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Despite the popularity of online food delivery systems in the foodservice industry, there have been few studies into customers’ decision-making process to use online food delivery services during the Coronavirus disease (COVID-19) pandemic. This study applied the technology acceptance model (TAM) to examine the factors affecting customers’ intention to use online food delivery services. Results showed (a) the perceived usefulness affects customer’s online food delivery usage directly and indirectly through customer attitude; (b) enjoyment and trust are also key factors determining behavior intention toward customer attitude using online food delivery services; (c) positive relationship between social influence and customer attitude; and (d) a positive relationship between customer attitude and behavior intention in the online food delivery service context. These findings provide theoretical and managerial implications that contribute to the online food delivery service industry.

Drainage of parenchymal waste through the lymphatic system maintains brain homeostasis. Age-related changes of glymphatic–lymphatic clearance lead to the accumulation beta-amyloid (Aβ) in dementia models. In this study, focused ultrasound treatment in combination with microbubbles (FUS-MB) improved Aβ drainage in early dementia model mice, 5XFAD. FUS-MB enhanced solute Aβ clearance from brain, but not plaques, to cerebrospinal fluid (CSF) space and then deep cervical lymph node (dCLN). dCLN ligation exaggerated memory impairment and progress of plaque formation and also the beneficial effects of FUS-MB upon Aβ removal through CSF-lymphatic routes. In this ligation model, FUS-MB improved memory despite accumulation of Aβ in CSF. In conclusion, FUS-MB enhances glymphatic–lymphatic clearance of Aβ mainly by increasing brain-to-CSF Aβ drainage. We suggest that FUS-MB can delay dementia progress in early period and benefits of FUS-MB depend on the effect of Aβ disposal through CSF-lymphatics.

The linkage between oxidative stress and idiopathic chronic fatigue (ICF) has not been explored in detail. This study thoroughly compared the serum levels of biomarkers for oxidative stress and antioxidants from 103 subjects with ICF (20 men and 83 women) to those of 82 healthy volunteers (27 men and 55 women). Oxidative parameters, which included reactive oxygen species (ROS), malondialdehyde (MDA) and F2-isoprotan, and tumor necrosis factor-alpha (TNF-α) were significantly elevated, while antioxidant parameters, which included total antioxidant activity (TAC), catalase, superoxide dismutase, SOD and GSH activity, were decreased compared to those of healthy subjects (by approximately 1.2- to 2.3-fold, p < 0.05 or 0.01). Our results confirmed that oxidative stress is a key contributor in the pathophysiology of ICF, and firstly explored the features of oxidative stress parameters in ICF subjects compared to a healthy population.

The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The purpose of this study is to generate 3D images from a sparsely sampled 2D images using an inpainting deep neural network that has a U-net-like structure and DenseNet sub-blocks. To train the network, not only fidelity loss but also perceptual loss based on the VGG network were considered. Various methods were used to assess the overall similarity between the inpainted and original 3D data. In addition, morphological analyzes were performed to investigate whether the inpainted data produced local features similar to the original 3D data. The diagnostic ability using the inpainted data was also evaluated by investigating the pattern of morphological changes in disease groups. Brain anatomy details were efficiently recovered by the proposed neural network. In voxel-based analysis to assess gray matter volume and cortical thickness, differences between the inpainted data and the original 3D data were observed only in small clusters. The proposed method will be useful for utilizing advanced neuroimaging techniques with 2D MRI data.

Purpose We conducted a comprehensive systematic review of the literature on volumetric parameters from 18 F-FDG PET and a meta-analysis of the prognostic value of metabolic tumour volume (MTV) and total lesion glycolysis (TLG) in patients with lung cancer. Methods A systematic search of MEDLINE and EMBASE was performed using the keywords “positron emission tomography (PET)”, “lung cancer”, and “volume”. Inclusion criteria were: 18 F-FDG PET used as an initial imaging tool; studies limited to non-small-cell lung cancer (NSCLC); volume measurement of lung cancer; patients who had not undergone surgery, chemotherapy, or radiotherapy before the PET scan; and studies that reported survival data. Event-free survival and overall survival were evaluated as outcomes. The impact of MTV and TLG on survival was measured in terms of the hazard ratio (HR) effect size. Data from each study were analysed using Review Manager 5.2. Results Thirteen eligible studies including 1,581 patients were analysed. Patients with high MTV showed a worse prognosis with an HR of 2.71 (95 % CI 1.82 – 4.02, p  < 0.00001) for adverse events and an HR of 2.31 (95 % CI 1.54 – 3.47, p  < 0.00001) for death. Patients with high TLG also showed a worse prognosis with an HR of 2.35 (95 % CI 1.91 – 2.89, p  < 0.00001) for adverse events and an HR of 2.43 (95 % CI 1.89 – 3.11, p  < 0.00001) for death. The prognostic value of MTV and TLG remained significant in a subgroup analysis according to TNM stage as well as the methods for defining cut-off values and tumour delineation. Conclusion Volumetric parameters from 18 F-FDG PET are significant prognostic factors for outcome in patients with NSCLC. Patients with a high MTV or TLG are at higher risk of adverse events and death. MTV and TLG were significant prognostic factors in patients with TNM stage I/II and stage III/IV NSCLC.

