Search

Perinatal exposure to valproic acid is commonly used for autism spectrum disorder (ASD) animal model development. The inhibition of histone deacetylases by VPA has been proposed to induce epigenetic changes during neurodevelopment, but the specific alterations in genetic expression underlying ASD-like behavioral changes remain unclear. We used qPCR-based gene expression and epigenetics tools and Western blotting in the hippocampi of neonatal valproic acid-exposed animals at 4 weeks of age and conducted the social interaction test to detect behavioral changes. Significant alterations in gene expression were observed in males, particularly concerning mRNA expression of Foxo3, which was significantly associated with behavioral changes. Moreover, notable differences were observed in H3K27ac chromatin immunoprecipitation, quantitative PCR (ChIP-qPCR), and methylation-sensitive restriction enzyme-based qPCR targeting the Foxo3 gene promoter region. These findings provide evidence that epigenetically regulated hippocampal Foxo3 expression may influence social interaction-related behavioral changes. Furthermore, identifying sex-specific gene expression and epigenetic changes in this model may elucidate the sex disparity observed in autism spectrum disorder prevalence. [ABSTRACT FROM AUTHOR]

Valproic acid (VPA) is a known drug for treating epilepsy and mood disorders; however, it is not recommended for pregnant women because of its possible teratogenicity. VPA affects neurotransmission and gene expression through epigenetic mechanisms by acting as a histone deacetylase inhibitor and has been used to establish animal models of autism spectrum disorder (ASD). However, studies on the long-term effects of early exposure to VPA on glucocorticoid and neurosteroid synthesis in the brain are lacking. Therefore, this study aimed to investigate the long-term changes in metabolic alterations and gene expression regulation according to sex, using metabolic steroid profiling data from cerebral cortex samples of rats four weeks after VPA exposure (400 mg/kg). In neonatal VPA-exposed models, estradiol levels decreased, and cytochrome P450 19A1 gene (Cyp19a1) expression was reduced in the prepubertal male cortex. Progesterone and allopregnanolone levels decreased, and 3β-hydroxysteroid dehydrogenase 1 gene (Hsd3b1) expression was also downregulated in the prepubertal female cortex. Furthermore, cortisol levels increased, and mRNA expression of the nuclear receptor subfamily 3 group C member 1 gene (Nr3c1) was downregulated in the cortices of both sexes. Unlike the neonatal VPA-exposed models, although a decrease in progestin and estradiol levels was observed in females and males, respectively, no differences were observed in cortisol levels in the cortex tissues of 8-week-old adult rats administered VPA for four weeks. These results indicate that early environmental chemical exposure induces long-term neurosteroid metabolic effects in the brain, with differences according to sex. [ABSTRACT FROM AUTHOR]

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k‐nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross‐validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs. [ABSTRACT FROM AUTHOR]

Alteration in stress response seems to affect the development of psychiatric disorders. In this study, we aimed to investigate whether baseline peripheral biomarkers could predict the reduction of stress response among patients with major depressive disorder (MDD) and panic disorder (PD). Patients with MDD (n = 41) and PD (n = 52) and healthy controls (HC, n = 59) were selected and regularly followed up with five visits for 12 weeks. The severity of stress at every visit was assessed using the Stress Response Inventory (SRI), and peripheral biomarkers were measured by blood tests at baseline and 2, 4, 8, and 12 weeks. Interleukin (IL)-6, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, C-reactive protein (CRP), adiponectin, and leptin levels were analyzed using enzyme-linked immunosorbent assays. Reduction of stress response was defined as the difference in SRI score between baseline and 12 weeks divided by the baseline score. SRI scores were significantly (p < 0.0001) higher in patients with MDD and PD than in HC at every visit after adjusting for variables. In multivariable linear regression, adiponectin levels at baseline were significantly associated with reduction of stress response in patients with PD. When adiponectin increased 1 mg/l, stress response decreased 0.781 points (β = −0.781, S.E. = 0.220, p = 0.001). Among the subscales of SRI, somatization had a moderate negative correlation with adiponectin levels (r = −0.469). There was no significant association between baseline peripheral biomarkers and reduction of stress response in patients with MDD. Our study showed an inverse association between baseline adiponectin levels and stress response changes in patients with PD, but not in patients with MDD. Thus, differentiated approaches for assessing and treating stress responses of patients with PD and MDD might be helpful. Larger and longitudinal studies are necessary to establish the role and mechanism of action of adiponectin in regulating stress responses in PD. [ABSTRACT FROM AUTHOR]

