. Chi-square tests were used for comparing rates, Mann-Whitney U tests were applied for comparing two groups with skewed distributions, and Kruskal-Wallis H rank-sum tests were used for multiple groups with skewed distributions. Pathogen characteristics were primarily analyzed using descriptive statistics. ResultsA total of 15 561 HFMD cases were reported from 2014 to 2024. The annual crude incidence rate ranged from 51.32/100 000 to 502.50/100 000, the age-standardized incidence rate ranged from 55.56/100 000 to 721.07/100 000, peaking in 2018, and the overall epidemic intensity generally showed an alternating-year pattern of intensification. A total of 85 severe cases were reported, showing a declining trend in the severe case rate (APC=AAPC=-37.57%, 95%CI∶ -51.49%‒21.33%， P<0.01). Severe cases were most common in the 1‑year‑old age group (43.53%), with a median age of 1.88 years. Recurrent infections accounted for 1 245 cases (8.00%), with 96.73% of these being reinfections. The median age at first infection for recurrent cases was 1.98 years, significantly lower than that of single-infection cases (2.69 years, P<0.001), and the median interval between consecutive infections was 1.19 years. The disease exhibited a bimodal seasonal pattern, with epidemic peaks occurring from May to July and from October to December, however, summer peaks extending into autumn were observed in 2018 and 2023. There were statistically significant differences in age distribution across different years (H=467.51, P<0.001), with the median age increasing from 2.25 years in 2018 to 4.18 years in 2024, and the interquartile range widening from 1.84 to 3.78 years. Since its initial detection in 2017, CVA6 has been identified annually, showing a peak proportion of 87.50% in 2023 and a second peak of 68.18% in 2018. CVA10 was detected during 2021‒2023, peaking at 18.75% in 2021 and decreasing to 14.29% in 2022. Male incidence rates were consistently higher than female rates each year (P<0.05). The proportion of males in the reinfection group was significantly higher than that in the single-infection group (χ²=9.951, P=0.002). Pathogen analyses showed that CVA16 was detected annually, EV71 detection declined fluctuantly and was not detected after 2022. CVA6 was first detected in 2017 and rapidly increased in prevalence, reaching 87.50% in 2023 and becoming the dominant strain. Among severe cases, EV71 accounted for the highest proportion (52.63%), while CVA16 was most common in non-severe cases (41.60%). There was a statistically significant difference in pathogen composition between severe and non-severe cases (χ²=10.402,P=0.011). ConclusionThe epidemiological profile of HFMD undergoes continuous changes over time. It is necessary to strengthen routine surveillance, with particular attention to changes in the pathogen spectrum and age distribution, so as to provide evidence for precise prevention and control measures and the adjustment of vaccination strategies.

