Comparing multimodal physiological responses to social and physical pain in healthy participants.

Background: Previous physiology-driven pain studies focused on examining the presence or intensity of physical pain. However, people experience various types of pain, including social pain, which induces negative mood; emotional distress; and neural activities associated with physical pain. In particular, comparison of autonomic nervous system (ANS) responses between social and physical pain in healthy adults has not been well demonstrated. Methods: We explored the ANS responses induced by two types of pain-social pain, associated with a loss of social ties; and physical pain, caused by a pressure cuff-based on multimodal physiological signals. Seventy-three healthy individuals (46 women; mean age = 20.67 ± 3.27 years) participated. Behavioral responses were assessed to determine their sensitivity to pain stimuli. Electrocardiogram, electrodermal activity, photoplethysmogram, respiration, and finger temperature (FT) were measured, and 12 features were extracted from these signals. Results: Social pain induced increased heart rate (HR) and skin conductance (SC) and decreased blood volume pulse (BVP), pulse transit time (PTT), respiration rate (RR), and FT, suggesting a heterogeneous pattern of sympathetic-parasympathetic coactivation. Moreover, physical pain induced increased heart rate variability (HRV) and SC, decreased BVP and PTT, and resulted in no change in FT, indicating sympathetic-adrenal-medullary activation and peripheral vasoconstriction. Conclusion: These results suggest that changes in HR, HRV indices, RR, and FT can serve as markers for differentiating physiological responses to social and physical pain stimuli.

Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.

PURPOSE: The methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma. METHODS: Data from 1,501 patients with asthma symptoms who underwent MCT between 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV1]; PC20 ≤ 16 mg/mL) was compared with the prediction models derived from five ML methods: logistic regression, support vector machine, random forest, extreme gradient boosting, and artificial neural network. The area under the receiver operator characteristic curves (AUROC) and area under the precision-recall curves (AUPRC) of each model were compared. The prediction models were further analyzed using different input combinations of FEV1, forced vital capacity (FVC), and forced expiratory flow at 25%-75% of forced vital capacity (FEF25%-75%) values obtained during MCT. RESULTS: In total, 545 patients (36.3%) were diagnosed with asthma. The AUROC of the conventional model was 0.856 (95% confidence interval [CI], 0.852-0.861), and the AUPRC was 0.759 (95% CI, 0.751-0.766). All the five ML prediction models had higher AUROC and AUPRC values than those of the conventional model, and random forest showed both highest AUROC (0.950; 95% CI, 0.948-0.952) and AUROC (0.909; 95% CI, 0.905-0.914) when FEV1, FVC, and FEF25%-75% were included as inputs. CONCLUSIONS: Artificial intelligence-based models showed excellent performance in asthma prediction compared to using PC20 ≤ 16 mg/mL. The novel technology could be used to enhance the clinical diagnosis of asthma.

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.

PKM2 Regulates HSP90-Mediated Stability of the IGF-1R Precursor Protein and Promotes Cancer Cell Survival during Hypoxia.

Insulin-like growth factor-1 receptor (IGF-1R), an important factor in promoting cancer cell growth and survival, is commonly upregulated in cancer cells. However, amplification of the IGF1R gene is extremely rare in tumors. Here, we have provided insights into the mechanisms underlying the regulation of IGF-1R protein expression. We found that PKM2 serves as a non-metabolic protein that binds to and increases IGF-1R protein expression by promoting the interaction between IGF-1R and heat-shock protein 90 (HSP90). PKM2 depletion decreases HSP90 binding to IGF-1R precursor, thereby reducing IGF-1R precursor stability and the basal level of mature IGF-1R. Consequently, PKM2 knockdown inhibits the activation of AKT, the key downstream effector of IGF-1R signaling, and increases apoptotic cancer cell death during hypoxia. Notably, we clinically verified the PKM2-regulated expression of IGF-1R through immunohistochemical staining in a tissue microarray of 112 lung cancer patients, demonstrating a significant positive correlation (r = 0.5208, p < 0.0001) between PKM2 and IGF-1R expression. Together, the results of a previous report demonstrated that AKT mediates PKM2 phosphorylation at serine-202; these results suggest that IGF-1R signaling and PKM2 mutually regulate each other to facilitate cell growth and survival, particularly under hypoxic conditions, in solid tumors with dysregulated IGF-1R expression.

Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks.

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

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.

Fasting-induced FGF21 signaling activates hepatic autophagy and lipid degradation via JMJD3 histone demethylase.

Autophagy is essential for cellular survival and energy homeostasis under nutrient deprivation. Despite the emerging importance of nuclear events in autophagy regulation, epigenetic control of autophagy gene transcription remains unclear. Here, we report fasting-induced Fibroblast Growth Factor-21 (FGF21) signaling activates hepatic autophagy and lipid degradation via Jumonji-D3 (JMJD3/KDM6B) histone demethylase. Upon FGF21 signaling, JMJD3 epigenetically upregulates global autophagy-network genes, including Tfeb, Atg7, Atgl, and Fgf21, through demethylation of histone H3K27-me3, resulting in autophagy-mediated lipid degradation. Mechanistically, phosphorylation of JMJD3 at Thr-1044 by FGF21 signal-activated PKA increases its nuclear localization and interaction with the nuclear receptor PPARα to transcriptionally activate autophagy. Administration of FGF21 in obese mice improves defective autophagy and hepatosteatosis in a JMJD3-dependent manner. Remarkably, in non-alcoholic fatty liver disease patients, hepatic expression of JMJD3, ATG7, LC3, and ULK1 is substantially decreased. These findings demonstrate that FGF21-JMJD3 signaling epigenetically links nutrient deprivation with hepatic autophagy and lipid degradation in mammals.

Predicting Individuals' Experienced Fear From Multimodal Physiological Responses to a Fear-Inducing Stimulus.

Emotions are experienced differently by individuals, and thus, it is important to account for individuals' experienced emotions to understand their physiological responses to emotional stimuli. The present study investigated the physiological responses to a fear-inducing stimulus and examined whether these responses can predict experienced fear. A total of 230 participants were presented with neutral and fear-inducing film clips, after which they self-rated their experienced emotions. Physiological measures (skin conductance level and response: SCL, SCR, heart rate: HR, pulse transit time: PTT, fingertip temperature: FT, and respiratory rate: RR) were recorded during the stimuli presentation. We examined the correlations between the physiological measures and the participants' experienced emotional intensity, and performed a multiple linear regression to predict fear intensity based on the physiological responses. Of the participants, 92.5% experienced the fear emotion, and the average intensity was 5.95 on a 7-point Likert scale. Compared to the neutral condition, the SCL, SCR, HR, and RR increased significantly during the fear-inducing stimulus presentation whereas FT and PTT decreased significantly. Fear intensity correlated positively with SCR and HR and negatively with SCL, FT, PTT, and RR. The multiple linear regression demonstrated that fear intensity was predicted by a combination of SCL, SCR, HR, FT, and RR. Our findings indicate that the physiological responses to experiencing fear are associated with cholinergic, sympathetic, and α-adrenergic vascular activation as well as myocardial ß-sympathetic excitation, and support the use of multimodal physiological signals for quantifying emotions.

Reliability of Physiological Responses Induced by Basic Emotions: A Pilot Study.

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.

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.

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.

Phosphorylation of hepatic farnesoid X receptor by FGF19 signaling-activated Src maintains cholesterol levels and protects from atherosclerosis.

The bile acid (BA) nuclear receptor, farnesoid X receptor (FXR/NR1H4), maintains metabolic homeostasis by transcriptional control of numerous genes, including an intestinal hormone, fibroblast growth factor-19 (FGF19; FGF15 in mice). Besides activation by BAs, the gene-regulatory function of FXR is also modulated by hormone or nutrient signaling-induced post-translational modifications. Recently, phosphorylation at Tyr-67 by the FGF15/19 signaling-activated nonreceptor tyrosine kinase Src was shown to be important for FXR function in BA homeostasis. Here, we examined the role of this FXR phosphorylation in cholesterol regulation. In both hepatic FXR-knockout and FXR-knockdown mice, reconstitution of FXR expression up-regulated cholesterol transport genes for its biliary excretion, including scavenger receptor class B member 1 (Scarb1) and ABC subfamily G member 8 (Abcg5/8), decreased hepatic and plasma cholesterol levels, and increased biliary and fecal cholesterol levels. Of note, these sterol-lowering effects were blunted by substitution of Phe for Tyr-67 in FXR. Moreover, consistent with Src's role in phosphorylating FXR, Src knockdown impaired cholesterol regulation in mice. In hypercholesterolemic apolipoprotein E-deficient mice, expression of FXR, but not Y67F-FXR, ameliorated atherosclerosis, whereas Src down-regulation exacerbated it. Feeding or treatment with an FXR agonist induced Abcg5/8 and Scarb1 expression in WT, but not FGF15-knockout, mice. Furthermore, FGF19 treatment increased occupancy of FXR at Abcg5/8 and Scarb1, expression of these genes, and cholesterol efflux from hepatocytes. These FGF19-mediated effects were blunted by the Y67F-FXR substitution or Src down-regulation or inhibition. We conclude that phosphorylation of hepatic FXR by FGF15/19-induced Src maintains cholesterol homeostasis and protects against atherosclerosis.

