Abstract:

Central nervous system tumors are characterized by high invasiveness， complex anatomical locations， and limitations in traditional treatment methods， resulting in low patient survival rates and a high likelihood of functional impairment. This underscores the urgent need for novel therapeutic targets. Cancerassociated fibroblasts（ CAFs）， as a key component of the tumor microenvironment， influence tumor initiation and progression through multiple mechanisms. This paper reviews the biological characteristics of CAFs， focusing on their mechanisms of action in central nervous system tumors， including enhancing tumor cell invasiveness and stemness， promoting angiogenesis， inducing immunosuppressive microenvironment， and enhancing ferroptosis resistance. At the same time， potential therapeutic strategies targeting CAFs and their pathways based on the above mechanisms are explored， aiming to provide new directions for overcoming the treatment bottleneck of central nervous system tumors.

Abstract:

The prevalence of mental disorders remains persistently high worldwide, imposing a substantial burden on public health systems. However， as the etiological mechanisms have yet to be fully elucidated， current clinical diagnosis still relies primarily on subjective symptomatic assessment, lacking objective biological criteria. This symptom-based classification system overlooks the biological heterogeneity of the disease， leading to significant differences in brain mechanisms among patients with similar symptoms， which increases the difficulty of precise diagnosis and treatment. Neuroimaging techniques offer new avenues for exploring neurobiological biomarkers. However， current research is largely confined to the population level， and the transition to personalized clinical applications still faces challenges regarding accuracy and generalizability. This review systematically summarizes the key findings from recent years in multimodal neuroimaging regarding structural and functional abnormalities in the brain associated with mental disorders， with a focus on analyzing the current challenges in neuroimaging research on mental disorders and proposing methods and strategies to address these challenges. Although there is currently a significant gap between neuroimaging findings and clinical translation in psychiatry， the integration of multimodal data， the use of artificial intelligence algorithms， and the conduct of large-scale longitudinal cohort studies can gradually lead to the precise diagnosis and treatment of mental disorders.

Abstract:

Objective To construct and validate a prediction model based on extreme gradient boosting （XGBoost） for early identification of the risk of treatment failure of olanzapine in schizophrenia patients at 8 weeks， so as to provide auxiliary support for individualized treatment decisions. Methods This study included 200 patients with schizophrenia who received olanzapine treatment and completed an 8-week follow-up at Xiamen Xianyue Hospital from January 2023 to December 2024. Treatment failure was defined as a Positive and Negative Syndrome Scale（ PANSS） reduction rate of＜30% after 8 weeks of treatment. Candidate predictors included subject demographic characteristics（ such as age， gender， disease duration）， clinical features（ baseline PANSS score， comorbidities， past medication history）， laboratory indicators［ such as complete blood count，liver and kidney function， serum interleukin-6（ IL-6）］， and genetic markers（ DRD2 rs1076560）. Multiple imputation by chained equations（ MICE， m=5） was used for missing values. The samples were randomly divided into a training set（ n=140） and a testing set（ n=60） in a 7∶3 ratio. The training set employed 5-fold cross-validation， with XGBoost hyperparameters tuned via grid search. When necessary， synthetic minority oversampling technique（ SMOTE） was used to address class imbalance. Model performance was evaluated in the independent testing set using area under the curve（ AUC）， sensitivity， specificity， accuracy， calibration curve （Hosmer-Lemeshow test）， and decision curve analysis（ DCA）， with variable importance interpreted using Shapley additive explanations（ SHAP） values. Statistical analysis was performed using Python（ XGBoost， scikit-learn） and R software. Results There were no statistically significant differences between patients in the training and testing sets in terms of gender， age， disease duration， baseline PANSS scores， comorbid anxiety disorder ratio， serum IL-6 levels， and genotype distribution at the DRD2 gene rs1076560 locus（ all P＞0.05）. The XGBoost model identified five important predictors during training/validation： baseline PANSS positive symptom score， disease duration， serum IL-6 levels， genotype distribution at the DRD2 gene rs1076560 locus， and comorbid anxiety disorder. The model performance in the testing set was as follows： accuracy 0.833， sensitivity 0.794， specificity 0.885， AUC of 0.897［ 95%CI（ 0.808，0.986）］， Hosmer-Lemeshow test P=0.620， with good calibration. DCA indicated that when the threshold probability exceeded 0.25， the model demonstrated greater clinical net benefit compared to a single predictor. Conclusions The XGBoost prediction model established in this retrospective study effectively identifies high-risk patients for olanzapine treatment failure at 8 weeks within this cohort. Key identified factors include symptom severity， disease duration， inflammatory indicators， genetic polymorphisms， and comorbidities. The model needs to be validated in external cohorts before it can be used for clinical decision support.

