Objective To investigate the correlation between gut microbiota characteristics and immunotherapy efficacy in patients with advanced microsatellite stable (MSS) advanced gastrointestinal tumors. Methods Thirty patients with advanced MSS gastrointestinal tumors, including fourteen cases of gastric cancer and sixteen cases of colorectal cancer, who were admitted from November 1, 2022, to July 31, 2025 were enrolled. All patients underwent gut microbiota testing and received corresponding interventions on the basis of the results, followed by treatment with antiangiogenic agents combined with immunotherapy. Some patients additionally received probiotics or fecal microbiota transplantation. The primary endpoints were disease control rate (DCR) and objective response rate (ORR). The secondary endpoints were progression-free survival (PFS) and overall survival (OS). Univariate and multivariate Cox regression analyses were used to assess potential prognostic factors. Results Among the thirty patients, no complete response was observed. Two cases of partial response (PR), twelve cases of stable disease, and sixteen cases of progressive disease were recorded. The ORR was 6.7%, and the DCR was 46.7%. The median PFS was 6.4 months, and the median OS was 8.3 months. Both PR patients were classified as having a medium risk of gut microbiota dysbiosis and exhibited a Bacteroides-dominated enterotype. Multivariate analysis indicated that tumor type, liver metastasis, and bone metastasis were potential prognostic factors for PFS and OS. Conclusion Patients with a medium risk of gut dysbiosis may exhibit a relatively favorable response to immunotherapy, suggesting that gut microbiota status could serve as a potential biomarker for predicting treatment efficacy in advanced MSS gastrointestinal tumors.

ObjectiveTo investigate the effects of paeoniflorin （PF） on neuroinflammation and cognitive impairment in the rat model of Alzheimer's disease （AD） and to clarify whether its mechanism is associated with regulation of the Janus kinase 2 （JAK2）/signal transducer and activator of transcription 3 （STAT3） signaling pathway. MethodsGlycosides related to AD-associated neuroinflammation were screened through bibliometric analysis， and the potential targets of PF were predicted through network pharmacology and molecular docking. A rat model of AD was established by intraperitoneal injection of D-galactose （100 mg·kg^-1·d^-1）. Rats were assigned into a control group， a model group， a PF （200 mg·kg^-1） group， and a donepezil （0.5 mg·kg^-1·d^-1） group， and drugs were administered by gavage for 21 days. The learning and memory abilities were evaluated by the Morris water maze （MWM）. Hematoxylin-eosin （HE） staining and Nissl staining were performed to observe hippocampal neuronal morphology. The mRNA levels of inflammatory factors were determined by Real-time quantitative polymerase chain reaction （Real-time PCR）， and the protein levels of molecules in the JAK2/STAT3 signaling pathway were determined by Western blot. ResultsCompared with the control group， the model group showed prolonged escape latency and decreased platform crossings and time spent in the target quadrant （P<0.05， P<0.01）， accompanied by increased expression of interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， as well as elevated ratios of phosphorylated （p）-JAK2/JAK2 and p-STAT3/STAT3 （P<0.01）. Compared with the model group， the PF group and donepezil group exhibited shortened escape latency and increased platform crossings and time spent in the target quadrant （P<0.05， P<0.01）， decreased expression levels of IL-1β and TNF-α， upregulated mRNA levels of interleukin-4 （IL-4） and interleukin-10 （IL-10）， and reduced ratios of p-JAK2/JAK2 and p-STAT3/STAT3 （P<0.05， P<0.01）. ConclusionPaeoniflorin can alleviate cognitive impairment and neuroinflammation in the rat model of AD by inhibiting the JAK2/STAT3 signaling pathway.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

AIM:To investigate the antiviral effica-cy and mechanism of Qingre Xiaoyanning(QRXYN)in vivo,and provide experimental basis for their prevention and treatment of influenza A virus.METHODS:We constructed a mouse model infect-ed with influenza A H3N2 virus.To evaluate the therapeutic effect of QRXYN on influenza A virus,we measured the body weight changes,pathologi-cal changes in lung tissue,hemagglutination titer,and viral load in mouse.To evaluate the possible mechanism of QRXYN's anti influenza A virus infec-tion,we used the ELISA to measure the levels of TNF-α,IL-1β,IL-4,IFN-γ,and vascular cell adhesion molecule-1(VCAM-1)in mouse bronchoalveolar Ia-vage fluid;used flow cytometry to assess the pro-portions of macrophages(F4/80),helper T lympho-cytes(CD4+T lymphocytes),and natural killer(NK)cells in lung tissue;and used Western blotting to detect the expression of Toll-like receptor 4(TLR4),myeloid differentiation factor 88(MYD88),inhibitor of kappa B kinase-β(IKK-β),NF-kappa-B inhibitor al-pha(IκBα),and phospho-IKB alpha(p-IκBα)in lung tissue.RESULTS:Compared to the model group,both Oseltamivir and QRXYN can alleviate the se-verity of lung tissue lesions in mice,decrease the blood coagulation titer and viral load of mouse lung tissue(P＜0.01),lower the levels of TNF-α,IL-4,and VCAM-1 in bronchoalveolar lavage fluid(P＜0.05,P＜0.01),reduce the proportion of macro-phages(P＜0.05,P＜0.01),and increase the propor-tion of CD4+T lymphocytes and NK cells(P＜0.05,P＜0.01).Additionally,oseltamivir can reduce the ex-pression of MYD88 protein in mouse lungs(P＜0.05),while QRXYN can decrease the expression of IKK-β and P-IκBα proteins in mouse lungs(P＜0.05).CONCLUSION:QRXYN have good in vivo antiviral ef-fects against the influenza A virus,and their mecha-nism may be related to the regulation of the immu-nologic function and NF-κB signal pathway.

