ObjectiveTo explore the effects of "dryness pathogen" on a bronchial asthma rat model from the perspective of the prostaglandin D₂ （PGD₂）/eosinophil （EOS） axis. MethodsForty SD rats were randomly divided into five groups， blank group， asthma group， mild dryness asthma group， moderate dryness asthma group， and severe dryness asthma group， with 8 rats in each group. The asthma model was induced in the asthma group using ovalbumin （OVA）， while the asthma-dryness syndrome groups were modeled using a climate chamber combined with OVA to establish animal models of bronchial asthma with Northwest dryness syndrome. During modeling， general behavioral performance of the rats in each group was observed and recorded， and body weight， water intake， and asthma symptom scores were compared. On day 21 of the modeling， small animal respiratory system testing was used to measure respiratory rate and intermittent respiratory contraction index. Pathological changes in rat lung tissues were observed using HE staining. Giemsa staining was used to observe eosinophil （EOS） infiltration in lung tissues and calculate EOS counts. ELISA was used to detect serum levels of prostaglandin D₂ （PGD₂）， interleukin-5 （IL-5）， and interleukin-4 （IL-4）. Western Blot was used to detect eosinophil peroxidase （EPX） and prostaglandin D₂ receptor （CRTH2） protein levels in rat lung tissues. ResultsCompared to the blank group， the average water intake of rats in all asthma-dryness groups increased （P＜0.01）. The body weight of rats decreased in the moderate and severe asthma-dryness groups on day 14， and in all modelling groups on day 21 （P＜0.05 or P＜0.01）. Across all model groups， the asthma symptom score， respiratory rate， intermittent respiratory contraction index， EOS count in lung tissue， serum levels of PGD₂， IL-5， and IL-4， as well as the relative protein expression levels of EPX and CRTH2 in lung tissue all significantly increased （P＜0.01）. Compared to the asthma group， the water intake of rats in the mild， moderate， and severe asthma-dryness groups increased （P＜0.01）. On day 21 of the experiment， the body weight of rats in the moderate and severe asthma-dryness groups decreased （P＜0.05）， and the respiratory rate of rats in all asthma-dryness groups increased， as well as the serum levels of PGD₂， IL-5， and IL-4， and the relative protein expression levels of EPX and CRTH2 in lung tissues （P＜0.05 or P＜0.01）. Furthermore， in the moderate and severe asthma-dryness groups， the asthma symptom scores and intermittent respiratory contraction indices were elevated， and the EOS counts in lung tissue increased （P＜0.05 or P＜0.01）. Compared to the mild asthma-dryness group， serum levels of PGD₂， IL-5， and IL-4 in the severe asthma-dryness group increased， as well as the relative protein expression levels of EPX and CRTH2 in lung tissue （P＜0.05 or P＜0.01）. Pathological observation showed that rats in all asthma-dryness syndrome groups exhibited increased inflammatory infiltration in the lung tissue， alveolar wall thickening， luminal narrowing， pulmonary interstitial congestion， and aggravated viscous secretions. These pathological manifestations worsened as the degree of dryness increased. ConclusionThe "dryness pathogenic factor" can exacerbate bronchial asthma symptoms and lung tissue pathological damage through the PGD₂/EOS axis， and the damage progressively worsens with the increasing severity of the dryness pathogen.

Objective:To explore the influence of different scanning positions based on chest phantom of computed tomography(CT)scan on chest on image quality and radiation dose.Methods:A thermoluminescent dosimeter(TLD)was placed at the breast area of simulating anthropoid chest phantom.GE Revolution evo CT was used to conduct scan on the conventional supine position(supine group)and prone position(prone group)for chest phantom.Different noise indexes(NI=10-23)were adjusted to control ration doses,and other parameters were fixed,and each group collected 12 sequence images.The average value(AV),standard deviation(SD)of the CT scan at region of interest(ROI)under different scanning positions were recorded to calculate the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of the image.The radiation dose at the breast area was measured by TLD,and the volume CT dose index(CTDIvol)and dose-length product(DLP)were recorded.Results:Under different scanning positions,the radiation dose of breast organs in the prone group was lower than that in the supine group,there was a statistically significant difference between the two groups(t=6.57,P<0.05),while there were not statistically significant differences in CTDIvol and DLP between the two groups(P>0.05).There were not statistically significant differences in the CT values,SD,SNR,CNR of lung tissue,and the CT values of breast tissue between the two groups of images(P>0.05).The SD,SNR and CNR of breast tissue in the prone group were lower than those in the supine group,and the differences were statistically significant(t=-13.33,-10.59,6.70,P<0.05).There were no statistically significant differences in the subjective scores of the clarity of the edge of the tissue within lung,the layers of soft tissue of the breast,noise,and artifacts in the bone tissue between the two groups of images(P>0.05).Conclusion:When low-dose CT physical examination on chest is conducted in clinical practice,the scanning of prone position during undergoing CT scan on chest can obtain image quality that can meet the requirements in diagnosing lung,and reduce the radiation dose on the breast,and conform to the technical principle of optimal radiation protection.

Objective To explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.

In order to effectively prevent and control the occurrence of catheter-associated urinary tract infection and ensure the safety of both patients and medical personnel,the National Health Commission of the People's Re-public of China officially released the recommended health industry standard"Standard for prevention and control of catheter-associated urinary tract infection"(WS/T862-2025)in Aug.2025.This paper provides an interpreta-tion of the standard,covering its drafting background,basis and content,to assist relevant medical personnel in healthcare institutions in enhancing their understanding and recognition of the standard,and to further promote its implementation and enforcement.

