H*ads dynamics engineering via bimetallic Pd-Cu@MXene catalyst for enhanced electrocatalytic hydrodechlorination.

Electrocatalytic hydrodechlorination (EHDC) is a promising approach to safely remove halogenated emerging contaminants (HECs) pollutants. However, sluggish production dynamics of adsorbed atomic H (H*ads) limit the applicability of this green process. In this study, bimetallic Pd-Cu@MXene catalysts were synthesized to achieve highly efficient removal of HECs. The alloy electrode (Pd-Cu@MX/CC) exhibited better EHDC performance in comparison to Pd@MX/CC electrode, resulting in diclofenac degradation efficiency of 93.3 ± 0.1%. The characterization analysis revealed that the Pd0/PdII ratio decreased by forming bimetallic Pd-Cu alloy. Density functional theory calculations further demonstrated the electronic configuration modulation of the Pd-Cu@MXene catalysts, optimizing binging energies for H* and thereby facilitating H*ads production and tuning the reduction capability of H*ads. Noteably, the amounts and reduction potential of H*ads for Pd-Cu@MXene catalysts were 1.5 times higher and 0.37 eV lower than those observed for the mono Pd electrode. Hence, the introduction of Cu into the Pd catalyst optimized the dynamics of H*ads production, thereby conferring significant advantages to EHDC reactions. This augmentation was underscored by the successful application of the alloy catalysts supported by MXene in EHDC experiments involving other HECs, which represented a new paradigm for EHDC for efficient recalcitrant pollutant removal by H*ads.

Associations between abnormal electroencephalogram microstates and childhood emotional abuse in adolescent depression. / é’å°‘å¹´æŠ‘éƒç—‡é™æ¯æ€è„‘ç”µå¾®çŠ¶æ€ä¸Žç«¥å¹´æœŸæƒ æ„Ÿè™å¾ çš„ç›¸å ³æ€§.

OBJECTIVES: Childhood traumatic experiences greatly influenced the brain network activities of patients with depression, and there is an urgent need to explore the temporal dynamics for these changes. This study aims to investigate the abnormalities of resting-state electroencephalogram (EEG) microstates in eye-open state of depressed adolescents and to explore the correlations between their EEG microstates and the childhood traumatic experience. METHODS: Using resting-state EEG microstate analysis, we explored the temporal dynamics of brain activity in patients with adolescent depression. This study selected 66 adolescents with depression as a patient group, and 27 healthy adolescents as a healthy control group. A modified k-means clustering algorithm was used to classify the 64-channel resting-state EEG data into different microstates. Independent sample t-tests were used to compare the microstate parameters between the 2 groups and further assciations between these parameters and childhood traumatic experience in patients were explored via using Spearman correlation. RESULTS: In this study, significant differences were observed in the occurrence and transition probabilities of EEG microstates between the healthy control and the patient group. Notably, there was a statistically significant difference (P<0.05) in the occurrence of microstate A across 2 groups, exhibiting a negative correlation with the emotional abuse component within the childhood trauma scores (Spearman's rho=-0.31, P=0.013). Furthermore, patient-specific, non-random transitions from microstate B to A (Spearman's rho=-0.30, P=0.015) and C to A (Spearman's rho=-0.31, P=0.013) were inversely associated with the scores of emotional abuse factors from childhood trauma in the patient group, showing statistically significant differences when comparing to the healthy controls (P<0.05). Upon stratification into quartiles based on the emotional abuse factor scores, the occurrence of microstate A, as well as the transition rates from microstates B to A and C to A, retained statistical significance following adjustment for multiple comparisons (all P<0.05). CONCLUSIONS: The abnormal temporal dynamics in brain networks of adolescents with depression are linked to childhood emotional abuse. Those who have suffered severe emotional abuse may show greater impairments in the brain's visual and central executive networks. EEG microstate analysis could be a potential tool for detecting adolescent depression with severe childhood trauma.

Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis.

We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.

Gender Differences of Antioxidant System and Thyroid Function in Depressed Adolescents with Non-Suicidal Self-Injury.

