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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Thioredoxin-reductase 2 (Txnrd2) belongs to the thioredoxin-reductase family of selenoproteins and is a key antioxidant enzyme in mammalian cells to regulate redox homeostasis. Here, we reported that Txnrd2 exerted a major influence in brain damage caused by Intracerebral hemorrhage (ICH) by suppressing endoplasmic reticulum (ER) stress oxidative stress and via Trx2/Prx3 pathway. Furthermore, we demonstrated that pharmacological selenium (Se) rescued the brain damage after ICH by enhancing Txnrd2 expression. ⋯ Mechanistically, we observed that loss of Txnrd2 leads to increased lipid peroxidation levels and ER stress protein expression in neurons and astrocytes. Additionally, it was revealed that Se effectively restored the expression of Txnrd2 in brain and inhibited both the activity of ER stress protein activity and the generation of reactive oxygen species (ROS) by promoting Trx2/Prx3 kilter when administrating sodium selenite in lateral ventricle. This study shed light on the effect of Txnrd2 in regulating oxidative stress and ER stress via Trx2/Prx3 pathway upon ICH and its promising potential as an ICH therapeutic target.
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Traumatic brain injury (TBI) is a prevalent form of cranial trauma that results in neural conduction disruptions and damage to synaptic structures and functions. Cannabidiol (CBD), a primary derivative from plant-based cannabinoids, exhibits a range of beneficial effects, including analgesic, sedative, anti-inflammatory, anticonvulsant, anti-anxiety, anti-apoptotic, and neuroprotective properties. Nevertheless, the effects of synaptic reconstruction and the mechanisms underlying these effects remain poorly understood. ⋯ This reduction inhibited the interaction between TNF-α and P2Y1 receptors, leading to a decrease in the release of neurotransmitters, including Ca2+ and glutamate, thereby initiating synaptic remodeling. Our study showed that CBD exhibits significant therapeutic potential for TBI-related synaptic dysfunction, offering valuable insights for future research and more effective TBI treatments. Further exploration of the potential applications of CBD in neuroprotection is required to develop innovative clinical strategies.
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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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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%.