Schizophrenia is a severe mental disorder affecting around 0.5–1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia ...patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients’ age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.
Background:
Schizophrenia, regarded as a neurodevelopmental disorder, is characterized by positive symptoms, negative symptoms, and cognitive dysfunction. Investigating the spontaneous brain activity ...in patients with schizophrenia can help us understand the underlying pathophysiologic mechanism of schizophrenia. However, results concerning abnormal neural activities and their correlations with cognitive dysfunction/psychopathology of patients with schizophrenia were inconsistent.
Methods:
We recruited 57 first-diagnosed and drug-naive patients with schizophrenia and 50 matched healthy controls underwent magnetic resonance imaging. The Positive and Negative Syndrome Scale (PANSS) and the MATRICS Consensus Cognitive Battery were used to assess the psychopathology/cognitive dysfunction. Regional homogeneity (ReHo) was used to explore neural activities. Correlation analyses were calculated between abnormal ReHo values and PANSS scores/standardized cognitive scores. Lastly, support vector machine analyses were conducted to evaluate the accuracy of abnormal ReHo values in distinguishing patients with schizophrenia from healthy controls.
Results:
Patients with schizophrenia showed cognitive dysfunction, and increased ReHo values in the right gyrus rectus, right inferior frontal gyrus/insula and left inferior frontal gyrus/insula compared with those of healthy controls. The ReHo values in the right inferior frontal gyrus/insula were positively correlated with negative symptom scores and negatively correlated with Hopkins verbal learning test-revised/verbal learning. Our results showed that the combination of increased ReHo values in the left inferior frontal gyrus/insula and right gyrus rectus had 78.5% (84/107) accuracy, 85.96% (49/57) sensitivity, and 70.00% specificity, which were higher than other combinations.
Conclusions:
Hyperactivities were primarily located in the prefrontal regions, and increased ReHo values in the right inferior frontal gyrus/insula might reflect the severity of negative symptoms and verbal learning abilities. The combined increases of ReHo values in these regions might be an underlying biomarker in differentiating patients with schizophrenia from healthy controls.
Cupriavidus necator H16 is a “Knallgas” bacterium with the ability to utilize various carbon sources and has been employed as a versatile microbial cell factory to produce a wide range of value-added ...compounds. However, limited genome engineering, especially gene regulation methods, has constrained its full potential as a microbial production platform. The advent of CRISPR/Cas9 technology has shown promise in addressing this limitation. Here, we developed an optimized CRISPR interference (CRISPRi) system for gene repression in C. necator by expressing a codon-optimized deactivated Cas9 (dCas9) and appropriate single guide RNAs (sgRNAs). CRISPRi was proven to be a programmable and controllable tool and could successfully repress both exogenous and endogenous genes. As a case study, we decreased the accumulation of polyhydroxyalkanoate (PHB) via CRISPRi and rewired the carbon fluxes to the synthesis of lycopene. Additionally, by disturbing the expression of DNA mismatch repair gene mutS with CRISPRi, we established CRISPRi-Mutator for genome evolution, rapidly generating mutant strains with enhanced hydrogen peroxide tolerance and robustness in microbial electrosynthesis (MES) system. Our work provides an efficient CRISPRi toolkit for advanced genetic manipulation and optimization of C. necator cell factories for diverse biotechnology applications.
Our study aimed to explore the abnormal spontaneous brain activity by regional homogeneity (ReHo) and its association with cognitive function to understand the neuropathology of major depressive ...disorder (MDD).
ReHo was used to investigate brain activities of 60 patients with first-episode drug-naive MDD and 60 healthy controls (HCs). Partial correlation analysis was conducted on altered ReHo values and the severity of symptoms and cognitive deficits. Moreover, support vector machine analysis was used to evaluate the accuracy of abnormal ReHo values in distinguishing patients with MDD from HCs.