ObjectiveTo investigate the in vivo anti-inflammatory effects of the triterpenoid saponin-enriched fraction Fr.B4 from the She medicine Clematis florida (C. florida) in a rat model of phenol-induced cervicitis and to preliminarily explore its potential mechanisms of action. MethodsThirty 6–8-week-old female SPF-grade SD rats were randomly assigned to three groups: control, cervicitis model, and C. florida treatment group, with 10 rats in each group. The model and C. florida treatment groups were induced with cervicitis by vaginal injection of 25% phenol gel (once every 2 d, for a total of three injections), and the vulva and cervix were examined to assess the effectiveness of model establishment. After successful modeling, the C. florida treatment group was administered Fr.B4 at 290 mg/kg daily by gavage (dissolved in normal saline), whereas the model group received an equal volume of normal saline; both treatments continued for 2 weeks. The control group received no modeling procedure and was administered normal saline for 2 weeks. After the last intervention, fresh feces and urine samples were collected from all rats. The rats were euthanized after anesthesia with 40 mg/kg Zoletil 50, and the serum and cervical tissue samples were harvested. ELISA was used to measure the levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6) in rat serum and cervical tissue. Hematoxylin and eosin (HE) staining was performed to observe the pathological changes in cervical tissue. Liquid chromatography-mass spectrometry was employed to analyze differential metabolites in feces and urine, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and core metabolite analysis. 16S rDNA sequencing was used to assess the gut microbiota structure and diversity in each group. Statistical analysis was conducted using GraphPad Prism 9.0. ResultsCompared with the control group, rats in the model group exhibited cervical redness, swelling, and erosion, with significant increases in TNF-α, IL-1β, and IL-6 levels in both serum and cervical tissue (P<0.05 or P<0.01). Pathological examination of cervical tissue showed thickening of the squamous epithelium, infiltration of inflammatory cells, and edema. Compared with the model group, rats with C. florida treatment showed marked alleviation of cervicitis symptoms, with significantly reduced levels of pro-inflammatory cytokines in serum and cervical tissue (P<0.05 or P<0.01). Cervical tissue edema was alleviated, inflammatory cells were reduced, and squamous epithelium thickening was ameliorated. Metabolomics analysis revealed 346 and 564 differential metabolites in the feces and urine between the model and control groups, respectively. After treatment with Fr.B4, 236 and 451 metabolites in the feces and urine showed significant shifts toward control levels, involving pathways including cyclic adenosine monophosphate (cAMP) signaling, AMP-activated protein kinase (AMPK) signaling, and glutathione metabolism. Anti-inflammatory metabolites like boswellic acid and oleanolic acid were increased (P<0.01 or P<0.001). Gut microbiota analysis showed dysbiosis in the model group, with an imbalance in the Firmicutes/Bacteroidetes ratio. The Fr.B4 treatment remodeled the gut microbiota. The α-diversity of microbiota shifted toward that of the control group. Specific bacterial genera were found to correlate significantly with metabolites (P<0.05 or P<0.01). ConclusionThe triterpenoid saponin-enriched fraction Fr.B4 from She medicine C. florida may exert anti-cervicitis effects by reducing pro-inflammatory cytokine levels, modulating the gut microbiota balance, and remodeling metabolism.

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.

BACKGROUND:Meningeal lymphatic vessels can drain cerebral spinal fluid and amyloid β-protein,promoting T lymphocyte to transport and home to deep cervical lymph nodes.A simple,quick and definite method of dural separation and accurate localization of deep cervical lymph nodes can provide strong support for the study of neurodegenerative diseases.OBJECTIVE:To establish a convenient and practical method for exfoliating dural and deep cervical lymph nodes.METHODS:ICR mice,3 months old,were taken,anesthetized and injected with Evans blue and tracer in the occipital pool for localizing deep cervical lymph nodes.A midline incision of about 3 cm in length was made about 5 mm above the clavicle,the superficial fat and fascia were bluntly separated,and the lateral sternocleidomastoid muscle was pulled to expose the deep cervical lymph nodes,which were removed under a stereomicroscope and frozen at-80℃.Subsequently,the mouse head was cut and the skin and muscles of the head were separated to expose the entire skull structure.The skull and brain tissue were separated from the foramen magnum along the lower parietal bone with scissors,and the complete skull top was obtained.The skull was sequentially fixed in 40 g/L paraformaldehyde solution for 24 hours,120 g/L paraformaldehyde for 24 hours,and 120 g/L paraformaldehyde for 10,20,30,and 40 minutes,and the dural structure was stripped.The drainage capacity of meningeal lymphatic vessels and deep cervical lymphatic vessels was verified by tracer,and the meningeal lymphatic vessels were identified by the lymphatic vessel endothelial hyaluronan receptor 1 using the immunofluorescence method.RESULTS AND CONCLUSION:(1)Obvious blue staining was observed in deep cervical lymph nodes 15 minutes after Evans blue staining.(2)The skull was sampled and fixed in 120 g/L paraformaldehyde for 24 hours,resulting in a less tight connection between the dura mater and the skull,and easier stripping of the dural structures with an intact shape.The dura mater fixed at 120 g/L concentration was more resilient and remained more intact during peeling compared with the conventional 40 g/L concentration;120 g/L paraformaldehyde fixed meninges for a short time,and 30-40 minutes was preferred.(3)The frozen section of deep cervical lymph nodes showed the presence of the tracer,complete meningeal lymphatic vessels were visible in the dura mater,and the tracer was observed at the tail of lymphatic vessels.Immunofluorescence staining for endothelial hyaluronan receptor 1 was positive in the deep cervical lymph nodes and dural lymphatics.In summary,the best peeling concentration and time is 120 g/L paraformaldehyde fixed for 24 hours.At this concentration,the dura mater has a stretched morphology,a better toughness,and is more intact after peeling,which is conducive to later use.Verified by Evans blue,tracers and immunofluorescence,deep cervical lymph nodes are located accurately,which can be used as a basis for the study of various neurodegenerative diseases.

