[Objective] To analyze the loss rate of platelet donors and evaluate the strategies for establishing a platelet donor bank. [Methods] A total of 1 443 donors who joined the HLA and HPA gene donor bank for platelets in Henan Province from 2018 to 2020 were included in this study. Data on the total number of apheresis platelet donations, annual donation frequency, age at enrollment, donation habits (including the number of platelets donated per session and whether they had previously donated whole blood), and enrollment location were collected from the platelet donor information management system. Donor loss was determined based on the date of their last donation. The loss rates of different groups under various conditions were compared to assess the enrollment strategies. [Results] By the time the platelet bank was officially operational in 2022, 421 donors had been lost, resulting in an loss rate of 29% (421/1 443). By the end of 2023, the overall cumulative loss rate reached 52% (746/1 443). The loss rate was lower than the overall level in groups meeting any of the following conditions: total apheresis platelet donations exceeding 50, annual donation frequency of 10 or more, age at enrollment of 40 years or older, donation of more than a single therapeutic dose per session, or a history of whole blood donation two or more times. Additionally, loss rates varied across different enrollment locations, with higher enrollment numbers generally associated with higher loss rates. [Conclusion] Through a comprehensive analysis of donor loss, our center has adjusted its strategies for establishing the donor pool. These findings also provide valuable insights for other blood collection and supply institutions in building platelet donor banks.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

As China accelerates its efforts in deep space exploration and long-duration space missions, including the operationalization of the Tiangong Space Station and the development of manned lunar missions, safeguarding astronauts’ physiological and cognitive functions under extreme space conditions becomes a pressing scientific imperative. Among the multifactorial stressors of spaceflight, microgravity emerges as a particularly potent disruptor of neurobehavioral homeostasis. Dopamine (DA) plays a central role in regulating behavior under space microgravity by influencing reward processing, motivation, executive function and sensorimotor integration. Changes in gravity disrupt dopaminergic signaling at multiple levels, leading to impairments in motor coordination, cognitive flexibility, and emotional stability. Microgravity exposure induces a cascade of neurobiological changes that challenge dopaminergic stability at multiple levels: from the transcriptional regulation of DA synthesis enzymes and the excitability of DA neurons, to receptor distribution dynamics and the efficiency of downstream signaling pathways. These changes involve downregulation of tyrosine hydroxylase in the substantia nigra, reduced phosphorylation of DA receptors, and alterations in vesicular monoamine transporter expression, all of which compromise synaptic DA availability. Experimental findings from space analog studies and simulated microgravity models suggest that gravitational unloading alters striatal and mesocorticolimbic DA circuitry, resulting in diminished motor coordination, impaired vestibular compensation, and decreased cognitive flexibility. These alterations not only compromise astronauts’ operational performance but also elevate the risk of mood disturbances and motivational deficits during prolonged missions. The review systematically synthesizes current findings across multiple domains: molecular neurobiology, behavioral neuroscience, and gravitational physiology. It highlights that maintaining DA homeostasis is pivotal in preserving neuroplasticity, particularly within brain regions critical to adaptation, such as the basal ganglia, prefrontal cortex, and cerebellum. The paper also discusses the dual-edged nature of DA plasticity: while adaptive remodeling of synapses and receptor sensitivity can serve as compensatory mechanisms under stress, chronic dopaminergic imbalance may lead to maladaptive outcomes, such as cognitive rigidity and motor dysregulation. Furthermore, we propose a conceptual framework that integrates homeostatic neuroregulation with the demands of space environmental adaptation. By drawing from interdisciplinary research, the review underscores the potential of multiple intervention strategies including pharmacological treatment, nutritional support, neural stimulation techniques, and most importantly, structured physical exercise. Recent rodent studies demonstrate that treadmill exercise upregulates DA transporter expression in the dorsal striatum, enhances tyrosine hydroxylase activity, and increases DA release during cognitive tasks, indicating both protective and restorative effects on dopaminergic networks. Thus, exercise is highlighted as a key approach because of its sustained effects on DA production, receptor function, and brain plasticity, making it a strong candidate for developing effective measures to support astronauts in maintaining cognitive and emotional stability during space missions. In conclusion, the paper not only underscores the centrality of DA homeostasis in space neuroscience but also reflects the authors’ broader academic viewpoint: understanding the neurochemical substrates of behavior under microgravity is fundamental to both space health and terrestrial neuroscience. By bridging basic neurobiology with applied space medicine, this work contributes to the emerging field of gravitational neurobiology and provides a foundation for future research into individualized performance optimization in extreme environments.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Aim To anticipate the mechanism of zuka- mu granules (ZKMG) in the treatment of bronchial asthma, and to confirm the projected outcomes through in vivo tests via using network pharmacology and molecular docking technology. Methods The database was examined for ZKMG targets, active substances, and prospective targets for bronchial asthma. The protein protein interaction network diagram (PPI) and the medication component target network were created using ZKMG and the intersection targets of bronchial asthma. The Kyoto Encyclopedia of Genes and Genomics (KEGG) and gene ontology (GO) were used for enrichment analysis, and network pharmacology findings were used for molecular docking, ovalbumin (OVA) intraperitoneal injection was used to create a bronchial asthma model, and in vivo tests were used to confirm how ZKMG affected bronchial asthma. Results There were 176 key targets for ZKMG's treatment of bronchial asthma, most of which involved biological processes like signal transduction, negative regulation of apoptotic processes, and angiogenesis. ZKMG contained 194 potentially active components, including quercetin, kaempferol, luteolin, and other important components. Via signaling pathways such TNF, vascular endothelial growth factor A (VEGFA), cancer pathway, and MAPK, they had therapeutic effects on bronchial asthma. Conclusion Key components had strong binding activity with appropriate targets, according to molecular docking data. In vivo tests showed that ZKMG could reduce p-p38, p-ERKl/2, and p-I

