Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules.

OBJECTIVE: To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). METHOD: A total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People's Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models. RESULTS: The combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively. CONCLUSION: The combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.

The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education.

The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined and explained based on the relevant theories of pedagogy. Next, gesture interactive robots and music perception education classrooms are studied based on recurrent neural networks (RNNs). Then, autistic children are treated by music perception, and an electroencephalogram (EEG) is used to collect the music perception effect and disease diagnosis results of children. Due to significant advantages of signal feature extraction and classification, RNN is used to analyze the EEG of autistic children receiving different music perception treatments to improve classification accuracy. The experimental results are as follows. The analysis of EEG signals proves that different people have different perceptions of music, but this difference fluctuates in a certain range. The classification accuracy of the designed model is about 72-94%, and the average classification accuracy is about 85%. The average accuracy of the model for EEG classification of autistic children is 85%, and that of healthy children is 84%. The test results with similar models also prove the excellent performance of the design model. This exploration provides a reference for applying the artificial intelligence (AI) technology in music perception education to diagnose and treat autistic children.

First survey of parasitic helminths of goats along the Han River in Hubei Province, China.

Diseases caused by parasitic helminths cause considerable production and economic losses in livestock worldwide. Understanding the epidemiology of these parasites has important implications for controlling them. The main purpose of the present study was to estimate the prevalence of key parasitic helminths in goats along the Han River in Zhanggang, Hubei Province (from January to December 2014). We used faecal flotation and sedimentation techniques as well as PCR-based DNA sequencing to detect and identify helminths. Results showed that the prevalence of helminths was high throughout the year, particularly for gastrointestinal nematodes. These first findings provide useful baseline information for goat helminths in Zhanggang, and a starting point for the implementation of control programs. With an increased expansion of the goat industry in China, the findings also emphasise the need to undertake prevalence surveys in other regions of China where extensive farming practices are used.

Characterization of the complete mitochondrial genome sequence of Homalogaster paloniae (Gastrodiscidae, Trematoda) and comparative analyses with selected digeneans.

Gastrodiscidae species are neglected but significant paramphistomes in small ruminants, which can lead to considerable economic losses to the breeding industry of livestock. However, knowledge about molecular ecology, population genetics, and phylogenetic analysis is still limited. In the present study, we firstly sequenced and analyzed the full mitochondrial (mt) genome of Homalogaster paloniae (14,490 bp). The gene contents and organization of the H. paloniae mt genome is the same as that of other digeneans, such as Fasciola hepatica and Paramphistomum cervi. It is interesting that unlike other paramphistomes, H. paloniae is flat in shape which is similar with Fasciola, such as F. hepatica. Phylogenetic analysis of H. paloniae and other 17 selected digeneans using concatenated amino acid sequences of the 12 protein-coding genes showed that Gastrodiscidae is closely related to Paramphistomidae and Gastrothylacidae. The availability of the mt genome sequence of H. paloniae should provide an important foundation for further molecular study of Gastrodiscidae and other digeneans.