Search Results (64)

Background/Objectives: The human insula is a key structure implicated in integrating internal states and external food cues, yet its precise role remains unclear, in part due to the temporal limitations of neuroimaging techniques like fMRI. To address this gap, we conducted an exploratory study using an intracranial EEG (iEEG) to investigate how the insula encodes both the subjective and objective properties of food-related stimuli, and how this encoding is modulated by hunger and satiety. Methods: Eight patients with drug-resistant epilepsy undergoing a pre-surgical evaluation between 2017 and 2023 participated in this study. Depth electrodes implanted in the insular cortex recorded event-related potentials (ERPs) in response to visual food cues. The sessions were conducted in two prandial states (hungry and satiated). The subjective ratings (appetite and palatability) and objective nutritional values (e.g., calories, carbohydrates) were collected and analyzed using paired t-tests, MANOVAs, and partial correlations. Results: Hunger increased the ERP amplitudes within the 350–450 ms interval, consistent with the EPIC model and positive alliesthesia, while satiety unexpectedly enhanced the early responses (150–250 ms). Importantly, the neural activity related to nutritional values was largely uncorrelated with the subjective ratings, suggestive of distinct processing streams. The mid- and posterior insula showed greater sensitivity to both subjective and nutritional information than the anterior insula. Conclusions: These findings offer novel electrophysiological insights into how the insula differentiates between implicit and explicit food-related signals, depending on the homeostatic state. This work supports a dual-route model of food cue processing, and may inform interventions targeting insular activity in disordered eating. Full article

Autism Spectrum Disorder (ASD) poses significant challenges in diagnosis due to its diverse symptomatology and the complexity of early detection. Atypical gait and gesture patterns, prominent behavioural markers of ASD, hold immense potential for facilitating early intervention and optimising treatment outcomes. These patterns can be efficiently and non-intrusively captured using modern computational techniques, making them valuable for ASD recognition. Various types of research have been conducted to detect ASD through deep learning, including facial feature analysis, eye gaze analysis, and movement and gesture analysis. In this study, we optimise a dual-stream architecture that combines image classification and skeleton recognition models to analyse video data for body motion analysis. The first stream processes Skepxels—spatial representations derived from skeleton data—using ConvNeXt-Base, a robust image recognition model that efficiently captures aggregated spatial embeddings. The second stream encodes angular features, embedding relative joint angles into the skeleton sequence and extracting spatiotemporal dynamics using Multi-Scale Graph 3D Convolutional Network(MSG3D), a combination of Graph Convolutional Networks (GCNs) and Temporal Convolutional Networks (TCNs). We replace the ViT model from the original architecture with ConvNeXt-Base to evaluate the efficacy of CNN-based models in capturing gesture-related features for ASD detection. Additionally, we experimented with a Stack Transformer in the second stream instead of MSG3D but found it to result in lower performance accuracy, thus highlighting the importance of GCN-based models for motion analysis. The integration of these two streams ensures comprehensive feature extraction, capturing both global and detailed motion patterns. A pairwise Euclidean distance loss is employed during training to enhance the consistency and robustness of feature representations. The results from our experiments demonstrate that the two-stream approach, combining ConvNeXt-Base and MSG3D, offers a promising method for effective autism detection. This approach not only enhances accuracy but also contributes valuable insights into optimising deep learning models for gesture-based recognition. By integrating image classification and skeleton recognition, we can better capture both global and detailed motion patterns, which are crucial for improving early ASD diagnosis and intervention strategies. Full article

Sex-related differences are found not only in the reproductive system but also across various biological systems, such as the cardiovascular system. Compared with premenopausal women, cardiovascular disease (CVD) tends to occur more frequently in adult men and postmenopausal women (Post-MW). Also, during the reproductive years, sex hormones synthesized and released into the blood stream affect vascular function in a sex-dependent fashion. Estrogen (E2) interacts with estrogen receptors (ERs) in endothelial cells, vascular smooth muscle, and the extracellular matrix, causing both genomic and non-genomic effects, including vasodilation, decreased blood pressure, and cardiovascular protection. These observations have suggested beneficial effects of female sex hormones on cardiovascular function. In addition, the clear advantages of E2 supplementation in alleviating vasomotor symptoms during menopause have led to clinical investigations of the effects of menopausal hormone therapy (MHT) in CVD. However, the findings from these clinical trials have been variable and often contradictory. The lack of benefits of MHT in CVD has been related to the MHT preparation (type, dose, and route), vascular ERs (number, variants, distribution, and sensitivity), menopausal stage (MHT timing, initiation, and duration), hormonal environment (progesterone, testosterone (T), gonadotropins, and sex hormone binding globulin), and preexisting cardiovascular health and other disorders. The vascular effects of sex hormones have also prompted further examination of the use of anabolic drugs among athletes and the long-term effects of E2 and T supplements on cardiovascular health in cis- and transgender individuals seeking gender-affirming therapy. Further analysis of the effects of sex hormones and their receptors on vascular function should enhance our understanding of the sex differences and menopause-related changes in vascular signaling and provide better guidance for the management of CVD in a gender-specific fashion and in Post-MW. Full article

