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.

Circadian rhythm is an endogenous biological clock mechanism that enables organisms to adapt to the earth’s alternation of day and night. It plays a fundamental role in regulating physiological functions and behavioral patterns, such as sleep, feeding, hormone levels and body temperature. By aligning these processes with environmental changes, circadian rhythm plays a pivotal role in maintaining homeostasis and promoting optimal health. However, modern lifestyles, characterized by irregular work schedules and pervasive exposure to artificial light, have disrupted these rhythms for many individuals. Such disruptions have been linked to a variety of health problems, including sleep disorders, metabolic syndromes, cardiovascular diseases, and immune dysfunction, underscoring the critical role of circadian rhythm in human health. Among the numerous systems influenced by circadian rhythm, the skin—a multifunctional organ and the largest by surface area—is particularly noteworthy. As the body’s first line of defense against environmental insults such as UV radiation, pollutants, and pathogens, the skin is highly affected by changes in circadian rhythm. Circadian rhythm regulates multiple skin-related processes, including cyclic changes in cell proliferation, differentiation, and apoptosis, as well as DNA repair mechanisms and antioxidant defenses. For instance, studies have shown that keratinocyte proliferation peaks during the night, coinciding with reduced environmental stress, while DNA repair mechanisms are most active during the day to counteract UV-induced damage. This temporal coordination highlights the critical role of circadian rhythms in preserving skin integrity and function. Beyond maintaining homeostasis, circadian rhythm is also pivotal in the skin’s repair and regeneration processes following injury. Skin regeneration is a complex, multi-stage process involving hemostasis, inflammation, proliferation, and remodeling, all of which are influenced by circadian regulation. Key cellular activities, such as fibroblast migration, keratinocyte activation, and extracellular matrix remodeling, are modulated by the circadian clock, ensuring that repair processes occur with optimal efficiency. Additionally, circadian rhythm regulates the secretion of cytokines and growth factors, which are critical for coordinating cellular communication and orchestrating tissue regeneration. Disruptions to these rhythms can impair the repair process, leading to delayed wound healing, increased scarring, or chronic inflammatory conditions. The aim of this review is to synthesize recent information on the interactions between circadian rhythms and skin physiology, with a particular focus on skin tissue repair and regeneration. Molecular mechanisms of circadian regulation in skin cells, including the role of core clock genes such as Clock, Bmal1, Per and Cry. These genes control the expression of downstream effectors involved in cell cycle regulation, DNA repair, oxidative stress response and inflammatory pathways. By understanding how these mechanisms operate in healthy and diseased states, we can discover new insights into the temporal dynamics of skin regeneration. In addition, by exploring the therapeutic potential of circadian biology in enhancing skin repair and regeneration, strategies such as topical medications that can be applied in a time-limited manner, phototherapy that is synchronized with circadian rhythms, and pharmacological modulation of clock genes are expected to optimize clinical outcomes. Interventions based on the skin’s natural rhythms can provide a personalized and efficient approach to promote skin regeneration and recovery. This review not only introduces the important role of circadian rhythms in skin biology, but also provides a new idea for future innovative therapies and regenerative medicine based on circadian rhythms.

Green prescription is a written prescription aimed at improving health by promoting physical activity and improving diet， with advantages such as high cost-effectiveness， strong feasibility， and minimal harm to patients. The theory of traditional Chinese medicine （TCM） green prescription integrates the health philosophy of "following rule of yin and yang， and adjusting ways to cultivating health"， the exercise philosophy of balancing yin-yang and the five elements， and the dietary philosophy of moderation and balance， which embody core TCM concepts such as treating disease before its onset and harmony between humans and nature. It has also developed traditional exercise practices like Tai Chi， Baduanjin， Wuqinxi， Yi-Gin-Ching， and Qigong， as well as dietary adjustments like medicated diet and herbal wines. However， it is believed that the TCM green prescription currently suffers from insufficient evidence-based research， low patient awareness and acceptance， and weak basic research. Based on this， it is proposed that large-sample clinical trials should be conducted in the future to improve the quality of evidence-based medicine， basic research can be carried out with the help of artificial intelligence and other methods in research design， the hospital information system （HIS） can be used for control at the implementation level， and publicity and patient education can be strengthened through the new media， so as to promote the development and application of the TCM green prescriptions in the field of global health treatment.

