Hypothalamic neuronal activation in non-human primates drives naturalistic goal-directed eating behavior.

Maladaptive feeding behavior is the primary cause of modern obesity. While the causal influence of the lateral hypothalamic area (LHA) on eating behavior has been established in rodents, there is currently no primate-based evidence available on naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHAGABA) neurons in eating using chemogenetics in three macaques. LHAGABA neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, predominantly for palatable food. Positron emission tomography and magnetic resonance spectroscopy validated chemogenetic activation. Resting-state functional magnetic resonance imaging revealed that the functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after LHAGABA neuron activation. Thus, our study elucidates the role of LHAGABA neurons in eating and obesity therapeutics for primates and humans.

Erratum: Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.

[This corrects the article on p. 57 in vol. 35, PMID: 38204745.].

Extremely Stable Ag-Based Photonics, Plasmonic, Optical, and Electronic Materials and Devices Designed with Surface Chemistry Engineering for Anti-Tarnish.

Silver (Ag) metal-based structures are promising building blocks for next-generation photonics and electronics owing to their unique characteristics, such as high reflectivity, surface plasmonic resonance effects, high electrical conductivity, and tunable electron transport mechanisms. However, Ag structures exhibit poor sustainability in terms of device performance because harsh chemicals, particularly S2- ions present in the air, can damage their structures, lowering their optical and electrical properties. Here, the surface chemistry of Ag structures with (3-mercaptopropyl)trimethoxysilane (MPTS) ligands at room temperature and under ambient conditions is engineered to prevent deterioration of their optical and electrical properties owing to S2- exposure. Regardless of the dimensions of the Ag structures, the MPTS ligands can be applied to each dimension (0D, 1D, and 3D). Consequently, highly sustainable plasmonic effects (Δλ < 2 nm), Fabry-Perot cavity resonance structures (Δλ < 2 nm), reflectors (ΔRReflectance < 0.5%), flexible electrodes (ΔRelectrical < 0.1 Ω), and strain gauge sensors (ΔGF < 1), even in S2- exposing conditions is achieved. This strategy is believed to significantly contribute to environmental pollution reduction by decreasing the volume of electronic waste.

Hypothalamic astrocyte NAD+ salvage pathway mediates the coupling of dietary fat overconsumption in a mouse model of obesity.

Nicotinamide adenine dinucleotide (NAD)+ serves as a crucial coenzyme in numerous essential biological reactions, and its cellular availability relies on the activity of the nicotinamide phosphoribosyltransferase (NAMPT)-catalyzed salvage pathway. Here we show that treatment with saturated fatty acids activates the NAD+ salvage pathway in hypothalamic astrocytes. Furthermore, inhibition of this pathway mitigates hypothalamic inflammation and attenuates the development of obesity in male mice fed a high-fat diet (HFD). Mechanistically, CD38 functions downstream of the NAD+ salvage pathway in hypothalamic astrocytes burdened with excess fat. The activation of the astrocytic NAMPT-NAD+-CD38 axis in response to fat overload induces proinflammatory responses in the hypothalamus. It also leads to aberrantly activated basal Ca2+ signals and compromised Ca2+ responses to metabolic hormones such as insulin, leptin, and glucagon-like peptide 1, ultimately resulting in dysfunctional hypothalamic astrocytes. Our findings highlight the significant contribution of the hypothalamic astrocytic NAD+ salvage pathway, along with its downstream CD38, to HFD-induced obesity.

Development of an assessment method for freely moving nonhuman primates' eating behavior using manual and deep learning analysis.

