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Background: Recent studies have identified empathy deficit as a core impairment and diagnostic criterion for people with autism spectrum disorders; however, the improvement of empathy focuses primarily on behavioural interventions without the target regulation. We sought to compare brain regions associated with empathy-like behaviours of fear and pain, and to explore the role of the oxytocinoxytocin receptor system in fear empathy. Methods: We used C57BL mice to establish 2 models of fear empathy and pain empathy. We employed immunofluorescence histochemical techniques to observe the expression of c-Fos throughout the entire brain and subsequently quantified the number of c-Fos-positive cells in different brain regions. Furthermore, we employed chemogenetic technology to selectively manipulate these neurons in Oxt-[Cre.sup.-/+] mice to identify the role of oxytocin in this process. Results: The regions activated by fear empathy were the anterior cingulate cortex, basolateral amygdala, nucleus accumbens, paraventricular nucleus (PVN), lateral habenula, and ventral and dorsal hippocampus. The regions activated by pain empathy were the anterior cingulate cortex, basolateral amygdala, nucleus accumbens, and lateral habenula. We found that increasing the activity of oxytocin neurons in the PVN region enhanced the response to fear empathy. This enhancement may be mediated through oxytocin receptors. Limitations: This study included only male animals, which restricts the broader interpretation of the findings. Further investigations on circuit function need to be conducted. Conclusion: The brain regions implicated in the regulation of fear and pain empathy exhibit distinctions; the activity of PVN neurons was positively correlated with empathic behaviour in mice. These findings highlight the role of the PVN oxytocin pathway in regulating fear empathy and suggest the importance of oxytocin signalling in mediating empathetic responses., Introduction Empathy refers to an individual's perception and recognition of the internal emotions and feelings of others, including the ability to identify and respond to their voices, expressions, and physical [...]

PurposeThe purpose of this study is to summarize the design and methodology of a large-scale trial in northern China, the Beijing Angle Closure Progression Study (BAPS). This trial is designed to explore the 5-year incidence of primary angle-closure suspect (PACS) progressing to primary angle-closure (PAC) or primary angle-closure glaucoma (PACG) and to determine the possible risk factors of disease progression.Methods/designThe BAPS is a clinic-based, multicenter, noninterventional trial conducted on a sample of urban Chinese adults. Consecutive eligible patients who meet PACS diagnostic criteria will be recruited from eight participating centers, with the trial commencing on August 4, 2022. The target sample size is set at 825 subjects, with follow up planned for a minimum period of 5 years. Baseline examination will include presenting visual acuity, best corrected visual acuity, intraocular pressure (IOP), undilated slit-lamp biomicroscopy, stereoscopic evaluation of the optic disc, visual field test, optical coherence tomography evaluation of retinal nerve fiber layer, ultrasound biomicroscopy and IOLMaster. Questionnaires will also be used to collect detailed personal history. Patients are scheduled to visit the glaucoma clinic every 12 months and may visit the emergency room in case of acute attack of angle closure. Study endpoints include acute PAC episodes, elevated IOP, peripheral anterior synechiae, glaucomatous visual field defect, or glaucomatous abnormality of optic nerve.DiscussionThe BAPS will provide data on the 5-year incidence of PACS progressing to PAC or PACG and determine the risk factors for disease progression. This study will also help redefine high-risk patients with PACS.

Abstract To address the problem of pillar instability in residual mining areas under repeated mining disturbances, the prediction of pillar instability time and mining technology is studied by using numerical simulations, theoretical examinations, and practical experimentation techniques utilizing the remaining facade of the Shenghua Coal Industry 3103. On the basis of the findings, employing a fully mechanized mining technique exerts greater pressure on the coal pillar, increasing the likelihood of plastic failure. However, adopting the segmental filling mining approach can effectively disperse this pressure, consequently mitigating potential damage to the coal pillar. Initially, the coal pillar undergoes a progressive yielding or collapsing process at its periphery, leading to instability once the area of the projectile core of the coal pillar decreases beyond a specific threshold. The dimensions of both the affected roadway and the coal pillar play a pivotal role in determining the coal pillar's load‐bearing capacity. A prediction model for pillar instability is established. If the area of the projectile core is less than 34% during remining, the mining method should be changed or pillar reinforcement should be implemented. The accurate prediction and adaptation of the model's mining approach ensure the secure extraction of coal pillars within the remaining mining region, with successful validation of the model's performance.

Abstract With the increasing depth of engineering in rock masses, the issue of significant rheological deformation becomes notably prominent. To accurately characterize the deformation and failure behavior of the surrounding rock near the goaf in deep small coal pillars, we have developed a viscoelastic–plastic constitutive model that takes into account the initial stress associated with creep. This model builds upon an existing viscoelastic–plastic constitutive framework and is implemented numerically using C++ in the FLAC3D. The Oedometer test method is used to check the creep components and the calculated parameter (η/G) ensure convergence of numerical operations. We conducted both pre‐improvement and post‐improvement tests of the constitutive model in a simple numerical analysis of roadways. The enhanced model demonstrates improved accuracy in deviational stress calculations and proves suitable for modeling deep, soft roadways. Furthermore, we applied the improved model to simulate actual working conditions at Zhaozhuang Mine, where mining operations near the working face induce a fracture zone of approximately 3 m in the small coal pillar located at the goaf's edge. The new model closely aligns with the observed results, further validating its suitability for tunneling along the goaf. Tunneling and mining activities result in significant deformation at various locations, including the shoulder socket of the working face, the top angle of the coal pillar, and the bottom angle of the floor. The crushing zone at the working face extends up to 4 m. Rheological effects transform the triangular elastic core, from its original high‐pressure elastic state to a fracture and plastic zone in the deep coal. Stress in deep coal is transmitted along the triangular elastic core. Numerical simulation results closely match the excavation profile of the tunneling, providing valuable insights into the impact of mining activities. This study can provide reference for deformation of similar geological conditions and engineering situation.

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis. Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries. Results: The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P < 0.05). Additionally, our model exhibited no gender bias (P > 0.05). Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings.