Construction and Verification of a Radiation Pneumonia Prediction Model Based on Multiple Parameters.

OBJECTIVE: Patients with lung cancer are at risk of radiation pneumonia (RP) after receiving radiotherapy. We established a prediction model according to the critical indicators extracted from radiation pneumonia patients. MATERIALS AND METHODS: 74 radiation pneumonia patients were involved in the training set. Firstly, the clinical data, hematological and radiation dose parameters of the 74 patients were screened by Logistics regression univariate analysis according to the level of radiation pneumonia. Next, Stepwise regression analysis was utilized to construct the regression model. Then, the influence of continuous variables on RP was tested by smoothing function. Finally, the model was externally verified by 30 patients in validation set and visualized by R code. RESULTS: In the training set, there was 40 patients suffered≥ level 2 acute radiation pneumonia. Clinical data (diabetes), blood indexes (lymphocyte percentage, basophil percentage, platelet count) and radiation dose (V15 > 40%, V20 > 30%, V35 >18%, V40 > 15%) were related to radiation pneumonia (P < 0.05). Particularly, stepwise regression analysis indicated that the history of diabetes, the basophils percentage, platelet count and V20 could be the best combination used for predicting radiation pneumonia. The column chart was obtained by fitting the regression model with the combined indicator. The receiver operating characteristic (ROC) curve showed that the AUC in the development term was 0.853, the AUC was 0.656 in the validation term. And calibration curves of both groups showed the high stability in efficiently diagnostic. Furthermore, the DCA curve showed that the model had a satisfactory positive net benefit. CONCLUSION: The combination of the basophils percentage, platelet count and V20 is available to build a predictive model of radiation pneumonia for patients with advanced lung cancer.

AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model.

Multimodal sentiment analysis is an essential task in natural language processing which refers to the fact that machines can analyze and recognize emotions through logical reasoning and mathematical operations after learning multimodal emotional features. For the problem of how to consider the effective fusion of multimodal data and the relevance of multimodal data in multimodal sentiment analysis, we propose an attention-based mechanism feature relevance fusion multimodal sentiment analysis model (AFR-BERT). In the data pre-processing stage, text features are extracted using the pre-trained language model BERT (Bi-directional Encoder Representation from Transformers), and the BiLSTM (Bi-directional Long Short-Term Memory) is used to obtain the internal information of the audio. In the data fusion phase, the multimodal data fusion network effectively fuses multimodal features through the interaction of text and audio information. During the data analysis phase, the multimodal data association network analyzes the data by exploring the correlation of fused information between text and audio. In the data output phase, the model outputs the results of multimodal sentiment analysis. We conducted extensive comparative experiments on the publicly available sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experimental results show that AFR-BERT improves on the classical multimodal sentiment analysis model in terms of relevant performance metrics. In addition, ablation experiments and example analysis show that the multimodal data analysis network in AFR-BERT can effectively capture and analyze the sentiment features in text and audio.

Four-dimensional echocardiography and left ventricular systolic strain measured via two-dimensional speckle-tracking for Danon disease: a case series.

BACKGROUND: Danon disease is an X-linked multisystemic disorder characterized by skeletal myopathy, cardiomyopathy, and intellectual disability. CASE SUMMARY: Herein, we describe two patients affected by Danon disease from the same family, a father (Patient 1) and his daughter (Patient 2). In Patient 1, a short PR interval with pre-excitation was evident. In Patient 2, over a 24-h period 2369 atrial premature beats and rare isolated ventricular ectopics were detected. Both patients exhibited left ventricular hypertrophy with non-compaction myocardium, and the left ventricular ejection fraction was impaired in Patient 1 and normal in Patient 2. In Patient 2, the total left ventricular strain value was reduced, and layer-specific strain revealed that subepicardial strain impaired more than in other layers. Late gadolinium enhancement was detected both in left and right ventricles in Patient 2, and cardiac fibrosis was more apparent in the subepicardium of left ventricular free wall. Four-dimensional (4D) echocardiography revealed that left atrial reservoir strain and left ventricular total longitudinal strain were induced. DISCUSSION: Novel 4D echocardiography and left ventricular systolic strain may play important role in diagnosis and myocardial functional evaluation in Danon disease.

M2 subtype tumor associated macrophages (M2-TAMs) infiltration predicts poor response rate of immune checkpoint inhibitors treatment for prostate cancer.

BACKGROUND: Prostate cancer (PCa) is poor response to the immunotherapy for its high heterogeneity of immune microenvironment. In this study, we aim to introduce a new immune subtype for PCa involving M2 tumour associated macrophages (M2-TAMs). METHODS: Three hundred and sixty-two PCa patients and matched normal prostate tissues were selected from the Cancer Genome Atlas and Gene Expression Omnibus databases. Patients' immune infiltration characters were then analyzed based on the gene expressions. The immune subtypes were identified by the method of unsupervised hierarchical clustering. Finally, the relationship between the M2-TAMs infiltration and anti-programmed death-ligand-1 (PD-L1) therapy was investigated in the IMvigor210 cohort. RESULTS: PCa expressed lower immune-related genes levels compared with the adjacent normal tissues. Based on the proved immunosuppressive mechanisms in PCa, tumour patients were classified into three independent subclasses with high infiltrated cytolytic activity (CYT), M2-TAMs and regulatory T cell (Tregs), respectively. Among these subtypes, M2-TAMs infiltration subtype showed the worst clinicopathological features and prognosis compared with the other two subtypes. The results of the IMvigor210 cohort demonstrated poor response of anti-PD-L1 therapy for patients with high M2-TAMs infiltration. CONCLUSION: Prostate tumours involved in significant immunosuppression, and high infiltration of M2-TAMs can be applied to predict the effect of anti-PD-L1 therapy.Key MessagesPCa patients can be classified into three immunotypes of high infiltrated CYT, M2-TAMS, and Tregs according to the immunosuppressive mechanisms.High M2-TAMs infiltration subtype reflected the worst clinical characters, immune infiltration, and lowest expression of immune checkpoint inhibitors among the three subclasses in PCa.High M2-TAMs infiltration predicts the low response rate of anti-PD-L1 therapy.

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode.

BACKGROUND: The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. MATERIALS AND METHODS: We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. RESULTS: We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3- and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). CONCLUSION: We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.