Neuroscience
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Children are disadvantaged compared to adults when they perceive speech in a noisy environment. Noise reduces their ability to extract and understand auditory information. Auditory-Evoked Late Responses (ALRs) offer insight into how the auditory system can process information in noise. ⋯ Different noise types had varying impacts, with the eight-talker babble noise causing more disruption. Children's auditory system responded similarly to adults but may be more susceptible to noise. This research emphasizes the need to understand noise's impact on children's auditory development, given their exposure to noisy environments, requiring further exploration of noise parameters in children.
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Armcx1 is a member of the ARMadillo repeat-Containing protein on the X chromosome (ARMCX) family, which is recognized to have evolutionary conserved roles in regulating mitochondrial transport and dynamics. Previous research has shown that Armcx1 is expressed at higher levels in mice after axotomy and in adult retinal ganglion cells after crush injury, and this protein increases neuronal survival and axonal regeneration. However, its role in traumatic brain injury (TBI) is unclear. ⋯ The results demonstrated that Armcx1 protein expression was elevated after TBI and that the Armcx1 protein was localized in neurons and astroglial cells in cortical tissue surrounding the injury site. In addition, inhibition of Armcx1 expression further led to impaired mitochondrial transport, abnormal morphology, reduced ATP levels, aggravation of neuronal apoptosis and neurological dysfunction, and decrease Miro1 expression. In conclusion, our findings indicate that Armcx1 may exert neuroprotective effects by ameliorating neurological injury after TBI through a mitochondrial transport pathway involving Miro1.
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This study investigated the potentials of hsa_circ_0018401 and miR-127-5p in traumatic brain injury (TBI) diagnosis, stratification and outcome prediction. A retrospective analysis of clinical data and blood samples of n = 109 TBI patients was performed. Expression levels of hsa_circ_0018401 and miR-127-5p were measured using Real-time PCR. ⋯ Hsa_circ_0018401 and miR-127-5p, used alone or combinedly, showed clinical values for TBI diagnosis and stratification, as well as outcome prediction. The proteins for target genes covered TBI-related functions and pathways. Therefore, hsa_circ_0018401 and miR-127-5p could represent promising new biomarkers to identify TBI from healthy, moderate/severe TBI from mild TBI, as well as to predict the TBI outcome.
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Contests may be highly effective in eliciting high levels of effort, but they also carry the risk of inefficient resource allocation due to excessive effort (overbidding), squandering valuable social resources. While a growing body of research has focused on how group identity exacerbates out-group conflict, its influence on in-group conflict remains relatively unexplored. This study endeavors to explore the impact of group identity on conflicts within and between groups in competitive environments, thereby addressing gaps in the current research landscape and dissecting the involved neurobiological mechanisms. ⋯ Subsequently, after the task, additional activation was observed in the right temporal lobe. Results from functional connectivity studies indicated that group identity tasks modify decision-making processes by promoting group norms, empathy, and blurred self-other boundaries for in-group decisions, while out-group decisions after the group identity task see heightened cognitive control, an increased dependence on rational judgment, introspection of self-environment relationships, and a greater focus on anticipating others' behaviors. This study reveals the widespread occurrence of overbidding behavior and demonstrates the role of group identity in mitigating this phenomenon, concurrently providing a comprehensive analysis of the underlying neural mechanisms.
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Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. ⋯ The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.