Neuropsychiatric Disease and Treatment最新文献

Purpose: This study aims to assess the diagnostic efficacy of a multi-region radiomics analysis utilizing conventional MRI sequences (T1-weighted imaging [T1WI] and T2-weighted imaging [T2WI]) for autism spectrum disorder (ASD), and to investigate the correlations between radiomics features and the severity of clinical symptoms, thereby exploring potential imaging biomarkers.

Methods: This retrospective study included 207 pediatric participants (91 ASD, 116 typically developing controls). Radiomics features were extracted from manually segmented bilateral hippocampus, thalamus, caudate nucleus, and lenticular nucleus on T1WI and T2WI images. Three distinct classifiers (T1WI-only, T2WI-only, T1WI+T2WI combined) were developed using logistic regression (LR), support vector machine (SVM), and a TabTransformer deep learning (DL) model. Diagnostic performance was evaluated via five-fold cross-validation.

Results: The TabTransformer DL model utilizing combined T1WI+T2WI features demonstrated superior performance, achieving an area under the curve of 0.900, accuracy of 0.834, sensitivity of 0.843, and specificity of 0.823. Specific radiomic features, predominantly from the left lentiform nucleus and bilateral caudate nucleus, were significantly correlated with clinical severity scores (ABC, CARS).

Conclusion: Radiomics models leveraging routine MRI sequences demonstrate robust diagnostic utility for ASD. The identified subcortical features, correlating with core symptoms, may serve as viable imaging biomarkers. Future work requires external validation, exploration of automated segmentation, and investigation in larger, multi-center cohorts..

Purpose: To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation antipsychotics (SGAs).

Patients and methods: Medical records of patients hospitalized between March 2019 and March 2025 were retrospectively reviewed. The optimal cut-off points for baseline serum ApoB levels and the ApoB/ApoA1 ratio were identified using a maximally selected log-rank statistic analysis. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (95% CIs). The Kaplan-Meier method with Log rank testing was used to compare the cumulative incidence of dyslipidemia between groups defined by these cut-off points.

Results: Of 311 enrolled patients, 33 (10.6%) lacking baseline ApoA1 measurements were excluded from ApoB/ApoA1 ratio analyses. The optimal cut-off points were 0.70 g/L for baseline ApoB and 0.45 for the ApoB/ApoA1 ratio. Multivariable Cox proportional hazards models, fully adjusted for covariates, demonstrated significantly elevated dyslipidemia risk for patients exceeding these thresholds vs low-risk groups: adjusted HR 2.98 (95% CI: 2.05-4.32, p < 0.001) for high ApoB and 3.17 (95% CI: 1.62-6.22, p = 0.001) for high ApoB/ApoA1 ratio. Continuous analysis showed each 0.1 g/L ApoB increase conferred a 34% higher risk (adjusted HR 1.34, 95% CI: 1.21-1.48, p < 0.001), while each 0.1-unit ApoB/ApoA1 ratio increase conferred a 20% higher risk (adjusted HR 1.20, 95% CI: 1.10-1.30, p < 0.001). Kaplan-Meier curves confirmed significantly higher cumulative dyslipidemia incidence in high vs low groups for both markers (Log rank test, both p < 0.001).

Conclusion: Baseline serum ApoB levels and the ApoB/ApoA1 ratio are valuable risk markers for dyslipidemia in patients treated with SGAs.

Purpose: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and stereotyped, repetitive behaviors or interests. Neuromodulation interventions have been employed in ASD, which can improve behavioral and cognitive outcomes in ASD, especially relief of comorbidities, as shown in preliminary evidence. However, their efficacy and safety remain unclear owing to the lack of high-quality synthetic evidence. We aim to systematically evaluate the therapeutic potential of neurostimulation in ASD and explore its underlying mechanisms.

Patients and methods: A narrative synthesis of peer-reviewed literature from 2000 to 2025 was conducted, sourced from the PubMed, Web of Science, and Cochrane Library. Seventy-three relevant studies were identified in this paper.

