ObjectiveTo identify the pathogen species responsible for the wilt disease of Tetradium ruticarpum in Chongqing， investigate there biological characteristics， and screen effective fungicides， so as to provide a theoretical basis for disease control in production. MethodsThe pathogen was isolated via the tissue culture method. Pathogenicity was verified according to Koch's postulates. The pathogen was identified based on morphological characteristics and multi-gene phylogenetic analysis. The mycelial growth rate method was used for biological characterization of the pathogen and fungicide screening. ResultsThe pathogen colonies were nearly circular with irregular edges， white， short， velvety aerial hyphae， and pale purple undersides. Macroconidia were colorless， sickle-shaped， with 3-5 septa， while microconidia were transparent， elliptical， aseptate or with 1-2 septa. Multi-gene phylogenetic analysis showed that the pathogen clustered in the same clade as Fusarium fujikuroi with 100% support， which， combined with morphological characteristics， identified the pathogen causing wilt of T. ruticarpum in Chongqing as F. fujikuroi. The optimal conditions for the mycelial growth of F. fujikuroi were mung bean agar （MBA） with glucose as the carbon source， beef extract and yeast powder as nitrogen sources， 28 ℃， pH 7.0， and alternating light/dark conditions. The optimal conditions for sporulation were potato dextrose agar （PDA） with glucose as the carbon source， beef extract as the nitrogen source， 28 ℃， pH 7.0， and complete darkness. Among chemical fungicides， phenazine-1-carboxylic acid exhibited the strongest inhibitory effect on F. fujikuroi. Shenqinmycin and tetramycin were the most effective bio-fungicides. ConclusionThis study is the first to report F. fujikuroi as the causal agent of wilt disease in T. rutaecarpa. The chemical fungicide phenazine-1-carboxylic acid and the bio-fungicides shenqinmycin and tetramycin showed strong inhibitory effects against F. fujikuroi.

Background: Malignant pleural effusion (MPE), a common complication of advanced cancers, is associated with poor prognosis and reduced quality of life. Although host–tumor interactions are known to drive MPE development, the associated immune dynamics during disease progression remain unclear. Using a Lewis lung carcinoma-induced MPE model in C57BL/6JNidfc mice, we systematically evaluated general parameters and immune cell changes at two-day intervals throughout disease progression. Results: The day of Lewis lung carcinoma cell injection into the pleural space was designated as day 0. By day 10 post-injection (p.i.), MPE-bearing mice exhibited ~ 10% body weight loss, marking the experimental endpoint. Pleural tumor mass and pleural effusion volume were minimal up to day 4 p.i. but increased sharply from day 6 onward.CD45⁺ immune cell counts rose over time, and days 6, 8, and 10 p.i. marked key stages of MPE progression. On day 6, B cells, T cells, and natural killer cells, but not macrophages and neutrophils, increased significantly compared to earlier timepoints. By day 8, all immune cell subsets except T cells exceeded day 6 levels, and at day 10, natural killer cell numbers declined while others continued to increase. Besides, the numbers of CD8⁺ T cells, Th1 cells, regulatory T cells, and M2 macrophages progressively increased from day 6 to 10. Based on these data, days 6 and 10 were defined as early and advanced MPE stages, respectively, with distinct immune phenotypes. In advanced MPE, CD8⁺ T cells displayed reduced IFN-γ, TNF-α, Granzyme B, Perforin, FasL, and Ki-67, but upregulated PD-1 and CTLA-4 relative to early stage. Similarly, Th1 cells showed decreased IFN-γ, TNF-α, and IL-2 production along with reduced Ki-67 expression. Advanced-stage M2 macrophages exhibited lower MHC-II levels and impaired phagocytosis, but higher PD-L1 and IL-10 production, while neutrophils showed reduced TNF-α release and phagocytic activity. Conclusions Our findings characterize the temporal immune dynamics associated with MPE progression in a mouse model, revealing a transition from an early immunostimulatory state to a late immunosuppressive state. This study enhances our understanding of MPE immunopathogenesis and provides a foundation for developing precise, stagespecific therapeutic strategies.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

