OBJECTIVE To investigate the current status of informatization tools applied to antimicrobial stewardship in county hospitals in China， and to provide a reference for promoting the informatization development of antimicrobial stewardship in county hospitals.METHODS From August to September 2025， an online questionnaire survey was conducted among 1 007 county hospitals across 31 provinces（autonomous regions， and municipalities） in China. The survey focused on the application of four types of informatization tools：the real-time monitoring system for antimicrobial use， the pre-prescription review system， the irrational use warning system， and the electronic prescription evaluation system（hereinafter referred to as the “monitoring system”“review system”“warning system”“evaluation system”，respectively）. Correlation analysis and binary Logistic regression analysis were employed to explore the influencing factors.RESULTS Of the county hospitals， 80.83% were equipped with at least one type of informatization tool for antimicrobial stewardship， while 19.17% had no relevant tool. Only 18.87% of the hospitals were equipped with all four types of tools. The monitoring system exhibited the highest equipped rate （70.80%）， whereas the warning system was equipped in only 33.96% of the hospitals. Four types of tools are evenly and adequately deployed in the eastern region， the central and western regions have far lower deployment rates of intervention tools including review system and warning system， accompanied by prominent intra-provincial gaps. The total number of the health technicians， hospital grade， and prescription review rate of outpatient antibacterial drug were significantly and positively correlated with the deployment rates of the four informatization tools （ P ＜0.05）. The results of the Logistic regression model fitting indicated that the model of the review system was rated “excellent” [area under the receiver operating characteristic curve （AUC）＝0.81]， and the model of the warning system was rated “further improvement” （AUC＝0.67）. The total number of health technicians was significantly positively correlated with the deployment rates of all four types of informatization tools （all P ＜0.05）.CONCLUSIONS The informatization of antimicrobial stewardship in county hospitals in China remains in the initial stage of transitioning from “passive statistics” to “active prevention and control”， with challenges such as structural imbalance and regional disparities. In the future， differentiated support strategies should be implemented， the allocation of resources should be optimized， to enhance the level of refined antimicrobial stewardship.

Objective To understand the situation of dental cone beam computed tomography (CBCT) quality control testing phantoms in radiation health technical service institutions in Guangdong province, analyze the differences among different phantoms, and provide a reference for dental CBCT quality control testing. Methods The testing phantoms of 49 radiation health technical service institutions were used as the research objects. The designated CBCT equipment was used for scanning and imaging. The Z-score method was used to evaluate the high-contrast resolution, low-contrast resolution, and distance measurement deviation of each phantom. Results The satisfaction rates of various items for the phantoms in 49 institutions ranged from 85.7% to 100%. The distance measurement deviations of four institutions were “suspicious”, and the high-contrast resolution of four institutions and the distance measurement deviation of one institution were “unsatisfactory”. Conclusion The overall performance of dental CBCT quality control testing phantoms in radiological health technical service institutions in Guangdong province is satisfactory. However, there are still some phantoms with poor results in items such as distance measurement deviation and high-contrast resolution. The structural design, material selection, and manufacturing process of the phantom may all affect the results of quality control testing. Therefore, appropriate phantoms, optimized exposure conditions, and suitable reconstruction algorithms should be used in CBCT quality control testing to ensure accurate and reliable measurements.

ObjectiveTo investigate the efficacy of ovarian tissue cryopreservation and autologous transplantation in preserving fertility and ovarian endocrine function in patients with cervical cancer. MethodsA 26-year-old patient with stage ⅡA1 cervical cancer underwent ovarian tissue harvesting and cryopreservation during cancer surgery. Following complete remission of the cancer， autologous ovarian tissue transplantation was performed. Follow-up monitoring included assessment of menopausal symptoms， hormone levels， and follicular development. ResultsSix months after transplantation， follicle-stimulating hormone levels decreased to 6.60 U/L， and estradiol levels increased from ＜10.00 ng/L to 89.00 ng/L. At 10 months after transplantation， ultrasound monitoring confirmed follicular development and physiological ovulation in the transplanted ovarian tissue. By 15 months after transplantation， follicle-stimulating hormone levels remained stable at 7.24 U/L， and estradiol levels further increased to 368.00 ng/L. Over 2 years after transplantation， the patient successfully gave birth to a healthy baby through assisted reproductive technology. ConclusionThe restoration of endocrine and ovulation functions in the transplanted cryopreserved ovarian tissue， followed by successful pregnancy， demonstrates the clinical success of ovarian tissue transplantation.

