Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

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.

Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

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.

ObjectiveThrough the correlation analysis between intestinal absorption profile and inhibition of macrophage foaming， the pharmacodynamic components of Zhuriheng dripping pills（ZRH） were explored to provide a basis for establishing its quality standard. MethodIntestinal absorption fluids with 0， 5， 10， 15， 20 times clinical equivalent doses were prepared by a rat everted gut sac（EGS）， and the oxidized low density lipoprotein（ox-LDL）-induced RAW264.7 macrophage foaming model was used to investigate the effect of intestinal absorption fluid with different doses on the accumulation of lipids in RAW264.7 cells by oil red O staining and cholesterol content determination， and to screen for the optimal dose. Ultra performance liquid chromatography-quadrupole-electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive Orbitrap MS） was used to analyze and identify intestinal absorption fractions of ZRH intestinal absorption fluids， and partial least squares-discriminant analysis（PLS-DA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were performed on different doses of ZRH intestinal absorption fluids using SIMCA 13.0 with peak area as the independent variable and the pharmacodynamic indicators as the dependent variables to screen the compounds with variable importance in the projection（VIP） value>1.0 as contributing components， and Pearson correlation analysis was used to determine the spectral effect relationship， determined the compounds and positive correlation with pharmacodynamic were as active ingredients. Molecular docking was used to verify the binding energy of peroxisome proliferator-activated receptor α（PPARα）， PPARγ， PPARβ， human retinoid X receptor α（RXRA） and nuclear transcription factor-κB（NF-κB） with the active ingredients in ZRH intestinal absorption fluids. Real-time fluorescence quantitative polymerase chain reaction（Real-time PCR） was performed to detect the mRNA levels of PPARγ， scavenger receptor A1（SRA1） and adenosine triphosphate-binding cassette transporter A1（ABCA1） in RAW264.7 cells， Westen blot was used to detect the expression level of PPARγ protein in RAW264.7 cells， and enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of interleukin（IL）-1β and NF-κB in RAW264.7 cells. ResultAccording to the results of oil red O staining and cholesterol content determination， the ZRH intestinal absorption fluids could significantly reduce macrophage foaming， and intestinal absorption fluids with 15， 20 times clinical equivalent doses had the best effect， the 15-fold ZRH intestinal absorption fluid was finally determined as the study subject. Spectral effect relationship showed that 52 corresponding peaks in the ZRH-containing intestinal fluid were positively correlated with the efficacy， including organic acids， phenylpropanoids， iridoids， flavonoids， bile acids， coumarins and chromones. Target validation results showed that 86.9%-96.2% of the total components processed good binding activities with the key targets of PPARα， PPARγ， PPARβ， RXRA and NF-κB， and the docking energy values were all less than -6.0 kcal·mol^-1（1 cal≈4.19 J）. The results of validation showed that， compared with the normal group， the model group showed a significant increase in the levels of SRA1 and PPARγ mRNA expression， a significant decrease in ABCA1 mRNA expression， a significant increase in the level of PPARγ protein expression， and a significant increase in the levels of IL-1β and NF-κB（P<0.01）， compared with the model group， the 15-fold intestinal absorption fluid group showed a significant decrease in the levels of SRA1 and PPARγ mRNA expression（P<0.05， P<0.01）， ABCA1 mRNA expression level was significantly up-regulated， the levels of IL-1β and NF-κB were significantly reduced（P<0.01）， and PPARγ protein expression level was significantly reduced（P<0.05）. ConclusionThis study identifies 52 components and their metabolites in ZRH intestinal absorption fluid that are positively correlated with the inhibition of macrophage foaming， which may be related to the regulation of the PPARs pathway in cells and the reduction of the levels of inflammatory factors， and can provide a reference for the quality control and clinical application of ZRH.

Objective To understand the molecular epidemiological characteristics of Norovirus outbreaks and the genome evolution of Norovirus epidemic strains in Hainan Province from 2020 to 2022.Methods The information and samples have been collected from the norovirus outbreaks from 2020 to 2022.Norovirus was detected by using the real-time PCR in these samples,then the detected sequences were amplified the analyzed.The Norovirus se-quences of 8 strains had been amplified and analyzed.Results From 2020 to 2022,39 gastroenteritis outbreaks were reported,and 25 outbreaks caused by Norovirus which mainly occurred in childcare institutions and schools(20/25,80％).The Norovirus outbreaks were mainly concentrated in counties around Haikou(northeast),which including Ding'an(5 cases),Wenchang(4 cases),Chengmai(4 cases),and Lingao(3 cases);following by western regions which included Baisha(2 cases),Ledong(2 cases),and Dongfang(3 cases).1 case was in Wanning in the southeast.Among individuals aged 2-17,the positive proportion of Norovirus in males was higher than that in females.Among individuals aged over 55,the proportion of Norovirus positive in females was higher than that in males.The gender of positive samples among individuals aged 18-40 was related to their profession.According to RT-PCR typing and sequencing,GⅡ group Norovirus were classified in13 outbreaks.There were 4 genotypes detected.GⅡ.2[P1 6]was the main epidemic strain with 60％(9/13),and the other three genotypes were GⅡ.4 Sydney[P31](15.4％,2/13)GⅡ.4 Sydney[P16](7.7％,1/13)and GⅡ.3[P12](7.7％,1/13).Further genic analysis of 8 Norovirus strains showed that all of them were still in the same branch as the previ-ous strain,and all exhibited a certain amount of amino acid variation.Conclusion Norovirus is the main pathogen of gastroenteritis outbreaks in Hainan province,and the main epidemic strain is GⅡ.2[P16].It is necessary to continue to strengthen the monitoring that provides scientific evidence for the prevention and control of norovirus out-breaks in Hainan region.