Objective To investigate the efficacy of leaflet augmentation technique to repair the recurrent mitral valve (MV) regurgitation after mitral repair in children. Methods A retrospective analysis was conducted on the clinical data of children who underwent redo MV repair for recurrent regurgitation after initial MV repair, using a leaflet augmentation technique combined with a standardized repair strategy at Fuwai Hospital, Chinese Academy of Medical Sciences, from 2018 to 2022. The pathological features of the MV, key intraoperative procedures, and short- to mid-term follow-up outcomes were analyzed. Results A total of 24 patients (12 male, 12 female) were included, with a median age of 37.6 (range, 16.5–120.0) months. The mean interval from the initial surgery was (24.9±17.0) months. All children had severe mitral regurgitation preoperatively. The cardiopulmonary bypass time was (150.1±49.5) min, and the aortic cross-clamp time was (94.0±24.2) min. There were no early postoperative deaths. During a mean follow-up of (20.3±9.1) months, 3 (12.5%) patients developed moderate or severe mitral regurgitation (2 severe, 1 moderate). One (4.2%) patient died during follow-up, and one (4.2%) patient underwent a second MV reoperation. The left ventricular end-diastolic diameter was significantly reduced postoperatively compared to preoperatively [ (43.5±8.6) mm vs. (35.8±7.8)mm, P<0.001]. Conclusion The leaflet augmentation technique combined with a standardized repair strategy can achieve satisfactory short- to mid-term outcomes for the redo mitral repair after previous MV repair. It can be considered a safe and feasible technical option for cases with complex valvular lesions and severe pathological changes.

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.

Objective: To investigate the impact of intergenerational parent-child separation (PCS) on allergic diseases among rural preschool children, providing theoretical guidance for developing targeted public health interventions. Methods: From March to June 2024, 10 kindergartens were selected from Nanling, Wuhu City, Anhui Province. A total of 2 279 children aged 3-6 years and their parents/primary caregivers participated in the survey by a combination of convenience sampling and cluster sampling method. Children s fathers and mothers reported the experiences of PCS during their childhood. The children s PCS experiences and allergies were reported by their primary caregivers. The International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire was used to supplement the allergies (allergic asthma, allergic rhinitis and atopic dermatitis). Analysis of variance (ANOVA) and Chi square tests were used to compare differences between children in different PCS groups. Logistic regression models assessed the association between PCS and the risk of allergic diseases in preschool children. Results: Among the preschoolers enrolled, the prevalence of allergic diseases in only parent-child separation group in childhood, only child separation group, and the intergenerational continuity of PCS groups were significantly higher than those of the none separation group (38.0%, 41.8%, 48.1%,30.4%; χ 2=40.45, P < 0.01 ). After adjusting for covariates including child age, sex and body mass index, Logistic regression model revealed that compared to children in the group without PCS, those in the only parent-child separation in childhood( OR =1.43, 95% CI =1.06-1.94), only child separation ( OR =1.82, 95% CI =1.22-2.71), and intergenerational continuity of PCS ( OR =2.33, 95% CI =1.68-3.24) exhibited higher allergic disease risk (all P <0.05). Conclusions Intergenerational continuity of PCS is related to the increased risk of allergies in preschool children. The multigenerational accumulation of adverse effects from PCS underscores the importance of breaking the cycle of disadvantage across generations.

