Objective: To explore the characteristics of the diversity and composition of oral microbial flora with age, and to provide a reference for understanding the succession of oral microecology at different ages. Methods: Oral rinse 16S rRNA (V4 region) sequencing data from 9 021 participants 14-69 years of age in the 2009-2012 National Health and Nutrition Examination Survey (NHANES) were analyzed. Alpha diversity (Observed OTUs, Faith’s PD, Shannon Index), beta diversity (Bray-Curtis and UniFrac), and genus-level composition were examined using weighted generalized linear models (GLMs), including quadratic terms for age and adjusting for key covariates (gender, race/ethnicity, BMI, smoking status, and periodontitis severity). Results: Alpha diversity demonstrated a clear inverted U-shaped trajectory across age, peaking at 25-30 years old and declining thereafter. This trend remained consistent across sex, race, smoking, and periodontal health strata. Beta diversity analyses revealed a modest but steady age-related shift in community structure. Genus-level analyses revealed that Rothia, Prevotella_6, and Lactobacillus increased steadily with age, while Haemophilus, Porphyromonas, and Corynebacterium declined significantly. Notably, potential periodontopathogens, such as Fusobacterium and Treponema_2, peaked in early adulthood before declining with age. Conclusion Age is an important driver of oral microbial succession, and the oral microbiome exhibits dynamic changes across different life stages. Future longitudinal and multi-omic studies are warranted to elucidate the mechanisms underlying these age-related trajectories.

Objective: To understand the allocation and use of safety seats for preschool children and explore its related factors, so as to provide a scientific reference for promoting the usage of safety seats. Methods: A stratified random cluster sampling method was used to select 3 143 parents of preschool children aged 3 to 6 from six kindergartens in Shunyi District, Beijing from January 3 to 10, 2022. An online questionnaire survey was conducted to collect and evaluate the equipment and use of child safety seats in different characteristics of preschool children, as well as their scores of health beliefs. Multiple factor Logistic regression analysis was used to investigated the related factors of safety seat configuration and use. Results: The equipping rate and usage rate of safety seats for preschool children were 66.56% and 58.45%, respectively. The proportion of equipped and used safety seats for preschool children in core families (69.52%, 62.23%) were higher than that in large families (64.35%, 55.62%), only child families ( 72.39 %, 64.87%) were higher than non only child families (61.49%, 52.86%), and urban families (71.63%, 63.04%) were higher than rural families (52.31%, 45.51%) ( χ 2=9.23, 13.86; 41.72, 46.44; 101.96 ,76.97,all P <0.05) . As the educational level of parents ( χ 2 trend =154.23,98.76) and annual income of the family ( χ 2 trend =155.78,127.69) rised, the reporting rates of the equipped and used child safety seats in the family also increased(all P <0.05 ). There were statistically significant differences in the scores of different dimensions of health beliefs for the provision ( t =-20.22-18.16) and use ( t =24.32-24.17) of safety seats for preschool children(all P <0.05). After adjusting for child sex, child age, family annual income, parental education level, family type, whether the child was an only child, and place of residence，multivariate Logistic regression analysis showed that preschool children with higher perceived susceptibility score( OR =1.11, 1.08), higher self efficacy score( OR =1.23, 1.33), and higher suggestive factors score( OR =1.08, 1.12) were more likely to have and use safety seats in their families, while preschool children with higher perceived impairments score( OR =0.82, 0.80) were less likely to have and use safety seats in their families (all P <0.05). Conclusions The installation rate of child safety seats needs to be improved, and there is also a certain gap in their use after installation. Parents of preschool children should improve susceptibility and self efficacy to safety seat equipment and use, and perceptual barriers should be reduced.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Chest CT pulmonary angiography (CTPA) has certain auxiliary diagnostic value for the clinical diagnosis of invasive pulmonary aspergillosis (IPA) . Three patients with hematological malignancies were reported, including 2 ones after allogeneic hematopoietic stem cell transplantation and 1 ones after chemotherapy for refractory recurrent leukemia. Each patient was treated with antibiotics for at least 48 hours after the onset of fever, they all underwent chest high-resolution CT (HRCT) scans without fever resolution. CT revealed at least one dense pulmonary consolidation shadow with a diameter greater than 10 mm, and subsequently a CTPA examination was performed to observe the effect of CTPA imaging signs for the diagnosis of IPA. There were 2 patients with positive vascular occlusion sign (VOS) and 1 patient with negative VOS detected by CTPA. Among them, 2 patients with positive VOS were diagnosed with possible IPA and received with diagnosis-driven antifungal treatment, which improved their conditions. One patient with negative VOS sign was diagnosed with diffuse large B-cell lymphoma involving the lungs. After receiving anti-lymphoma treatment, the lesions significantly reduced in size. The vascular occlusion sign detected by CTPA is relatively characteristic. For high-risk IPA patients, it helps to improve the specificity of imaging diagnosis and guide clinical treatment decisions.

Colon cancer is a complex disease characterized by the impairment of body,qi and spirit,as well as the establishment of a tumor immune microenvironment(TIME)induced by chronic stress.Chronic stress is classified as a micro-level mental disorder,while TIME serves as the biological foundation for qi disorders.The observable manifestation of colon cancer is the tangible representation of physical disease.The interconnected mechanism of"chronic stress-TIME-colon cancer"aligns with the traditional Chinese medicine's understanding of disease as involving the interplay between the body,qi and spirit.In treatment,we should cooperate to improve the"regulating mind"of chronic stress and reshape the"invigorating qi"of TIME,and finally achieve the purpose of shape treatment to delay the progression of colon cancer.The paper is to provide new insights into the treatment of colon cancer with traditional Chinese medicine.