Purpose: Although eating is imperative for survival, few comprehensive methods have been developed to assess freely moving nonhuman primates' eating behavior. In the current study, we distinguished eating behavior into appetitive and consummatory phases and developed nine indices to study them using manual and deep learning-based (DeepLabCut) techniques. Method: The indices were utilized to three rhesus macaques by different palatability and hunger levels to validate their utility. To execute the experiment, we designed the eating behavior cage and manufactured the artificial food. The total number of trials was 3, with 1 trial conducted using natural food and 2 trials using artificial food. Result: As a result, the indices of highest utility for hunger effect were approach frequency and consummatory duration. Appetitive composite score and consummatory duration showed the highest utility for palatability effect. To elucidate the effects of hunger and palatability, we developed 2D visualization plots based on manual indices. These 2D visualization methods could intuitively depict the palatability perception and hunger internal state. Furthermore, the developed deep learning-based analysis proved accurate and comparable with manual analysis. When comparing the time required for analysis, deep learning-based analysis was 24-times faster than manual analysis. Moreover, temporal and spatial dynamics were visualized via manual and deep learning-based analysis. Based on temporal dynamics analysis, the patterns were classified into four categories: early decline, steady decline, mid-peak with early incline, and late decline. Heatmap of spatial dynamics and trajectory-related visualization could elucidate a consumption posture and a higher spatial occupancy of food zone in hunger and with palatable food. Discussion: Collectively, this study describes a newly developed and validated multi-phase method for assessing freely moving nonhuman primate eating behavior using manual and deep learning-based analyses. These effective tools will prove valuable in food reward (palatability effect) and homeostasis (hunger effect) research.

Activation of ChAT+ neuron in dorsal motor vagus (DMV) increases blood glucose through the regulation of hepatic gene expression in mice.

The brain and peripheral organs communicate through hormones and neural connections. Proper communication is required to maintain normal whole-body energy homeostasis. In addition to endocrine system, from the perspective of neural connections for metabolic homeostasis, the role of the sympathetic nervous system has been extensively studied, but understanding of the parasympathetic nervous system is limited. The liver plays a central role in glucose and lipid metabolism. This study aimed to clarify the innervation of parasympathetic nervous system in the liver and its functional roles in metabolic homeostasis. The liver-specific parasympathetic nervous system innervation (PNS) was shown by tissue clearing, immunofluorescence and transgenic mice at the three-dimensional histological level. The parasympathetic efferent signals were manipulated using a chemogenetic technique and the activation of ChAT+ parasympathetic neurons in dorsal motor vagus (DMV) results in the increased blood glucose through the elevated hepatic gluconeogenic and lipogenic gene expression in the liver. Thus, our study showed the evidence of ChAT+ parasympathetic neurons in the liver and its role for hepatic parasympathetic nervous signaling in glucose homeostasis through the regulation of hepatic gene expression.

Case Reports of Binge Eating Patterns in the Recovery Phase of Anorexia Nervosa Patients With and Without Food Addiction.

Food addiction refers to a condition in which individuals exhibit addictive-like behaviors toward food, like those observed in substance abuse. Although still debated, evidence supporting the validity and usefulness of the concept of food addiction is growing. Food addiction is particularly associated with obesity and eating disorders involving binge eating. This study discusses the cases of two adolescent patients who presented with anorexia nervosa. During the recovery phase of anorexia nervosa, binge eating was observed, and the patterns of binge eating significantly differed between patients, with and without food addiction. Therefore, healthcare professionals treating eating disorders should be aware of food addiction and modify their treatment strategies accordingly.

Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.

Autism spectrum disorder (ASD) can be associated with eating problems. However, currently, there is a lack of established guidelines for assessing and addressing eating behaviors in individuals with ASD. This gap in research exists due to the challenges associated with using traditional assessment methods, which may lead to discrepancies in responses and unintentional potential biases from caregivers. In this review, we provided a comprehensive overview of various eating behaviors commonly observed in individuals with ASD. These behaviors include 1) food neophobia, 2) selective eating, 3) binge eating, 4) food avoidance, 5) chewing and swallowing problems, 6) pica, 7) rumination, 8) rituals, and 9) problematic behaviors. Furthermore, we provide a perspective of utilizing digital tools: 1) augmentative and alternative communication; 2) ecological momentary assessment; and 3) video analysis, behavioral analysis, and facial expression analysis. This review explores existing assessment methods and suggests novel assessment aiding together.

Ultra-Low Noise Level Infrared Quantum Dot Photodiodes with Self-Screenable Polymeric Optical Window.