Compared with HCs, patients with MDD showed significantly increased ReHo values in the right cerebellum crus2 and right thalamus and decreased ReHo values in the right angular gyrus (AG) and right precuneus (PCUN). The ReHo values in right cerebellum crus2 and right AG were positively associated with working memory and visual learning, respectively. Furthermore, the combination of ReHo values in the right cerebellum crus2 and right PCUN discriminated the patients with MDD from HCs with specificity, sensitivity, and accuracy of 0.9688, 0.6250, and 0.90, respectively.
The design of repeated cross-sectional surveys does not allow analyses of within individual changes.
Our study revealed that the pathophysiology mechanism of cognitive deficits in MDD may be related to abnormal spontaneous brain activity. Moreover, the combination of ReHo values in the right cerebellum crus2 and right PCUN can be used to discriminate patients with MDD from HCs effectively.
•Widespread ReHo alterations and cognitive dysfunction in MDD•Cognitive deficits in MDD were related to abnormal spontaneous brain activity.•Abnormalities might distinguish patients with MDD from healthy controls.
Diagnostic information for psychiatric research often depends on both clinical interviews and medical records. Although discrepancies between these two sources are well known, there have been few ...studies into the degree and origins of inconsistencies.
We compared data from structured interviews and medical records on 1,970 Han Chinese women with recurrent DSM-IV major depression (MD). Correlations were high for age at onset of MD (0.93) and number of episodes (0.70), intermediate for family history (+0.62) and duration of longest episode (+0.43) and variable but generally more modest for individual depressive symptoms (mean kappa = 0.32). Four factors were identified for twelve symptoms from medical records and the same four factors emerged from analysis of structured interviews. Factor congruencies were high but the correlation of factors between interviews and records were modest (i.e. +0.2 to +0.4).
Structured interviews and medical records are highly concordant for age of onset, and the number and length of episodes, but agree more modestly for individual symptoms and symptom factors. The modesty of these correlations probably arises from multiple factors including i) inconsistency in the definition of the worst episode, ii) inaccuracies in self-report and iii) difficulties in coding medical records where symptoms were recorded solely for clinical purposes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Genetic liability to ADHD and ASD confers risk for one another.•Genetic liability to extraversion confers a causal effect on ADHD, but not on ASD.•The cross-trait meta-analysis identified three ...novel pleiotropic genomic loci between ADHD and ASD.
Deciphering the genetic relationships between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) may uncover underlining shared pathophysiology as well as inform treatment.
The summary results of genome-wide association studies on ADHD, ASD, and extraversion were utilized for the analyzes. Genetic correlations between ADHD, ASD, and extraversion were tested using linkage disequilibrium score regression. Causal relationships between ADHD, ASD, and extraversion were investigated using Mendelian randomization (MR) analysis. Novel pleiotropic genomic loci shared by ADHD and ASD were identified using a cross-trait meta-analysis.
Extraversion was positively correlated with ADHD (rg = 0.205) and negatively correlated with ASD (rg = −0.193). The MR analysis showed that ADHD confers a causal effect on ASD (OR: 1.35, 95% confidence interval (CI):1.20–1.52) and vice versa (1.46, 1.38–1.55). Extraversion exerts a causal effect on ADHD only (1.19, 1.05–1.33). The cross-trait meta-analysis identified three novel pleiotropic genomic loci for ADHD and ASD, involving two pleiotropic genes, LINC00461 and KIZ.
Our study provides new insights into the shared genetics of ADHD and ASD and their connections with extraversion.
Pd was electro-deposited on Ni foam using nitrogen-doped carbon as an inter-layer (EDPd/CNx/Ni). The as-prepared material was employed as the cathode for the electrochemical dechlorination of ...3,6-dichloropicolinic acid (3,6-D) in an aqueous solution. When compared to the electrodes that direct the electrodeposition or chemical deposition of Pd on Ni foam, EDPd/CNx/Ni exhibited higher activity and selectivity during the electrocatalytic hydrogenation of 3,6-D to form the intermediates 3-chloropicolinic acid and 6-chloropicolinic acid, as well as the final product, 2-picolinic acid. Furthermore, the modified electrode maintained its high dechlorinating activity after continuous operation for three batches. On the basis of physical-chemical characterization and electrochemical measurements, it is proposed that the superior performance of the EDPd/CNx/Ni electrode benefits sufficiently from the accelerated generation of adsorbed hydrogen (Hads). The electrodeposition method facilitates the generation of Pd with a higher percentage of exposed {111} facets. The CNx functions as an efficient support for the enlargement of the electrochemically active surface area, the reduction of Pd particle size, and improvement of the Pd dispersion. Moreover, CNx may also act as an electrocatalyst for the water-splitting reaction and favors the stabilization of chemisorbed active hydrogen.
