Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods.

The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.

Comparison of Peripheral Biomarkers and Reduction of Stress Response in Patients With Major Depressive Disorders vs. Panic Disorder.

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 (ß = -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.

Predictive inflammatory biomarkers for change in suicidal ideation in major depressive disorder and panic disorder: A 12-week follow-up study.

Previous studies have investigated the role of inflammatory markers in suicidality of patients with major depressive disorder (MDD) or panic disorder (PD). However, few studies have investigated associations between serum inflammatory cytokine levels and suicidality. We hypothesized that MDD and PD status might be significantly associated with serum inflammatory cytokines and that we could predict levels of improvement in suicide ideation intensity using serum inflammatory biomarkers in patients with MDD and PD. For this study, 41 patients with MDD, 52 patients with PD, and 59 healthy control (HC) subjects were enrolled. Psychological measurements and serum inflammatory markers such as interleukin (IL) -6, -10, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, and C reactive protein (CRP) were examined. A total of five visits were completed during 12 weeks. After controlling for confounding factors, log-transformed IL-6 (ln_IL-6) at baseline (MDD: 0.297 ± 0.626; PD: 0.342 ± 0.723; HC: -0.121 ± 0.858; p = 0.007, >0.0017, 0.05/30) and mean ln_IL-6 (MDD: 0.395 ± 0.550, PD: 0.249 ± 0.544, HC: -0.139 ± 0.622, p = 0.002, >0.0017, 0.05/30) levels were trends towards significantly higher in patients with MDD and PD than in HC. In MDD patients, a higher level of basal ln_TNF-α was a significant predictor of ΔSSI (changes in SSI scores between baseline and week 12) even after controlling for changes of depression symptoms and baseline SSI scores (standardized ß = 0.541, p = 0.002 < 0.0028, 0.05/18). In conclusion, we could predict ΔSSI using baseline inflammatory biomarkers for patients with MDD.

Near-infrared-emitting nanoparticles activate collagen synthesis via TGFß signaling.

Research efforts towards developing near-infrared (NIR) therapeutics to activate the proliferation of human keratinocytes and collagen synthesis in the skin microenvironment have been minimal, and the subject has not been fully explored. Herein, we describe the novel synthesis Ag2S nanoparticles (NPs) by using a sonochemical method and reveal the effects of NIR irradiation on the enhancement of the production of collagen through NIR-emitting Ag2S NPs. We also synthesized Li-doped Ag2S NPs that exhibited significantly increased emission intensity because of their enhanced absorption ability in the UV-NIR region. Both Ag2S and Li-doped Ag2S NPs activated the proliferation of HaCaT (human keratinocyte) and HDF (human dermal fibroblast) cells with no effect on cell morphology. While Ag2S NPs upregulated TIMP1 by only twofold in HaCaT cells and TGF-ß1 by only fourfold in HDF cells, Li-doped Ag2S NPs upregulated TGF-ß1 by tenfold, TIMP1 by 26-fold, and COL1A1 by 18-fold in HaCaT cells and upregulated TGF-ß1 by fivefold and COL1A1 by fourfold in HDF cells. Furthermore, Ag2S NPs activated TGF-ß1 signaling by increasing the phosphorylation of Smad2 and Smad3. The degree of activation was notably higher in cells treated with Li-doped Ag2S NPs, mainly caused by the higher PL intensity from Li-doped Ag2S NPs. Ag2S NPs NIR activates cell proliferation and collagen synthesis in skin keratinocytes and HDF cells, which can be applied to clinical light therapy and the development of anti-wrinkle agents for cosmetics.

Pre-treatment peripheral biomarkers associated with treatment response in panic symptoms in patients with major depressive disorder and panic disorder: A 12-week follow-up study.