Results: Up to date, noninvasive brain stimulation has become a potential intervention to reduce autism-related symptoms and improve neuropsychological function in ASDs, while a marked alleviation of comorbidities including aggression, anxiety and epilepsy was observed following invasive brain stimulation interventions. Both of the neuromodulation techniques are believed to be safe and well-tolerated.

Conclusion: Neuromodulation interventions could be a hopeful option to improve patients' symptoms and control comorbidities of ASD. Further high-quality trials should be conducted to optimize long-term prognosis of ASD.

Objective: This double-blinded randomized trial investigated the therapeutic effects of theta/beta mobile neurofeedback (MNF) in 8-15-year-old ADHD and neurotypical children (randomized N = 139; analyzed N = 120).

Methods: Participants were divided into three groups: ADHD Combination (MNF + Medication), ADHD MNF alone, and Neurotypical, and each group underwent 3 months of MNF intervention, either active or sham, the latter using randomly generated training results. Pre- and post-intervention electroencephalography tests were conducted, including an analysis of theta-gamma coupling (TGC), along with assessments of clinical variables.

Results: Regarding clinical outcomes, MNF was not superior to sham in alleviating ADHD symptoms based on self-reports or in cognitive test performance, as indicated by limited changes in clinical variables. Before the intervention, the Neurotypical group exhibited higher TGC scores than the ADHD groups, reaffirming TGC as a neurophysiological marker for attention and working memory. Post-intervention, the ADHD Combination group with active MNF showed increased TGC in Fp2, F3, F4, F8, P4, O1, and O2, while the ADHD MNF alone group had mixed results. Notably, active MNF had no significant effect on TGC in the Neurotypical group, while sham MNF reduced TGC.

Conclusion: MNF produced measurable neurophysiological modulation-reflected in enhanced TGC-when used as an adjunct to medication. Although clinical improvements were modest, these findings support TGC as a sensitive biomarker of MNF-induced brain regulation in ADHD.

Objective: To develop a nomogram model for individualized prediction of non-suicidal self-injury (NSSI) risk in adolescent depression patients.

Methods: Clinical data from 270 adolescent depression patients (August 2022-January 2025) were randomly divided into modeling and validation groups. The modeling group was split into NSSI and non-NSSI subgroups based on NSSI occurrence. Logistic regression identified risk factors. R software was used to construct the nomogram, while ROC and DCA evaluated its discrimination and clinical utility.

Results: A total of 189 patients from our hospital were retrospectively selected, among whom 72 patients (38.10%) were identified as having engaged in NSSI behavior within the past year. Disease duration, depression level, childhood abuse, family dysfunction, school bullying, sleep disorder, and Barratt Impulsiveness were significant risk factors (P<0.05). AUCs were 0.899 (modeling) and 0.954 (validation). H-L tests showed good fit: χ²=7.243 (P=0.721) and χ²=7.010 (P=0.711). The DCA curve indicated high clinical value when probability ranged from 0.05 to 0.97.

Conclusion: Disease course, severity of depression, childhood abuse, dysfunctional family environment during childhood, experiences of school bullying, sleep disorders, and Barratt Impulsiveness Scale scores were identified as influencing factors for NSSI in adolescents with depression. Based on these factors, a nomogram model was constructed, which showed good predictive consistency and high clinical applicability. This model can assist clinicians in identifying high-risk individuals for early prevention. Although the model may help guide interventions to reduce the incidence of NSSI, further validation through rigorously designed implementation studies is still required.

Purpose: Schizophrenia (SCZ) is a profound psychosomatic illness with an unidentified cause and no definitive biomarkers. This study sought to investigate plasma biomarkers linked to schizophrenia by untargeted metabolomics.