Small Heterodimer Partner and Fibroblast Growth Factor 19 Inhibit Expression of NPC1L1 in Mouse Intestine and Cholesterol Absorption.

BACKGROUND & AIMS: The nuclear receptor subfamily 0 group B member 2 (NR0B2, also called SHP) is expressed at high levels in the liver and intestine. Postprandial fibroblast growth factor 19 (human FGF19, mouse FGF15) signaling increases the transcriptional activity of SHP. We studied the functions of SHP and FGF19 in the intestines of mice, including their regulation of expression of the cholesterol transporter NPC1L1 )NPC1-like intracellular cholesterol transporter 1) and cholesterol absorption. METHODS: We performed histologic and biochemical analyses of intestinal tissues from C57BL/6 and SHP-knockout mice and performed RNA-sequencing analyses to identify genes regulated by SHP. The effects of fasting and refeeding on intestinal expression of NPC1L1 were examined in C57BL/6, SHP-knockout, and FGF15-knockout mice. Mice were given FGF19 daily for 1 week; fractional cholesterol absorption, cholesterol and bile acid (BA) levels, and composition of BAs were measured. Intestinal organoids were generated from C57BL/6 and SHP-knockout mice, and cholesterol uptake was measured. Luciferase reporter assays were performed with HT29 cells. RESULTS: We found that the genes that regulate lipid and ion transport in intestine, including NPC1L1, were up-regulated and that cholesterol absorption was increased in SHP-knockout mice compared with C57BL/6 mice. Expression of NPC1L1 was reduced in C57BL/6 mice after refeeding after fasting but not in SHP-knockout or FGF15-knockout mice. SHP-knockout mice had altered BA composition compared with C57BL/6 mice. FGF19 injection reduced expression of NPC1L1, decreased cholesterol absorption, and increased levels of hydrophilic BAs, including tauro-α- and -ß-muricholic acids; these changes were not observed in SHP-knockout mice. SREBF2 (sterol regulatory element binding transcription factor 2), which regulates cholesterol, activated transcription of NPC1L1. FGF19 signaling led to phosphorylation of SHP, which inhibited SREBF2 activity. CONCLUSIONS: Postprandial FGF19 and SHP inhibit SREBF2, which leads to repression of intestinal NPC1L1 expression and cholesterol absorption. Strategies to increase FGF19 signaling to activate SHP might be developed for treatment of hypercholesterolemia.

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.

Postprandial FGF19-induced phosphorylation by Src is critical for FXR function in bile acid homeostasis.

Farnesoid-X-Receptor (FXR) plays a central role in maintaining bile acid (BA) homeostasis by transcriptional control of numerous enterohepatic genes, including intestinal FGF19, a hormone that strongly represses hepatic BA synthesis. How activation of the FGF19 receptor at the membrane is transmitted to the nucleus for transcriptional regulation of BA levels and whether FGF19 signaling posttranslationally modulates FXR function remain largely unknown. Here we show that FXR is phosphorylated at Y67 by non-receptor tyrosine kinase, Src, in response to postprandial FGF19, which is critical for its nuclear localization and transcriptional regulation of BA levels. Liver-specific expression of phospho-defective Y67F-FXR or Src downregulation in mice results in impaired homeostatic responses to acute BA feeding, and exacerbates cholestatic pathologies upon drug-induced hepatobiliary insults. Also, the hepatic FGF19-Src-FXR pathway is defective in primary biliary cirrhosis (PBC) patients. This study identifies Src-mediated FXR phosphorylation as a potential therapeutic target and biomarker for BA-related enterohepatic diseases.