Quantum dot photodiodes (QPDs) have garnered significant attention because of their unparalleled near-infrared (NIR) detection capabilities, primarily attributable to their size-dependent bandgap tunability. Nevertheless, the broadband absorption spectrum of QPD engenders substantial noise floor within superfluous visible light regions, notably hindering their use in several emerging applications necessitating the detection of faint micro-light signals. To overcome these hurdles, a self-screenable NIR QPD featuring an internal optical filter with a thick polymeric interlayer to reduce electronic noise is demonstrated. This effectively screens out undesirable visible light regions while reducing the ionized defect owing to decreased density of state, yielding an extremely low dark current (≈1010 A cm-2 at V = -1 V). Consequently, the electronic noise spectral density is attained at levels below ≈10-27 -10-28 A2 Hz-1 , and responsivity (R) dropped to 92% within the visible light spectrum.

High-Performance Self-Powered Quantum Dot Infrared Photodetector with Azide Ion Solution Treated Electron Transport Layer.

The demand for self-powered photodetectors (PDs) capable of NIR detection without external power is growing with the advancement of NIR technologies such as LIDAR and object recognition. Lead sulfide quantum dot-based photodetectors (PbS QPDs) excel in NIR detection; however, their self-powered operation is hindered by carrier traps induced by surface defects and unfavorable band alignment in the zinc oxide nanoparticle (ZnO NP) electron-transport layer (ETL). In this study, an effective azide-ion (N3 - ) treatment is introduced on a ZnO NP ETL to reduce the number of traps and improve the band alignment in a PbS QPD. The ZnO NP ETL treated with azide ions exhibited notable improvements in carrier lifetime and mobility as well as an enhanced internal electric field within the thin-film heterojunction of the ZnO NPs and PbS QDs. The azide-ion-treated PbS QPD demonstrated a increase in short-circuit current density upon NIR illumination, marking a responsivity of 0.45 A W-1 , specific detectivity of 4 × 1011 Jones at 950 nm, response time of 8.2 µs, and linear dynamic range of 112 dB.

CETP and APOA2 polymorphisms are associated with weight loss and healthy eating behavior changes in response to digital lifestyle modifications.

Response to digital healthcare lifestyle modifications is highly divergent. This study aimed to examine the association between single nucleotide polymorphism (SNP) genotypes and clinical efficacy of a digital healthcare lifestyle modification. We genotyped 97 obesity-related SNPs from 45 participants aged 18-39 years, who underwent lifestyle modification via digital cognitive behavioral therapy for obesity for 8 weeks. Anthropometric, eating behavior phenotypes, and psychological measures were analyzed before and after the intervention to identify their clinical efficacy. CETP (rs9939224) SNP significantly predict "super-responders" with greater body mass index (BMI) reduction (p = 0.028; GG - 2.91%, GT - 9.94%), while APOA2 (rs5082) appeared to have some potential for predicting "poor-responders" with lower BMI reduction (p = 0.005; AA - 6.17%, AG + 2.05%, and GG + 5.11%). These SNPs was also associated with significant differences in eating behavior changes, healthy diet proportions, health diet diversity, emotional and restrained eating behavior changes. Furthermore, classification using gene-gene interactions between rs9939224 and rs5082 significantly predicted the best response, with a greater decrease in BMI (p = 0.038; - 11.45% for the best response group (CEPT GT/TT × APOA2 AA) vs. + 2.62% for the worst response group (CEPT GG × APOA2 AG/GG)). CETP and APOA2 SNPs can be used as candidate markers to predict the efficacy of digital healthcare lifestyle modifications based on genotype-based precision medicine.Trial registration: NCT03465306, ClinicalTrials.gov. Registered March, 2018.

Voltage control of magnetism in Fe3-xGeTe2/In2Se3 van der Waals ferromagnetic/ferroelectric heterostructures.

We investigate the voltage control of magnetism in a van der Waals (vdW) heterostructure device consisting of two distinct vdW materials, the ferromagnetic Fe3-xGeTe2 and the ferroelectric In2Se3. It is observed that gate voltages applied to the Fe3-xGeTe2/In2Se3 heterostructure device modulate the magnetic properties of Fe3-xGeTe2 with significant decrease in coercive field for both positive and negative voltages. Raman spectroscopy on the heterostructure device shows voltage-dependent increase in the in-plane In2Se3 and Fe3-xGeTe2 lattice constants for both voltage polarities. Thus, the voltage-dependent decrease in the Fe3-xGeTe2 coercive field, regardless of the gate voltage polarity, can be attributed to the presence of in-plane tensile strain. This is supported by density functional theory calculations showing tensile-strain-induced reduction of the magnetocrystalline anisotropy, which in turn decreases the coercive field. Our results demonstrate an effective method to realize low-power voltage-controlled vdW spintronic devices utilizing the magnetoelectric effect in vdW ferromagnetic/ferroelectric heterostructures.