The molecular changes that occur with the progression of Alzheimer’s disease (AD) are well known, but an understanding of the spatiotemporal heterogeneity of changes in the brain is lacking. Here, we investigated the spatially resolved transcriptome in a 5XFAD AD model at different ages to understand regional changes at the molecular level. Spatially resolved transcriptomic data were obtained from 5XFAD AD models and age-matched control mice. Differentially expressed genes were identified using spots clustered by anatomical structures. Gene signatures of activation of microglia and astrocytes were calculated and mapped on the spatially resolved transcriptomic data. We identified early alterations in the white matter (WM) of the AD model before the definite accumulation of amyloid plaques in the gray matter (GM). Changes in the early stage of the disease involved primarily glial cell activation in the WM, whereas the changes in the later stage of pathology were prominent in the GM. We confirmed that disease-associated microglia (DAM) and astrocyte (DAA) signatures also showed initial changes in WM and that activation spreads to GM. Trajectory inference using microglial gene sets revealed the subdivision of DAMs with different spatial patterns. Taken together, these results help to understand the spatiotemporal changes associated with reactive glial cells as a major pathophysiological characteristic of AD. The heterogeneous spatial molecular changes apply to identifying diagnostic and therapeutic targets caused by amyloid accumulation in AD. Unraveling alzheimer’s: molecular changes across space and time Researchers used spatially resolved transcriptomic analysis to study spatiotemporal patterns of disease progression in 5XFAD Alzheimer’s disease (AD) mouse models. They found that initial molecular changes related to glial cell activation occurred in white matter (WM) before gray matter (GM) changes. The study also identified distinct activation patterns of microglia and astrocytes that change with AD progression. These findings provide insights into the pathophysiology of AD and could help identify potential molecular targets for AD treatment. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Fibromyalgia (FM), chronic widespread pain, exhibits spontaneous pain without external stimuli and is associated with altered brain activities during resting state. To understand the topological features of brain network in FM, we employed persistent homology which is a multiple scale network modeling framework not requiring thresholding. Spontaneous magnetoencephalography (MEG) activity was recorded in 19 healthy controls (HCs) and 18 FM patients. Barcode, single linkage dendrogram and single linkage matrix were generated based on the proposed modeling framework. In theta band, the slope of decrease in the number of connected components in barcodes showed steeper in HC, suggesting FM patients had decreased global connectivity. FM patients had reduced connectivity within default mode network, between middle/inferior temporal gyrus and visual cortex. The longer pain duration was correlated with reduced connectivity between inferior temporal gyrus and visual cortex. Our findings demonstrated that the aberrant resting state network could be associated with dysfunction of sensory processing in chronic pain. The spontaneous nature of FM pain may accrue to disruption of resting state network.

Discrepant incidence has been reported regarding the incidence of herb-induced liver injury (HILI). To address the growing worldwide concern of HILI, we evaluated the risk of HILI in a nationwide prospective study. Between April 2013 and January 2016, 1001 inpatients (360 males and 641 females) from 10 tertiary hospitals throughout South Korea were treated with herbal drugs and had their liver enzymes periodically measured. A total of six patients met the criteria for HILI with RUCAM scores ranging from 4 to 7. All these participants were women and developed the hepatocellular type of HILI. One HILI participant met the criteria for Hy ’s law; however, none of six cases presented clinical symptoms related to liver injury. This is the first nationwide prospective study that estimated the extent of the incidence of HILI [total: 0.60%, 95% confidence interval (CI) 0.12–1.08; women: 0.95%, 95% CI 0.19–1.68] and described its features in hospitalized participants.

Diverse pheromones and pheromone-based traps, as well as images acquired from insects captured by pheromone-based traps, have been studied and developed to monitor the presence and abundance of pests and to protect plants. The purpose of this study is to construct models that detect three species of pest moths in pheromone trap images using deep learning object detection methods and compare their speed and accuracy. Moth images in pheromone traps were collected for training and evaluation of deep learning detectors. Collected images were then subjected to a labeling process that defines the ground truths of target objects for their box locations and classes. Because there were a few negative objects in the dataset, non-target insects were labeled as unknown class and images of non-target insects were added to the dataset. Moreover, data augmentation methods were applied to the training process, and parameters of detectors that were pre-trained with the COCO dataset were used as initial parameter values. Seven detectors—Faster R-CNN ResNet 101, Faster R-CNN ResNet 50, Faster R-CNN Inception v.2, R-FCN ResNet 101, Retinanet ResNet 50, Retinanet Mobile v.2, and SSD Inception v.2 were trained and evaluated. Faster R-CNN ResNet 101 detector exhibited the highest accuracy (mAP as 90.25), and seven different detector types showed different accuracy and speed. Furthermore, when unexpected insects were included in the collected images, a four-class detector with an unknown class (non-target insect) showed lower detection error than a three-class detector.

In recent years, investigations into the biochemistry of insect-associated bacteria have increased. When combined with analytical dereplication processes, these studies provide a powerful strategy to identify structurally and/or biologically novel compounds. Non-ribosomally synthesized cyclic peptides have a broad bioactivity spectrum with high medicinal potential. Here, we report the discovery of three new cyclic tripeptides: natalenamides A⁻C (compounds 1⁻3). These compounds were identified from the culture broth of the fungus-growing termite-associated Actinomadura sp. RB99 using a liquid chromatography (LC)/ultraviolet (UV)/mass spectrometry (MS)-based dereplication method. Chemical structures of the new compounds (1⁻3) were established by analysis of comprehensive spectroscopic methods, including one-dimensional (¹H and 13C) and two-dimensional (¹H-¹H-COSY, HSQC, HMBC) nuclear magnetic resonance spectroscopy (NMR), together with high-resolution electrospray ionization mass spectrometry (HR-ESIMS) data. The absolute configurations of the new compounds were elucidated using Marfey's analysis. Through several bioactivity tests for the tripeptides, we found that compound 3 exhibited significant inhibitory effects on 3-isobutyl-1-methylxanthine (IBMX)-induced melanin production. The effect of compound 3 was similar to that of kojic acid, a compound extensively used as a cosmetic material with a skin-whitening effect.