Background: Neurogenic bladder (NB), resulting from neurological disorders, significantly affects quality of life and increases healthcare costs. Although percutaneous tibial nerve stimulation (PTNS) is an established therapy for central nervous system-related lower urinary tract dysfunction (LUTD), its efficacy in treating intervertebral discogenic LUTD remains unexplored. This study presents the first documented case of PTNS applied to NB secondary to severe lumbosacral herniated intervertebral disc (HIVD). Methods: A 39-year-old female, hospitalized twice for worsening HIVD, presented with LUTD, including urgency, weak stream, and nocturia. Magnetic resonance imaging confirmed progressive L5-S1 disc extrusion with sacral nerve compression. PTNS, delivered via electronic stimulation through acupuncture needles at SP6 and KI3, was administered daily for 10 days during hospitalization. Symptom scores relating to LUTD, pain, and physical disability were evaluated. Result: The American Urological Association symptom score showed significant improvement (from 20 to 6 and 22 to 15 at 12 weeks after the first and second hospitalizations, respectively). Recovery of voiding function was slower during the second hospitalization, possibly due to increased sacral nerve compression and chronic pathologic condition. Pain and functional disability, assessed using the NRS and ODI, improved by approximately 50% (from 55 to 25 and 80 to 45 during the first and second hospitalizations, respectively) and two-thirds (from 66 to 42 and 93 to 66, respectively). Conclusions: This case suggests that PTNS may be a viable conservative therapy for HIVD-associated LUTD. Further research is required to elucidate its mechanistic effects and clinical efficacy in peripheral nerve-related bladder dysfunction. Full article

Background: Vitamin D insufficiency/deficiency is a highly prevalent condition worldwide. At the same time, chronic inflammation is a versatile pathophysiological feature and a common correlate of various disorders, including vitamin D deficiency. Methods: We investigated the possible association of inflammation with 25-hydroxyvitamin D3 (25OHD3) levels and its down-stream pathway by exploring vitamin D-binding protein (DBP) and vitamin D receptor (VDR) genes for single-nucleotide polymorphisms (SNPs), in healthy non-elderly Bahraini adults. Plasma levels of 25OHD3 were measured by chemiluminescence, and six SNPs, four in the GC gene (rs2282679AC, rs4588CA, rs7041GT, and rs2298849TC) and two in the VDR gene (rs731236TC and rs12721377AG) were genotyped by real-time PCR. The concentrations of five inflammatory biomarkers, IL6, IL8, procalcitonin (PCT), TREM1, and uPAR, were measured by ELISA. Results: The results showed no association between the 25OHD3 level and any of the inflammatory markers’ levels. However, three tested SNPs were significantly associated with the concentrations of tested biomarkers except for IL6. The TT mutant genotype of rs2298849TC was associated with lower levels of IL8 and higher levels of PCT and TREM1, the AA mutant genotype of rs2282679AC was associated with decreased levels of IL8 (p ≤ 0.001) and increased levels of TREM1 (p = 0.005), and the GG wild genotype of rs12721377AG was associated with increased levels of 25OHD3 (p = 0.026). Conclusions: Although chronic inflammation is not associated with the vitamin D system in the blood, it is downstream, as revealed by DBP and VDR genotyping. Alternatively, DBP and VDR pursue other functions beyond the vitamin D pathway. Full article

This review describes our current understanding of the role of the mitochondria in the repurposing of the anti-diabetes drugs metformin, gliclazide, GLP-1 receptor agonists, and SGLT2 inhibitors for additional clinical benefits regarding unhealthy aging, long COVID, mental neurogenerative disorders, and obesity. Metformin, the most prominent of these diabetes drugs, has been called the “Drug of Miracles and Wonders,” as clinical trials have found it to be beneficial for human patients suffering from these maladies. To promote viral replication in all infected human cells, SARS-CoV-2 stimulates the infected liver cells to produce glucose and to export it into the blood stream, which can cause diabetes in long COVID patients, and metformin, which reduces the levels of glucose in the blood, was shown to cut the incidence rate of long COVID in half for all patients recovering from SARS-CoV-2. Metformin leads to the phosphorylation of the AMP-activated protein kinase AMPK, which accelerates the import of glucose into cells via the glucose transporter GLUT4 and switches the cells to the starvation mode, counteracting the virus. Diabetes drugs also stimulate the unfolded protein response and thus mitophagy, which is beneficial for healthy aging and mental health. Diabetes drugs were also found to mimic exercise and help to reduce body weight. Full article