Highly Conductive and Sensitive Wearable Strain Sensors with Metal/Nanoparticle Double Layer for Noninterference Voice Detection.

Human voice recognition via skin-attachable devices has significant potential for gathering important physiological information from acoustic data without background noise interference. In this study, a highly sensitive and conductive wearable crack-based strain sensor was developed for voice-recognition systems. The sensor was fabricated using a double-layer structure of Ag nanoparticles (NPs) and Ag metal on a biocompatible polydimethylsiloxane substrate. The top metal layer acts as a conducting active layer, whereas the bottom Ag NP layer induces channel cracks in the upper layer, effectively hindering current flow. Subsequently, the double-layer film exhibits a low electrical resistance value (<5 × 10-5 Ω cm), ultrahigh sensitivity (gauge factor = 1870), and a fast response/recovery time (252/168 µs). A sound wave was detected at a high frequency of 15 kHz with a signal-to-noise ratio (SNR) over 40 dB. The sensor exhibited excellent anti-interference characteristics and effectively differentiated between different voice qualities (modal, pressed, and breathy), with a systematic analysis revealing successful detection of the laryngeal state and glottal source. This ultrasensitive wearable sensor has potential applications in various physiological signal measurement methods, personalized healthcare systems, and ubiquitous computing.

Mitochondria-derived peptide SHLP2 regulates energy homeostasis through the activation of hypothalamic neurons.

Small humanin-like peptide 2 (SHLP2) is a mitochondrial-derived peptide implicated in several biological processes such as aging and oxidative stress. However, its functional role in the regulation of energy homeostasis remains unclear, and its corresponding receptor is not identified. Hereby, we demonstrate that both systemic and intracerebroventricular (ICV) administrations of SHLP2 protected the male mice from high-fat diet (HFD)-induced obesity and improved insulin sensitivity. In addition, the activation of pro-opiomelanocortin (POMC) neurons by SHLP2 in the arcuate nucleus of the hypothalamus (ARC) is involved in the suppression of food intake and the promotion of thermogenesis. Through high-throughput structural complementation screening, we discovered that SHLP2 binds to and activates chemokine receptor 7 (CXCR7). Taken together, our study not only reveals the therapeutic potential of SHLP2 in metabolic disorders but also provides important mechanistic insights into how it exerts its effects on energy homeostasis.

Generation of Conventional 18F-FDG PET Images from 18F-Florbetaben PET Images Using Generative Adversarial Network: A Preliminary Study Using ADNI Dataset.

Background and Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PETFDG) image can visualize neuronal injury of the brain in Alzheimer's disease. Early-phase amyloid PET image is reported to be similar to PETFDG image. This study aimed to generate PETFDG images from 18F-florbetaben PET (PETFBB) images using a generative adversarial network (GAN) and compare the generated PETFDG (PETGE-FDG) with real PETFDG (PETRE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). Materials and Methods: Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, 110 participants with both PETFDG and PETFBB images at baseline were included. The paired PETFDG and PETFBB images included six and four subset images, respectively. Each subset image had a 5 min acquisition time. These subsets were randomly sampled and divided into 249 paired PETFDG and PETFBB subset images for the training datasets and 95 paired subset images for the validation datasets during the deep-learning process. The deep learning model used in this study is composed of a GAN with a U-Net. The differences in the SSIM and PSNR values between the PETGE-FDG and PETRE-FDG images in the cycleGAN and pix2pix models were evaluated using the independent Student's t-test. Statistical significance was set at p ≤ 0.05. Results: The participant demographics (age, sex, or diagnosis) showed no statistically significant differences between the training (82 participants) and validation (28 participants) groups. The mean SSIM between the PETGE-FDG and PETRE-FDG images was 0.768 ± 0.135 for the cycleGAN model and 0.745 ± 0.143 for the pix2pix model. The mean PSNR was 32.4 ± 9.5 and 30.7 ± 8.0. The PETGE-FDG images of the cycleGAN model showed statistically higher mean SSIM than those of the pix2pix model (p < 0.001). The mean PSNR was also higher in the PETGE-FDG images of the cycleGAN model than those of pix2pix model (p < 0.001). Conclusions: We generated PETFDG images from PETFBB images using deep learning. The cycleGAN model generated PETGE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PETFDG images. These may provide additional information for the management of Alzheimer's disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses.