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

OBJECTIVES: Osteoarthritis (OA) is one of the most common chronic degenerative diseases, with chondrocyte apoptosis and extracellular matrix (ECM) degradation as the major pathological changes. The mechanical stimulation can attenuate chondrocyte apoptosis and promote ECM synthesis, but the underlying molecular mechanisms remain unclear. This study aims to investigate the role of primary cilia (PC) in mediating the effects of mechanical stimulation on OA progression. METHODS: In vivo, conditional knockout mice lacking intraflagellar transport 88 (IFT88^flox/flox IFT88 knockout; i.e., primary cilia-deficient mice) were generated, with wild-type mice as controls. OA models were established via anterior cruciate ligament transection combined with destabilization of the medial meniscus, followed by treadmill exercise intervention. OA progression was evaluated by hematoxylin-eosin staining, safranin O-fast green staining, and immunohistochemistry; apoptosis was assessed by TUNEL staining; and limb function by rotarod testing. In vitro, primary articular chondrocytes were isolated from mice and transfected with lentiviral vectors to suppress IFT88 expression, thereby constructing a primary cilia-deficient cell model. Interleukin-1β (IL-1β) was used to induce an inflammatory environment, while cyclic tensile strain (CTS) was applied via a cell stretcher to mimic mechanical loading on chondrocytes. Immunofluorescence and Western blotting were used to detect the protein expression levels of type II collagen α1 chain (COL2A1), primary cilia, IFT88, and caspase-12; reverse transcription polymerase chain reaction was performed to assess COL2A1 mRNA levels; and flow cytometry was used to evaluate apoptosis. RESULTS: In vivo, treadmill exercise significantly reduced Osteoarthritis Research Society International (OARSI) scores and apoptotic cell rates, and improved balance ability in wild-type OA mice, whereas IFT88-deficient OA mice showed no significant improvement. In vitro, CTS inhibited IL-1β-induced ECM degradation and apoptosis in primary chondrocytes; however, this protective effect was abolished in cells with suppressed primary cilia expression. CONCLUSIONS Mechanical stimulation delays OA progression by mediating signal transduction through primary cilia, thereby inhibiting cartilage degeneration and chondrocyte apoptosis.
Animals ; Chondrocytes/cytology* ; Apoptosis/physiology* ; Mice ; Cilia/metabolism* ; Osteoarthritis/pathology* ; Extracellular Matrix/metabolism* ; Mice, Knockout ; Disease Progression ; Interleukin-1beta ; Male ; Cells, Cultured

OBJECTIVES: Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability. METHODS: This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI). RESULTS: Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58). CONCLUSIONS Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
Humans ; Sepsis/complications* ; Machine Learning ; Nomograms ; Venous Thrombosis/complications* ; Retrospective Studies ; Hospital Mortality ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Prognosis ; Bayes Theorem

