Publications by authors named "Jiahui Pan"

Objective: This study aimed to explore differences in sleep electroencephalogram (EEG) patterns in individuals with prolonged disorders of consciousness, utilizing polysomnography (PSG) to assist in distinguishing between the vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), thereby reducing misdiagnosis rates and enhancing the quality of medical treatment.

Methods: A total of 40 patients with prolonged disorders of consciousness (pDOC; 27 patients in the VS/UWS and 13 in the MCS) underwent polysomnography. We analyzed differential EEG indices between VS/UWS and MCS groups and performed correlation analyses between these indices and the Coma Recovery Scale-Revised (CRS-R) scores.

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Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major-BCI major-has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major.

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Reversion inducing cysteine rich protein with kazal motifs (RECK), a Kazal motif-containing protein, regulates pro-inflammatory cytokines production, migration of inflammatory cells, vascular endothelial growth factor (VEGF) and Wnt pathways and plays critical roles in septic inflammatory storms and vascular endothelial dysfunction. Recently, RECK has been defined as the negative regulator of adisintegrin and metalloproteinases (ADAMs) and matrix metalloproteinases (MMPs), which are both membrane "molecular scissors" and aggravate the poor prognosis of sepsis. To better understand the roles of RECK and the related mechanisms, we make here a systematic and in-depth review of RECK.

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Objective: The association of tea consumption with life expectancy in US adults remains unclear. This study aimed to evaluate the correlation between tea consumption and life expectancy among US adults.

Methods: Tea consumption records and available mortality data from National Health and Nutrition Examination Survey 2001 to 2018 for adults ≥ 20 years of age were used (n = 43,276).

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Article Synopsis
  • Sleep staging is important for understanding sleep patterns, yet current deep learning methods face challenges like generalization issues and the lack of labeled data, particularly in patients with disorders of consciousness (DOC).
  • The paper introduces MultiConsSleepNet, a network designed to extract universal features from EEGs and EOGs using both unimodal and multimodal approaches, which helps address the scarcity of labeled data through self-supervised learning.
  • Experimental results show that MultiConsSleepNet performs exceptionally well in sleep staging across different datasets, demonstrating its ability to work effectively with limited labeled data and making it a promising tool for research involving DOC patients.
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Lignification of the cell wall in pear (Pyrus) fruit results in the formation of stone cells, which affects the texture and quality of the fruit. However, it is still unclear that how different transcription factors (TFs) work together to coordinate the synthesis and deposition of lignin. Here, we examined the transcriptome of pear varieties with different stone cell contents and found a key TF (PbAGL7) that can promote the increase of stone cell contents and secondary cell wall thicknesses.

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  • Coxsackievirus B (CVB) is linked to serious illnesses like myocarditis, meningitis, and pancreatitis, with no effective antiviral treatments available due to incomplete understanding of its pathogenesis.
  • The study identifies that the 3D protein of CVB3 undergoes K48-linked polyubiquitination, leading to its degradation by the proteasome, with E3 ligase TRIM56 playing a crucial role in this process.
  • Findings suggest that TRIM56 acts as a cellular defense mechanism against CVB infection, indicating that boosting viral protein degradation may provide a new strategy for managing CVB infections.
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Article Synopsis
  • Group B Coxsackieviruses (CVB) can lead to myocarditis and potentially cardiomyopathy, but there's currently no effective antiviral treatment available.
  • The study investigates the effects of N-acetylcysteine (NAC), an antioxidant, which was found to significantly reduce viral replication and cardiac injury caused by CVB3.
  • The research reveals that NAC downregulates a specific protein, eEF1A1, that facilitates viral replication in infected cells, and promotes its degradation through autophagy, contributing to its antiviral effects.
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Objective: Attention regulation is an essential ability in daily life that affects learning and work efficiency and is closely related to mental health. The effectiveness of brain-computer interface (BCI) systems in attention regulation has been proven, but most of these systems rely on bulky and expensive equipment and are still in the experimental stage. This study proposes a wearable BCI system for real-time attention regulation and cognitive monitoring.