OBJECTIVE To study the effects of Phellodendron amurense polysaccharides （PAP） on improving gouty nephropathy （GN） in rats， and to investigate its mechanism primarily by interfering the p38 mitogen-activated protein kinase （p38 MAPK）/nuclear factor-κB（NF-κB）/tumor necrosis factor-α（TNF-α）. METHODS Sixty rats were randomly divided into normal group （water）， model group （water）， allopurinol group （positive control， 20 mg/kg）， PAP high-dose， medium-dose and low-dose groups （100， 50， 25 mg/kg， by raw material） after being stratified by body weight， with 10 rats in each group. Except for the normal group， the other groups were induced to construct GN model by giving 1 500 mg/kg potassium oxazinate and 100 mg/kg adenine intragastrically for 14 days. After modeling， the rats in each group were given relevant medicine/water intragastrically， once a day， for consecutive 28 days. After the last medication， the levels of biochemical parameters related to renal function [uric acid， creatinine （Cr）， blood urea nitrogen （BUN）， xanthine oxidase （XOD）] were detected in rats， and the histopathological changes in the rat kidney were observed. The protein expressions of monocyte chemoattractant protein-1（MCP-1），TNF-α and interleukin-6（IL-6） as well as the phosphorylation levels of p38 MAPK and NF-κB p65 protein were determined in renal tissue of rats. RESULTS Compared with the normal group， the model group suffered from the dilatation of renal tubules， structural damage to glomeruli， accompanied by inflammatory infiltration and fibrosis； the contents of uric acid， Cr， BUN and XOD， the protein expressions of MCP-1，TNF-α and IL-6 and the phosphorylation levels of p38 MAPK and NF-κB p65 protein were all increased significantly （P＜0.05 or P＜0.01）. Compared with the model group， the pathological symptoms of renal tissue in rats had been improved to varying degrees in different dose groups of PAP； the contents of uric acid， Cr， BUN and XOD， protein expressions of MCP-1， TNF-α and IL-6， the phosphorylation levels of p38 MAPK and NF-κB p65 protein in PAP high-dose and PAP medium-dose groups were all decreased significantly （P＜0.05 or P＜0.01）. CONCLUSIONS PAP exhibits an anti-GN effect， the mechanism of which may be associated with inhibiting the p38 MAPK/NF-κB/TNF-α signaling pathway.