Patients and methods: A total of 50 medication-naïve SCZ patients and 25 healthy controls were eligible and participated in this study. Psychiatric symptomatology was evaluated employing the Positive and Negative Syndrome Scale. We quantified the concentration of lipid metabolites in plasma from all participants using untargeted metabolomics and classified metabolites that were significantly different between both groups. We subsequently assessed the diagnostic potential of metabolites on the basis of receiver operating characteristic curves and examined metabolites affiliated with psychotic symptomatology in SCZ patients.

Results: Fourteen metabolites exhibited compelling disparities in schizophrenia patients relative to healthy controls. Eight metabolites, including methylphosphatidylcholine, acylcarnitine, sphingomyelin, and (O-acyl)-1-hydroxy fatty acids (38:4), could significantly distinguish between schizophrenia and healthy controls, with all their areas under the curve (AUC) exceeding 0.7. The peak area under the curve (CV-AUC) for AcCa(20:4) was 0.92 ± 0.03. In schizophrenia patients, negative symptoms exhibited a negative correlation with acylcarnitines, while cognitive symptoms had a substantial positive correlation with methylphosphatidylcholine and phosphatidylcholine.

Conclusion: The findings reveal lipid metabolism dysregulation as a potential pathophysiological mechanism of schizophrenia. The identified metabolites, such as AcCa and phosphatidylcholine, serve as promising biomarkers for the diagnosis and symptom evaluation, suggesting their direct involvement in the disease's pathogenesis.

Introduction: We reviewed the historical, longitudinal concept, and current cross-sectional manifestations of posttraumatic stress disorder (PTSD). Commonalities include anxiety, dysphoria, defensiveness, dissociation, dysregulation (disinhibition), sleep disturbance, and somatization. Each of these, with the exception of somatization, is represented in the Diagnostic and Statistical Manual of Mental Disorders - 5-Text Revision (DSM-5-TR).

Methods: This cross-sectional pilot study sought to develop a time sequence, including demographics, trauma type, traumatic response, posttraumatic symptoms, and subjective interference (or impairment), and sought to determine how each factor in the time series relates to all later factors in the time series. The aims included (a) the effects demographics have on trauma type, traumatic response, posttraumatic symptoms, and subjective impairment, (b) the effects trauma type have on traumatic response, posttraumatic symptoms, and subjective impairment, (c) the effects traumatic response have on posttraumatic symptoms and subjective impairment, and (d) the effects posttraumatic symptoms have on subjective impairment. This was an exploratory qualitative and quantitative inquiry using cross-sectional information from a pre-existing dataset. Variable types were defined. Then statistical tests were determined using IBM Statistical Package for the Social Sciences (SPSS) software, Version 26.

Results: Along the time series, both qualitative and quantitative variables were assessed. Qualitatively, demographics affected seven later variables, but did not affect impairment. Trauma type affected ten later variables, but did not affect impairment. Traumatic response affected seven later variables, and did not affect impairment, except for personal physical injury predicting fun and/or leisure activity interference. Otherwise, posttraumatic symptoms were the only variables affecting later impairment and did so considerably with twenty associations. Quantitative findings were congruent with qualitative findings.

Discussion: Along the time series of PTSD, there is a tendency for factors to affect those variables that are adjacent or proximal, but not to affect those variables that are more distant or further out.

Background: The pathogenesis of schizophrenia (SZ) remains incompletely understood; although neuroinflammation and the NLRP3 inflammasome have been implicated, the key regulatory genes involved are still unidentified.

Objective: To investigate the association between SZ and NLRP3 inflammasome-related genes, and to screen for hub genes as potential biomarkers and therapeutic targets.

Methods: We analyzed the GEO dataset GSE27383, comprising 43 SZ patients and 29 controls, and identified 1,672 differentially expressed genes (DEGs). NLRP3-related genes were obtained from GeneCards, and weighted gene co-expression network analysis (WGCNA) highlighted the green, yellow, and red modules. The intersection of DEGs, NLRP3-related genes, and module genes was further refined using LASSO and Random Forest algorithms. Immune cell infiltration was profiled with CIBERSORT, and the diagnostic utility of candidate genes was evaluated using ROC curves. Molecular docking was performed to predict compound binding, and hub gene expression was validated in an independent cohort of 20 SZ patients and 20 controls using RT-qPCR on PBMCs.

