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.

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*

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

OBJECTIVES: This study aimed to explore the clinical efficacy of severe early childhood caries (SECC) treatment combined with local anesthesia under general anesthesia. METHODS: A total of 108 children under 6 years old who underwent SECC dental treatment under general anesthesia at the Department of Pediatric Dentistry, Third Affiliated Hospital of Air Force Medical University from March to December 2023 were selected as the study subjects, with American Society of Anesthesiologists (ASA) classification of classⅠor Ⅱ. The study subjects were divided into a control group (n=54) and an experimental group (n=54) by retrieving intraoperative cases and postoperative follow-up records. The control group was given general anesthesia through inhalation combined with nasotracheal intubation, whereas the experimental group was given local anesthesia with 2% lidocaine on each treated tooth on the basis of general anesthesia. The basic information, preoperative anesthesia depth, hemodynamic changes during different surgical procedures, postoperative pain, and adverse reactions in the two groups were recorded and analyzed. RESULTS: No statistically significant difference was found in the basic information and preoperative anesthesia depth between the two groups (P>0.05). Among the three procedures (pulpotomy, root canal treatment, and tooth extraction), the three observed indicators in the experimental group were significantly lower than those in the control group (P<0.05). The proportion of patients in the experimental group who needed to take analgesic measures in accordance with the modified facial pain scale (FPS-R) score was significantly lower than that in the control group at postoperative wakefulness and 2 h after surgery (P<0.05). Meanwhile, no statistically significant difference was observed between the groups at 24 h after surgery (P>0.05). The proportion of patients in the experimental group who needed to take analgesic measures on the basis of the parent posto-perative pain measurement (PPPM) score was significantly lower than that in the control group when they were awake after surgery (P<0.05). No statistically significant difference was found between the groups at 2 and 24 h after surgery (P>0.05). Moreover, no statistically significant difference was observed in the incidence of adverse reactions between the two groups at 24 h after surgery (P>0.05). CONCLUSIONS The combination of local anesthesia during SECC dental treatment under general anesthesia results in minimal changes in intraoperative hemodynamics and mild postoperative pain response, hence worthy of clinical promotion.
Humans ; Anesthesia, General ; Child, Preschool ; Dental Caries/therapy* ; Pain, Postoperative/prevention & control* ; Anesthesia, Local/methods* ; Male ; Hemodynamics ; Female ; Lidocaine/administration & dosage* ; Child ; Anesthetics, Local/administration & dosage* ; Anesthesia, Dental/methods*

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.

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.