Obtaining quartz nanocrystals (NCs) of high purity and uniform sizes remains a challenging problem. In this report, the synthesis and characterization of quartz NCs under hydrothermal conditions was investigated and the corresponding mathematical models were introduced to elucidate the growth kinetics of quartz NCs. Amorphous silica nanoparticles were dissolved in aqueous solutions followed by mild hydrothermal reactions, resulting in NCs with relatively uniform sizes and shapes. The NCs were made from highly crystalline α-quartz. Their hydrothermal growth process over an induction period of ∼3 hr initially yielded amorphous silica nanoparticles that were aggregated into clusters. The crystallinity of α-quartz emerged from the products of the nanoparticle clusters after the induction period, which likely involved an amorphous to crystalline transition. The NCs continued to grow with increasing time. The growth kinetics exhibited a dependence on the square root of time, which has not been observed for other quartz nanocrystalline systems. The analysis suggests that the process is reaction-limited, not diffusion-limited, likely governed by the dissolved silicate monomer flux to the surface of the growing NCs followed by first-order rate-limiting attachment kinetics. This study highlights the growth kinetics of quartz NCs by unveiling the complex nature of multi-step growth processes, offering an improved hydrothermal method for fine-tuning the size and morphology of quartz NCs, which have potential optoelectronics, sensing, and rechargeable battery, and novel biorefinery process applications. [ABSTRACT FROM AUTHOR]

We report on the synthesis and characterization of highly monodisperse amorphous silica nanoparticles (ASNs) and mesoporous silica nanoparticles (MSNs) with particle sizes of 15–60 nm. We demonstrate adsorption of Cr(VI) ions on amino-functionalized ASNs (NH2–ASNs) and MSNs (NH2–MSNs) and their removal from aqueous environments and show the specific surface area (SSA) of NH2–MSNs is four times as larger as that of NH2–ASNs and that more than 70% of the total SSA of NH2–MSNs is due to the presence of nanopores. Analyses of Cr(VI) adsorption kinetics on NH2–ASNs and NH2–MSNs exhibited relatively rapid adsorption behavior following pseudo-second order kinetics as determined by nonlinear fitting. NH2–ASNs and NH2–MSNs exhibited significantly higher Cr(VI) adsorption capacities of 34.0 and 42.2 mg·g−1 and removal efficiencies of 61.9 and 76.8% than those of unfunctionalized ASNs and MSNs, respectively. The Langmuir model resulted in best fits to the adsorption isotherms of NH2–ASNs and NH2–MSNs. The adsorption of Cr(VI) on NH2–ASNs and NH2–MSNs was an endothermic and spontaneous process according to the thermodynamic analyses of temperature-dependent adsorption isotherms. The removal efficiencies of NH2–ASNs and NH2–MSNs exhibited a moderate reduction of less than 25% of the maximum values after five regeneration cycles. Furthermore, NH2–MSNs were also found to reduce adsorbed Cr(VI) into less harmful Cr(III). [ABSTRACT FROM AUTHOR]

Background: The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires.Objective: Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated.Methods: The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification.Results: The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2.Conclusion: Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition. [ABSTRACT FROM AUTHOR]

Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states. [ABSTRACT FROM AUTHOR]

Background/Aims: A limited number of studies addressed MRI-based neurodegenerative changes in subjective memory impairment (SMI). We investigated changes in white matter (WM) microstructures as well as gray matter (GM) macrostructures in subjects with SMI of high and low risk for progression. Methods: A modeling scale (score range, 0-6) developed for prediction of SMI progression was used to divide SMI subjects (n = 46) into two groups: a high risk of progression (score ⩾ 3; n = 19) and a low risk of progression (score ⩽ 2; n = 27). Cross-sectional comparisons were performed using a region-of-interest-based diffusion tensor imaging (DTI) analysis, cortical thickness analysis, and hippocampal volumetry. Results: The high-risk group had more microstructural disruption shown by lower fractional anisotropy in the hippocampus, parahippocampal gyrus, supramarginal gyrus, and parts of frontotemporal lobes. On the other hand, GM macrostructural changes did not differ between the groups and were not associated with modeling scale scores. Conclusion: SMI subjects with a high risk of progression had more WM microstructural disruption than those with a low risk, and the changes were not explained by GM atrophy. Our findings suggest that the degree of microstructural alterations in SMI may be distinctive according to the risk factors and may precede GM atrophy. [ABSTRACT FROM AUTHOR]

We report an enhancement in light emission efficiency of Si nanocrystal (NC) light-emitting diodes (LEDs) by employing 5.5 periods of SiCN/SiC superlattices (SLs). SiCN and SiC layers in SiCN/SiC SLs were designed by considering the optical bandgap to induce the uniform electron sheet parallel to the SL planes. The electrical property of Si NC LED with SiCN/SiC SLs was improved. In addition, light output power and wall-plug efficiency of the Si NC LED with SiCN/SiC SLs were also enhanced by 50% and 40%, respectively. This was attributed to both the formation of two-dimensional electron gas, i.e., uniform electron sheet parallel to the SiCN/SiC SL planes due to the conduction band offset between the SiCN layer and SiC layer, and an enhanced electron transport into the Si NCs due to a lower tunneling barrier height. We show here that the use of the SiCN/SiC SL structure can be very useful in realizing a highly efficient Si NC LED. [ABSTRACT FROM AUTHOR]

