ObjectiveThis study aims to compare the modeling effects of submaxillary gland antigen and salivary gland antigen in the establishment of Sjögren syndrome (SS) mouse models, and to characterize the phenotypic and immunological features of these models in comparison with spontaneous SS-prone non-obese diabetic (NOD)/LtJ mice. MethodsAdult C57BL/6J mice (equal numbers of males and females) were immunized with submaxillary gland antigen or salivary gland antigen, respectively, combined with Freund's adjuvant to induce SS models. Mice immunized with phosphate-buffered saline (PBS) combined with Freund's adjuvant served as the control group. Immunization was induced via multiple subcutaneous injections in the back with antigen combined with Freund's complete adjuvant (FCA) on Days 1 and 7. A booster immunization was administered via multiple subcutaneous injections in the back with antigen combined with Freund's incomplete adjuvant (FIA) on Day 14. Female NOD/LtJ mice were used as the spontaneous SS model group, with ICR mice as the corresponding control strain for comparative analysis. Body weight, water intake, and salivary flow rate of mice were dynamically monitored for 4 weeks. At the end of the experiment, tissue and serum samples were collected, the weights of submaxillary glands, thymus, and spleen were measured, and organ indices (organ-to-body weight ratios) were calculated. Pathological morphological analysis of the submaxillary gland and spleen was performed with hematoxylin and eosin (HE) staining. Serum interleukin-17 (IL-17) level was detected using enzyme-linked immunosorbent assay (ELISA). Real-time quantitative polymerase chain reaction was used to detect the mRNA expression levels of SS type A (SSA) and SS type B (SSB) in submaxillary gland tissues. ResultsFemale mice in the submaxillary gland antigen group exhibited significantly increased water intake (P<0.05) and reduced salivary flow rate (P<0.05) compared with the female control group. No statistically significant differences were observed in the submaxillary gland index, thymus index and spleen index (P>0.05). Focal lymphocytic infiltration was observed in the submaxillary glands, and the splenic marginal zone was enlarged. Serum IL-17 levels were significantly increased (P<0.05). There was no significant difference in submaxillary gland SSA/SSB expression levels (P>0.05). Compared with the female control group, female mice in the salivary gland antigen group showed no statistically significant differences in water intake, salivary flow rate, submaxillary gland index, and spleen index (P>0.05), whereas the thymus index was significantly reduced (P<0.01). Mild inflammatory cell infiltration and glandular atrophy were observed in the submaxillary glands, and the splenic white pulp and marginal zone were slightly enlarged. Serum IL-17 levels and submaxillary gland SSB mRNA expression levels were significantly increased (P<0.01), whereas no significant change was observed in submaxillary gland SSA expression levels (P>0.05). Compared with the male control group, mild submaxillary gland atrophy was observed in male mice in the submaxillary gland antigen group, whereas no obvious changes were found in other modeling-related indicators (P>0.05). Compared with the ICR control group, NOD/LtJ model mice exhibited elevated water intake (P<0.05), significantly reduced salivary flow rate (P<0.01), no significant differences in the submaxillary gland index or spleen index (P>0.05), but a significantly increased thymus index (P<0.05). Marked focal infiltration was observed in the submaxillary glands, the splenic marginal zone was obviously enlarged, and serum IL-17 concentrations as well as submaxillary gland SSA/SSB expression levels were significantly increased (P<0.05). ConclusionSubmaxillary gland antigen and salivary gland antigen can induce SS-related features in female C57BL/6J mice. The SS-related phenotype is more pronounced in the submaxillary gland antigen group than in the salivary gland antigen group, but weaker than that in spontaneously SS-prone female NOD/LtJ mice. Immunization of male C57BL/6J mice with submaxillary or salivary gland antigens fails to induce an obvious SS phenotype.