ObjectiveTo investigate the impact of early intervention with Yishen Huazhuo prescription （YHP） on the learning and memory of accelerated aging model mice， as well as its underlying mechanism. MethodForty-eight 3-month-old male SAMP8 mice were randomly assigned into four groups， including the model group， low-dose YHP group， high-dose YHP group， and donepezil group. Additionally， 24 SAMR1 mice of the same age were divided into a control group and a YHP treatment control group， each consisting of 12 mice. The YHP groups received YHP at doses of 6.24 g·kg^-1 and 12.48 g·kg^-1， while the donepezil group was treated with donepezil at a dose of 0.65 mg·kg^-1. The model group and control groups were given physiological saline. The mice were gavaged once daily for a duration of four weeks. Spatial learning and memory abilities of mice were assessed using the Morris water maze test. Immunofluorescence staining was employed to evaluate neuronal density as well as expression levels of M1 microglial （MG） polarization marker inducible nitric oxide synthase （iNOS） and M2 MG polarization marker arginase-1 （Arg-1） in the hippocampus region. Enzyme-linked immunosorbent assay （ELISA） was used to measure serum levels of pro-inflammatory factor interleukin 1β （IL-1β） and anti-inflammatory factor transforming growth factor-β₁ （TGF-β₁）. Furthermore， Western blot analysis was conducted to determine expressions of amyloid β peptide1-42 （Aβ_1-42） along with triggering receptor expressed on myeloid cells 2 （TREM2）/nuclear factor kappa B （NF-κB） signaling pathway-related proteins TREM2， phospho （p）-NF-κB p65， and phospho-inhibitory kappa B kinase β （IKKβ） in the hippocampus. ResultCompared with the control group， the model group exhibited a significantly prolonged escape latency （P<0.01）， a significant reduction in neuron-specific nuclear protein （NeuN） expression in the hippocampus， a significant increase in iNOS expression in MG， and a significant decrease in Arg-1 expression. The serum IL-1β content was significantly increased， while the TGF-β₁ content was significantly decreased. Additionally， there was a significant decrease in TREM2 expression in the hippocampus and significant increases in p-NF-κB p65， p-IKKβ， and Aβ_1-42 expressions （P<0.05， P<0.01）. However， no significant changes were observed in escape latency， times of crossing the platform， and hippocampal NeuN expression in the YHP treatment control group. Conversely， iNOS expression in MG as well as the hippocampal p-NF-κB p65， p-IKKβ， and Aβ_1-42 expressions were significantly decreased. Furthermore， TREM2 expression was significantly increased （P<0.05， P<0.01）. In comparison to the model group， the low-dose YHP group showed a significantly shortened escape latency and an increased number of crossing the platform （P<0.05， P<0.01）. In the high-dose YHP group， the escape latency was significantly shortened （P<0.05）. In the low-dose YHP group， high-dose YHP group， the expression of NeuN in the hippocampus was significantly increased， the expression of iNOS in MG was significantly decreased， and the expression of Arg-l was significantly increased. The serum IL-1β content was significantly decreased， while the TGF-β₁ content was significantly increased. Furthermore， the expression of TREM2 in the hippocampus was significantly increased， and the expressions of p-NF-κB p65， p-IKKβ， and Aβ_1-42 were significantly decreased （P<0.01）. ConclusionEarly YHP intervention may promote the transformation of hippocampal MG from M1 to M2 by regulating the TREM2/NF-κB signaling pathway， reduce the release of neuroinflammatory factors， protect hippocampal neurons， and reduce the deposition of Aβ_1-42， and finally delay the occurrence of learning and memory decline in SAMP8 mice.

A derivatizaton method combined with gas chromatography-mass spectrometry(GC-MS)was established for detection of isobutyl chloroformate(IBCF)residue in active pharmaceutical ingredient of agatroban.The extraction and derivatization reagents,derivatization time,qualitative and quantitative ions were selected and optimized,respectively.The possible mechanism of derivatization and characteristic fragment ions fragmentation were speculated.The agatroban samples were dissolved and extracted by methanol,and the residual IBCF was derived with methanol to generate methyl isobutyl carbonate(MIBCB).After 24 h static derivatization at room temperature,IBCF was completely transformed into MIBCB,which could be used to indirectly detect IBCF accurately.The results showed that the linearity of this method was good in the range of 25-500 ng/mL(R2=0.9999).The limit of detection(LOD,S/N=3)was 0.75 μg/g,and the limit of quantification(LOQ,S/N=10)was 2.50 μg/g.Good recoveries(95.2%-97.8%)and relative standard deviations(RSDs)less than 3.1%(n=6)were obtained from agatroban samples at three spiked levels of IBCF(2.50,25.00,50.00 μg/g),which showed good accuracy of this method.Good precision of detection results was obtained by different laboratory technicians at different times,the mean value of spiked sample solution(25.00 μg/g)was 24.28 μg/g,and the RSD was 2.1%(n=12).The durability was good,minor changes of detection conditions had little effect on the results.Under the original condition and conditions with initial column temperature±5℃,heating rate±2℃/min,column flow rate±0.1 mL/min,the IBCF content of spiked sample solution(25.00 μg/g)was detected,the mean value of detection results was 24.16 μg/g,and the RSD was 2.2%(n=7).Eight batches of agatroban samples from two manufacturers were detected using the established method,and the results showed that no IBCF residue was detected in any of these samples.The agatroban samples could be dissolved by methanol,and then the IBCF residue could be simultaneously extracted and derived with methanol as well.This detection method had the advantages of simple operation,high sensitivity,low matrix effect and accurate quantification,which provided a new effective method for detection of IBCF residue in agatroban.

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.