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.

The World Health Organization has declared that the outbreak of coronavirus disease 2019(COVID-19) is a global pandemic. As mutations occurred in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the global epidemic still needs further concern. Worryingly, the effectiveness and neutralizing activity of existing antibodies and vaccines against SARS-CoV-2 variants is declining. There is an urgent need to find an effective antiviral medication with broad-spectrum inhibitory effects on novel coronavirus mutant strains against the SARS-CoV-2 infection. Neutralizing antibodies play an important role in the prevention and treatment of COVID-19. The interaction of spike-receptor-binding domain (Spike-RBD) of SARS-CoV-2 and human angiotensin-converting enzyme 2 (ACE2) is the first and critical step of SARS-CoV-2 infection. Hence, the SARS-CoV-2 Spike-RBD is a hot target for neutralizing antibodies development. Evusheld, the combination of Tixagevimab and Cilgavimab monoclonal antibodies (mAbs) targeting Spike-RBD exhibits neutralizing activity against BA.2.12.1, BA.4 and BA.5, which could be used as pre-exposure prophylaxis against SARS-CoV-2 infection. The nucleocapsid (N) protein is a conservative and high-abundance structural protein of SARS-CoV-2. The nCoV396 monoclonal antibody, isolated from the blood of convalescent COVID-19 patients against the N protein of SARS-CoV-2. This mAb not only showed neutralizing activity but also inhibits hyperactivation of complement and lung injury induced by N protein. The mAb 3E8 targeting ACE2 showed broadly neutralizing activity against SARS-CoV-2 and D614G, B.1.1.7, B.1.351, B.1.617.1 and P.1 variants in vitro and in vivo, but did not impact the biological activity of ACE2. Compared with neutralizing antibodies, small molecule inhibitors have several advantages, such as broad-spectrum inhibitory effect, low cost, and simple administration methods. Several small-molecule inhibitors disrupt viral binding by targeting the ACE2 and N-terminal domain (NTD) of SARS-CoV-2 spike protein. Known drugs such as chloroquine and hydroxychloroquine could also block the infection of SARS-CoV-2 by interacting with residue Lys353 in the peptidase domain of ACE2. The transmembrane protease serine 2 (TMPRSS2) inhibitors Camostat mesylate and Proxalutamide inhibit infection by blocking TMPRSS2 mediates viral membrane fusion. The main protease inhibitor Paxlovid and RNA-dependent RNA polymerase inhibitor Azvudine have been approved for treatment of COVID-19 patients. This review summarizes the current research status of neutralizing antibodies and small molecule inhibitors and prospects for their application. We expect to provide more valuable information for further studies in this field.