Objective To investigate the compliance of hormone receptor-positive breast cancer patients with prolonged endocrine therapy and analyze its influencing factors.Methods This study was a retrospective cohort study.A total of 347 patients with breast cancer who received prolonged endocrine therapy in our hospital from June 2017 to March 2023 were selected.Relevant data of the patients were collected and they were divided into two groups according to whether they adhered to prolonged endocrine therapy:the compliance group and the non-compliance group.Using the x2 test to analyze the impact of patients'disease-related data on prolonging compliance with endocrine therapy.Use Logistic regression to analyze its influencing factors.Results Among 347 breast cancer patients who received extended endocrine therapy,during the median follow-up of 28 months(ranging from 12 to 60 months),319 patients(91.9％)adhered to extended endocrine therapy(compliance group),and their treatment compliance was acceptable.Twenty-eight cases(8.1％)of patients did not adhere to prolonged endocrine therapy(non-compliance group).Multivariate analysis showed that the independent factors Influencing the compliance of breast cancer patients with prolonged endocrine therapy were comorbidities and radiotherapy(P＜0.05).Conclusion Based on the characteristics of influencing factors,behavioral interventions such as increasing follow-up frequency and strengthening health education content can be implemented for some patients without comorbidities and those who have not received radiation therapy,and to improve treatment compliance.

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.

BACKGROUND:Studies have shown that immune cells are involved in all processes of ischemic stroke,in which cuproptosis also plays a key role.OBJECTIVE:To screen diagnostic biomarkers related to the progression of ischemic stroke through bioinformatics,and analyze and validate cuproptosis-related genes closely related to the occurrence and development of ischemic stroke.METHODS:The GSE16561 microarray was obtained from the GEO database,containing data from 39 cases of ischemic stroke(ischemic stroke group)and 24 controls(control group).Differentially expressed genes from the ischemic stroke microarray data were analyzed.Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.By using LASSO and Random Forest methods,key genes affecting the occurrence and development of ischemic stroke were screened,and a diagnostic model was established and validated.Differential gene analysis was performed through immune cell infiltration and weighted gene co expression network.The differentially expressed immune-related genes were intersected with cuproptosis genes to obtain the hub genes related to cuproptosis immunity.In vitro cell experiments were conducted to divide rat hippocampal neurons into a normal group and an ischemic stroke group,and qPCR experiments were performed to verify the results.RESULTS AND CONCLUSION:(1)573 differentially expressed genes were obtained by differential analysis.Differentially expressed genes were mainly enriched in biological processes,such as positive regulation of immune response,and signaling pathways such as lipid and atherosclerosis.(2)Machine learning methods were used to screen diagnostic genes such as MFN2,PKM2,CREG1,and FOXO3A,which may have some diagnostic value for ischemic stroke.(3)Immune infiltration analysis revealed resting plasma cells,NK cells,macrophages,etc.,indicating that immune cells play a certain role in the pathogenesis of ischemic stroke.(4)Weighted gene co-expression network analysis combined with immune infiltration analysis obtained 118 key module genes,which were intersected with cuproptosis genes to obtain 2 cuproptosis and immune characteristic genes.The correlation analysis between four diagnostic genes and Hub genes showed that the expression of FOXO3A and MFN2,PKM2 and BCL2L1,MTF1 and MFN2,ATP7B and BCL2L1 were correlated.(5)The qPCR results showed significant differences in the genes MTF1 and ATP7B between the ischemic stroke group and the blank group.To conclude,ATP7B and MTF1 can serve as characteristic genes for cuproptosis in ischemic stroke.It is possible to improve ischemic stroke by intervening in ATP7B and MTF1 to regulate cuproptosis.