Contribution of small airway inflammation to the development of COPD

BACKGROUND

Little attention has been paid to the pathophysiological changes in the natural history of chronic obstructive pulmonary disease (COPD). The destructions of the small airways were visualized on thoracic micro-computed tomography scan. We investigated whether small airway inflammation (SAI) was the risk for the development of COPD.

METHODS

A total of 1062 patients were enrolled and analyzed in the study. The partitioned airway inflammation was determined by exhaled nitric oxide (NO) of FnNO, FeNO50, FeNO200, and calculated CaNOdual. Both FeNO200 and CaNOdual were compared to detect the promising predictor for peripheral airway/alveolar inflammation in COPD. The correlation between exhaled NO and white cell classification was evaluated to determine the inflammation type during the development of COPD.

RESULTS

Exhaled NO levels (FnNO, FeNO50, FeNO200, and CaNOdual) were the highest in the COPD group compared with all other groups. Furthermore, compared with controls, exhaled NO levels (FeNO50, FeNO200, and CaNOdual) were also significantly higher in the emphysema, chronic bronchitis, and smoking groups. FeNO200 was found to be a promising predictor for peripheral airway/alveolar inflammation (area under the curve [AUC] of the receiver operating characteristic [ROC] curve, area under the curve [AUC] = 0.841) compared with CaNOdual (AUC ROC = 0.707) in COPD. FeNO200 was the main risk factor (adjusted odds ratio, 2.191; 95% CI, 1.797-2.671; p = 0.002) for the development of COPD. The blood eosinophil and basophil levels were correlated with FeNO50 and FeNO200.

CONCLUSION

The complete airway inflammations were shown in COPD, whereas SAI was the main risk factor for the development of COPD, which might relate to eosinophil and basophil levels.

DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network

Non-coding RNAs (ncRNAs) play crucial roles in drug resistance and sensitivity, making them important biomarkers and therapeutic targets. However, predicting ncRNA-drug associations is challenging due to issues such as dataset imbalance and sparsity, limiting the identification of robust biomarkers. Existing models often fall short in capturing local and global sequence information, limiting the reliability of predictions. This study introduces DMGAT (diffusion map and heterogeneous graph attention network), a novel deep learning model designed to predict ncRNA-drug associations. DMGAT integrates diffusion maps for sequence embedding, graph convolutional networks for feature extraction, and GAT for heterogeneous information fusion. To address dataset imbalance, the model incorporates sensitivity associations and employs a random forest classifier to select reliable negative samples. DMGAT embeds ncRNA sequences and drug SMILES using the word2vec technique, capturing local and global sequence information. The model constructs a heterogeneous network by combining sequence similarity and Gaussian Interaction Profile kernel similarity, providing a comprehensive representation of ncRNA-drug interactions. Evaluated through five-fold cross-validation on a curated dataset from NoncoRNA and ncDR, DMGAT outperforms seven state-of-the-art methods, achieving the highest area under the receiver operating characteristic curve (0.8964), area under the precision-recall curve (0.8984), recall (0.9576), and F1-score (0.8285). The raw data are released to Zenodo with identifier 13929676. The source code of DMGAT is available at https://github.com/liutingyu0616/DMGAT/tree/main.

Automatic registration of urban high-resolution remote sensing images based on characteristic spatial objects

Automatic registration of high-resolution remote sensing images (HRRSIs) has always been a severe challenge due to the local deformation caused by different shooting angles and illumination conditions. A new method of characteristic spatial objects (CSOs) extraction and matching is proposed to deal with this difficulty. Firstly, the Mask R-CNN model is utilized to extract the CSOs and their positioning points on the images automatically. Then, an encoding method is provided to encode each object with its nearest adjacent 28 objects according to the object category, relative distance, and relative direction. Furthermore, a code matching algorithm is applied to search the most similar object pairs. Finally, the object pairs need to be filtered by position matching to construct the final control points for automatic image registration. The experimental results demonstrate that the registration success rate of the proposed method reaches 88.6% within a maximum average error of 15 pixels, which is 28.6% higher than that of conventional optimization method based on local feature points. It is reasonable to believe that it has made a beneficial contribution to the automatic registration of HRRSIs more accurately and efficiently.

A nomogram model based on clinical and 3D-EIT parameters for CTEPH diagnosis

BACKGROUND

Chronic thromboembolic pulmonary hypertension (CTEPH) is easily misdiagnosed. Three-dimensional (3D) electrical impedance tomography (EIT) can monitor the whole-lung perfusion at the bedside. In this study, three-dimensional electrical impedance tomography (3D-EIT) features in patients with suspected chronic thromboembolic pulmonary hypertension (CTEPH) was investigated, and nomogram models based on clinical and 3D-EIT parameters were constructed to identify CTEPH.

METHODS

Patients with pulmonary hypertension (PH) due to left heart disease and chronic hypoxia were excluded. The enrolled patients were divided into CTEPH and Non-CTEPH groups by confirmatory tests. Then, history and laboratory results were collected and 3D-EIT examination was performed. Out of 70 enrolled patients, 50 cases were used as the training set to construct the nomogram model. Obtained nomogram diagnostic model was calibrated and then evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC).

RESULTS

Through a comprehensive univariate analysis, Wald test, Akaike information criterion (AIC), and Bayesian information criterion (BIC), the nomogram model for CTEPH diagnosis based on 50 patients was constructed using venous thromboembolism (VTE) history, D-dimer, maximum of corresponding regional ventilation/perfusion ratio (V/Qmax), range between the maximum and minimum values of regional perfusion (P-Range) and the percentage of ventilation/perfusion match area (VQMatch). The C-index of the nomogram model in the training set was 0.926 (95% CI: 0.859-0.993). In the training set and test set, the nomogram model had a larger area under the curve (AUC) than models containing only VTE history, VTE history + D-dimer and EIT parameters. Both DCA and CIC analyses indicate that this model can provide significant clinical benefits.

CONCLUSIONS

A nomogram model combining clinical and 3D-EIT parameters facilitated the diagnosis of CTEPH.

CLINICAL TRIAL NUMBER

Not applicable.