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.

Sodium ; Wastewater ; Potassium ; Diet ; Blood Pressure ; China ; Sodium, Dietary ; Sodium Chloride, Dietary

Polysaccharides, predominantly extracted from traditional Chinese medicinal herbs such as Lycium barbarum, Angelica sinensis, Astragalus membranaceus, Dendrobium officinale, Ganoderma lucidum, and Poria cocos, represent principal bioactive constituents extensively utilized in Chinese medicine. These compounds have demonstrated significant anti-inflammatory capabilities, especially anti-liver injury activities, while exhibiting minimal adverse effects. This review summarized recent studies to elucidate the hepatoprotective efficacy and underlying molecular mechanisms of these herbal polysaccharides. It underscored the role of these polysaccharides in regulating hepatic function, enhancing immunological responses, and improving antioxidant capacities, thus contributing to the attenuation of hepatocyte apoptosis and liver protection. Analyses of molecular pathways in these studies revealed the intricate and indispensable functions of traditional Chinese herbal polysaccharides in liver injury management. Therefore, this review provides a thorough examination of the hepatoprotective attributes and molecular mechanisms of these medicinal polysaccharides, thereby offering valuable insights for the advancement of polysaccharide-based therapeutic research and their potential clinical applications in liver disease treatment.
Humans ; Drugs, Chinese Herbal/pharmacology* ; Liver Diseases/drug therapy* ; Antioxidants ; Polysaccharides/therapeutic use* ; Medicine, Chinese Traditional

ObjectiveDetection and quantification of RNA synthesis in cells is a widely used technique for monitoring cell viability, health, and metabolic rate.After exposure to environmental stimuli, both the internal reference gene and target gene would be degraded. As a result, it is imperative to consider the accurate capture of nascent RNA and the detection of transcriptional levels of RNA following environmental stimulation. This study aims to create a Click Chemistry method that utilizes its property to capture nascent RNA from total RNA that was stimulated by the environment. MethodsThe new RNA was labeled with 5-ethyluridine (5-EU) instead of uracil, and the azido-biotin medium ligand was connected to the magnetic sphere using a combination of “Click Chemistry” and magnetic bead screening. Then the new RNA was captured and the transcription rate of 16S rRNA was detected by fluorescence molecular beacon (M.B.) and quantitative reverse transcription PCR (qRT-PCR). ResultsThe bacterial nascent RNA captured by “Click Chemistry” screening can be used as a reverse transcription template to form cDNA. Combined with the fluorescent molecular beacon M.B.1, the synthesis rate of rRNA at 37℃ is 1.2 times higher than that at 15℃. The 16S rRNA gene and cspI gene can be detected by fluorescent quantitative PCR,it was found that the measured relative gene expression changes were significantly enhanced at 25℃ and 16℃ when analyzed with nascent RNA rather than total RNA, enabling accurate detection of RNA transcription rates. ConclusionCompared to other article reported experimental methods that utilize screening magnetic columns, the technical scheme employed in this study is more suitable for bacteria, and the operation steps are simple and easy to implement, making it an effective RNA capture method for researchers.

ObjectiveTo screen optimized protocol of acupuncture for post-prostatectomy urinary incontinence （PPUI） patients， and to provide evidence for clinical practice. MethodsMEDLINE， Embase， Cochrane Library， Web of Science， Chinese Biomedical Literature Database， China National Knowledge Infrastructure， WanFang and VIP databases were searched to collect randomized controlled trials of acupuncture for PPUI. The search was conducted from the establishment of the database to February 1， 2024， and the quality of the literature was evaluated to exclude the studies with a high risk of overall bias or modified Jadad ＜3， and constructed acupuncture protocol and performed meta-analysis. We used International Consultation on Urinary Incontinence Short Form （ICI-Q-SF） scores， quality of life scores， overall effective rate， and modified Jadad scores as beneficial indicators， and the number of acupoints selected， stimulation duration， the number of acupuncture， and the duration of the treatment course as costly indicators， to derive the standardized protocol matrix， and used the entropy method to determine the weights of the different decision-making indicators， and finally combined with the Technique for Order Preference by Similarity to Ideal Solution （TOPSIS） for comprehensive evaluation. ResultsNine studies met the criteria， and the acupuncture treatments involved were constructed as six protocols including electrical acupoint stimulation with low-frequency， electroacupuncture at four acupoints of sacral region， replenishing qi and tonifying kidney acupuncture， body acupuncture plus pelvic floor muscle training， auricular acupuncture， and electroacupuncture plus pelvic floor muscle training. The ICI-Q-SF， number of acupuncture sessions， and total effectiveness rate were given higher weights in the decision-making indexes， while the stimulation duration and the duration of treatment course were given lower weights； the entropy method of TOPSIS was used for the evaluation and proved that the best protocol was the electroacupuncture at four acupoints of sacral region which used continuous-wave electroacupuncture with a frequency of 2 Hz for 60 min each time， and required the needle sensation to radiate to the root of the penis， with the advantages of streamlined selection of acupoints， a significant reduction in ICI-Q-SF， and an increase in the effectiveness rate. ConclusionThe final optimized protocol was electroacupuncture at four acupoints of sacral region， which can provide an evidence-based basis for clinical decision-making and guideline development.