With the rapid aging of the global population, the demand for prosthodontic rehabilitation of partial edentulism continues to increase. Among the available treatment options, removable partial dentures remain an essential clinical modality for restoring partially edentulous arches and masticatory function because of their broad indications, minimal invasiveness, and cost-effectiveness. However, unlike fixed prosthodontics, which have undergone substantial digitalization, removable partial denture therapy is characterized by dual tooth-tissue support, complex biomechanics, and highly variable anatomical morphology. As a result, the digital design and fabrication of removable partial dentures have long faced major bottlenecks, including limited standardization, steep learning curves, and heavy dependence on expert experience. In recent years, artificial intelligence (AI) has undergone a paradigm shift from early expert systems to machine learning and deep learning, creating new opportunities to address the complexity and precision demands of removable partial denture design. Based on a comprehensive literature review and our team's clinical experience, this article systematically elaborates on the specific applications and normative standards of AI throughout the entire removable partial denture fabrication workflow. In the data acquisition phase, we emphasize multimodal data fusion and digital pressure impression strategies. In the intelligent diagnosis and planning phase, deep learning is utilized to achieve the automatic identification of edentulous areas and undercuts, while machine learning and expert systems assist in abutment selection and treatment plan generation. Regarding automated design logic, the article analyzes the process from the intelligent planning of the common path of insertion to parametric design based on biomechanical optimization. At the clinical operation level, a standardized human-machine collaborative workflow of “AI generation-clinician review-local fine-tuning” is proposed. In terms of ethics and accountability, the principles of algorithmic transparency and “the clinician as the ultimate responsible subject” are emphasized. Furthermore, integrating clinical practice, this article innovatively proposes a “capability grading system for AI-assisted removable partial denture design (comprising the auxiliary analysis level, the rule-based design level, and the intelligent adaptive level)”. The aim is to offer practical recommendations for dental clinicians, dental technicians, and researchers, thereby facilitating the transition of removable partial denture prosthodontics toward greater precision, intelligence, and standardization.

A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

Objective To investigate the mechanism by which Guijianyu ameliorates podocyte injury in a mouse model of diabetic kidney disease(DKD)induced by advanced glycation endproducts(AGEs).Methods Sixty db/db mouse models of DKD were randomized equally into 5 groups for treatment with saline,Guijianyu extract at 3 doses or irbesartan for 12 weeks,and the changes in renal pathology and structure were observed using transmission electron microscopy,and the expressions of related genes and key proteins were detected using RT-qPCR and immunohistochemistry.In cultured MPC-5 cells incubated with 50 mg/L AGEs-BSA for 24 h,the effect of different concentrations of Guijianyu extract on cell viability was examined with CCK-8 assay;Western blotting was performed to detect the protein expressions of RAGE,VEGFA,TNF-α,NF-κB(p65),IL-6 and caspase-3,and the mRNA expressions of RAGE,NF-κB(p65),VEGFA and IL-6 were detected with RT-qPCR.Results In mouse models of DKD,treatment with high-dose Guijianyu extract significantly reduced renal expressions of RAGE,VEGFA,NF-κB(p65),and IL-6 proteins and the mRNA expressions of RAGE,NF-κB,and IL-6.In MPC-5 cells,exposure to AGEs significantly reduced cell viability and increased the protein expressions of RAGE,NF-κB(p65),VEGFA,TNF-α,IL-6 and caspase-3(P<0.05)and mRNA expressions of RAGE,NF-κB(p65),VEGFA,and IL-6.Treatment with Guijianyu extract obviously improved cell viability and reduced the expressions of RAGE,NF-κB(p65),VEGFA,TNF-α,IL-6,and caspase-3.Furthermore,Guijianyu extract effectively reversed RAGE agonist-induced elevation of protein expressions of RAGE,VEGFA,TNF-α,IL-6,and caspase-3 and mRNA expressions of RAGE,NF-κB(p65),IL-6,and VEGFA in MPC-5 cells.Conclusion Guijianyu extract ameliorates AGEs-induced mouse renal podocyte injury in DKD by inhibiting the activation of AGEs-RAGE signaling pathway and reducing the expressions of pro-inflammatory cytokines and vascular endothelial growth factors.

Objective To understand the current situation of reprocessing reusable sterilized items in the central sterile supply departments(CSSD)of hospitals and provide a scientific basis for further standardized management.Methods Using the convenience sampling method,a self-designed questionnaire on the current situation of reprocessing of reusable disinfection items in CSSDs was used to conduct a survey in 31 provinces(autonomous regions and municipalities)from June 11th to 23rd,2024.The questionnaire covered 2 aspects,including the basic situation of the hospital and the reprocessing of disinfection items.Results A total of 1 835 questionnaires were distributed,and 1 603 valid questionnaires were retrieved.Regarding the use of reusable disinfection items,with the most frequently used items being humidification bottles,tourniquets,breathing bags and their accessories,and ventilator tubing systems etc.Among them,1 558 hospitals(97.19％)established standardized disposal procedures,and 996 hospitals(62.13％)assigned dedica-ted personnel to be responsible.In terms of the reprocessing procedures of reusable disinfection items,all 1 603 hospitals carried out drying process for the items;the main packaging method was plastic self-sealing bags(59.95％),and 541 hospitals(33.75％)sterilized the packaging materials.Additionally,935 hospitals(58.33％)used oil-free air compressors;among them,51 hospitals(58.62％)installed activated carbon filters at the end of the compressors.Conclusion The current situation of reprocessing reusable sterilized items in CSSDs of nationwide hospitals needs improvement.It is recommended to strengthen the training of professional disinfection knowledge,unify the sterilization standards for plastic self-sealing bags,develop a storage management system,and adopt a medical air compressor system without oil and water to reduce the risk of hospital infections.