OBJECTIVE: Peripheral biomarkers have been studied to predict treatment response of panic symptoms. We hypothesized that depressive disorder (MDD) vs. panic disorder (PD) would exhibit different peripheral biomarkers, and their correlation with severity of panic attacks (PA) would also differ. METHODS: Forty-one MDD patients, 52 PD patients, and 59 healthy controls were followed for 12 weeks. We measured peripheral biomarkers along with the Panic Disorder Severity Scale (PDSS) at each visit-pre-treatment, 2, 4, 8, and 12 weeks on a regular schedule. Peripheral biomarkers including serum cytokines, plasma and serum brain-derived neurotrophic factor (BDNF), leptin, adiponectin, and C-reactive protein (CRP) were quantified using enzyme-linked immunosorbent assay (ELISA). RESULTS: Patients with MDD and PD demonstrated significantly higher levels of pre-treatment IL-6 compared to controls, but no differences were seen in plasma and serum BDNF, leptin, adiponectin, and CRP. Pre-treatment leptin showed a significant clinical correlation with reduction of panic symptoms in MDD patients at visit 5 (p=0.011), whereas pre-treatment IL-6 showed a negative correlation with panic symptom reduction in PD patients (p=0.022). An improvement in three panic-related items was observed to be positively correlated with pre-treatment leptin in MDD patients: distress during PA, anticipatory anxiety, and occupational interference. CONCLUSION: Higher pre-treatment leptin was associated with better response to treatment regarding panic symptoms in patients with MDD, while higher IL-6 was associated with worse response regarding panic symptoms in PD patients. Different predictive peripheral biomarkers observed in MDD and PD suggest the need for establishing individualized predictive biomarkers, even in cases of similar symptoms observed in different disorders.

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from healthy controls was developed based on linear and nonlinear heart rate variability (HRV), which reflects the autonomic cardiovascular regulation. METHODS: HRV data were collected from 37 MDD patients and 41 healthy controls during five 5-min experimental phases: the baseline, a mental stress task, stress recovery, a relaxation task, and relaxation task recovery. The experimental protocol was designed to assess the autonomic responses to stress and recovery. Twenty HRV indices were extracted from each phase, and a total of 100 features were used for classification using a support vector machine (SVM). SVM-recursive feature elimination (RFE) and statistical filter were employed to perform feature selection. RESULTS: We achieved 74.4% accuracy, 73% sensitivity, and 75.6% specificity with two optimal features selected by SVM-RFE, which were extracted from the stress task recovery and mental stress phases. Classification performance worsened when individual phases were used separately as input data, compared to when all phases were included. The SVM-RFE using nonlinear and Poincaré plot HRV features performed better than that using the linear indices and matched the best performance achieved by using all features. CONCLUSIONS: We demonstrated the machine learning-based diagnosis of MDD using HRV analysis. Monitoring the changes in linear and nonlinear HRV features for various autonomic nervous system states can facilitate the more objective identification of MDD patients.

Ratio of plasma BDNF to leptin levels are associated with treatment response in major depressive disorder but not in panic disorder: A 12-week follow-up study.

BACKGROUND: A link between brain-derived neurotrophic factor (BDNF) expression and the mood regulatory effect of leptin has been suggested in the pathophysiology of major depressive disorder (MDD). We investigated treatment response and pre-treatment leptin and BDNF in patients with MDD and with panic disorder (PD). METHODS: We recruited 41 patients with MDD, 52 patients with PD, and 59 matched healthy controls. All subjects completed five visits (at baseline, 2, 4, 8, and 12 weeks), and both MDD and PD patients were treated with standard pharmacotherapy for 12 weeks. Plasma BDNF (pBDNF) and blood leptin levels were obtained along with a 17-item Hamilton Depression Scale rating (HDRS-17) score at every visit. RESULTS: The ratio of pre-treatment pBDNF to leptin was significantly lower in patients with MDD and PD compared to healthy controls (p = 0.024), but was not associated with severity of depressive or anxiety symptoms. Pre-treatment pBDNF:leptin ratio was significantly higher in treatment responders than in non-responders (p = 0.012) in MDD but not in PD. This difference was larger in MDD patients with appetite loss (p = 0.034). In multivariate analysis, pre-treatment pBDNF:leptin ratio was significantly associated with treatment responsiveness (Adjusted Odds Ration [AOR] = 2.50, 95% CI 1.02-6.14) in MDD. LIMITATION: small sample size; limited information on detailed pharmacological effects. CONCLUSIONS: A relatively higher ratio of pre-treatment pBDNF to leptin was associated with greater treatment response in MDD but not in PD. Further research should focus on exploration of a link between BDNF and leptin underlying neuronal plasticity in depression.

Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.

