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.

[Objective] To explore the predictive value of HALP score for prognosis in patients with multiple myeloma (MM). [Methods] A retrospective analysis was conducted on laboratory indicators and related clinical data of newly diagnosed multiple myeloma (NDMM) patients, treated at the Affiliated Hospital of North Sichuan Medical College from January 2016 to October 2023, prior to their first treatment. The HALP score was calculated, and the optimal cutoff value for HALP was determined using X-tile software. Survival analysis was performed using Kaplan-Meier curves for high HALP and low HALP groups. Univariate and multivariate analyses were conducted using the Cox regression model, and a forest plot was generated using Graphpad Prism to illustrate factors that may impact patient prognosis. The predictive ability of HALP score combined with β2-microglobulin and ECOG score for prognosis in MM patients was evaluated using receiver operating characteristic curve (ROC) analysis. [Results] A total of 203 MM patients were included, with the optimal cutoff value for HALP score being 29.15 (P<0.05). Among them, 101 patients were in the low HALP score group, and 102 patients were in the high HALP score group. The results of univariate and multivariate analysis using the Cox regression model showed that a HALP score <29.15 was an independent risk factor for progression-free survival (PFS) and overall survival (OS) (P<0.05). ROC curve analysis indicated that the combination of HALP score with β2-microglobulin and ECOG score had a higher predictive value for prognosis in MM patients compared to using HALP score alone. [Conclusion] The HALP score is closely related to the prognosis of patients with NDMM. A low HALP score indicates a poorer prognosis, while the combination of HALP score with β2-microglobulin and ECOG score provides a higher predictive value when assessed together.

BACKGROUND: Asthma is a common chronic inflammatory airway disease and intermittent hypoxia is increasingly recognized as a factor that may impact disease progression. The present study investigated whether intermittent hypoxia (IH) could aggravate asthma by promoting hypoxia-inducible factor-1α (HIF-1α)/nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing protein 3 (NLRP3)/interleukin (IL)-1β-dependent pyroptosis and the inflammatory response and further elucidated the underlying molecular mechanisms involved. METHODS: A total of 49 patients diagnosed with severe bronchial asthma and diagnosed by polysomnography were enrolled at Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, between January 2022 and December 2022, and their general data and induced sputum were collected. BEAS-2B cells were treated with IL-13 and subjected to IH. An ovalbumin (OVA)-treated mouse model was also used to assess the effects of chronic intermittent hypoxia (CIH) on asthma. Pyroptosis, the inflammatory response, and related signalling pathways were assessed in vivo and in vitro . RESULTS: In this study, as the apnoea and hypopnea index (AHI) increased, the proportion of patients with uncontrolled asthma increased. The proportions of neutrophils and the levels of IL-6, IL-8, HIF-1α and NLRP3 in induced sputum were related to the AHI. NLRP3-mediated pyroptosis, which could be mediated by the HIF-1α signalling pathway, was activated in IL-13 plus IH-treated BEAS-2B cells and in the lungs of OVA/CIH mice. HIF-1α downregulation significantly reduced lung pyroptosis and ameliorated neutrophil inflammation by modulating the NLRP3/IL-1β pathway both in vitro and in vivo . Similarly, pretreatment with LW6, an inhibitor of HIF-1α, effectively blocked the generation of inflammatory cytokines in neutrophils. In addition, administration of the NLRP3 activator nigericin obviously increased lung neutrophil inflammation. CONCLUSIONS Obstructive sleep apnoea-hypopnea syndrome (OSAHS) is a risk factor for asthma exacerbation. IH aggravates neutrophil inflammation in asthma via NLRP3/IL-1β-dependent pyroptosis mediated by the HIF-1α signalling pathway, which should be considered a potential therapeutic target for the treatment of asthma with OSAHS.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Humans ; Asthma/metabolism* ; Animals ; Pyroptosis/physiology* ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Mice ; Signal Transduction/physiology* ; Male ; Hypoxia/metabolism* ; Female ; Interleukin-1beta/metabolism* ; Adult ; Inflammation/metabolism* ; Middle Aged ; Mice, Inbred C57BL