Objective:To construct a Boston matrix-based management model for operational quality of hospital's equipment for diagnosis and treatment,so as to achieve management with comprehensive process for operational quality of them.Methods:Combining evaluation indicators of operational quality of equipment for diagnosis and treatment,the stability and safety were quantified to construct the management model for operational quality of the safety of hospital's equipment for diagnosis and treatment by using the Boston matrix.From January 2023 to January 2024,a total of 300 equipment for diagnosis and treatment in clinical use of JuYe County People's Hospital were enrolled.Using a random number table method,150 equipment adopted conventional management mode,while the other 150 equipment adopted the Boston matrix-based management model for operational quality of hospital's equipment for diagnosis and treatment(Boston matrix management mode)to conduct management.The standardization of test for equipment performance,management quality of operation,and multi-dimensional efficacy scores were compared between the two management modes.A self-designed questionnaire was adopted to investigate satisfactions of engineers,doctors,nurses,and patients for two kinds of management modes,who involved in using equipment.Results:In 150 tests for performance,which adopted Boston matrix-based management mode,the standardization degrees of environmental management,parameter settings,equipment operation and record feedback were respectively 94%,93.33%,92.67%and 92.67%,all of them were significantly higher than those of conventional management mode(x2=12.997,14.361,13.830,12.891,P<0.05).The scores of resource allocation,technical support,information infrastructure,and management performance for equipment were(92.36±3.57),(91.69±4.36),(92.54±3.69)and(93.54±4.36)points in Boston matrix management mode,respectively,and all of them were significantly higher than those in conventional management mode,and the differences were significant(t=18.673,12.439,12.377,12.509,P<0.05).The scores of utilization efficacy,maintenance efficacy,and repair efficacy in using equipment under Boston matrix management mode were all significantly higher than those in conventional management mode,and the differences were significant(t=13.149,12.344,12.864,P<0.05).Satisfaction scores from engineers for equipment,doctors,nurses,and patients for operation of equipment for diagnosis and treatment,which adopted Boston matrix management mode,were all higher than those of adopting conventional management mode,and the differences were significant(t=13.1504,16.064,11.139,13.605,P<0.05).Conclusion:The Boston matrix-based management model for operational quality of hospital's equipment for diagnosis and treatment can be used in the quantitative analysis of safety and stability of equipment,and realize safe management with comprehensive process for equipment,and enhance medical service quality,and improve utilization efficacy of equipment.

X-ray is usually used to determine anatomy and localization during conventional permanent pacemaker implantation.But X-ray exposure might induce radiation injury to fetus.In this paper,we reported 2 cases successful double cavity permanent pacemaker implantation in pregnant women woman at 13 weeks of gestation under the guidance of transthoracic echocardiography.The postoperative pacing parameters were good,the whole process was finished with zero radiation,and there were complications during pregnancy.Both cases resulted in full-term pregnancies and the natural delivery of healthy newborns.Pacemaker electrodes were in normal position as confirmed by post-delivery X-ray examinations.

"Technology-consumption separation"in pricing of medical testing programs,refers to the medical testing programs in the pricing process will be the cost of technology and consumables cost accounting separately,this is the basis for large-scale centralized procurement of test reagents,for the standardization of medical testing programs to carry out,reduce the cost of medical testing programs is critical.The medical testing programs pricing"technology and consumption separation"of the connotation is deeply analyzed.It is considered that the main difficulties in the implementation of the"technology-consumption separation"in the pricing of medical testing programs are the difficulty of costing,the difficulty of calculating the use of special reagents,and the difficulty of policy implementation.The implementation of the"technology-consumption separation"in the pricing of medical testing programs are proposed from"pricing according to the current price minus the cost of specialized reagents","based on big data to measure the loss of specialized reagents"and"appropriate increase in the pricing level".