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Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emotion recognition. In this study, we propose a cross-subject EEG emotion recognition method based on a semi-supervised fine-tuning self-supervised graph attention network (SFT-SGAT).

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Numerous studies have shown that musical stimulation can activate corresponding functional brain areas. Electroencephalogram (EEG) activity during musical stimulation can be used to assess the consciousness states of patients with disorders of consciousness (DOC). In this study, a musical stimulation paradigm and verifiable criteria were used for consciousness assessment.

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Background: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations.

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Purpose Of Review: Autoimmune diseases manifest as an immune system response directed against endogenous antigens, exerting a significant influence on a substantial portion of the population. Notably, a leading contributor to morbidity and mortality in this context is cardiovascular disease (CVD). Intriguingly, individuals with autoimmune disorders exhibit a heightened prevalence of CVD compared to the general population.

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Assessing communication abilities in patients with disorders of consciousness (DOCs) is challenging due to limitations in the behavioral scale. Electroencephalogram-based brain-computer interfaces (BCIs) and eye-tracking for detecting ocular changes can capture mental activities without requiring physical behaviors and thus may be a solution. This study proposes a hybrid BCI that integrates EEG and eye tracking to facilitate communication in patients with DOC.

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Objective: This study aimed to determine whether patients with disorders of consciousness (DoC) could experience neural entrainment to individualized music, which explored the cross-modal influences of music on patients with DoC through phase-amplitude coupling (PAC). Furthermore, the study assessed the efficacy of individualized music or preferred music (PM) versus relaxing music (RM) in impacting patient outcomes, and examined the role of cross-modal influences in determining these outcomes.

Methods: Thirty-two patients with DoC [17 with vegetative state/unresponsive wakefulness syndrome (/UWS) and 15 with minimally conscious state (MCS)], alongside 16 healthy controls (HCs), were recruited for this study.

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Article Synopsis
  • Hybrid crops show better yields and resilience, but the genes behind hybrid vigor (heterosis) are not well understood, making breeding predictions difficult.
  • Researchers created detailed genomes of two pear hybrids, 'Yuluxiang' and 'Hongxiangsu,' to study gene expression differences and develop a pangenome graph for pears.
  • They identified nearly 6000 genes with allele-specific expression related to fruit quality traits, highlighting the significance of certain genes like Ma1 in determining acid levels in fruit, offering insights into how these genes contribute to hybrid advantages.
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Objective: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet.

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In order to enhance crop harvesting efficiency, an automatic-driving tracked grain vehicle system was designed. Based on the harvester chassis, we designed the mechanical structure of a tracked grain vehicle with a loading capacity of 4.5 m and a grain unloading hydraulic system.

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Modern medicine has unveiled that essential oil made from Aquilaria possesses sedative and hypnotic effects. Among the chemical components in Aquilaria, nerolidol, a natural sesquiterpene alcohol, has shown promising effects. This study aimed to unravel the potential of nerolidol in treating depression.

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Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC.

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Anemia and acute heart failure (AHF) frequently coexist. Several published studies have investigated the association of anemia with all-cause mortality and all-cause heart failure events in AHF patients, but their findings remain controversial. This study is intended to evaluate the relationship between anemia and AHF.

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Ribosomal protein 25 (RPS25) has been related to male fertility diseases in humans. However, the role of RPS25 in spermatogenesis has yet to be well understood. RpS25 is evolutionarily highly conserved from flies to humans through sequence alignment and phylogenetic tree construction.

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Article Synopsis
  • Road traffic safety is increasingly important as more vehicles fill the roads, with driver fatigue detection being a key challenge that requires efficient and accurate methods.
  • Researchers propose an attention-based Ghost-LSTM neural network (AGL-Net) for detecting fatigue through EEG signals, employing an attention mechanism and Ghost bottlenecks for better efficiency and feature extraction.
  • AGL-Net outperforms existing models with improved computational efficiency (2.67 M FLOPs) and accuracy (around 87.3%), showcasing its potential for practical use in monitoring driver fatigue on mobile devices.
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Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of EEG sleep staging.

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