Fasting-induced JMJD3 histone demethylase epigenetically activates mitochondrial fatty acid ß-oxidation.

Jumonji D3 (JMJD3) histone demethylase epigenetically regulates development and differentiation, immunity, and tumorigenesis by demethylating a gene repression histone mark, H3K27-me3, but a role for JMJD3 in metabolic regulation has not been described. SIRT1 deacetylase maintains energy balance during fasting by directly activating both hepatic gluconeogenic and mitochondrial fatty acid ß-oxidation genes, but the underlying epigenetic and gene-specific mechanisms remain unclear. In this study, JMJD3 was identified unexpectedly as a gene-specific transcriptional partner of SIRT1 and epigenetically activated mitochondrial ß-oxidation, but not gluconeogenic, genes during fasting. Mechanistically, JMJD3, together with SIRT1 and the nuclear receptor PPARα, formed a positive autoregulatory loop upon fasting-activated PKA signaling and epigenetically activated ß-oxidation-promoting genes, including Fgf21, Cpt1a, and Mcad. Liver-specific downregulation of JMJD3 resulted in intrinsic defects in ß-oxidation, which contributed to hepatosteatosis as well as glucose and insulin intolerance. Remarkably, the lipid-lowering effects by JMJD3 or SIRT1 in diet-induced obese mice were mutually interdependent. JMJD3 histone demethylase may serve as an epigenetic drug target for obesity, hepatosteatosis, and type 2 diabetes that allows selective lowering of lipid levels without increasing glucose levels.

AhR and SHP regulate phosphatidylcholine and S-adenosylmethionine levels in the one-carbon cycle.

Phosphatidylcholines (PC) and S-adenosylmethionine (SAM) are critical determinants of hepatic lipid levels, but how their levels are regulated is unclear. Here, we show that Pemt and Gnmt, key one-carbon cycle genes regulating PC/SAM levels, are downregulated after feeding, leading to decreased PC and increased SAM levels, but these effects are blunted in small heterodimer partner (SHP)-null or FGF15-null mice. Further, aryl hydrocarbon receptor (AhR) is translocated into the nucleus by insulin/PKB signaling in the early fed state and induces Pemt and Gnmt expression. This induction is blocked by FGF15 signaling-activated SHP in the late fed state. Adenoviral-mediated expression of AhR in obese mice increases PC levels and exacerbates steatosis, effects that are blunted by SHP co-expression or Pemt downregulation. PEMT, AHR, and PC levels are elevated in simple steatosis patients, but PC levels are robustly reduced in steatohepatitis-fibrosis patients. This study identifies AhR and SHP as new physiological regulators of PC/SAM levels.

Synergistic Use of Gold Nanoparticles (AuNPs) and "Capillary Enzyme-Linked Immunosorbent Assay (ELISA)" for High Sensitivity and Fast Assays.

Using gold nanoparticles (AuNPs) on "capillary enzyme-linked immunosorbent assay (ELISA)", we produced highly sensitive and rapid assays, which are the major attributes for point-of-care applications. First, in order to understand the size effect of AuNPs, AuNPs of varying diameters (5 nm, 10 nm, 15 nm, 20 nm, 30 nm, and 50 nm) conjugated with Horseradish Peroxidase (HRP)-labeled anti-C reactive protein (antiCRP) (AuNP•antiCRP-HRP) were used for well-plate ELISA. AuNP of 10 nm produced the largest optical density, enabling detection of 0.1 ng/mL of CRP with only 30 s of incubation, in contrast to 10 ng/mL for the ELISA run in the absence of AuNP. Then, AuNP of 10 nm conjugated with antiCRP-HRP (AuNP•antiCRP-HRP) was used for "capillary ELISA" to detect as low as 0.1 ng/mL of CRP. Also, kinetic study on both 96-well plates and in a capillary tube using antiCRP-HRP or AuNP•antiCRP-HRP showed a synergistic effect between AuNP and the capillary system, in which the fastest assay was observed from the "AuNP capillary ELISA", with its maximum absorbance reaching 2.5 min, while the slowest was the typical well-plate ELISA with its maximum absorbance reaching in 13.5 min.

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.