Abstract:

Objective To explore the abnormalities of the white matter structural network and resting state functional network in patients with schizophrenia（ SCZ）， as well as the differences in structuralfunctional connectivity（ SC-FC） coupling. Methods This study was designed as a cross-sectional study. From June 2023 to March 2025， 35 patients with SCZ（ SCZ group） at the Department of Psychiatry of the Second Affiliated Hospital of Qiqihar Medical University and the Qiqihar Mental Health Center as well as 39 healthy volunteers（ control group） recruited from the general public during the same period， were selected as study subjects. Diffusion tensor imaging and resting-state functional magnetic resonance imaging data were collected from the participants. Graph theory analysis and the SC-FC coupling method were used to analyze the imaging data. Pearson correlation was used to analyze the relationship between network index values of nodes in brain regions showing abnormal changes and clinical symptoms. Results In the structural network， compared with controls， SCZ group patients showed abnormal changes in the node network indicators of Cp value in the right olfactory cortex， Efficiency value in the left cingulate gyrus， Cp and Eloc values in the right cingulate gyrus， Cp， Eloc and Dc values in the left anterior cingulate gyrus and collateral cingulate gyrus， Efficiency value in the left orbital superior frontal gyrus， and Efficiency value in the left caudate nucleus， with statistically significant differences（ t=-5.595，4.049，-5.852，-5.852，-4.158，-4.158，4.158，4.192，3.982， all P＜0.001）. In the functional network， compared with control group， SCZ group showed abnormal changes in the node network indicators of aDc and aEfficiency values in the left caudate nucleus， aDc value in the right caudate nucleus， aDc and aEfficiency values in the left putamen， aDc value in the right putamen， aDc value in the left globus pallidus， aDc value in the right suboccipital gyrus， aDc and aEfficiency values in the right fusiform gyrus， aDC value in the right middle temporal gyrus and the differences were statistically significant（ t=3.824，3.744，3.934， 4.343，4.234，3.786，3.810，-3.693，-3.960，-3.730，-3.660， all P ＜ 0.001）. The SC-FC coupling strength in the whole-brain network of SCZ group patients was higher than that of control group， but the difference was not statistically significant（ t=0.166，P=0.869）. Correlation analysis revealed that the SC-FC coupling values in the left superior marginal gyrus of SCZ group were negatively correlated with the General Psychopathological Symptom Scale score， with the difference being statistically significant（ r=-0.350， P=0.049）. After Bonferroni correction， there was no correlation between the abnormal network index values and SC-FC coupling values in SCZ group and any clinical characteristics（ all P ＞ 0.006 3）. Conclusions SCZ patients exhibit unique brain structural and functional network characteristics， as well as SC-FC coupling model， which provide a new perspective for understanding the potential pathogenesis of SCZ.

Abstract:

Objective To explore the differences in degree centrality（ DC） of brain functional networks in adolescents with comorbidity of major depressive disorder（ MDD） and obesity based on resting state functional magnetic resonance imaging（ rs-fMRI）. Methods A total of 75 adolescents with MDD who received outpatient or inpatient treatment at Suzhou Guangji Hospital between October 2022 and July 2023 were selected as research subjects and divided into a MDD comorbid with obesity group（ fMDD group， n=37） and MDD group（ n=38）. During the same period， 35 healthy individuals were recruited from the community as the healthy control group （HC group）. All participants underwent rs-fMRI scans. The statistical analysis module in the DPABI software was used to perform a one-way analysis of variance（ ANOVA） on the three sets of image data collected， in order to compare the differences in DC values among the three groups of subjects. The correlations between DC values and the Eysenck Personality Questionnaire for Children（ EPQ-C）， Adolescent Self-Rating Life Events Check List（ ASLEC）， Patient Health Questionnaire-9（ PHQ-9） and Simplified Coping Style Questionnaire（ SCSQ） were analyzed. Results Compared with HC group， fMDD group showed statistically significant reductions in DC values in the left thalamus， right thalamus， and right insula（ t=-4.82、-5.14、-4.81，all P＜0.001）. Compared with HC group， MDD group showed lower DC values in the left thalamus and right thalamus， with the differences being statistically significant（ t=-4.53、-4.72，all P＜0.001）. Compared with MDD group， fMDD group showed higher DC values in the left orbitofrontal cortex， with the difference being statistically significant（ t=4.60， P＜ 0.001）. Pearson correlation analysis revealed that DC values in the right insula were negatively correlated with extraversion-introversion scores in the EPQ-C （r=-0.432， P=0.008）， and with learning stress factor and loss factor scores in the ASLEC（ r=-0.367， -0.417；P=0.025、0.010）， and DC values in the right thalamus were negatively correlated with learning stress factor and loss factor scores in the ASLEC（ r=-0.331， -0.390； P=0.046、0.017）， all these differences were statistically significant. Spearman correlation analysis revealed a statistically significant negative correlation between the DC values in the left thalamus and the positive coping scores in the SCSQ（ r=-0.338， P=0.041）. Conclusions Adolescents with comorbidity of MDD and obesity exhibit abnormalities in DC of specific brain functional networks， and functional abnormalities in the thalamus and insula are closely associated with personality traits and life stresses. Increased DC in the left orbitofrontal cortex may be a characteristic neuroimaging manifestation that distinguishes MDD comorbid with obesity from MDD alone.