Objective To explore the predictive value of sequential organ failure score(SOFA)combined with serum Clara cell protein 16(CC16)and angipoietin-2(Ang-2)in the prognosis of patients with acute respiratory distress syndrome(ARDS)caused by sepsis.Methods A total of 173 sepsis patients were divided into the concurrent group(n=76)and the non-concurrent group(n=97),based on whether ARDS occurred within 72 h after admission.According to the death situation within 30 days,patients in the concurrent group were divided into the death group(n=35)and the survival group(n=41).Enzyme-linked immunosorbent assay(ELISA)was used to detect serum CC16,Ang-2,C-reactive protein(CRP)and interleukin(IL)-6.The levels of triglycerides(TG),total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C),high-density lipoprotein cholesterol(HDL-C)and white blood cell count(WBC)were detected by automatic biochemical analyzer and hemocytometer.SOFA scores were performed on patients at admission.The biochemical indexes,SOFA score,CC16 and Ang-2 levels were compared between the two groups,and the poor prognosis of the patients was analyzed.Binary Logistic regression was used for influencing factor analysis.The receiver operating characteristic(ROC)curve was used to analyze the efficacy of related factors in the poor prognosis of patients with sepsis complicated by ARDS.Results The serum levels of CRP,IL-6,CC16,Ang-2 and SOFA scores were higher in the concurrent group than those in the non-concurrent group(P＜0.05).The SOFA score,mechanical ventilation time,CRP,CC16 and Ang-2 were higher in the death group than those in the survival group(P＜0.05).High SOFA score,CC16 and Ang-2 were independent risk factors for poor prognosis in the concurrent group(P＜0.05).The area under the curve(AUC)of SOFA score,serum CC16 and Ang-2 levels in predicting poor prognosis in patients with sepsis complicated with ARDS were 0.806(0.700-0.888),0.801(0.693-0.884),0.845(0.743-0.918)and 0.945(0.867-0.984),respectively.Conclusion SOFA score combined with changes in CC16 and Ang-2 expression levels can be used to comprehensively evaluate prognosis of patients with sepsis complicated with ARDS.

Objective To investigate the diagnostic accuracy and clinical applicability of the Linknet-VGG16 deep learning algorithm for measuring the effective optical zone(EOZ)after corneal refractive surgery.Methods This single-center retrospective cohort study included 69 patients(69 eyes)who underwent femtosecond laser-assisted in situ kerato-mileusis(FS-LASIK)(34 eyes)or small incision lenticule extraction(SMILE)(35 eyes)at the Refractive Surgery Center of Affiliated Zhengzhou Aier Eye Hospital of Henan University from June 2023 to June 2024.Data from the right eyes of all patients were selected for statistical analysis.During the surgery,patients in the FS-LASIK group adopted the VisuMax fem-tosecond laser system combined with the Amaris 750S excimer laser system,while those in the SMILE group only used the VisuMax femtosecond laser system.A total of 276 Pentacam images were re-examined postoperatively.A Linknet segmenta-tion model based on the VGG16 encoder was constructed,and image normalization techniques were applied to accelerate model convergence.Model performance was assessed using accuracy,intersection over union(IoU),and the Dice coeffi-cient.The traditional EOZ measurement method based on corneal tangential curvature served as the reference standard.Bland-Altman analysis was conducted to evaluate consistency across all images and within each group,and the time effi-ciency of both methods was compared.Results Six representative medical image segmentation architectures(U-Net,U-Net++,DeepLabv3-ResNet50,DeepLabv3+-ResNet50,Unet-Densenet169,and Linknet-VGG16)were systematically evaluated.The Linknet-VGG16 model demonstrated superior performance over the other 5 models in pixel-level accuracy,IoU and Dice coefficient,which were 99.83％,99.48％and 99.74％,respectively.Although there was no significant differ-ence in accuracy and Dice coefficient between Linknet-VGG16 and U-Net models(whose accuracy was 99.82％and Dice coefficient was 99.72％),the inference speed of the U-Net model(62.46 ms)was 31.76％slower than that of the Linknet-VGG16 model(42.62 ms).The evaluation results of a clinically applicable comprehensive scoring model(weights:accura-cy 20％,IoU 20％,Dice coefficient 20％,speed 25％,model size 15％)showed that the Linknet-VGG16 model achieved a score of 88.01,surpassing other architectures(U-Net:86.29;DeepLabv3+-ResNet50:80.41;DeepLabv3-ResNet50:73.82;U-Net++:73.22;Unet-Densenet169:66.66).Bland-Altman analysis revealed that the mean difference of the 136 images in the FS-LASIK group was 0.01 mm[95％limits of agreement(LoA):-0.36 to 0.35 mm],with 96.3％of data points falling within the LoA.The mean difference of the 140 images in the SMILE group was-0.01 mm(95％LoA:-0.36 to 0.33 mum),with 95.7％of data points falling within the LoA.The mean difference of all 276 images was 0.00 mm(95％LoA:-0.36 to 0.34 mm),with 96.4％of data points falling within the LoA.These results indicated excellent consistency.The average measurement time per image using the traditional EOZ measurement method was 13.00 minutes,whereas the deep learning model required only 3.22 seconds.Conclusion The traditional EOZ measurement method based on corne-al tangential curvature exhibits good consistency with the fully automatic EOZ measurement method based on deep learning algorithms,achieving high image recognition accuracy.Additionally,the deep learning algorithm significantly reduces measurement time,compared with the traditional method based on corneal tangential curvature.

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.