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.

Skin conductance responses in Major Depressive Disorder (MDD) under mental arithmetic stress.

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.

Inhibition of lactate dehydrogenase A suppresses inflammatory response in RAW 264.7 macrophages.

Lactate is an important metabolite in cellular metabolism and fluctuates in certain disease conditions including cancer and immune diseases. It was hypothesized that a decrease in lactate would modulate the inflammatory response elicited by lipopolysaccharides (LPS) in macrophages. When RAW 264.7 macrophages were treated with FX11, a specific lactate dehydrogenase (LDHA) inhibitor, the expression of the cytokines, inducible nitric oxide synthase (iNOS) and cyclooxygenase 2 (COX­2) was downregulated due to reduced cellular lactate levels. Genetic suppression of LDHA by small interfering RNA (siRNA) downregulated the LPS­activated expression of interleukin (IL)­6, iNOS, and COX­2, and reduced the production of IL­6 and nitrites. Pharmacological and genetic suppression of LDHA inhibited the phosphorylation of p38 mitogen­activated protein kinase. Microarray gene expression profile demonstrated that the genes involved in cell proliferation and inflammation were mainly altered by siRNA­mediated LDHA suppression. Collectively, the present observations suggest that lactate may be an important metabolite and implicated in regulation of inflammatory response.

Heart rate variability for treatment response between patients with major depressive disorder versus panic disorder: A 12-week follow-up study.

BACKGROUND: Heart Rate Variability (HRV) parameters have been used to evaluate the autonomic nervous system. We hypothesized that patients with major depressive disorder (MDD) and panic disorder (PD) showed different HRV profiles compared to healthy controls. We also hypothesized that we could predict the responder groups in the MDD and PD patients, using differences in HRV indices between the stress and rest phases. METHODS: 28 MDD patients and 29 PD patients were followed for 12 weeks, and we also followed 39 healthy control subjects. We measured HRV parameters at the rest, stress, and recovery phases. RESULTS: Patients with MDD and PD demonstrated lower pNN50 than controls during the stress (F = 7.49, p = 0.001), and recovery phases (F = 9.43, p = 0.0001). Patients with MDD and PD also showed higher LF/HF ratio than controls during the stress phase (F = 6.15, p = 0.002). Responders in the PD group presented a lower level of LF/HF ratio during the stress phase compared to non-responders (F = 10.14, p = 0.002), while responders in the MDD group showed a lower level of heart rate during all three phases, compared to non-responders. Additionally, we could predict treatment response in patients with MDD using ΔLF/HF ratio (OR: 1.33, 95% CI = 1.07-1.65, p = 0.011) and ΔpNN50 (OR: 1.49, 95% CI 1.09-1.77, p = 0.014). CONCLUSION: The changes of HRV parameters of pNN50 and LF/HF ratio between the stress and recovery phase may be clinical markers of predictors of treatment responsiveness in MDD and PD patients.

Automatic detection of major depressive disorder using electrodermal activity.

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.

Silicone-Based Adhesives with Highly Tunable Adhesion Force for Skin-Contact Applications.

A fundamental approach to fabricating silicone-based adhesives with highly tunable adhesion force for the skin-contact applications is presented. Liquid blends consisting of vinyl-multifunctional polydimethylsiloxane (V-PDMS), hydride-terminated PDMS (H-PDMS), and a tackifier composed of a silanol-terminated PDMS/MQ resin mixture and the MQ resin are used as the adhesive materials. The peel adhesion force of addition-cured adhesives on the skin is increased by increasing the H-PDMS molecular weights and the tackifier content, and decreasing the H-PDMS/V-PDMS ratio. There is an inverse relationship between the adhesion force and the Young's modulus. The low-modulus adhesives with a low H-PDMS/V-PDMS ratio exhibit enhanced adhesion properties. The low-modulus adhesives with the high MQ resin content show significantly enhanced adhesion properties. These adhesives exhibit a wide range of modulus (2-499 kPa), and their adhesion force (0.04-5.38 N) is superior to commercially available soft silicone adhesives (0.82-2.79 N). The strong adhesives (>≈2 N) provide sufficient adhesion for fixing the flexible electrocardiogram (ECG) device to the skin in most daily activity. The human ECG signals are successfully recorded in real time. These results suggest that the silicone-based adhesives should be useful as an atraumatic adhesive for the skin-contact applications.