Abstract:

Recent clinical studies and multimodal neuroimaging evidence have revealed that patients with obsessive-compulsive disorder（ OCD） primarily exhibit impairments in inhibitory control， cognitive flexibility， and working memory updating within executive functions. Attention deficits primarily manifest as difficulty disengaging from threat-related information， and memory impairments are characterized by impaired episodic memory retrieval and insufficient metamemory confidence. In terms of neural mechanisms， the aforementioned deficits are closely associated with dysfunction in the prefrontal-striatal circuit and impaired coordination between large-scale brain networks such as the executive control network and the default mode network. This paper reviews behavioral manifestations and neural mechanisms of OCD patients across three major cognitive dimensions of execution， attention， and memory， so as to elucidate the role of cognitive deficits in symptom occurrence and maintenance. The paper further emphasizes that multidimensional cognitive impairments， coupled with network dysregulation， collectively form the foundation for persistent OCD symptoms， aiming to provide a systematic reference for understanding pathological mechanisms and developing targeted intervention strategies.

Abstract:

Objective To evaluate the impact of cholesterol， high-density lipoprotein， and glucose （CHG） index levels on progressive ischemic stroke（ PIS）， so as to provide evidence and guidance for the comprehensive management of blood lipids and blood glucose in patients with PIS. Methods This study was a retrospective study. A total of 1 356 patients primarily diagnosed with ischemic stroke at Cangzhou People's Hospital from January 2019 to December 2024 were included as the research subjects. According to whether the National Institute of Health Stroke Scale（ NIHSS） score increased by ≥ 2 compared to admission score within 7 days after onset， patients were divided into a progressive group（ NIHSS score increased by ≥ 2 compared to admission score） and a non-progressive group. The correlation between the CHG index and PIS was investigated using the Logistic regression， receiver operating characteristic（ ROC） curve， and subgroup analysis. Results During hospitalization of 1 356 patients， a total of 375 patients experienced PIS. The age， baseline NIHSS score， proportion of atrial fibrillation history， fasting blood glucose， glycated hemoglobin， total cholesterol， low-density lipoprotein cholesterol， CHG index of progressive group were all higher than those of non-progressive group， and the differences were statistically significant（ all P＜0.05）. After adjusting for confounding factors with P ＜ 0.05 in univariate Logistic regression analysis using multivariate Logistic regression， the results showed that the CHG index［ OR=1.532， 95%CI（ 1.206，1.945）， P＜0.001］ remained an independent risk factor for PIS. Subgroup analysis revealed that the CHG index was positively associated with PIS risk in male gender［ OR=1.589， 95%CI（ 1.225，2.061）， P＜0.001］， female gender［ OR=1.970， 95%CI （1.460，2.658）， P＜0.001］， no history of smoking［ OR=1.870， 95%CI（ 1.491，2.346）， P＜0.001］， no history of alcohol consumption［ OR=1.806， 95%CI（ 1.460， 2.235）， P＜0.001］， history of hypertension［ OR=1.783， 95%CI（ 1.428，2.227）， P＜0.001］， no history of hypertension［ OR=1.555， 95%CI（ 1.068，2.264）， P=0.021］， history of diabetes［ OR=2.093， 95%CI（ 1.507，2.907）， P ＜ 0.001］， no history of diabetes［ OR=1.584， 95%CI（ 1.229，2.042）， P＜ 0.001］， history of coronary heart disease［ OR=1.457， 95%CI（ 1.032，2.058）， P=0.033］， no history of coronary heart disease［ OR=1.851， 95%CI（ 1.471， 2.329）， P＜ 0.001］， no history of cerebral infarction［ OR=1.739， 95%CI（ 1.430， 2.116）， P＜0.001］， history of peripheral vascular disease ［OR=3.869， 95%CI（ 2.002，7.478）， P＜ 0.001］， and no history of peripheral vascular disease［ OR=1.570， 95%CI（ 1.284， 1.921）， P ＜ 0.001］. Significant interaction effects were observed in the subgroups classified by TOAST（ Pinteraction ＜ 0.001）. The area under the ROC curve was 0.696. Conclusions A high CHG index is significantly associated with an increased risk of PIS.

