Objective To develop and validate a machine learning model based on preoperative clinical characteristics, laboratory indices, and radiological features for the non-invasive prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma. Methods Preoperative data from patients with early-stage lung adenocarcinoma who underwent surgical resection at Northern Jiangsu People's Hospital between January 2020 and August 2025 were retrospectively collected. The data included clinical characteristics, laboratory indices, and radiological features. Patients were divided into a STAS-positive and a STAS-negative group based on postoperative pathological findings. The dataset was randomly split into a training set and a testing set at a 7 : 3 ratio. Feature variables were selected using the maximum relevance and minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed: logistic regression (LR), random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The shapley additive explanations (SHAP) method was employed to interpret the optimal prediction model. Results A total of 377 patients were included, comprising 177 (46.9%) males and 200 (53.1%) females, with a mean age of (63.31±9.73) years. There were 261 patients in the training set and 116 patients in the testing set. In the training set, statistically significant differences were observed between the STAS-positive group (n=130) and STAS-negative group (n=131) across multiple features, including age, sex, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), clinical T stage, and maximum solid component diameter (P<0.05). A final set of 10 feature variables was selected by combining mRMR and LASSO regression, and five machine learning models (LR, RF, SVM, LightGBM, XGBoost) were developed. The XGBoost model demonstrated optimal performance in both the training and testing sets, achieving AUCs of 0.947 [95%CI (0.920, 0.975)] and 0.943 [95%CI (0.894, 0.993)], respectively. DCA indicated that the XGBoost model provided a high net clinical benefit across a wide range of threshold probabilities. SHAP analysis revealed that the vessel convergence sign, clinical T stage, age, consolidation-to-tumor ratio (CTR), and MLR were the features with the highest contributions to STAS prediction. Conclusion The XGBoost model effectively predicts preoperative STAS status in early-stage lung adenocarcinoma, exhibiting excellent discriminative performance and good clinical interpretability. Key predictors such as the vessel convergence sign, clinical T stage, age and CTR provide a crucial reference for preoperative risk assessment and the individualized selection of surgical strategies, ultimately benefiting patients.

A 29-year-old man visited Zhongshan Hospital, Fudan University in December 2021. The patient presented with recurrent coughing, sputum, and wheezing, high level of serum total IgE, positive aspergillus fumigatus-specific IgE and extremely severe mixed ventilatory dysfunction. These features and thoracic CT results scan showed bronchiectasis and allergic bronchopulmonary aspergillosis. In consideration of his clinical characteristics, including low levels of fractional exhaled nitric oxide (FeNO), and nasal nitric oxide (nNO), persistent cough after birth, consanguineous marriage of his parents, etc. we ratiocinated a possibility of hereditary diseases, especially primary ciliary dyskinesia (PCD). From this perspective, whole exome sequencing (WES) was performed and the diagnosis of PCD was ultimately confirmed.

A system adapted for depot management in hospital based on RFID technology is introduced. With resolution of RFID from PHILIPS and RF transmission from Nordic VLSI Norway, the storage of medical materials and medical equipments can be identified, labeled and statistically controlled accurately and expediently. The system can not only save a mass of work force, but also improve work efficiency.

This paper develops a USB signal acquisition card to introduce ECG signal into the computer for display, storagee and analysis. Such aspects get involved in as the acquisition, amplification, filter and AD conversion as well as the principle and structure of USB transmission circuit. MSP430 series of SCMs of TI Corp and SPCP825 chip of SunPlus Corp are adopted as the micro-controller and USB transmission chip respectively.

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