Purpose: The aim of our study was to explore the relation between serum levels of non-enzymatic antioxidants, thyroid function with the risk of non-suicidal self-injury (NSSI) in depressed adolescents. Patients and Methods: We retrospected the electronic records of 454 hospitalized patients aged 13-17 years old with a diagnosis of major depressive disorder (239 patients with NSSI and 215 subjects without NSSI), and collected their demographic and clinical information, including serum levels of total bilirubin (Tbil), uric acid (UA), free triiodothyronine (FT3), free thyroxine (FT4) and thyroid stimulating hormone (TSH). Results: The incidence of NSSI was 52.6% among depressed adolescents aged 13-17, 57.1% in female and 38.5% in male. After using the propensity scoring method to exclude the influence of age between the two groups, it was found that patients with NSSI showed lower levels of Tbil (P=0.046) and UA (P=0.015) compared with those without NSSI. Logistic regression results showed that serum UA was associated with NSSI behavior in female patients (OR=0.995, 95% CI: 0.991-0.999, P=0.014), and TSH was associated with NSSI in male participants (OR=0.499, 95% CI: 0.267-0.932, P=0.029). Conclusion: Female and male may have different pathological mechanisms of NSSI. NSSI is more likely to be related to antioxidant reaction in female adolescent patients, while more likely to be related to thyroid function in male depressed adolescent patients.

Personality traits predict treatment outcome of an antidepressant in untreated adolescents with depression: An 8-week, open-label, flexible-dose study.

BACKGROUND: Antidepressant response in adults with major depressive disorder (MDD) is probably influenced by personality dimensions. However, personality dimensions in depression and their association with antidepressant treatment in adolescents are relatively unknown. We sought to investigate whether personality traits (PTs) can influence antidepressant treatment response in adolescents with depression. METHODS: Eighty-two adolescents with MDD who had completed the 8 weeks of treatment with selective serotonin reuptake inhibitors (SSRI) were enrolled. The Revised NEO Five-Factor Inventory (NEO-FFI-R) was used to measure their personality at baseline, and the 17-item Hamilton Depression Rating Scale (HAMD-17) and Children's Depression Rating Scale-Revised (CDRS-R) were used to evaluate depressive symptoms at baseline and 8 weeks. Moreover, logistic regression was performed to investigate the relationship between personality dimensions and antidepressant response. Receiver operating characteristic analyses were employed to determine the accuracy of a PT-based model in predicting the antidepressant response rate. RESULTS: Adolescents with MDD had significantly different PTs at baseline. Multivariable logistic regression analysis showed that extroversion scores were associated with response to antidepressant treatment, the lower the extroversion score, the better the response to antidepressant treatment, after correcting for variables with significant differences and trends or all potential confounding variables. It was also found that the combination of disease duration, extraversion-gregariousness, and agreeableness-trust effectively predicted antidepressant response in adolescents with MDD, with a sensitivity of 79.4 % and specificity of 68.7 %. CONCLUSION: Personality dysfunction in adolescents is associated with MDD. The antidepressant treatment response is influenced by the degree of extroversion in adolescents with MDD.

EEG microstate analysis reveals large-scale brain network alterations in depressed adolescents with suicidal ideation.

BACKGROUND: Accumulating evidence showed abnormalities in brain network connectivity in depressive individuals with suicidal ideation (SI). We aimed to investigate the large-scale brain network dynamics in adolescents with SI and major depressive disorder (MDD). METHODS: We recruited 47 first-episode drug-naïve adolescents with MDD and SI, 26 depressed adolescents without SI (noSI), and 26 age-matched healthy controls (HC). The Columbia Suicidal Ideation Severity Scale (C-SSRS) was utilized to assess suicide ideation. We acquired 64-channel resting-state EEG recordings from all subjects and used microstate analysis to investigate the large-scale brain network dynamics. RESULTS: We observed a significant reduction in the occurrence and coverage of microstate B within the SI group when contrasted with the noSI group. Conversely, there was a significant increase in the occurrence and coverage of microstate A in the SI group as compared to the HC group. Additionally, we observed heightened transition probabilities from microstates D and C to microstate A in the SI group; meanwhile, transitions from microstate D to B were more prevalent in the noSI group. Furthermore, the noSI group exhibited a significant decline in the transition probabilities from microstate D to microstate C. LIMITATIONS: The cross-sectional nature limits the capacity to determine whether microstate dynamics have prognostic significance for SI. CONCLUSION: We provided evidence that depressed adolescents with SI have a distinct pattern in microstate dynamics compared to those without SI. These findings suggest that microstate dynamics might serve as a potential neurobiomarker for identifying SI in depressed adolescents.