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.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Objective To observe the effects of Tianma Gouteng Decoction combined with magnesium sulfate and Labetalol on lowering blood pressure and improving hemorheology in patients with gestational hypertension.Methods Ninety patients with gestational hypertension of liver-yang hyperactivity type were randomly divided into the combination group and the control group,with 45 cases in each group.The control group was treated with magnesium sulfate combined with Labetalol,and the combination group was treated with Tianma Gouteng Decoction on the basis of treatment for the control group.The course of treatment lasted for 5 days.The changes of systolic blood pressure(SBP),diastolic blood pressure(DBP),urinary protein level,and hemorheological indicators of the two groups were observed before and after the treatment.Moreover,the adverse pregnancy outcomes,adverse reactions,and patients'satisfaction of the two groups were compared.Finally,the influencing factors of patients'adverse pregnancy outcomes were investigated by logistic regression analysis.Results(1)After treatment,the SBP,DBP and urinary protein level of patients in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the combination group was significantly superior to that in the control group(P＜0.01).(2)After treatment,the hemorheological indicators of plasma viscosity,whole blood viscosity and hematocrit of patients in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the combination group was significantly superior to that in the control group(P＜0.05 or P＜0.01).(3)The total incidence of adverse pregnancy outcomes in the combination group was 11.11%(5/45),which was significantly lower and that in the control group(33.33%,15/45),the difference being statistically significant(P＜0.05).(4)The patients'satisfaction of the combination group was 97.78%(44/45),which was significantly higher than that of the control group(84.44%,38/45),and the difference was statistically significant(P＜0.05).(5)The total incidence of adverse reactions in the combination group was 13.33%(6/45)and that in the control group was 8.89%(4/45),but the intergroup comparison showed no significant difference between the two groups(P＞0.05).(6)Logistic regression analysis of influencing factors showed that no medication of Tianma Gouteng Decoction combined with Labetalol and magnesium sulfate,and poor antihypertensive effect were the independent risk factors for adverse pregnancy outcomes in patients with gestational hypertension(all OR＞1,P＜0.05).Conclusion Tianma Gouteng Decoction combined with magnesium sulfate and Labetalol in treating gestational hypertension exerts certain antihypertensive effect,and the therapy can effectively improve the hemorheological indicators and the adverse pregnancy outcomes,and enhance the patients'satisfaction.

Objective To investigate the protective effect and mechanism of acellular nerve allografts(ANA)combined with electroacupuncture on spinal ganglia in rats with sciatic nerve injury(SNI).Methods Totally 50 male adult SD rats were randomly selected for this experiment.Ten rats were prepared for the ANA.Forty male SD rats were randomly divided into normal group,model group,ANA group and combinational group,with 10 rats in each group.The SNI model was established by cutting off the nerves 10 mm at the 5 mm on the inferior border of piriformis after separating the right sciatic nerves.The rats in the ANA group were bridged with ANA to the two broken ends of injured nerves.The rats in the combinational group were treated with electroacupuncture 2 days after ANA bridging,Huantiao(GB30)and Yanglingquan(GB34)were performed as the acupuncture points,each electroacupuncture lasted 15 minutes and 7 days as a course of treatment,4 courses in all.Sciatic nerve conduction velocity was measured by electrophysiology to evaluate the regeneration of damaged axons.Morphology of spinal ganglia was observed by Nissl staining.The expression of nerve growth factor(NGF)and brain-derived neurotrophic factor(BDNF)were detected by Western blotting and immunofluorescent staining.Results Compared with the normal group,the sciatic nerve conduction velocity in model group decreased significantly(P<0.01),Nissl bodies in neurons of spinal ganglia were swollen and dissolved,with incomplete structure and the number decreased dramatically(P<0.01),while the level of NGF and BDNF also decreased significantly(P<0.01).Compared with the model group,the sciatic nerve conduction velocity in ANA and combinational groups strongly increased(P<0.01),the damage of Nissl bodies in neurons of spinal ganglia reduced and the number obviously increased(P<0.01),the level of NGF and BDNF increased considerably(P<0.01).Compared with the ANA group,the sciatic nerve conduction velocity in combinational group increased significantly(P<0.01),the morphology of Nissl bodies in neurons of spinal ganglia were more regular and the number increased(P<0.01),moreover,the level of NGF also increased significantly(P<0.01).Conclusion ANA combined with electroacupuncture can enhance the sciatic nerve conduction velocity,improve the morphology of neurons in spinal ganglia and play a protective effect on spinal ganglia.The mechanism can be related to the higher expression of NGF and BDNF proteins,especially the expression of NGF protein.

Objective:To investigate the precipitating and aggravating factors in patients with fibromyalgia (FMS) compared to patients with rheumatoid arthritis (RA).Methods:This study was conducted from January 2015 to November 2021, using a cross-sectional survey research method, based on references to develop a patient-reported "onset and exacerbation triggers questionnaire", and surveyed patients with FMS and RA at the same time, and counted the types and proportions of onset and exacerbation triggers in the two groups of patients and used the chi-square test to make comparisons between the groups.Results:A total of 415 patients with FMS and 200 patients with RA participated the survey. 146 patients with FMS (35.2%) and 38 patients with RA (19.0%) reported morbidity triggers. Experiencing physical injury (71, 17.1%), wind-cold/cold-dampness (30 patients, 7.2%), mental stress (26, 6.2%), and exercise fatigue (10 patients, 2.4%) were the common morbidity triggers for FMS. More FMS patients reported to have experienced physical injuries and mental stress before the onset of the disease compared to RA patients [8.2%(17/200), χ2=5.41, P=0.020; 1.5%(3/200), χ2=6.82, P=0.009]. Exacerbation triggers were reported by 319 patients with FMS (76.9%) and 137 patients with RA (68.5%), in the order of weather changes (219 patients, 52.7%), physical labor (192 patients, 46.2%), mood swings (147 patients, 35.4%), sleep deprivation (145 patients, 34.9%), and mental stress (130 patients, 31.3%). The proportion of FMS patients with symptom exacerbation due to physical labor [46.2%(192/415)], mood swings[35.4%(147/415)], sleep deprivation[34.9%(145/415)], mental stress[31.3%(130/415)], and infection [9.3%(39/415)] was significantly higher than that of RA patients [35.0%(70/200), χ2=7.00, P=0.008; 19.5%(39/200), χ2=16.22, P<0.001; 13.5%(27/200), χ2=30.79, P<0.001; 17.5%(35/200), χ2=13.14, P<0.001; 3.0%(6/200), χ2=8.15, P=0.004). Conclusion:More than a third of FMS patients reported precipitating factors, and nearly four fifths FMS patients reported at least one aggravating trigger. FMS patients are likely to be more sensitive to environmental changes and perceived stress than RA patients.

italic>Candida albicans (C. albicans) stands as the primary opportunistic fungal pathogen responsible for fungal infections. The formation of biofilms constitutes a key virulence trait of C. albicans and a pivotal factor in drug resistance. Consequently, the development of antifungal drugs possessing biofilm inhibitory properties holds importance in the treatment of fungal infectious diseases. This study conducted in-depth research of the novel biofilm inhibitor named 1-(cyclopentylamino)-3-(4-(2,4,4-trimethylpentan-2-yl)phenoxy)propan-2-ol (IMB-H12). IMB-H12 showed good inhibitory activity on the formation of biofilms and a certain scavenging effect on mature biofilms. Preliminary research on the mechanism of action has found that IMB-H12 can inhibit the transformation from yeast to hyphal phase, inhibit the formation of mycelium, and reduce the adhesion activity and hydrophobicity of Candida albicans. IMB-H12 could also induce changes in the content of cell wall components, downregulate the expression of multiple genes related to adhesion and hyphal formation. Therefore, further research on this compound is expected to discover new lead compounds with antifungal activity.

This experiment aims to study the taste-masking effects of different kinds of corrigent used individually and in combination on ibuprofen oral solution, in order to optimize the taste-masking formulation. Firstly, a wide range of corrigent and the mass fractions were extensively screened using electronic tongue technology. Subsequently, a combination of sensory evaluation, analytic hierarchy process (AHP)-fuzzy mathematics evaluation, and Box-Behnken experimental design were employed to comprehensively assess the taste-masking effects of different combinations of corrigent on ibuprofen oral solution, optimize the taste-masking formulation, and validate the results. The study received ethical approval from the Review Committee of the Beijing University of Chinese Medicine (ethical code: 2024BZYLL0102). The results showed that corrigent fractions and types were screened separately through single-factor experiments. Subsequently, a Box-Behnken response surface design combined with AHP and fuzzy mathematics evaluation was used to fit a functional model: Z = 688.310 11 - 3 023.722 22X₁ - 11.477 00X₂ + 62.721 67X₃ + 14.600 00X₁X₂ - 179.666 67X₁X₃ - 3.152 00X₂X₃ + 4 031.111 11X₁² + 0.525 28X₂² + 9.772 00X₃². This model is stable and reliable, determining the optimal taste-masking formulation to be 3.9 g·L^-1 of stevioside, 100 g L^-1 of xylitol, and 30 g L^-1 of methyl-β-cyclodextrin. The comprehensive score of the verification test is 88.14, with a relative RSD of 0.39%, indicating the feasibility of this model. This study achieved the improvement of the taste of ibuprofen oral solution through a combination of objective and subjective methods. Three types of corrigent were identified for enhancing drug taste, leading to the selection of the optimal taste-masking formulation for ibuprofen oral solution. This significantly enhanced the taste of the original formulation, improved patient compliance, and offered a new approach to mitigating the undesirable taste of formulations.

Objective Hypertriglyceridemic waist(HW),hypertriglyceridemic waist-to-height ratio(HWHtR),and waist-to-hip ratio(WHR)have been shown to be indicators of cardiometabolic risk factors.However,it is not clear which indicator is more suitable for children and adolescents.We aimed to investigate the relationship between HW,HWHtR,WHR,and cardiovascular risk factors clustering to determine the best screening tools for cardiometabolic risk in children and adolescents. Methods This was a national cross-sectional study.Anthropometric and biochemical variables were assessed in approximately 70,000 participants aged 6-18 years from seven provinces in China.Demographics,physical activity,dietary intake,and family history of chronic diseases were obtained through questionnaires.ANOVA,x2 and logistic regression analysis was conducted. Results A significant sex difference was observed for HWHtR and WHR,but not for HW phenotype.The risk of cardiometabolic health risk factor clustering with HW phenotype or the HWHtR phenotype was significantly higher than that with the non-HW or non-HWHtR phenotypes among children and adolescents(HW:OR = 12.22,95%CI:9.54-15.67;HWHtR:OR = 9.70,95%CI:6.93-13.58).Compared with the HW and HWHtR phenotypes,the association between risk of cardiometabolic health risk factors(CHRF)clustering and high WHR was much weaker and not significant(WHR:OR = 1.14,95%CI:0.97-1.34). Conclusion Compared with HWHtR and WHR,the HW phenotype is a more convenient indicator with higher applicability to screen children and adolescents for cardiovascular risk factors.

