Inflammatory cytokines derived from peripheral blood contribute to the modified electroconvulsive therapy-induced cognitive deficits in major depressive disorder.

Little is known about the pathophysiology of memory deficits in patients with major depressive disorder (MDD) treated with modified electroconvulsive therapy (MECT). This study examined the profiles of cytokines, the memory function, and their association in MECT-treated MDD patients. Forty first-episode, drug-free MDD patients and 40 healthy controls were recruited. MECT was started with antidepressant treatment at a stable initial dose. The Wechsler Memory Scale (WMS) and Hamilton Rating Scale for Depression 17 (HRSD-17) were used to assess the cognitive function. MDD patients were divided into the memory impairment group (WMS < 50) and the non-memory impairment group (WMS ≥ 50) based on the total WMS scores after MECT. The levels of NOD-like receptor 3 (NLRP3) inflammasome, interleukin-18 (IL-18) and nuclear factor kappa-B (NF-κB) in the serum were measured. MDD patients showed significantly higher levels of NLRP3 inflammasome, IL-18 and NF-κB than that in the controls prior to MECT, and the levels also significantly increased after MECT. In MDD patients, the serum levels of these inflammatory cytokines were negatively associated with the total WMS scores and likely contributed to the scores independently. The receiver operating characteristic curve showed that the serum levels of these inflammatory cytokines may predict the cognitive impairment risk in MDD patients receiving MECT. Abnormal levels of NLRP3 inflammasome, IL-18 and NF-κB reflecting the disturbed balance of pro-inflammatory and anti-inflammatory mechanisms likely contribute to the MECT-induced cognitive deficits in MDD patients.

The role of BDNF exon I region methylation in the treatment of depression with sertraline and its clinical diagnostic value.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is considered to be one of the best candidate genes for depression. However, whether sertraline treatment affects the methylation level of this gene remains unknown. METHODS: Fifty-three patients with depression and 51 healthy controls were included in the study. The methylation level of BDNF exon I was determined in blood samples from these subjects. The Hamilton Depression Scale was used to evaluate the depression status of patients. Single nucleotide polymorphism detection was used for genotyping, and a receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the methylation level of this locus in patients with depression. RESULTS: There was a significant difference in the methylation level of BDNF exon I between the control and depression groups. No effect of sertraline monotherapy on BDNF methylation was found in subjects with depression. Moreover, no interaction was found between BDNF genotype and the per cent methylation of BDNF exon I. However, methylation at this site was positively correlated with diurnal variation and retardation scores. Blood homocysteine concentrations were significantly reduced by sertraline treatment. No influence of genotype on serum BDNF concentration was found in subjects with depression. The ROC curve showed that methylation of BDNF exon I may be used to distinguish patients from healthy people, to a certain extent. CONCLUSION: Methylation of BDNF exon I may be used as a biomarker of depression and may be a therapeutic target for previously untreated depression.

HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation.

Both Convolutional Neural Networks (CNNs) and Transformers have shown great success in semantic segmentation tasks. Efforts have been made to integrate CNNs with Transformer models to capture both local and global context interactions. However, there is still room for enhancement, particularly when considering constraints on computational resources. In this paper, we introduce HAFormer, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges. Specifically, we design a Hierarchy-Aware Pixel-Excitation (HAPE) module for adaptive multi-scale local feature extraction. During the global perception modeling, we devise an Efficient Transformer (ET) module streamlining the quadratic calculations associated with traditional Transformers. Moreover, a correlation-weighted Fusion (cwF) module selectively merges diverse feature representations, significantly enhancing predictive accuracy. HAFormer achieves high performance with minimal computational overhead and compact model size, achieving 74.2% mIoU on Cityscapes and 71.1% mIoU on CamVid test datasets, with frame rates of 105FPS and 118FPS on a single 2080Ti GPU. The source codes are available at https://github.com/XU-GITHUB-curry/HAFormer.