Objective To compare the effects of different detector combinations of 320-row CT on the image quality and radiation dose of head CT to explore the feasibility of using a wide detector for head CT scanning.Methods Totally 100 patients underwent head CT scanning due to trauma or cerebrovascular disease at some hospital from June to August 2023 were collected prospectively and divided into group A and group B by using block randomization grouping method,with the length of the block group being 2 and 50 patients in each group.In group A,all the detectors had the widths at z-axis direction being 40×0.5 mm and head scanning was completed after multiple exposures;in group B,detector combinations with widths of 280×0.5 mm or 320×0.5 mm were chosen based on the patient's head size in the head-foot direction(z-axis direction),and head scanning was performed with a single-turn exposure.The remaining scanning and image reconstruction parameters in the two groups were kept completely consistent.The head image quality of the 2 groups was evaluated objectively and scored subjectively by 2 radiologists.The volume CT dose index(CTDIvol),dose length product(DLP)and exposure time of the 2 groups were recorded,and the effective dose(ED)was calculated.SPSS 22.0 software was used for statistical analysis.Results In terms of objective evaluation of image quality,at the level of the parietal skull group B had the CT value of gray matter,image noise and contrast to noise ratio(CNR)of the images higher than those of group A,and the differences were statistically significant(all P<0.05);at the level of the posterior skull group B had the CT values of gray and white matter,image noise and air noise lower while CNR higher than those of group A,and the differences were statistically significant(all P<0.05).In terms of subjective scoring of image quality,at the levels of parietal and posterior skull group A behaved better than group B,and the differences were statistically significant(all P<0.05).In group A 5 patients had obvious motion artifacts affecting the diagnosis and the image quality scores not higher than 2,and secondary scanning had to be carried out;In group B all the patients had no obvious motion artifacts and met the diagnosis requirements.When compared with group A Group B had the CTDIvol,DLP,ED and exposure time decreased by 17.44%,17.24%,17.48%and 85.53%,respectively,and the differences were statistically significant(all P<0.05).Conclusion A wide detector gains advantages over a 20 mm detector in image quality when 320-row CT is used for head CT scanning,with the diagnosis requirements satisfied.[Chinese Medical Equipment Journal,2025,46(4):57-62]

Objective:To describes the probability and rate of native liver survival (NLS) in biliary atresia (BA) patients after Kasai portoenterostomy (KPE)over various time periods and analyzes the perioperative factors associated with liver transplantation or death.Methods:A retrospective case-summary.BA patients administrated at the Department of Fetal and Neonatal Surgery in Hunan Children′s Hospital between January 2015 and December 2021.Probability and rate of NLS were calculated by life table.Cox proportional hazards regression model and Logistic model was applied to explore the perioperative factors related to post-Kasai liver transplantation/death.Results:The median age at Kasai surgery was 62 days.The rate of jaundice clearance (JC) was 64.5% within 3 months after Kasai, and 58.3% of the patients had cholangitis.The probability of NLS reached its lowest point in the first 1 year after Kasai (76.2%) and ranged from 93.2% to 98.0% in years 2-8 after Kasai.The rates of NLS in 2 years, 5 years and 8 years were 71.1%, 62.8% and 56.0%, respectively.Cytomegalovirus (CMV) infection before or on the day of Kasai without antiviral treatment can increase the risk of liver transplantation or death[ HR(95% CI): 1.628 (1.081-2.452), P=0.020].Preoperative gamma-glutamyl transferase increased the risk of liver transplantation/death within 1 year after Kasai[ OR(95% CI): 1.001 (1.000-1.001), P=0.021], and early cholangitis was a risk factor for liver transplantation/death within 5 years after Kasai[ OR(95% CI): 1.934 (1.004-3.726), P=0.048].JC within 3 months post-KPE was a protective factor of NLS. Conclusions:The first year after Kasai was the highest risk period for liver transplantation/death, which should be the focus of follow-up management.JC within 3 months after surgery is the protective factor for overall NLS, 1-year NLS and 5-year NLS.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Methodological quality assessment is a pivotal link between primary studies and reliable evidence-based practice,and an essential pathway for operationalizing the core principles of the Guide on Methodological Standards in Pharmacoepidemiology(2nd edition).A prevalent challenge in practice,however,is the conflation of appraising methodological robustness(risk of bias assessment)with verifying reporting transparency(adherence to reporting guidelines).This paper systematically addresses this fundamental challenge,beginning with a clear distinction between the essence and boundaries of these two concepts.On this basis,the article provides a comprehensive review of mainstream quality assessment tools,covering the methodological features and evolutionary trajectory of numerous instruments for interventional(e.g.,RoB 2,ROBINS-I),observational(e.g.,NOS,the JBI/SIGN/NIH series),secondary(e.g.,AMSTAR 2),and other specific types of studies such as health economic evaluations.Furthermore,a complete case study is used to illustrate the practical application of the ROBINS-I tool.The paper's central thesis advocates for an"appraisal-informed design"philosophy,urging a conceptual shift from the retrospective critique of existing literature to the prospective quality control of new research by internalizing appraisal standards as design principles,while also exploring the emerging paradigm of artificial intelligence in assisting assessment.This paper provides a comprehensive methodological reference for researchers and practitioners to prudently select appropriate assessment tools and to conduct rigorous critical appraisals of pharmacoepidemiological evidence.

The time-sensitive medical treatment advantages of unmanned aerial vehicles(UAVs)in casualty evacuation were described,along with their typical application scenarios and the principles of casualty evacuation.The development status of UAV-based casualty evacuation,both domestically and internationally,was reviewed.Key life-support technologies,such as intelligent injury recognition,fully automated cardiopulmonary resuscitation(CPR)and auto cruise-based mecha-nical ventilation,were examined as important research directions of casualty evacuation UAVs.Finally,future development trends in UAV casualty evacuation and transport system were envisioned from four dimensions of autonomous flight technology,life support systems,information interaction and policy regulations.

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.