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.

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.

Medicinal plants are a valuable source of essential medicines and herbal products for healthcare and disease therapy. Compared with chemical synthesis and extraction, the biosynthesis of natural products is a very promising alternative for the successful conservation of medicinal plants, and its rapid development will greatly facilitate the conservation and sustainable utilization of medicinal plants. Here, we summarize the advances in strategies and methods concerning the biosynthesis and production of natural products of medicinal plants. The strategies and methods mainly include genetic engineering, plant cell culture engineering, metabolic engineering, and synthetic biology based on multiple "OMICS" technologies, with paradigms for the biosynthesis of terpenoids and alkaloids. We also highlight the biosynthetic approaches and discuss progress in the production of some valuable natural products, exemplifying compounds such as vindoline (alkaloid), artemisinin and paclitaxel (terpenoids), to illustrate the power of biotechnology in medicinal plants.

This paper investigates the effect of myricetin (MYR) on renal fibrosis induced by unilateral ureteral obstruction (UUO) and common bile duct ligation (CBDL) in mice and its mechanism. The animal experiment has been approved by the Ethics Committee of China Pharmaceutical University (NO: 2022-10-020). Thirty-five ICR mice were divided into control, UUO, UUO+MYR, CBDL and CBDL+MYR groups. H&E and Masson staining were used to detect pathological changes in kidney tissues. Western blot (WB) was used to detect the expression of fibrosis-related proteins in renal tissue, and total superoxide dismutase (SOD) activity detection kit (WST-8) was used to detect the changes of total SOD in renal tissue of CBDL mice. In vitro, HK-2 cells and transforming growth factor beta 1 (TGF-β1, 10 ng·mL^-1) were used to induce fibrotic model, and high glucose (30 mmol·L^-1) was used to induce oxidative stress model, and then treated with different concentrations of MYR, WB was used to detect the expression of fibrosis and oxidative stress-related proteins, while NIH/3T3 cells were treated with different concentrations of MYR, and their effects on cell proliferation were detected by 5-bromo-2′-deoxyuridine (Brdu). The results showed that the renal lesions in UUO group and CBDL group were severe, collagen deposition was obvious, the expression of collagen-Ⅰ (COL-Ⅰ), α-smooth muscle actin (α-SMA), fibronectin (FN), vimentin and plasminogen activator inhibitor-1 (PAI-1) protein was up-regulated, and the activity of SOD enzyme in CBDL group was significantly decreased. MYR partly reversed the above changes after treatment. MYR inhibited the proliferation of NIH/3T3 cells but had no effect on the proliferation of HK-2 cells, and decreased the upregulation of PAI-1, FN and vimentin in HK-2 cells stimulated by TGF-β1. MYR can also up-regulate the down-regulation of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) in HK-2 cells stimulated by high glucose. To sum up, MYR can improve renal fibrosis in vivo and in vitro, probably by inhibiting the proliferation of fibroblasts and activating Nrf2/HO-1 signal pathway to inhibit oxidative stress.

This article discussed the evolution of the traditional preparation process of Pinelliae Rhizoma Fermentata.The production methods for Pinelliae Rhizoma Fermentata in Song Dynasty include cake-making of Pinelliae Rhizoma together with ginger juice and fermentation after cake-making,and the former method of cake-making was the mainstream.The process technology in Jin and Yuan Dynasties inherited from that in Song Dynasty,and the application of Pinelliae Rhizoma Fermentata had certain limitations.The medical practitioners of Ming Dynasty elucidated the mechanism of processing of Pinelliae Rhizoma Fermentata,and proposed the view of"sliced Pinelliae Rhizoma being potent while fermented Pinelliae Rhizoma being mild".In the Ming Dynasty,LI Shi-Zhen defined the cake-making process and fermentation process for Pinelliae Rhizoma,and HAN Mao's Han Shi Yi Tong(Han's Clear View of Medicine)contained five prescriptions for the processing of Pinelliae Rhizoma Fermentata,which had the epoch-making signficance in the expansion of prescriptions for the processing of Pinelliae Rhizoma Fermentata.In the Qing Dynasty,HAN Fei-Xia's ten methods for making Pinelliae Rhizoma Fermentata were summarized on the basis of the methods recorded in Han Shi Yi Tong,and at that time,the processing of Pinelliae Rhizoma Fermentata and the preparation of Massa Medicata Fermentata interacted with each other.After the founding of the People's Republic of China,the local experience in the preparation of Pinelliae Rhizoma Fermentata was deeply influenced by the methods in the Qing Dynasty,and the local preparation technical standards gradually became the same.Moreover,this article also explored the issues of the importance of"Pinelliae Rhizoma"and"ingredients for fermentation",the pre-treatment of Pinelliae Rhizoma,the distinction between cake-making process and fermentation process for Pinelliae Rhizoma,the amount of flour added as well as the timing of adding,the addition of Massa Medicata Fermentata powder,the role of Alum in Pinelliae Rhizoma Fermentata and so on.