Objective To investigate the epidemiological characteristics and mutation spectrum of monogenic diseases in Chinese population through a large-scale,multicenter carrier screening.Methods This study was conducted among a total of 33 104 participants(16 610 females)from 12 clinical centers across China.Carrier status for 223 genes was analyzed using high-throughput sequencing and different PCR methods.Results The overall combined carrier frequency was 55.58%for 197 autosomal genes and 1.84%for 26 X-linked genes in these participants.Among the 16 669 families,874 at-risk couples(5.24%)were identified.Specifically,584 couples(3.50%)were at risk for autosomal genes,306(1.84%)for X-linked genes,and 16 for both autosomal and X-linked genes.The most frequently detected autosomal at-risk genes included GJB2(autosomal recessive deafness type 1A,393 couples),HBA1/HBA2(α-thalassemia,36 couples),PAH(phenylketonuria,14 couples),and SMN1(spinal muscular atrophy,14 couples).The most frequently detected X-linked at-risk genes were G6PD(G6PD deficiency,236 couples),DMD(Duchenne muscular dystrophy,23 couples),and FMR1(fragile X syndrome,17 couples).After excluding GJB2 c.109G>A,the detection rate of at-risk couples was 3.91%(651/16 669),which was lowered to 1.72%(287/16 669)after further excluding G6PD.The theoretical incidence rate of severe monogenic birth defects was approximately 4.35‰(72.5/16 669).Screening for a battery of the top 22 most frequent genes in the at-risk couples could detect over 95%of at-risk couples,while screening for the top 54 genes further increased the detection rate to over 99%.Conclusion This study reveals the carrier frequencies of 223 monogenic genetic disorders in the Chinese population and provides evidence for carrier screening strategy development and panel design tailored to the Chinese population.In carrier testing,genetic counseling for specific genes or gene variants can be challenging,and the couples need to be informed of these difficulties before testing and provided with options for not screening these genes or gene variants.

Objective:To construct a risk prediction model for the inter-hospital transfer of critically ill children using machine learning methods, identify key medical features affecting transfer outcomes, and improve the success rate of transfers.Methods:A prospective study was conducted on critically ill children admitted to the pediatric transfer center of Hunan Children's Hospital from January 2020 to January 2021. Medical data on critical care features and relevant data from the Pediatric Risk of Mortality (PRISMⅢ) scoring system were collected and processed. Three machine learning models, including logistic regression, decision tree, and Relief algorithm, were used to construct the risk prediction model. A back propagation neural network was employed to build a referral outcome prediction model to verify and analyze the selected medical features from the risk prediction model, exploring the key medical features influencing inter-hospital transfer risk.Results:Among the 549 transferred children included in the study, 222 were neonates (40.44%) and 327 were non-neonates (59.56%). There were 50 children in-hospital deaths, resulting in a mortality rate of 9.11%. After processing 151 critical care medical feature data points, each model selected the top 15 important features influencing transfer outcomes, with a total of 34 selected features. The decision tree model had an overlap of 72.7% with PRISMⅢ indicators, higher than logistic regression (36.4%) and Relief algorithm (27.3%). The training prediction accuracy of the decision tree model was 0.94, higher than the accuracy of 0.90 when including all features, indicating its clinical utility. Among the top 15 important features selected by the decision tree model, the impact on transfer outcomes was ranked as follows based on quantitative feature violin plots: base excess, total bilirubin, ionized calcium, total time, arterial oxygen pressure, blood parameters (including white blood cells, platelets, prothrombin time/activated partial thromboplastin time), carbon dioxide pressure, blood glucose, systolic blood pressure, heart rate, organ failure, lactate, capillary refill time, temperature, and cyanosis. Eight of these important features overlapped with PRISMⅢ indicators, including systolic blood pressure, heart rate, temperature, pupillary reflex, consciousness, acidosis, arterial oxygen pressure, carbon dioxide pressure, blood parameters, and blood glucose. The decision tree was used to select the top 15 medical features with high impact on the neonatal and non-neonatal datasets, respectively. A total of 19 features were selected, among which there were 8 differences and 11 overlap terms between the important features of the neonatal and non-neonatal.Conclusions:Machine learning models could serve as reliable tools for predicting the risk of inter-hospital transfer of critically ill children. The decision tree model exhibits superior performance and helps identify key medical features affecting inter-hospital transfer risk, thereby improving the success rate of inter-hospital transfers for critically ill children.