Abstract:

Objective To explore the relationship between sleep duration and depressive disorder， and further analyze the moderating effects of inflammatory markers， sociodemographic characteristics， and lifestyle factors. Methods Based on the publicly available cross-sectional database of the National Health and Nutrition Examination Survey（ NHANES）， after excluding pregnant women， participants with incomplete demographic data， and those lacking questionnaire assessments of depressive disorder， sleep duration， and physical activity or inflammatory marker tests， a total of 22 201 participants were ultimately included in the analysis. Multivariate Logistic regression and restricted cubic spline were used to analyze the association between sleep duration and depressive disorder， This study delved into the moderating effects of multidimensional factors such as demographic characteristics， lifestyle factors， and inflammatory indicators on the relationship between sleep duration and depressive symptoms and to examine multiplicative interactions across subgroups. Results Of the 22 201 study participants， 1 909 exhibited depressive symptoms（ PHQ-9 score ≥ 10）， resulting in a positive detection rate of 8.6%. Multivariate Logistic regression and restricted cubic spline analysis revealed a U-shaped relationship between sleep duration and depression scores. Both insufficient sleep［ OR=1.76， 95%CI （1.52，2.03）］and excessive sleep［ OR=1.63， 95%CI（ 1.41，1.88）］were associated with an increased risk of depressive disorder， and these differences were statistically significant（ P ＜ 0.05）. Multiplicative interaction analysis revealed a statistically significant interaction between age and sleep duration in relation to depressive disorder（ F=3.389， Pinteraction=0.041）. In addition， smoking， body mass index， physical activity， poverty income ratio， and platelet-to-lymphocyte ratio exerted moderating effects on the association between sleep duration and depressive disorder， with statistically significant differences（ Pinteraction ＜ 0.05）. Conclusions Both insufficient and excessive sleep increase the risk of depressive disorder， particularly among older adults， individuals with high levels of inflammation， those who quit smoking， those with low levels of physical activity and high-income. Sleep health management should be incorporated into depressive disorder prevention and treatment strategies， as ensuring adequate sleep duration helps reduce the risk of depressive disorder.

Abstract:

Parkinson disease is a neurodegenerative disorder. Dysarthria and cognitive impairment are common motor and non-motor symptoms of Parkinson disease. Recent studies have found that dysarthria may be closely linked to cognitive impairments in Parkinson disease. This paper reviews recent research on the correlation between the two， covering risk factors， pathogenesis， treatment， and nursing， with the aim of providing reference for clinical diagnosis， treatment， and practice.

Abstract:

The pathophysiology mechanisms of anxiety disorders are closely associated with neuroinflammation mediated by the nucleotide-binding oligomerization domain-like receptor protein 3（ NLRP3） inflammasome. Research indicates that NLRP3 inflammasomes activate via classical and non-classical pathways under stress or environmental stimuli， releasing proinflammatory factors such as interleukin-1β（ IL-1β） and interleukin-18（ IL-18）， leading to impaired synaptic plasticity in the hippocampus and prefrontal cortex， as well as neuronal pyroptosis， ultimately inducing anxiety-like behaviors. Its regulatory network exhibits multidimensional characteristics： brain region specificity， environment-gene interactions， gut-brain axis， and epigenetic mechanisms. In terms of targeted interventions， drugs and natural ingredients exert therapeutic effects by inhibiting NLRP3 inflammasome assembly， modulating microglial phenotypic polarization， and activating antioxidant pathways. Future research should focus on brain region regulatory heterogeneity， mechanisms of neuronal subpopulation interactions， and the development of efficient small-molecule inhibitors. This paper systematically analyzes the multidimensional regulatory network of the NLRP3 inflammasome， aiming to provide a theoretical basis for mechanistic interventions in anxiety disorders.

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