Results: Five hub genes-HSPA8, SCAP, FLNA, TRAF2, and PINK1-AS-were significantly down-regulated in SZ (P < 0.05). The combined ROC-AUC reached 0.883. Molecular docking revealed strong binding affinities of ellagic acid to FLNA (-4.70 kcal mol^{- 1}), and of hydralazine to HSPA8 (-4.27), TRAF2 (-4.84), and SCAP (-4.98). Differential expression was confirmed in PBMCs. Additionally, SZ patients exhibited increased naive B cells and neutrophils, along with reduced resting NK cells and M2 macrophages.

Conclusion: Five genes (HSPA8, SCAP, FLNA, TRAF2, PINK1-AS) were identified as potential novel biomarkers and therapeutic targets for SZ, providing a theoretical foundation for elucidating disease mechanisms and advancing precision medicine in SZ.

Anxiety disorders are complex neuropsychiatric conditions with incompletely understood mechanisms, which hinders the development of targeted therapies. Natural products have attracted attention as promising anxiolytic candidates due to their multi-component nature, synergistic effects, and ability to modulate multiple molecular targets and signaling pathways. However, a systematic integration of anxiety pathology with the mechanisms of natural products is still lacking. This review summarizes recent advances in the molecular and cellular basis of anxiety and explores how natural products (such as flavonoids, terpenoids, and alkaloids) interact with key genes, receptors, and intracellular signaling pathways. It also addresses current research limitations, including insufficient depth of mechanistic studies, lack of standardized models, and challenges in clinical translation. By synthesizing mechanistic and therapeutic evidence, this work aims to support the development of effective, evidence-based natural anxiolytics with improved clinical applicability.

Purpose: In traditional Chinese medicine, Ligusticum wallichii is a prominent herb, acclaimed for its therapeutic roles, including anti-tumor, antioxidant, and anti-inflammatory benefits. Studies conducted recently suggest it may help reduce cognitive deficits linked to Alzheimer's disease. However, the precise neuroprotective pathways through which Ligusticum wallichii exerts its effects on Alzheimer's disease are not yet fully understood. Network pharmacology is utilized in this research to understand the mechanisms through which Ligusticum wallichii's active ingredient might protect against Alzheimer's disease.

Methods: The TCMSP database was utilized to extract the bioactive compounds of Ligusticum wallichii, and their related molecular targets were identified. By querying the GeneCards and OMIM databases, targets associated with Alzheimer's disease were identified. Using Cytoscape 3.8.2, a regulatory network mapping the interactions between active compounds and their respective targets was constructed. A protein-protein interaction network was generated by analyzing the target genes influenced by Ligusticum wallichii in Alzheimer's disease using the String database. The DAVID database was utilized to perform functional enrichment analysis, encompassing Gene Ontology (GO) and KEGG pathway analyses, to identify possible biological pathways related to these targets. Following this, molecular docking studies were carried out to confirm the interaction strength of the active compounds to the pivotal targets. Finally, in vitro experimental validation was performed to corroborate the findings.

Results: Seven bioactive compounds were identified from Ligusticum wallichii, interacting with 269 potential targets. Molecular docking revealed that Myricanone, Mandenol, and Sitosterol exhibited stable binding affinities with STAT3, HSP90AA1, and EGFR, with binding energies ranging from -4.04 to -5.87 kcal/mol. In vitro studies demonstrated that these compounds significantly downregulated the expression of STAT3, EGFR, and HSP90AA1 in Neuro 2A cells.

Conclusion: In conclusion, the results indicate that Ligusticum wallichii significantly downregulated STAT3, EGFR, and HSP90AA1 expression in Neuro 2A cells, providing mechanistic evidence that targeting these proteins may ameliorate neurodegenerative processes in Alzheimer's disease and highlighting Ligusticum wallichii's promising therapeutic potential.