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

Objective:To investigate the influencing factors of the quality of inpatient medical record home page information in public hospitals under the background of the reform of diagnosis related groups(DRG)-based payment system and their mechanisms of action, so as to provide references for continuous improvement of the quality of medical record home page information.Methods:From July to August 2024, semi-structured interviews were conducted with clinical physicians and personnel engaged in the filling, quality control, review, and management of inpatient medical record home pages in public hospitals in Hangzhou, using purposeful sampling. The interview data were qualitatively analyzed based on the grounded theory to identify the influencing factors of the quality of medical record home page information and construct a theoretical model of the mechanisms of action of these factors.Results:Through open coding of the 17 interview materials, 53 initial concepts and 13 categories were identified. Further induction through axial coding led to the extraction of four main categories: external environment, organizational resources, person-job fit, and work ecology. The theoretical model of the mechanisms of action of the influencing factors on the quality of medical record home page information was as follows: external environmental factors such as policies and regulations played a macroscopic guiding role in the quality of medical record home pages; organizational resource factors such as the hospital′s information construction played a mesoscopic regulating role in the quality of medical record home pages; person-job fit factors such as the professional skills of clinical physicians and medical record-related staff played a direct driving role in the quality of medical record home pages; and work ecology factors such as the workload of clinical physicians and medical record-related staff played an external modulating role in the quality of medical record home pages.Conclusions:Under the background of DRG reform, the quality of inpatient medical record home page information was influenced by multiple factors. To systematically improve the quality of medical record home page information, it is necessary for multiple parties to work together. Relevant government departments should improve the top-level design and optimize the external environment, guide and assist medical institutions in building quality control systems, and strengthen resource allocation. Medical institutions should focus on enhancing the job competence and person-job fit of relevant personnel and also use intelligent means to optimize work processes to reduce workload.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

ObjectiveTo explore the main mechanism of self-precipitation formed during the decoction of Sinitang（SNT）， and to provide a research basis for exploring the differences in the toxic and effective components of this compound. MethodsThe average precipitation yields of SNT， Glycyrrhizae Radix et Rhizoma（GRR）-Aconiti Lateralis Radix Praeparata（ALRP） decoction（GF）， ALRP-Zingiberis Rhizoma（ZR） decoction（FJ）， GRR-ZR decoction（GJD）， ALRP decoction（FZ）， ZR decoction（GJ） and GRR decoction（GC） were determined. The four main self-precipitation samples of SNT， GF， FZ and GC were physically characterized by particle size， scanning electron microscopy（SEM）， pH， total dissolved solids（TDS）， conductivity， and Fourier transform infrared spectroscopy（FT-IR） analysis. The chemical compositions of SNT decoction and its different phases was identified by ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive Orbitrap-MS） for SNT， SNT self-precipitation and SNT supernatant， and the contents of its main toxic and effective components were determined by high performance liquid chromatography（HPLC）. ResultsPrecipitation yield results of the 7 samples of SNT decoction and single decoction showed that SNT had the highest self-precipitation yield. The formation of SNT self-precipitation was mainly related to the reaction between ALRP and GRR components to form complexes， and FT-IR showed that GRR had the greatest influence on the formation of self-precipitation. A total of 110 components were identified in the SNT decoction， including 100 components in the SNT self-precipitation and 106 components in the SNT supernatant. And quantitative results of the main toxic and effective components revealed that the reaction between ALRP and GRR components formed complexes， resulting in the following content hierarchy for free components：SNT decoctionsupernatantself-precipitation， these components included free liquiritin， benzoylmesaconine， benzoylaconitine， benzoylhypacoitine， liquiritigenin， aconitine， hypoaconitine， isoliquiritigenin and ammonium glycyrrhizinate. ConclusionSNT exhibits spontaneous precipitation during compound decoction， with GRR exerting the greatest influence on its formation. This suggests GRR plays a significant role in the detoxification of SNT. The differences in the self-precipitated toxic-effective components of SNT compound decoction primarily manifest as changes in component content， reflecting the characteristics of SNT "deposition in vitro and sustained release in vivo" and the importance of "administered at draught" in the clinical application of SNT.