Ultrastretchable Analog/Digital Signal Transmission Line with Carbon Nanotube Sheets.

Stretchable conductors can be used in various applications depending on their own characteristics. Here, we demonstrate simple and robust elastomeric conductors that are optimized for stretchable electrical signal transmission line. They can withstand strains up to 600% without any substantial change in their resistance (≤10% as is and ≤1% with passivation), and exhibit suppressed charge fluctuations in the medium. The inherent elasticity of a polymeric rubber and the high conductivity of flexible, highly oriented carbon nanotube sheets were combined synergistically, without losing both properties. The nanoscopic strong adhesion between aligned carbon nanotube arrays and strained elastomeric polymers induces conductive wavy folds with microscopic bending of radii on the scale of a few micrometers. Such features enable practical applications such as in elastomeric length-changeable electrical digital and analog signal transmission lines at above MHz frequencies. In addition to reporting basic direct current, alternating current, and noise characterizations of the elastomeric conductors, various examples as a stretchable signal transmission line up to 600% strains are presented by confirming the capability of transmitting audio and video signals, as well as low-frequency medical signals without information distortion.

Attogram mass sensing based on silicon microbeam resonators.

Using doubly-clamped silicon (Si) microbeam resonators, we demonstrate sub-attogram per Hertz (ag/Hz) mass sensitivity, which is extremely high sensitivity achieved by micro-scale MEMS mass sensors. We also characterize unusual buckling phenomena of the resonators. The thin-film based resonator is composed of a Si microbeam surrounded by silicon nitride (SiN) anchors, which significantly improve performance by providing fixation on the microbeam and stabilizing oscillating motion. Here, we introduce two fabrication techniques to further improve the mass sensitivity. First, we minimize surface stress by depositing a sacrificial SiN layer, which prevents damage on the Si microbeam. Second, we modify anchor structure to find optimal design that allows the microbeam to oscillate in quasi-one dimensional mode while achieving high quality factor. Mass loading is conducted by depositing Au/Ti thin films on the local area of the microbeam surface. Using sequential mass loading, we test effects of changing beam dimensions, position of mass loading, and distribution of a metal film on the mass sensitivity. In addition, we demonstrate that microbeams suffer local micro-buckling and global buckling by excessive mass loading, which are induced by two different mechanisms. We also find that the critical buckling length is increased by additional support from the anchors.

Flexible Nonstick Replica Mold for Transfer Printing of Ag Ink.

We report the fabrication of flexible replica molds for transfer printing of Ag ink on a rigid glass substrate. As mold precursors, acrylic mixtures were prepared from silsesquioxane-based materials, silicone acrylate, poly(propylene glycol) diacrylate, 3,3,4,4,5,5,6,6,7,7,8,8, 9,9,10,10,10-heptadecafluorodecyl methacrylate, and photoinitiator. By using these materials, the replica molds were fabricated from a silicon master onto a flexible substrate by means of UV-assisted molding process at room temperature. The wettability of Ag ink decreased with increase in the water contact angle of replica molds. On the other hand, the transfer rate of Ag ink onto adhesive-modified substrates increased with increase in the water contact angle of replica molds. Transferred patterns were found to be thermally stable on the photocurable adhesive layer, whereas Ag-ink patterns transferred on non-photocurable adhesives were distorted by thermal treatment. We believe that these characteristics of replica molds and adhesives offer a new strategy for the development of the transfer printing of solution-based ink materials.

Fabrication of Large-Area Hierarchical Structure Array Using Siliconized-Silsesquioxane as a Nanoscale Etching Barrier.