Objective To analyze the clinical characteristics of high energy metabolism in lung cancer patients and its correlation with body composition,nutritional status,and quality of life,and to develop a corresponding risk prediction model.Methods Retrospectively analyzed 132 primary lung cancer patients admitted to the First Hospital of Shanxi Medical University from January 2022 to May 2023,and categorized into high(n=94)and low energy metabolism group(n=38)based on their metabolic status.Differences in clinical data,body composition,Patient Generated Subjective Global Assessment(PG-SGA)scores,and European Organization for Research and treatment of Cancer(EORTC)Quality of Life Questionnaire-Core 30(QLQ-C30)scores were compared between the two groups.Logistic regression was used to identify the risk factors for high energy metabolism in lung cancer patients,and a risk prediction model was established accordingly;the Hosmer-Lemeshow test was used to assess the model fit,and the ROC curve was used to test the predictive efficacy of the model.Results Of the 132 patients with primary lung cancer,94(71.2%)exhibited high energy metabolism.Compared with low energy metabolism group,patients in high-energy metabolism group had a smoking index of 400 or higher,advanced disease staging of stage Ⅲ or Ⅳ,and higher levels of IL-6 level,low adiposity index,low skeletal muscle index,and malnutrition(P<0.05),and lower levels of total protein,albumin,hemoglobin level,and prognostic nutritional index(PNI)(P<0.05).There was no significant difference in age,gender,height,weight,BMI and disease type between the two groups(P>0.05).Logistic regression analysis showed that smoking index≥400,advanced disease stage,IL-6≥3.775 ng/L,and PNI<46.43 were independent risk factors for high energy metabolism in lung cancer patients.The AUC of the ROC curve for the established prediction model of high energy metabolism in lung cancer patients was 0.834(95%CI 0.763-0.904).Conclusion The high energy metabolic risk prediction model of lung cancer patients established in this study has good fit and prediction efficiency.

Objective To investigate the therapeutic effect of Euryale ferox seed shell extract on oral ulcer in rats and its underlying mechanism. Methods The contents of polyphenols and flavonoids in Euryale ferox seed shells were determined by Folin-phenol assay and aluminum nitrate colorimetry, respectively. DPPH · , ABTS+· , · OH and · O2- scavenging experiments were performed to evaluate the antioxidant activities of Euryale ferox seed shell extract in vitro. In a rat model of oral ulcer induced by burning with glacial acetic acid, the therapeutic effect of Euryale ferox seed shell extract was assessed by detecting changes in serum levels of oxidative factors by enzyme-linked immunosorbent assay (ELISA) and observing pathological changes of the ulcerous mucosa using HE staining; the therapeutic mechanism of the extract was explored by detecting the expression levels of Keap1, Nrf2, Nes-Nrf2 and HO-1 proteins in ulcerous mucosa using Western blotting. Results The ethyl acetate extract of Euryale ferox seed shells contained 306.74±1.04 mg/g polyphenols and 23.43±0.61 mg/g flavonoids and had IC50 values for scavenging DPPH · and ABTS+· free radicals of 3.42 ± 0.97 μg/mL and 3.32 ± 0.90 μg/mL, respectively. In the rat models, the ethyl acetate extract significantly ameliorated oral mucosal ulcer, increased serum CAT level, and decreased serum MDA level. The protein expression levels of Nes-Nrf2 and HO-1 were increased and Keap1 protein expression was lowered significantly in the ulcerous mucosa of the rats after treatment with the extract (P<0.05 or 0.01). Conclusion The therapeutic effect of Euryale ferox seed shell extract on oral ulcers in rats is mediated probably by activation of the Keap1/Nrf2/HO-1 signaling pathway.

Objective To develop a damage control operation(DCO)simulation training platform for traumatic brain injury(TBI)in wartime based on mixed reality to open up a new path for surgical skills training of military surgeons.Methods The platform mainly consisted of wartime TBI DCO simulation training software,a surgical manikin and a HoloLens 2 MR device.The simulating training software was developed with C# language and the technologies of MR,basic gestures,spatial scanning positioning and etc on the basis of constructed surgical decision-making training system,virtual surgical environment and functional modules.The surgical manikin was customized with reference to the standard body type of an adult male with a height of 180 cm,and an electronic chip was developed and placed inside the head of the manikin to execute data matching with the simulation training software.The simulation training software was installed and run in the HoloLens 2 MR device to realize TBI DCO simulation training on the virtual reality interactive model.Results The platform developed implemented the functions of virtual reality interactive model reset positioning,operation simulation training,examination and on-site demonstration,which gained advantages in stimulating learning interest and facilitating risk-free,time-and space-indepen-dent,immersive and interactive learning and was generally recognized by the trainees.Conclusion The simulation training platform can be a supplementary to other training means to improve the ability of military surgeons in damage control operation.[Chinese Medical Equipment Journal,2024,45(2):17-21]

Objective To construct a big data sharing platform for clinical scientific research to solve the problems of clinical research in decentralized application systems and data sharing safety.Methods A clinical research information data usage management system was developed through the formulation of management methods in line with the actual situation of the institution,normalized standard data usage processes and a data usage service team.Then a clinical scientific research big data sharing platform including the components for sharing environment construction,research application integration,data desensitization and encryption and file management was established based on the existing hospital systems,the requirements of clinical research data usage management and the habits of clinical researchers.Results The platform realized the balance between open sharing of clinical research data and data security control,which improved the efficiency of clinical researchers while reducing data security risks during data transmission and data analysis.Conclusion The clinical scientific research big data sharing platform meets the needs of clinical scientific research application and data security management,and provides references for the co-construction-sharing of medical big data resources.[Chinese Medical Equipment Journal,2024,45(4):27-31]