We report an enhancement in light emission efficiency form Si nanocrystal (NC) light-emitting diodes (LEDs) via surface plasmons (SPs) by employing Au nanoparticles (NPs). Photoluminescence intensity of Si NCs with Au NPs was enhanced by 2 factors of magnitude due to the strong coupling of Si NCs and SP resonance modes of Au NPs. The electrical characteristics of Si NC LED were significantly improved, which was attributed to an increase in an electron injection into the Si NCs due to the formation of inhomogeneous Schottky barrier at the SiC-indium tin oxide interface. Moreover, light output power from the Si NC LED was enhanced by 50% due to both SP coupling and improved electrical properties. The results presented here can provide a very promising way to significantly enhance the performance of Si NC LED. [ABSTRACT FROM AUTHOR]

Valproic acid (VPA) is an antiepileptic drug found to induce mitochondrial dysfunction and autophagy in cancer cell lines. We treated the SH-SY5Y cell line with various concentrations of VPA (1, 5, and 10 mM). The treatment decreased cell viability, ATP production, and mitochondrial membrane potential and increased reactive oxygen species production. In addition, the mitochondrial DNA copy number increased after VPA treatment in a dose-dependent manner. Western blotting showed that the levels of mitochondrial biogenesis-related proteins (PGC-1α, TFAM, and COX4) increased, though estrogen-related receptor expression decreased after VPA treatment. Further, VPA treatment increased the total and acetylated FOXO3a protein levels. Although SIRT1 expression was decreased, SIRT3 expression was increased, which regulated FOXO3 acetylation in the mitochondria. Furthermore, VPA treatment induced autophagy via increased LC3-II levels and decreased p62 expression and mTOR phosphorylation. We suggest that VPA treatment induces mitochondrial biogenesis and autophagy via changes in FOXO3a expression and posttranslational modification in the SH-SY5Y cell line. [ABSTRACT FROM AUTHOR]

Trimethyltin (TMT) is an irreversible neurotoxicant. Because prenatal TMT exposure has been reported to induce behavioral changes, this study was conducted to observe gender differences and epigenetic changes using a mouse model. In behavioral testing of offspring at 5 weeks of age, the total times spent in the center, corner, or border zones in the male prenatal TMT-exposed mice were less than those of control unexposed mice in the open-field test. Female TMT-exposed mice scored lower on total numbers of arm entries and percentages of alternations than controls in the Y-maze test with lower body weight. We found that only TMT-exposed males had fewer copies of mtDNA in the hippocampus and prefrontal cortex region than controls. Additional epigenetic changes, including increased 5-methyl cytosine/5-hydroxymethyl cytosine levels in the male TMT hippocampus, were observed. After methylation binding domain (MBD) sequencing, multiple signaling pathways related to metabolism and neurodevelopment, including FoxO signaling, were identified by pathway analysis for differentially methylated regions (DMRs). Increased FOXO3 and decreased ASCL1 expression were also observed in male TMT hippocampi. This study suggests that sex differences and epigenetics should be more carefully considered in prenatal toxicology studies. [ABSTRACT FROM AUTHOR]

Background: Although emotion-specific autonomic responses based on the discrete theory of emotion have been widely studied, studies on the reliability of physiological responses to emotional stimuli are limited. In this study, we aimed to assess the reliability of physiological changes induced by the six basic emotions (happiness, sadness, anger, fear, disgust, and surprise) that were measured during 10 weekly repeated experiments. Methods: Twelve college students participated, and in each experiment, physiological signals were collected before and while participants were watching emotion-provoking film clips. Additionally, the participants self-evaluated the emotions that they experienced during the film presentation at the end of each emotional stimulus. To avoid adaptation of participants to identical stimuli during repeated measurements, we used 10 different film clips for each emotion, and thus a total of 60 film clips over 10 weeks were used. Physiological features, such as skin conductance level (SCL), fingertip temperature (FT), heart rate (HR), and blood volume pulse (BVP), were extracted from the physiological signals. Two reliability indices, Cronbach's alpha and intraclass correlation coefficient, were calculated from the physiological features to assess internal consistency and interrater reliability, respectively. Results: We found that SCL, HR, and BVP measured during the emotion-provoking phase over the 10 weekly sessions were more reliable than those assessed at baseline. Furthermore, SCL, HR, and BVP from the emotion-provoking phase exhibited excellent internal consistency and interrater reliability. Conclusions: Our findings suggest that these features can be used as reliable physiological indices in emotion studies. The results also support the significance of physiological signals as meaningful indicators for emotion recognition in HCI (human computer interface) area. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power. [ABSTRACT FROM AUTHOR]