A material approach to fabricate a large-area hierarchical structure array is presented. The replica molding and oxygen (O2) plasma etching processes were combined to fabricate a large-area hierarchical structure array. Liquid blends consisting of siliconized silsesquioxane acrylate (Si-SSQA), ethylene glycol dimethacrylate (EGDMA), and photoinitiator are developed as a roughness amplifying material during O2 plasma etching. Microstructures composed of the Si-SSQA/EGDMA mixtures are fabricated by replica molding. Nanoscale roughness on molded microstructures is realized by O2 etching. The nanoscale roughness on microstructures is efficiently controlled by varying the etching time and the weight ratio of Si-SSQA to EGDMA. The hierarchical structures fabricated by combining replica molding and O2 plasma etching showed superhydrophilicity with long-term stability, resulting in the formation of hydroxyl-terminated silicon oxide layer with the reorientation limit. On the other hand, the hierarchical structures modified with a perfluorinated monolayer showed superhydrophobicity. The increment of water contact angles is consistent with increment of the nano/microroughness of hierarchical structures and decrement of the top contact area of water/hierarchical structures.

Leptin is associated with mood status and metabolic homeostasis in patients with bipolar disorder.

BACKGROUND: Patients with bipolar disorder are at a high risk for becoming obese. Adipokines are associated with depression and obesity via the inflammatory process. However, few studies have investigated the associations between depression and leptin, adiponectin and resistin levels in patients with bipolar disorder. We explored the associations between serum levels of leptin, adiponectin and resistin and mood and metabolic status in patients with bipolar disorder. METHODS: Body mass index (BMI) and serum leptin, adiponectin and resistin levels were assessed in 94 Korean patients with bipolar disorder. The Hamilton Rating Scale for Depression-17 and the Young Mania Rating Scale were used to assess mood state. RESULTS: Leptin (17.19 ± 13.08 vs. 10.47 ± 10.05 ng/ml; p = 0.008) and adiponectin (10.51 ± 8.37 vs. 5.91 ± 2.82 µg/ml; p = 0.001) levels were higher in female than in male patients. After adjusting for mood state, age, smoking, alcohol habit, and BMI in a multivariate analysis of covariance (MANCOVA), leptin (17.86 ± 1.22 vs. 10.05 ± 1.48 ng/ml; p < 0.001) and adiponectin (10.18 ± 0.98 vs. 6.40 ± 1.19 µg/ml; p = 0.027) levels were still higher in female than in male patients. Compared to euthymic patients, depressed patients had higher levels of leptin (17.37 ± 14.69 vs. 11.65 ± 9.04 ng/ml; p = 0.024), but there was no significant difference in adiponectin and resistin levels between the two groups. After adjusting for age, gender and BMI in the MANCOVA, leptin levels were also significantly higher in depressed (16.78 ± 1.34 ng/ml) than in euthymic patients (10.73 ± 1.22 ng/ml; p = 0.001). CONCLUSION: Leptin is closely associated with the regulation of mood and metabolic homeostasis in patients with bipolar disorder.

Assessment of risk factors related to suicide attempts in patients with bipolar disorder.

We compared the characteristics of patients with bipolar disorder with and without a history of suicide attempts to identify the risk factors of suicide in this disorder. Among 212 patients with bipolar disorder, 44 (21.2%) patients had histories of suicide attempts. Suicide attempters were younger and more likely to be diagnosed with bipolar II. The variables that differentiated those who did from those who did not attempt suicide included age at first contact, lifetime history of antidepressant use, major depressive episode, mixed episode, auditory hallucinations, rapid cycling, the number of previous mood episodes, age of first depressive episode, and age of first psychotic symptoms. Strong predictors of suicide attempts were younger age at onset, lifetime history of auditory hallucinations, and history of antidepressant use. Antecedent depressive episodes and psychotic symptoms predicted the first suicide attempt in patients with bipolar disorder. This study could help clinicians to understand the major risk factors of suicidal behavior in bipolar disorder.

Production of large-scale, freestanding vanadium pentoxide nanobelt porous structures.

Large-scale, freestanding, porous structures of vanadium pentoxide nanobelts (VPNs) were successfully prepared using the template-free freeze-drying method. The porous and multi-layered VPN macrostructures are composed of randomly oriented long nanobelts (over 100 µm) and their side length can be controlled up to a few tens of centimetres. Also, the bulk density and surface area of these macrostructures are 3-5 mg cm(-3) and 40-80 m(2) g(-1), respectively, which are similar to those of the excellent adsorbents. In addition, the removal efficiency measurements of ammonia molecules revealed that the VPN porous structures can adsorb the ammonia molecules with the combinations of van der Waals forces and strong chemical bonding by functional groups on the VPN surface.