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.

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.

Child ; Humans ; Mpox (monkeypox)/prevention & control* ; Disease Outbreaks

Objective:To analyze the risk factors for asparaginase-associated pancreatitis (AAP) in children with acute lymphoblastic leukemia (ALL) after treatment with pegaspargase and evaluate the predictive value of pediatric sequential organ failure assessment (SOFA) score, pediatric acute pancreatitis severity (PAPS) score, Ranson′s score and pediatric Ministry of Health, Labour and Welfare of Japan (JPN) score for severe AAP.Methods:Cross-sectional study.The clinical data of 328 children with ALL who received pegaspargase treatment in the Department of Pediatric Hematology, Zhujiang Hospital, Southern Medical University from January 2014 to August 2021, as well as their clinical manifestations, laboratory examinations, and imaging examinations were collected.The SOFA score at the time of AAP diagnosis, PAPS score and Ranson′s score at 48 hours after AAP diagnosis, and JPN score at 72 hours after AAP diagnosis were calculated, and their predictive value for severe AAP was evaluated by the receiver operating characteristic (ROC) curve.Results:A total of 6.7%(22/328) of children had AAP, with the median age of 6.62 years.AAP most commonly occurred in the induced remission phase (16/22, 72.7%). Three AAP children were re-exposed to asparaginase, and 2 of them developed a second AAP.Among the 22 AAP children, 16 presented with mild symptoms, and 6 with severe symptoms.The 6 children with severe AAP were all transferred to the Pediatric Intensive Care Unit (PICU). There were no significant differences in gender, white blood cell count at first diagnosis, immunophenotype, risk stratification, and single dose of pegaspargase between the AAP and non-AAP groups.The age at diagnosis of ALL in the AAP group was significantly higher than that in the non-AAP group ( t=2.385, P=0.018). The number of overweight or obese children in the AAP group was also higher than that in the non-AAP group ( χ2=4.507, P=0.034). The areas under the ROC curve of children′s JPN score, SOFA score, Ranson′s score, and PAPS score in predicting severe AAP were 0.919, 0.844, 0.731, and 0.606, respectively.The JPN score ( t=4.174, P=0.001) and the SOFA score ( t=3.181, P=0.005) showed statistically significant differences between mild and severe AAP. Conclusions:AAP is a serious complication in the treatment of ALL with combined pegaspargase and chemotherapy.Older age and overweight or obesity may be the risk factors for AAP.Pediatric JPN and SOFA scores have predictive value for severe AAP.

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.

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.

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 explore the correlation between serum beta 2-microglobulin(B2M)level and cerebral microbleeds(CMB)in the elderly.Methods A retrospective analysis of 636 elderly patients with chronic diseases admitted to the Department of Neurology of our hospital from Janu-ary 2020 to November 2022 was made.On the second day after admission,venous blood samples were collected to detect the serum B2M level,and brain magnetic resonance susceptibility weigh-ted imaging was performed.Then these patients were assigned into CMB group(82 cases)and CMB-free group(554 cases).Binary logistic regression analysis was employed to identify the inde-pendent risk factors for CMB.Results Binary logistic regression analysis showed that serum B2M level was an independent risk factor for CMB in elderly patients(Model 1:β=0.179,OR=1.196,95％CI:1.017-1.407,P=0.031;Model 2:β=0.215,OR=1.240,95％CI:1.048-1.468,P=0.012)after adjusting confounding factors.ROC curve analysis indicated that the optimal cutoff value of serum B2M level in diagnosing CMB was 1.805 mg/L,with a sensitivity of 70.7％and a specificity of 52.5％,and an AUC value of 0.657(95％CI:0.595-0.719,P＜0.01).Conclusion The increment of serum B2M level is closely related to CMB in the elderly population,so the pro-tein can be used as one of indicators for prediction of CMB in the population.

Objective:To summarize the clinical and genetic characteristics of children with β-ketothiolase deficiency (BKTD).Methods:The clinical characteristics, biochemical, markers detected by tandem mass spectrometry (MS/MS) and gas chromatography-mass spectrometry (GC/MS), as well as the variants in ACAT1 gene among 5 children with BKTD in Children′s Hospital of Chongqing Medical University between October 2018 and December 2022 were retrospectively analyzed.Results:The onset age of the disease in 5 patients (4 males and 1 female) ranged from 9.7 to 28.0 months. During the acute phase, severe metabolic acidosis was observed with a pH of 6.9-7.1, as well as hypoglycaemia (2.3-3.4 mmol/L) and positive urinary ketone bodies (+-++++). Blood levels of methylcrotonyl carnitine, methylmalonyl carnitine and malonyl carnitine were 0.03-0.42, 0.34-1.43 and 0.83-3.53 μmol/L respectively and were significantly elevated. Urinary 2-methyl-3-hydroxybutyric acid was 22-202 and 3-hydroxybutyric acid was 4-6 066, both were higher than the normal levels. Methylcrotonylglycine was mild elevated (0-29). The metabolites detected by MS/MS and GC/MS were significantly reduced after treatment. Analysis of ACAT1 gene mutation was performed in 5 children. Most variants were missense (8/9). Four previously unreported variants were identified: c.678G>T (p.Trp226Cys), c.302A>G (p.Gln101Arg), c.627_629dupTGA (p.Asn209_Glu210insAsp) and c.316C>T (p.Gln106Ter), the first 2 variants were predicted to be damaging by SIFT, PolyPhen-2 and Mutation Taster software. c.316C>T (p.Gln106Ter) is a nonsense variant.Conclusions:β-ketothiolase deficiency is relatively rare, lacks specific clinical manifestations, however severe metabolic acidosis, hypoglycemia, and ketosis during the acute onset were consistent findings. Missense mutations in the ACAT1 gene are common genetic causes of β-ketothiolase deficiency.