In this study, deep eutectic solvents (DESs) were used as excipients to prepare solid dispersion (SD) of scutellarin. The SD of scutellarin were prepared by melting method with cumulative dissolution rate as the index of investigation. The preparation conditions of SD of scutellarin were optimized by single factor experiment, which investigated the type of the carrier material, the type of DESs, and the ratio of the drug to the carrier. The optimum preparation conditions of DESs-SD were as follows: using Poloxamer 407 as the carrier material, PEG 200/urea (2∶1) as the DESs system, and the ratio of carrier, DESs, and drug was 6∶1∶1. The drug loading capacity of scutellarin in SD was 12.53% under the optimum preparation conditions. Differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray powder diffraction and scanning electron microscope exhibited that scutellarin was amorphous form in the SD system. Furthermore, the stability of the DESs-based SD of scutellarin was evaluated by high temperature, high humidity, and strong light tests, which showed that the cumulative dissolution rate and scutellarin content of SD decreased with time under these conditions. Finally, the result of pharmacokinetic studies indicated that the oral absorption of the scutellarin could be increased using DESs as an excipient in the preparation of SD. The animal experiment was approved by the Experimental Animal Ethics Committee of Fujian University of Traditional Chinese Medicine (approval number: FJTCMIACUC 2023048). Consequently, this research offers a novel and effective approach for using DESs to enhance the oral bioavailability of active substances with low water solubility.

Nicotinic acetylcholine receptors (nAChRs) belong to ligand-gated ion channel receptors, of which α7 nAChR subtype is widely distributed in the cerebral cortex, thalamus, hippocampus, and also identified in microglia, macrophages, bone marrow cells, etc. Previous studies revealed that α7 nAChR is closely related to the function of the cholinergic anti-inflammatory pathway, and is a vital target for drug development of Alzheimer's disease and schizophrenia. The establishment of a stable α7 nAChR in vitro drug screening system is crucial for the efficient screening of novel drugs targeting this target. Recombinant expression of different subtypes of nAChRs on Xenopus laevis oocyte membranes and current detected by two-electrode voltage clamp (TEVC) is an advanced and complex model for novel drug screening. Molecular chaperones can assist the assembly of some nAChR subunits to form functional receptors, providing a stable expression model for the screening of compounds targeting this receptor. In this study, a molecular chaperone gene of α7 nAChR, transmembrane protein 35A (Tmem35a), was isolated and cloned from rats. We constructed the recombinant expression vector and obtained the cRNA of Tmem35a by in vitro transcription technique. Two cRNAs (Tmem35a and α7) were mixed and injected into X. laevis oocytes for expression. Then, the effects of this molecular chaperone on the current expression and pharmacological properties of α7 nAChR were evaluated by the TEVC. The results revealed that TMEM35A, also known as novel acetylcholine receptor chaperone (NACHO) could effectively increase the expression of α7 nAChR protein on oocyte membranes, and the amount of α7 nAChR protein was increased about 1-fold. The peak current induced by agonist acetylcholine (ACh) was increased about 10-fold. After injection of Tmem35a cRNA, the median effect concentration (EC₅₀) value of α7 nAChR to agonist ACh is 228.5 μmol·L^-1, which shows almost no difference from native α7 nAChR (EC₅₀: 223.3 μmol·L^-1), indicating the preservation of the normal properties of α7 nAChR. The results of this investigation indicate that the molecular chaperone NACHO effectively assists the heterologous expression of α7 nAChR in X. laevis oocytes, which provides a model for screening the potency of lead compounds targeting α7 nAChR. All animal experiments in this study were reviewed and approved by the Ethics Committee of Guangxi University (approval number: GXU-2023-0249).

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.