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•Pd was electro-deposited on Ni foam using nitrogen-doped carbon as an inter-layer.•CNx facilitated the electrochemical dechlorination of 3,6-dichloropicolinic acid.•Accelerated generation of adsorbed hydrogen.
•Abnormalities were mainly in the default mode network and sensorimotor regions.•The related patterns of cognition of patients were different with healthy controls.•Abnormalities might distinguish ...patients with schizophrenia from healthy controls.
The neurodevelopmental hypothesis states that schizophrenia is a brain disease. Exploring abnormal brain activities can improve understanding of the neural pathologic mechanism of clinical characteristics and determine subjective biomarkers to differentiate patients with schizophrenia from healthy controls. We collected clinical characteristics (i.e., demographics, positive and negative syndrome scale (PANSS) scores, and cognitive scores) and magnetic resonance imaging (MRI) data from 57 first-diagnosed drug-naïve patients with schizophrenia and 50 healthy controls. The fractional amplitude of low-frequency fluctuation (fALFF) was used to detect local activities. Partial correlation analysis was applied to estimate the relationship between abnormal regions and clinical characteristics. The support vector machine (SVM) analysis was used to calculate the accuracy of classification in abnormal regions. In our study, the fALFF values in the right postcentral gyrus, left precentral gyrus/postcentral gyrus, left postcentral gyrus/superior parietal lobule, bilateral supplementarymotor area, bilateral paracentral lobule, and bilateral precuneus were decreased in patients with schizophrenia and associated with clinical characteristics. However, the related patterns of cognition of patients were different from those of healthy controls. Additionally, the combination of fALFF values in the bilateral paracentral lobule and right postcentral gyrus might distinguish patients with schizophrenia from healthy controls with high accuracy (98.13%), specificity (98.00%), and sensitivity (98.25%). Our study suggests that reduced local activities in the default mode network and sensorimotor network may be regarded as neural underpinnings of clinical characteristics and may discriminate patients with schizophrenia from healthy controls.
The neurodevelopmental hypothesis states that schizophrenia is a brain disease. Exploring abnormal brain activities can improve understanding of the neural pathologic mechanism of clinical ...characteristics and determine subjective biomarkers to differentiate patients with schizophrenia from healthy controls. We collected clinical characteristics (i.e., demographics, positive and negative syndrome scale scores, and cognitive scores) and magnetic resonance imaging data from 57 first-diagnosed drug-naïve patients with schizophrenia and 50 healthy controls. The fractional amplitude of low-frequency fluctuation (fALFF) was used to detect local activities. Partial correlation analysis was applied to estimate the relationship between abnormal regions and clinical characteristics. The support vector machine analysis was used to calculate the accuracy of classification in abnormal regions. In our study, the fALFF values in the right postcentral gyrus, left precentral gyrus/postcentral gyrus, left postcentral gyrus/superior parietal lobule, bilateral supplementarymotor area, bilateral paracentral lobule, and bilateral precuneus were decreased in patients with schizophrenia and associated with clinical characteristics. However, the related patterns of cognition of patients were different from those of healthy controls. Additionally, the combination of fALFF values in the bilateral paracentral lobule and right postcentral gyrus might distinguish patients with schizophrenia from healthy controls with high accuracy (98.13%), specificity (98.00%), and sensitivity (98.25%). Our study suggests that reduced local activities in the default mode network and sensorimotor network may be regarded as neural underpinnings of clinical characteristics and may discriminate patients with schizophrenia from healthy controls.
Abstract Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of ...well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.
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