A Case of Adenomyosis Showing Size Reduction in a Follicular Lymphoma Patient Receiving Chemotherapy With R-Bendamustine.

ABSTRACT: We describe a case of adenomyosis that reduced in size in a patient with lymphoma on receiving chemotherapy. A 48-year-old woman with worsening left flank pain was diagnosed with follicular lymphoma. [ 18 F]FDG PET/CT revealed multiple hypermetabolic lymph nodes in the bilateral cervical, axillary, mediastinal, mesenteric, retroperitoneal, iliac, and inguinal regions. In addition, adenomyosis with mild hypermetabolism was demonstrated on [ 18 F]FDG PET/CT. The size and metabolism of adenomyosis decreased after chemotherapy with R-bendamustine; in addition, along with decrease in estradiol levels, the patient experienced amenorrhea and hot flushes. The patient was diagnosed with chemotherapy-induced early menopause.

The Use of F-18 FDG PET-Based Cognitive Reserve to Evaluate Cognitive Decline in Alzheimer's Disease, Independent of Educational Influence.

Background and Objectives: The optimal assessment of cognitive function, including the impact of education, is crucial in managing Alzheimer's disease (AD). This study aimed to evaluate the role of cognitive reserve (CR), represented by the metabolic status of regions of the cerebral cortex, to evaluate cognitive decline considering the educational attainment of patients with AD. Materials and Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative database, and selected 124 patients who underwent both baseline F-18 fluorodeoxyglucose (FDG) and F-18 florbetaben (FBB) positron emission tomography (PET) scans. Demographics, cognitive function variables (Clinical Dementia Rating-Sum of Boxes [CDR]; AD Assessment Scale 11/13 [ADAS11/13] Mini-Mental State Examination [MMSE]), and the average standardized uptake value ratio (SUVR) of cerebral cortex regions to those of the cerebellum were obtained from the data. The participants' education level was divided into low and high education subgroups using four cut-offs of 12, 14, 16, and 18 years of educational attainment (G12, G14, G16, and G18, respectively). Demographic and cognitive function variables were compared between the two subgroups in each of the four groups, and their correlations with the SUVRs were evaluated. Results: There was no significant difference between the high and low education subgroups in each of the four groups, except for ADAS11/13 and MMSE in G14 and age in G16. The SUVRs of FDG PET (FDGSUVR) were significantly correlated with CDR, ADAS11/13, and MMSE scores. FDGSUVR showed different trajectories of neurodegeneration between the low and high education groups. Conclusions: FDGSUVR correlated moderately but significantly with neuropsychological test results, without being influenced by education level. Therefore, FDG PET may reflect CR independent of education level, and therefore could be a reliable tool to evaluate cognitive decline in AD.

Obesity mechanism after hypothalamic damage: Cohort analysis of neuroimaging, psychological, cognitive, and clinical phenotyping data.

Objective: The hypothalamus regulates energy homeostasis, and its damage results in severe obesity. We aimed to investigate the multifaceted characteristics of hypothalamic obesity. Methods: We performed multidimensional analyses of brain structure/function and psychological and behavioral phenotypes in 29 patients with hypothalamic damage (HD) (craniopharyngioma) and 31 controls (non-functional pituitary adenoma). Patients underwent structural and functional magnetic resonance imaging and completed self-reports and cognitive tasks. Results: Patients with HD showed significantly higher postoperative weight gain than controls. The HD group also showed significant hypothalamic damage and lower neural activation in the left caudate nucleus in response to food images. The HD group had significantly higher food inattention, lower satiety, and higher restrained eating behavior. Within the HD group, higher restrained eating behavior was significantly associated with lower activation in the bilateral fusiform gyrus. Conclusion: These results suggest that hypothalamic damage contributes to weight gain by altering the brain response, attention, satiety, and eating behaviors. The present study proposes novel neuro-psycho-behavioral mechanisms targeted for patients with hypothalamic obesity.