Background A diverse cohort of patients and susceptible individuals congregate in healthcare facilities, where exposure to pathogenic microorganisms associated with respiratory infectious diseases constitutes a significant risk factor for cross-infection. Central air-conditioning ventilation systems improve some indoor environment indicators while exacerbating the risk of transmission of respiratory infectious diseases. Objective To investigate the distribution characteristics of microbial communities in the central air-conditioning ventilation systems of hospitals, providing a scientific basis for the selection of microbial indicators in hygiene standards for hospital central air-conditioning ventilation systems and for hospital risk early warning systems. Methods In October 2023, two central air-conditioning ventilation systems were selected from a Grade 3A hospital in Shanghai: one was an all-air air-conditioning system serving the waiting area on the ground floor, and the other was a fan coil plus fresh air system serving the outpatient area on the third floor. Samples from four different components of the ventilation systems—air outlets, filters, surface coolers, and condensate trays—were collected for high-throughput sequencing of the 16S rRNA gene to analyze bacterial communities. Alpha-diversity and beta-diversity analyses were performed to investigate the microbial community composition and diversity characteristics of the hospital central air-conditioning ventilation systems. Functional analysis was conducted to determine the relative abundance of bacterial functions in these systems.Results A total of 528 operational taxonomic units (OTUs) were identified, encompassing 20 bacterial phyla, 37 classes, 79 orders, 123 families, and 240 genera. The analysis revealed that the bacterial community was predominantly composed of Proteobacteria, Gemmatimonadates, Bacteroidetes, and Actinobacteria. The diversity analysis indicated that bacterial community richness and diversity were highest in the condensate trays, while no statistically significant differences (P > 0.05) were observed in the bacterial community composition among the air outlets, filters, and surface coolers. The functional analysis showed that the bacterial communities in the central air-conditioning ventilation systems primarily exhibited chemoheterotrophic, oxidative energy-dependent heterotrophic, and ureolytic functional characteristics. Conclusion The dominance of Proteobacteria suggests that this phylum exhibits strong adaptability in the central air-conditioning ventilation systems, possibly related to its ability to survive and reproduce under varying environmental conditions. The diversity analysis indicates that the condensate tray is a critical area for bacterial proliferation in the central air-conditioning ventilation systems. The similarity in environmental conditions among the air outlets, filters, and surface coolers result in similar bacterial community structures. The functional analysis reveals that the bacterial communities possess robust energy conversion and metabolic capabilities, potentially contributing to processes such as organic matter decomposition and nitrogen cycling within the central air-conditioning ventilation systems.

Objective: To explore the potential application of PAK4-PROTAC targeted degradation drug (PpD) in renal cancer immunotherapy. Methods: TIMER 2.0 and TISIDB databases were used to analyze the relationship among PAK4 expression, tumor purity and abundance of immune cell infiltration in renal tumor microenvironment (TEM).Renal cancer cell lines OS-RC-2, 786-O and ACHN were treated with 0, 125 and 250 nmol/L PpD, and the effects of Jurkat cell co-culture on the results were investigated.The cell apoptosis was detected with flow cytometry, and the expression of programmed cell death 1 ligand 1 (PD-L1) in renal cancer cells was detected with immunoblotting. Results: The high expression of PAK4 was positively related to immune purity, and inhibited the abundance of immune killer cells in TEM, such as CD8 T cells, CD4 T cells, natural killer cells and dendritic cells.With 250 nmol/L PpD treatment, there were 21.02% apoptotic cells in OS-RC-2, 29.67% apoptotic cells in 786-O, and 15.39% apoptotic cells in ACHN, respectively.However, with the same concentration of 250 nmol/L PpD treatment, cell apoptotic rate was sharply increased to 70.13% in OS-RC-2/Jurkat, 70.68% in 780-O/Jurkat, and 60.27% in ACHN/Jurkat co-culture models, respectively. Conclusion: PpD can promote apoptosis of renal cancer cells by reducing the expression of PAK4 protein, and enhance the killing effects of immune cells on tumor cells.

OBJECTIVES: To investigate the role of ferroptosis in diquat-induced acute kidney injury (AKI) and its molecular mechanisms. METHODS: Transgenic zebrafish models with Tg (Eco.Tshb:EGFP) labeling of the renal tubules and Tg (lyz:dsRed2) labeling of the neutrophils were both divided into control group, gentamicin (positive control) group, diquat poisoning group, ferroptosis inhibitor group. The indicators of kidney injury, inflammatory response, and ferroptosis were examined in the zebrafish, and the changes in expressions of voltage-dependent anion-selective channel protein 1 (VDAC1) and mitochondrial ferritin (FTMT) were detected using Western blotting. RESULTS: AKI induced by diquat exhibited a significant dose-effect relationship, and the severity of injury was proportional to the exposure concentration. Diquat also caused marked oxidative stress and inflammatory responses in the zebrafish models. Rhodamine metabolism assay and HE staining revealed significantly declined glomerular filtration function of the zebrafish as diquat exposure concentration increased. Immunofluorescence staining highlighted significant changes in the expressions of ferroptosis markers GPX4 and FTH1 in zebrafish renal tissues following diquat exposure. In diquat-exposed zebrafish, treatment with ferrostatin-1, a ferroptosis inhibitor, obviously upregulated GPX4 and downregulated FTH1 expressions and improved the metabolic rate of glucan labeled with rhodamine B. Diquat exposure significantly upregulated the expression of VDAC1 and FTMT in zebrafish, and the application of ferrostatin-1 and VBIT-12 (a VDAC1 inhibitor) both caused pronounced downregulation of FTMT expression. CONCLUSIONS Ferroptosis is a critical mechanism underlying diquat-induced AKI, in which VDAC1 and FTMT play important regulatory roles, suggesting their potential as therapeutic target for AKI caused by diquat.
Animals ; Zebrafish ; Ferroptosis/drug effects* ; Acute Kidney Injury/chemically induced* ; Diquat/toxicity* ; Animals, Genetically Modified ; Voltage-Dependent Anion Channel 1/metabolism* ; Ferritins/metabolism* ; Oxidative Stress

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*