Objective To look for cognitive assessment paradigms and electrocardiographic(ECG)characteristics sensitive to mental fatigue through an experimental study.Methods Data were collected from 10 healthy students of the University of Shanghai for Science and Technology who participated in the 4-day National Undergraduate Electronics Design Contest,including their Stanford sleepiness scale(SSS)scores,sleep duration,cognitive task performance,and ECG signal data.Ten ECG features,including time-domain,frequency-domain,and information-domain characteristics,were extracted during cognitive tasks.The cognitive task performance and ECG features sensitive to changes in mental fatigue were analyzed.Results Significant differences were observed in SSS scores(x2=23.116,P＜0.001)and sleep duration(x2=19.608,P＜0.001)across the 4 d.For cognitive task performance,word-color congruent accuracy and word-color incongruent accuracy in the Stroop task and Single-visual target stimulus accuracy and Visual target-auditory non-target stimulus accuracy in the audiovisual competition task all showed significant negative correlations with SSS scores(all P＜0.05).Regarding ECG features,Poincaré plot SD2 during the Stroop task was positively correlated with sleep duration(P＜0.05),while Poincaré plot SD2 during the 2-back task was positively correlated with mental fatigue assessment scores and was negatively correlated with sleep duration(both P＜0.05).Conclusion The accuracy of the Stroop task and audiovisual competition task is a cognitive ability indicator sensitive to mental fatigue,while the Poincaré plot SD2 during Stroop and 2-back tasks is an ECG indicator sensitive to mental fatigue.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To investigate the effect of gender on prognosis after transcatheter patent foramen ovale(PFO)closure in patients with cerebral infarction or transient ischemic attack.Methods A retrospective cohort study was conducted involving patients with cerebral infarction or transient ischemic attack(TIA)who underwent PFO closure at our hospital between January 2013 and December 2023.The patients were grouped by gender,and related data were collected,including age,comorbidities,Risk of Paradoxical Embolism(RoPE)score,laboratory results,findings of transthoracic/transesophageal echocardiography(TTE/TEE),and post-procedural complications,such as device-related thrombosis(DRT),recurrent stroke,bleeding,and atrial fibrillation(AF).Results A total of 112 patients were enrolled,including 59 males and 53 females,at a mean age of 42.47±12.35 years.The females had significantly higher preoperative RoPE score than the males(6.6±1.4 vs 6.0±1.5,P=0.046),and a statistical difference was observed in the distribution of infarction sites between them(Chi-square=10.25,P=0.006),indicating that the males were prone to posterior circulation infarction.Intraoperative transthoracic echocardiography revealed a greater distance from the PFO to the aortic root in the females(9.3±2.4 mm vs 7.6±2.0 mm,P<0.001).During a median follow-up of 4 years,the male group had 1 case of myocardial infarction,1 cerebral hemorrhage,1 paroxysmal AF,2 gingival bleeding episodes,and 1 skin ecchymosis.In the female group,1 case experienced pulmonary embolism,1 paroxysmal atrial fibrillation,3 gingival bleeding episodes,2 skin ecchymoses,2 recurrent cerebral infarctions,and 2 recurrent TIAs.There was no statistical difference in overall adverse events between gender(P=0.291).Although the females had higher rates of recurrent cerebral infarction and TIA,this difference lacked statistical significance(P=0.222).Multivariate Cox regression analysis indicated that after adjusting for various potential confounding factors,such as RoPE score,age,hypertension,coronary heart disease,and other factors,gender was not an independent predictor of composite endpoint events after surgery.Conclusion Gender does not significantly affect overall prognosis after PFO closure in patients with cerebral infarction or TIA.However,females showed a trend toward higher rates of recurrent cerebral infarction and TIA.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

BACKGROUND:As a common disease in middle-aged and elderly,osteoarthritis is difficult to cure,and the pathogenesis is not clear.Long non-coding RNA participates in the pathogenesis of osteoarthritis through many ways,such as regulating translation,promoting or inhibiting mRNA,and adsorbing miRNAs. OBJECTIVE:To review the types of common long non-coding RNA in osteoarthritis,and the influence of multiple long non-coding RNAs on the pathological factors related to osteoarthritis,to analyze the future application of long non-coding RNAs in osteoarthritis. METHODS:Literature retrieval was conducted in CNKI,WanFang Data,VIP database,PubMed,Web of Science and Sciencedirect databases,using the search terms of"osteoarthritis,degenerative joint disease,degenerative arthritis,OA,LncRNA,long non-coding RNA,long noncoding RNA,long intergenic non-coding RNA"in Chinese and English.All relevant literature published from 1976 and May 2024 was retrieved.After literature screening,induction,analysis and summary,93 articles were finally included for review. RESULTS AND CONCLUSION:This review collected 25 long non-coding RNAs that are well studied with osteoarthritis.Long non-coding RNAs,as a molecular sponge for miRNA,are competing endogenous RNAs to competitively adsorb miRNAs and then affect downstream targets.Long non-coding RNAs can regulate physiopathological processes such as chondrocyte apoptosis and proliferation,cartilage extracellular matrix degradation,and inflammatory responses.Long non-coding RNAs are expected to become a biomarker and potential therapeutic target for the clinical diagnosis and therapeutic prognosis of osteoarthritis,and it may become a new strategy for the clinical treatment of osteoarthritis in the future.

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.