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Prolonged disorder of consciousness (DoC) is a rising challenge. Pediatric data on diagnosis and prognosis of prolonged DoC were too limited and heterogeneous, making it difficult to define the natural course and evaluate the prognosis. The present study explored the emergence from the Minimally Conscious State (eMCS) incidence at different months postinjury drawing the natural course, and detected the predictors of the incidence in children with prolonged DoC. A hospital-based prospective cohort study was conducted. Kaplan–Meier curves, as well as univariate and multivariate COX regression analysis, were performed. The study enrolled 383 pediatric DoC individuals, including 220 males (57.4%), with an average age of 3.9 (1.9–7.3) years. The median duration between onset and rehabilitation is 30.0 (21.0–46.0) days. At enrollment, the ratio of vegetative state/unresponsive wakefulness syndrome (VS/WUS) to MCS is 78.9%–21.1%. Traumatic brain injury and infection are the major etiologies (36.8% and 37.1%, respectively), followed by hypoxia cerebral injury (12.3%). For children with prolonged DoC, the cumulative incidence of eMCS at months 3, 6, 12, and 24 was 0.510, 0.652, 0.731, 0.784 VS 0.290, 0.418, 0.539, 0.603 in the traumatic VS non-traumatic subgroup, respectively. For children in a persistent vegetative state (PVS), the cumulative incidence of emergence at months in 3, 6, 12, 24, 36 and 48 was testified as 0.439, 0.591, 0.683, 0.724, 0.743 and 0.743 in the traumatic subgroup, and 0.204, 0.349, 0.469, 0.534, 0.589 and 0.620 in the non-traumatic subgroup. Participants who exhibit any of the following four demographical and/or clinical characteristics—namely, older than 4 years at onset, accepted rehabilitation within 28 days of onset, remained MCS at enrollment, or with etiology of traumatic brain injuries—had a significantly positive outcome of consciousness recovery (eMCS). Moreover, both prolongation of the central somatosensory conductive time (CCT) (level 2) and absence of N20 (level 3) independently predict a negative outcome. In children with prolonged DoC, we found that 12 months postinjury was critical to eMCS, and a preferred timepoint to define chronic vegetative state (VS). The characteristics including age, etiology, time before rehabilitation, consciousness state, and SEP results were useful predictors of conscious recovery. Trial registration Registered 06/11/2018, the registration number is chiCTR1800019330 (chictr.org.cn). Registered prospectively. [ABSTRACT FROM AUTHOR]

This study aimed to construct a non-invasive diagnostic nomogram based on high-frequency ultrasound and magnetic resonance imaging results for early liver cirrhosis patients with chronic hepatitis B (CHB) which cannot be detected by conventional non-invasive examination methods but can only be diagnosed through invasive liver puncture for pathological examination. 72 patients with CHB were enrolled in this prospective study, and divided into S4 stage of liver cirrhosis and S0-S3 stage of non-liver cirrhosis according to pathological findings. Binary logistic regression analysis was performed to identify independent predictors, and a diagnostic nomogram was constructed for CHB-related early cirrhosis. It was validated and calibrated by bootstrap self-extraction. Binary logistic regression analysis showed that age (OR 1.14, 95% CI (1.04–1.27)), right hepatic vein diameter (OR 0.43, 95% CI 0.23–0.82), presence or absence of nodules (OR 31.98, 95% CI 3.84–266.08), and hepatic parenchymal echogenicity grading (OR 12.82, 95% CI 2.12–77.51) were identified as independent predictive indicators. The nomogram based on the 4 factors above showed good performance, with a sensitivity and specificity of 90.70% and 89.66%, respectively. The area under the curve (AUC) of the prediction model was 0.96, and the predictive model showed better predictive performance than APRI score (AUC 0.57), FIB-4 score (AUC 0.64), INPR score (AUC 0.63), and LSM score (AUC 0.67). The calibration curve of the prediction model fit well with the ideal curve, and the decision curve analysis showed that the net benefit of the model was significant. The nomogram in this study can detect liver cirrhosis in most CHB patients without liver biopsy, providing a direct, fast, and accurate practical diagnostic tool for clinical doctors. [ABSTRACT FROM AUTHOR]

Generative AI, represented by GPT (Generative Pre-trained Transformer), is now leading the technological revolution and is reconstructing the journalism and communication industries’ ecosystems because of its powerful generative capacity and diverse range of outputs. While GPT is busy revolutionizing and innovating the production of news content, working patterns, and operation modes, it has also given rise to ethical concerns in regard to news authenticity, data security, humanistic values, and other related aspects. Therefore, it is imperative to initiate strategies and approaches, such as establishing a mechanism for verifying information authenticity, enhancing data security and privacy regulations, and instituting an ethical supervision and governance framework for AI, in order to facilitate the systematic advancement of AI-based news production while reinstating public trust. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology provides a novel approach for studying the damage and fracture behavior of wood‐plastic composites (WPCs) during their application process. This method allows rapid and precise analysis of the mechanical properties of materials, which is a significant advancement for the emerging WPC industry. Hence, a blend of waste floor sanding powder (FSP) and rice husk (RH) reinforced recycled high‐density polyethylene (Re‐HDPE) was prepared by the extrusion method. The AE technique, scanning electron microscopy, and finite element method (FEM) were used to analyze the damage and fracture behavior of the composite materials under a three‐point bending test. The results indicated that the bearing capacity of RH/Re‐HDPE composites was slightly higher than that of FSP/Re‐HDPE composites. The AE technique can better illustrate the damage evolution and fracture process of composites and determine the damage degree and mode. Matrix cracking and deformation were more pronounced in the FSP/Re‐HDPE composites. Fiber breakage and delamination were more prominent in the RH/Re‐HDPE composites. In addition, repeated loading caused irreversible damage to the composite. The increase in temperature led to the attenuation of the AE signal and a decrease in matrix strength. However, when the temperature reached a certain point, the cumulative AE ringing count increased again because the higher temperature improved the fiber–matrix interface strength, which partially canceled out the negative effect. In addition, Abaqus was used for simulation analysis, and the running results coincided with the experimental value. Highlights: RH/Re‐HDPE and FSP/Re‐HDPE are sustainability and low‐cost composites.AE can determine the degree and mode of damage of WPC at different temperatures.The AE curves of RH/Re‐HDPE and FSP/Re‐HDPE all pass through four stages.Heating reduced the matrix strength and improved the interface strength of WPC.The results of FEM were consistent with the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we propose an emotion recognition model based on convolutional neural network (CNN), long short term memory (LSTM) and channel attention mechanism, aiming at the low classification accuracy of machine learning methods and the uneven spatial distribution of electroencephalogram (EEG) electrodes. This model can effectively integrate the frequency, space and time information of EEG signals, and improve the accuracy of emotion recognition by adding channel attention mechanism after the last convolutional layer of the model. Firstly, construct a 4-dimensional structure representing EEG signals. Then, a CLSTM model structure combining CNN and LSTM is designed. CNN is used to extract frequency and spatial information from 4-dimensional input, and LSTM is used to extract time information. Finally, the channel attention module is added after the last convolutional layer of CLSTM model structure to allocate the weight of different electrodes. In this paper, an emotion recognition model based on CLSTM and channel attention mechanism was proposed from the perspective of integrating the frequency, space and time 3-dimensional information of EEG signals. The average classification accuracy of the model on SEED public data set reached 93.36%, which was significantly improved over the existing CNN and LSTM emotion recognition models.