italic>Salmonella has emerged as a promising tumor-targeting strategy in recent years due to its good tumor targeting ability and certain safety. In order to further optimize its therapeutic effect, scientists have tried to modify Salmonella, including its attenuation and drug loading. This paper summarizes the mechanism and research progress of Salmonella-mediated targeted tumor therapy, and introduces the strategies and related progress of its modification and optimization. At the same time, the advantages, current challenges and future development directions of Salmonella-mediated tumor therapy are summarized.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Sonogenetics is an emerging synthetic biology technique that uses sound waves to activate mechanosensitive ion channel proteins on the cell surface to regulate cell behavior and function.Due to the widespread presence of mechanically sensitive ion channel systems in cells and the advantages of non-invasion,strong penetrability,high safety and high accuracy of sonogenetics technology,it has great development potential in basic biomedical research and clinical applications,especially in neuronal regulation,tumor mechanism research,sonodynamic therapy and hearing impairment.This review discusses the basic principles of sonogenetics,the development status of sonogenetics and its application in the prevention and treatment of noise-induced hearing loss,summarizes and analyzes the current challenges and future development direction,thus providing a reference for further research and development of sonogenetics in the field of military medicine.

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.

Immunoassays are widely used in medicine, food, environment and other fields due to having the advantages of simpleness, rapidness and accuracy. Combining immunoassays with nanomaterials can improve the performance of immunoassays. Compared with traditional nanomaterials, upconversion nanoparticles (UCNPs) have excellent optical properties such as good photostability, long luminescence lifetime and narrow and tunable emission bands, which can significantly reduce background noise and improve analytical sensitivity when combined with immunoassay. This paper briefly introduces the luminescence mechanism of UCNPs, summarizes the synthesis and surface modification methods of UCNPs. And then 5 UCNPs-based immunoassay techniques, namely, fluorescence resonance energy transfer, inner filter effect, magnetic separation technique, upconversion-linked immunosorbent assay and upconversion immunochromatography, are discussed in detail. These sensing protocols of UCNPs-based immunoassays have been successfully utilized to detect various targets, including small molecules, macromolecules, and pathogens, all of which closely related to food safety, human health, and environmental pollution. Finally, the challenges and prospects of this technique are summarized and prospected. Although the UCNPs immunoassays based on antibodies and antigens have made great progress, most of the research is still in the stage of laboratory, and there is a long way to go to realize its social applications. There is a series of challenges need to be overcome. (1) Designing excellent water soluble and dispersive upconversion nanomaterials is needed. Hydrophilic ligands are bound to smaller upconversion nanoparticles and removing hydrophobic surface ligands are the most widely used methods to improve solubility and dispersity. (2) Multi-detection technology platforms and multi-mode simultaneous detection platforms have great potential, which will improve the efficiency of point of care detection. (3) The researchers also need to focus on some important problems. For examples, the upconversion luminescence efficiency of UCNPs is difficult to maintain, the synthesis method is complex, and the surface modification degree and functionalization are difficult to control.