Objective:To construct an explainable artificial intelligence(AI) model of risk characteristics of papillary thyroid carcinoma(PTC), and to explore its value of it combined with clinical features in predicting cervical lymph node metastasis(CLNM) in PTC patients.Methods:From January 2021 to September 2022, 422 patients(422 nodules) with pathologically confirmed PTC underwent thyroidectomy and neck lymph node dissection in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively collected, the patients were randomly divided into training set and test set according to the ratio of 7∶3. Ultrasonographic features highly correlated with PTC risk characteristics were extracted by traditional machine learning method, and an intelligent prediction model with optimal probability of risk characteristics was established. Then, a risk model for predicting CLNM of PTC patients was constructed in combination with clinical features. The diagnostic effectiveness of the model was evaluated by drawing a ROC curve and calculating the area under curve (AUC).Results:In the AI explaineable model of PTC risk characteristics in the test set, the intelligent diagnosis model of calcification based on logistic regression classification showed the highest diagnostic efficiency, with an AUC of 0.87 ( P<0.05). Compared with the probability model of risk characteristic of PTC alone, the comprehensive model combined with clinical characteristics showed higher diagnostic efficiency in predicting CLNM of PTC patients, with AUC of 0.97, diagnostic critical value of 0.15, corresponding accuracy, sensitivity and specificity of 92.65%, 92.76% and 92.54%, respectively (all P<0.05). Conclusions:The explaineble risk characteristics of PTC AI model combined with clinical features can effectively predict the cervical lymph node metastasis of PTC, and then provide effective information for clinical decision-making of PTC patients.

Acute liver injury (ALI) is one of the common severe diseases in clinic, which is characterized by redox imbalance and inflammatory storm. Untimely treatment can easily lead to liver failure and even death. Rosmarinic acid (RA) has been proved to have anti-inflammatory and antioxidant activity, but it is not clear how to protect ALI through antioxidation and inhibition of inflammation. Therefore, this study explored the therapeutic effect and molecular mechanism of RA on ALI through in vitro and in vivo experiments. In the mouse ALI model, the effects of RA on liver function and inflammatory indexes were studied, the pathological changes of liver were observed by HE, the effect of RA on reactive oxygen species in liver was detected by fluorescence method, and the level of F4/80 in liver tissue was detected by immunohistochemical method. The levels of thioredoxin interacting protein (TXNIP), NOD-like receptor protein 3 (NLRP3) and cysteinyl aspartate specific proteinase-1 (CASPASE-1) in liver tissue were measured by Western blot. All animal welfare and experimental procedures follow the rules of the Animal Ethics Committee of Southwest Minzu University. In vitro, human hepatoma cell line HepG2 was used to establish the model of oxidative damage induced by H₂O₂. The cell viability was detected by CCK-8 method. The level of interleukin-1β (IL-1β) in the supernatant was detected by enzyme linked immunosorbent assay (ELISA), the activity of lactatede hydrogenase (LDH) was detected by LDH kit, and the level of ROS was detected by fluorescence probe DCFH-DA labeling. The mRNA expression of Txnip, Nlrp3, Caspase 1, Il1β was detected by real-time fluorescence quantitative PCR (qPCR), and the protein levels of nuclear factor erythroid-2 related factor 2 (NRF2), TXNIP, NLRP3 and CASPASE-1 were measured by Western blot. The results showed that compared with the model group, the degree of liver swelling, tissue injury, liver function index in RA group were significantly lower than those in model group. And RA significantly attenuated the increases of ROS in liver tissue. The expression levels of TXNIP, NLRP3 and CASPASE-1 in liver tissue were significantly lower than those in model group. Additionally, RA inhibited the expressions of F4/80 and IL-1β. In vitro experiment, compared with model group, RA effectively inhibited the secretion of IL-1β and LDH. The level of ROS also decreased significantly. RA inhibited the mRNA expressions of Txnip, Caspase 1, Il1β. Furthermore, RA significantly increased the expression level of NRF2 protein in nucleus, and decreased the expression level of TXNIP and NLRP3 protein. Specifically, with the addition of ML385, the effect of RA on NRF2, TXNIP, NLRP3, CASPASE-1 protein expression was reversed. Collectively, these findings suggested that RA may inhibit the production of ROS by promoting NRF2 nuclear transfer, and then reduce the activation of NLRP3 inflammatory bodies by TXNIP, reduce cell death and inflammatory response to prevent the liver injury.