Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

[Purpose]To analyze the cost-effectiveness and cost-utility conducted on the lung can-cer screening project in urban areas of Anhui Province,and to provide suggestions for the formu-lation of lung cancer screening policies in Anhui Province.[Methods]A Markov decision model for low-dose computed tomography(LDCT)lung cancer screening intervention was established based on on-site survey data and literature data.The development of the population under different interventions was simulated,using saved life years(LYS)and quality-adjusted life years(QALY)as effectiveness indicators,to conduct cost-effectiveness and cost-utility analyses of different screening strategies.Cost data were discounted at a 3％discount rate.[Results]The screening schemes of once a year,once every two years,once every three years,and once every five years all meet the cost-effectiveness principle for saving one LYS or QALY.Among them,the best screening strategy in terms of cost-effectiveness and cost-utility was the LDCT lung cancer screening strategy once every two years,with costs of 72 441.54 CNY and 71 050.24 CNY,respectively.[Conclusion]The LDCT lung cancer screening program demonstrates good cost-effectiveness,with strategies of dif-ferent screening frequencies being viable options.The optimal screening strategy is screening once every two years.

To summarize the nursing experience of a patient with coronary heart disease who was left in the radial artery during the removal of the radial artery sheath after coronary angiography via the radial artery pathway.Nursing points:to start the emergency transfer process,to shorten the treatment transfer time;to assist to locate the position of the sheath to provide a basis for the selection of surgical incision;to conduct dynamic assessment of hemostatic effect,prevention of radial artery occlusion;to closely monitor pain and signs to prevent vasovagal reflex;to implement the whole psychological intervention,and to reduce the psychological burden of patients and their families.The ruptured sheath tube was successfully removed by emergency surgery of vascular surgery.A total of 6 days after the operation,the patient was transferred to cardiac surgery for coronary artery bypass grafting,and was discharged 23 days later.After 3 months of follow-up,the blood supply of the limbs was good,and the incision healed well.

Objective:To explore patterns of traditional Chinese medicine (TCM) use among patients with inflammatory bowel disease (IBD) in Jiangsu Province, China from 2019 to 2023.Methods:Using data from the IBD health data platform of the National Healthcare Big Data (Eastern) Center, a retrospective cohort study was conducted. We performed descriptive analyses on hospitalised patients diagnosed with IBD between 2019 and 2023, who received TCM treatment.Results:The study included 11 095 case records from 4 760 patients, with TCM diagnoses primarily indicating diarrhoea and abdominal pain. Ulcerative colitis (UC) accounted for 4 782 hospitalizations (3 103 patients), while Crohn's disease (CD) accounted for 6 313 hospitalizations (1 657 patients). Patient demographics showed a trend towards younger age and a higher proportion of males. Treatment utilisation was highest in southern Jiangsu compared with the central and northern regions. In terms of disease burden, all treatment costs showed a downward trend. In terms of external TCM therapies, UC patients tend to prefer plasters and enemas, while CD patients are more inclined to use acupuncture. Regarding herbal medicine, licorice, white atractylodes, and white peony root are commonly used single herbs for IBD patients.Conclusions:The number of IBD patients treated with TCM in Jiangsu Province has steadily increased from 2019 to 2023. It is important to identify effective TCM treatment methods to reduce the burden of patients.

Stereotactic radiotherapy is widely favored because of its high treatment precision and less fractionations.ZND-A is a new domestic gamma-ray cone-beam focused stereotactic radiotherapy system.Herein the technical characteristics of ZND-A system are described in detail from the aspects of the treatment frame,gamma-ray module,collimator module,six-dimensional treatment couch module and image-guided system module,and the main parameters are compared with the mainstream gamma knife equipments at home and abroad.With reference to Response Evaluation Criteria in Solid Tumors(RECIST 1.1),the initial efficacy of the patients treated by the ZND-A system is analyzed to evaluate the advantages and disadvantages of the ZND-A system for providing a reference for the hospital clinical use of this type of gamma knife.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective:To investigate the predictive value of machine learning-based CT radiomics model for radioresistance in patients with esophageal squamous cell carcinoma (ESCC).Methods:Clinical data of 185 patients with ESCC treated with radical radiotherapy in the First Affiliated Hospital of Anhui Medical University from December 2015 to July 2022 were retrospectively analyzed, and all patients were randomly divided into a training set ( n=129) and a validation set ( n=56) at a ratio of 7 : 3. The radiomics parameters of the primary lesion of esophageal cancer and the surrounding 5 cm region in the patients' CT arterial phase images were extracted, and 6 machine learning methods were used to screen the optimal radiomics model to obtain the optimal radiomics score (Radscore). Independent prognostic predictors of radioresistance in ESCC were obtained by univariate and multivariate logistic regression analyses, which was used as the basis for constructing the nomogram. The predictive performance of different models was compared by the area under the receiver operating characteristic (ROC) curve (AUC). The predictive efficacy and clinical value of the combined model were evaluated using calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results:The combined intratumoral and peritumoral radiomics model based on naive Bayesian classifier yielded the optimal prediction performance, with AUC of 0.859 and 0.936 in the training set and validation set, respectively. Multivariate logistic regression analysis showed that Radscore and T stage were the independent prognostic predictors of radioresistance in ESCC patients, and the AUC of the combined model constructed based on these predictors in the training and validation sets were 0.942 and 0.959, respectively. Calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) all indicated higher clinical benefit and more consistent predictive efficacy of the combined model.Conclusions:Machine learning-based CT radiomics model is useful for the prediction of radioresistance in ESCC. The nomogram of radiomics and clinical parameters can further improve the prediction accuracy and provide novel reference for individualized treatment of patients with ESCC.