OBJECTIVES: To explore the potential of pyrroline-5-carboxylate reductase 1 (PYCR1) as a pan-cancer biomarker and investigate its expression, function, and clinical significance in bladder cancer (BLCA). METHODS: Bioinformatics analysis was conducted to evaluate the associations of PYCR1 with prognosis, immune microenvironment remodeling, tumor mutation burden (TMB), and microsatellite instability (MSI) in cancer patients. Using the TCGA-BLCA dataset, univariate and multivariate regression analyses were performed to assess the potential of PYCR1 as an independent prognostic risk factor for BLCA, and a clinical decision model was constructed. The IMvigor210 cohort was utilized to evaluate the potential of PYCR1 for independently predicting the efficacy of immunotherapy. The pRRophetic was employed to screen candidate chemotherapeutic agents for treating BLCA with high PYCR1 expression. The CMap-XSum algorithm and molecular docking techniques were used to explore and validate small molecule inhibitors of PYCR1. RESULTS: A high expression of PYCR1 was significantly associated with poor prognosis, immune cell infiltration, TMB and MSI in various tumors (r>0.3). PYCR1 was overexpressed in BLCA, and high PYCR1 expression was closely related to poor prognosis in BLCA patients (HR: 1.14, 95% CI: 1.02-1.68, P=0.006). The IC₅₀ of the anti-cancer drugs cetuximab, 5-fluorouracil, and doxorubicin increased significantly in BLCA cell lines with high PYCR1 expressions (P<0.0001). CONCLUSIONS High PYCR1 expression is an independent risk factor for poor prognosis in BLCA patients and can serve as a significant indicator for clinical decision-making as well as a marker for predicting sensitivity to chemotherapeutic agents and the efficacy of immunotherapy.
Humans ; Urinary Bladder Neoplasms/genetics* ; Immunotherapy ; Prognosis ; Pyrroline Carboxylate Reductases/metabolism* ; Biomarkers, Tumor/genetics* ; delta-1-Pyrroline-5-Carboxylate Reductase ; Microsatellite Instability ; Tumor Microenvironment ; Mutation ; Computational Biology ; Molecular Docking Simulation

Radiochemotherapy-induced oral mucositis (OM) is a common oral complication in patients with tumors following head and neck radiotherapy or chemotherapy. Erosion and ulcers are the main features of OM that seriously affect the quality of life of patients and even the progress of tumor treatment. To date, differences in clinical prevention and treatment plans for OM have been noted among doctors of various specialties, which has increased the uncertainty of treatment effects. On the basis of current research evidence, this expert consensus outlines risk factors, clinical manifestations, clinical grading, ancillary examinations, diagnostic basis, prevention and treatment strategies and efficacy indicators for OM. In addition to strategies such as basic oral care, anti-inflammatory and analgesic agents, anti-infective agents, pro-healing agents, and photobiotherapy recommended in previous guidelines, we also emphasize the role of traditional Chinese medicine in OM prevention and treatment. This expert consensus aims to provide references and guidance for dental physicians and oncologists in formulating strategies for OM prevention, diagnosis, and treatment, standardizing clinical practice, reducing OM occurrence, promoting healing, and improving the quality of life of patients.
Humans ; Chemoradiotherapy/adverse effects* ; Consensus ; Risk Factors ; Stomatitis/etiology*

Objective:To evaluate whether the reconstructed image on the basis of deep learning(DL)can improve the success rate and display quality of automatic segmentation of computed tomography(CT)with ultralow dose for chest of children on airway.Methods:The clinical data of 41 consecutive cases who adopted ultralow dose CT to underwent reexamination on chest at Beijing Children's Hospital,Capital Medical University from February 2020 to September 2020 were selected,whose average age was(4.43±1.61 years).The scan protocol of ultralow dose CT was(0.05 mGy).The reconstructed images included 6 groups,which were respectively filtered reflection projection(FBP)image with 0.625 mm thickness,50%adaptive iterative recombination(ASIR-V)images,100%ASIR-V images,low energy DL(DL-L),medium energy DL(DL-M),and high energy DL(DL-H).The automatically segmentation software was used to conduct automatically segmentation for airway,and the success rate of automatic segmentation was recorded.For images that were successful segmented,a 5-point scale was adopted to subjectively evaluate the displayed quality for airway(5 point is the best).In addition,the CT values and noise values of the images of 6 groups for airway were objectively measured.Results:The success rate of automatic segmentation of DL-H image was the highest(60.98%),and that of the 100%ASIR-V was the lowest(39.02%).The subjective score of DL-H image of the automatic segmentation was the highest(4.06±0.55)point,and that of 100%ASIR-V was the lowest(2.44±0.76)point.DL-H can display more fine and small airways.The noise values of objective measurement showed that both of DL-H and 100%ASIR-V had the lowest noise value,and there was no statistical difference in that between them.Conclusion:The use of high energy deep learning iterative reconstruction(DLIR)algorithm can improve the success rate and display effect of automatic segmentation of ultralow dose CT for chest of children on airway,and DLIR is contribute to improve the accuracy of automatic segmentation algorithm of artificial intelligence.