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 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.

Objective:To summarize and analyze the composition characteristics and problems of basic medical insurance policies for traditional Chinese medicine in various provinces of China,providing reference for optimizing and improving subsequent basic medical insurance policies for traditional Chinese medicine.Methods:Based on the perspective of policy instrument,combined with two dimensions of policy instrument types and policy development process,the content analysis method is used to quantitatively analyze the content of the basic medical insurance policies for traditional Chinese medicine released at the provincial level from 2011 to 2023.Results:The 93 included policy documents were coded and sorted,with a cumulative total of 487 codes.From the perspective of policy instrument dimensions,subcategories of policy instruments involve diverse themes,but there are differences in the level of attention paid to each policy tool.From the perspective of policy development process,each link also presents a discrete trend,indicating a dominant feature of policy planning and implementation.Conclusion:To improve the basic medical insurance policy system of traditional Chinese medicine in China,it is necessary to optimize the combination of policy instrument and construct a coordinated and balanced policy instrument framework;Overall planning of the development process of traditional Chinese medicine medical insurance policies,highlighting the unique advantages of traditional Chinese medicine;Emphasize policy synergy between dimensions and strengthen the implementation of traditional Chinese medicine medical insurance policies.

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]

Respiratory syncytial virus (RSV) is an enveloped, negative-sense, single-stranded RNA virus of the Orthopneumovirus  genus of the Pneumoviridae family in the order Mononegavirales. RSV can cause acute upper and lower respiratory tract infections, sometimes with extrapulmonary complications. The disease burden of RSV infection is enormous, mainly affecting infants and older adults aged 75 years or above. Currently, treatment options for RSV are largely supportive. Prevention strategies remain a critical focus, with efforts centered on vaccine development and the use of prophylactic monoclonal antibodies. To date, three RSV vaccines have been approved for active immunization among individuals aged 60 years and above. For children who are not eligible for these vaccines, passive immunization is recommended. A newly approved prophylactic monoclonal antibody, Nirsevimab, which offers enhanced neutralizing activity and an extended half-life, provides exceptional protection for high-risk infants and young children. This review provides a comprehensive and detailed exploration of RSV's virology, immunology, pathogenesis, epidemiology, clinical manifestations, treatment options, and prevention strategies.
Humans ; Respiratory Syncytial Virus Infections/prevention & control* ; Respiratory Syncytial Viruses/pathogenicity* ; Respiratory Syncytial Virus, Human/pathogenicity* ; Antiviral Agents/therapeutic use*

Objective: The scientific community knows little about the long-term influence of coronavirus disease 2019 (COVID-19) on olfactory dysfunction (OD). With the COVID-19 pandemic ongoing worldwide, the risk of imported cases remains high. In China, it is necessary to understand OD in imported cases. Methods: A prospective follow-up design was adopted. A total of 11 self-reported patients with COVID-19 and OD from Xi'an No. 8 Hospital were followed between August 19, 2021, and December 12, 2021. Demographics, clinical characteristics, laboratory and radiological findings, and treatment outcomes were analyzed at admission. We surveyed the patients via telephone for recurrence and sequelae at the 1-, 6-, and 12-month follow-up. Results: Eleven patients with OD were enrolled; of these, 54.5% (6/11) had hyposmia and 45.5% (5/11) had anosmia. 63.6% (7/11) reported OD before or on the day of admission as their initial symptom; of these, 42.9% (3/7) described OD as the only symptom. All patients in the study received combined treatment with traditional Chinese medicine and Western medicine, and 72.7% (8/11) had partially or fully recovered at discharge. In terms of OD recovery at the 12-month follow-up, 45.5% (5/11) reported at least one sequela, 81.8% (9/11) had recovered completely, 18.2% (2/11) had recovered partially, and there were no recurrent cases. Conclusions Our data revealed that OD frequently presented as the initial or even the only symptom among imported cases. Most OD improvements occurred in the first 2 weeks after onset, and patients with COVID-19 and OD had favorable treatment outcomes during long-term follow-up. A better understanding of the pathogenesis and appropriate treatment of OD is needed to guide clinicians in the care of these patients.
COVID-19/complications* ; Follow-Up Studies ; Humans ; Olfaction Disorders/etiology* ; Pandemics ; Prospective Studies ; SARS-CoV-2

Objective To explore the morphology and the tissue structure of acromioclavicular joint. Methods Anatomical analysis was performed on 27 fresh adult cadavers and the morphology of the acromioclavicular joint was observed. The relevant bone structure and ligament parameters were measured, and the specimens were randomly divided into group A and group B. Group A reserved the acromioclavicular ligament and coracoclavicular ligament, and group B reserved only the acromioclavicular ligament. The difference in tension between the two groups was compared. Results The distance from the midpoint of the conical ligament to the distal end of the clavicle was (42.68 ± 6.34) mm, the width of the end point was (16.97 ± 4.28) mm, and the thickness of the center point was (5.39 ± 0.34) mm; the distance from the midpoint of the trapezoidal ligament to the clavicle was (20.35 mm ± 4.18) mm, the width of the end point was (10.35± 1.31) mm, the thickness of the center point was (5.19 ± 0.342) mm; the average vertical distance from the base of the coracoid process to the surface of the clavicle was 30.75 mm, and the mean coracoclavicular gap was 12.02 mm; the length of the central axis of the conical ligament was (15.68 ± 3.30) mm and the angle was (117.25 ± 10.80) °, while the length of the central axis of the trapezoidal ligament was (9.67 ± 2.25) mm, and the angle was (75.42± 11.37) °. The distance between the start joint of the trapezoidal ligament and the trapezium was (8.96± 3.00) mm, and the distance between the end points (13.09± 3.50) mm. The average tensile force of group A was higher than that of group B [(610.04 ± 51.24) N vs (560.41 ± 44.63) N, P < 0.05]. Conclusion During distal clavicular resection, the resection of the distal clavicle shall be within 10-30 mm. The depth shall not exceed 42 mm when drilling under the coracoid process. The reconstruction of the coracoclavicular ligament during acromioclavicular joint dislocation has an anatomical and biomechanical basis.

Antiviral Agents/therapeutic use* ; Bibliometrics ; COVID-19/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Humans ; SARS-CoV-2