Canine-assisted interactions (CAIs) have been explored to offer therapeutic benefits to human participants in various contexts, from addressing cancer-related fatigue to treating post-traumatic stress disorder. Despite their widespread adoption, there are still unresolved questions regarding the outcomes for both humans and animals involved in these interactions. Previous attempts to address these questions have suffered from core methodological weaknesses, especially due to absence of tools for an efficient objective evaluation and lack of focus on the canine perspective. In this article, we present a first-of-its-kind system and study to collect simultaneous and continuous physiological data from both of the CAI interactants. Motivated by our extensive field reviews and stakeholder feedback, this comprehensive wearable system is composed of custom-designed and commercially available sensor devices. We performed a repeated-measures pilot study, to combine data collected via this system with a novel dyadic behavioral coding method and short- and long-term surveys. We evaluated these multimodal data streams independently, and we further correlated the psychological, physiological, and behavioral metrics to better elucidate the outcomes and dynamics of CAIs. Confirming previous field results, human electrodermal activity is the measure most strongly distinguished between the dyads’ non-interaction and interaction periods. Valence, arousal, and the positive affect of the human participant significantly increased during interaction with the canine participant. Also, we observed in our pilot study that (a) the canine heart rate was more dynamic than the human’s during interactions, (b) the surveys proved to be the best indicator of the subjects’ affective state, and (c) the behavior coding approaches best tracked the bond quality between the interacting dyads. Notably, we found that most of the interaction sessions were characterized by extended neutral periods with some positive and negative peaks, where the bonded pairs might display decreased behavioral synchrony. We also present three new representations of the internal and overall dynamics of CAIs for adoption by the broader field. Lastly, this paper discusses ongoing options for further dyadic analysis, interspecies emotion prediction, integration of contextually relevant environmental data, and standardization of human–animal interaction equipment and analytical approaches. Altogether, this work takes a significant step forward on a promising path to our better understanding of how CAIs improve well-being and how interspecies psychophysiological states can be appropriately measured. Full article

The etiology of autism spectrum disorder (ASD) has not yet been completely elucidated. Through time, multiple attempts have been made to uncover the causes of ASD. Different theories have been proposed, such as being caused by alterations in the gut–brain axis with an emphasis on gut dysbiosis, post-vaccine complications, and genetic or even autoimmune causes. In this review, we present data covering the main streams that focus on ASD etiology. Data collection occurred in many countries covering ethnically diverse subjects. Moreover, we aimed to show how the progress in genetic techniques influences the explanation of medical White Papers in the ASD area. There is no single evidence-based pathway that results in symptoms of ASD. Patient management has constantly only been symptomatic, and there is no ASD screening apart from symptom-based diagnosis and parent-mediated interventions. Multigene sequencing or epigenetic alterations hold promise in solving the disjointed molecular puzzle. Further research is needed, especially in the field of biogenetics and metabolomic aspects, because young children constitute the patient group most affected by ASD. In summary, to date, molecular research has confirmed multigene dysfunction as the causative factor of ASD, the multigene model with metabolomic influence would explain the heterogeneity in ASD, and it is proposed that ion channel dysfunction could play a core role in ASD pathogenesis. Full article

This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders’ treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth. Full article

Human action recognition has facilitated the development of artificial intelligence devices focusing on human activities and services. This technology has progressed by introducing 3D point clouds derived from depth cameras or radars. However, human behavior is intricate, and the involved point clouds are vast, disordered, and complicated, posing challenges to 3D action recognition. To solve these problems, we propose a Symmetric Fine-coarse Neural Network (SFCNet) that simultaneously analyzes human actions’ appearance and details. Firstly, the point cloud sequences are transformed and voxelized into structured 3D voxel sets. These sets are then augmented with an interval-frequency descriptor to generate 6D features capturing spatiotemporal dynamic information. By evaluating voxel space occupancy using thresholding, we can effectively identify the essential parts. After that, all the voxels with the 6D feature are directed to the global coarse stream, while the voxels within the key parts are routed to the local fine stream. These two streams extract global appearance features and critical body parts by utilizing symmetric PointNet++. Subsequently, attention feature fusion is employed to capture more discriminative motion patterns adaptively. Experiments conducted on public benchmark datasets NTU RGB+D 60 and NTU RGB+D 120 validate SFCNet’s effectiveness and superiority for 3D action recognition. Full article

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user’s health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability. Full article

It is estimated that three million people annually experience homelessness, with about a third of the homeless population being served by Federally Qualified Health Centers (FQHCs). Thus, FQHCs, dependent on government funding for financial viability, are vital to the infrastructure addressing the complex issues facing people experiencing homelessness. This study examines the relationship between various government funding streams and the number of homeless patients served by FQHCs. Data for this study come from three publicly available databases: the Uniform Data System (UDS), the IRS Core files, and the Area Resource File. Fixed-effects models employed examine changes across six years from 2014 to 2019. The results suggest that, on average, an additional homeless patient served increases the expenses of FQHCs more than other patients and that federal funding, specifically Health Care for the Homeless (HCH) funding, is a vital revenue source for FQHCs. We found that the number of homeless patients served is negatively associated with contemporaneous state and local funding but positively associated with substance use and anxiety disorders. Our findings have important implications for the effective management of FQHCs in the long term and for broader public policy supporting these vital elements of the social safety net. Full article

Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection. Full article

We explore the possibility of attaining valley-dependent tunnelling and confinement using proximity-induced spin-orbit couplings (SOCs) in graphene-based heterostructures. We consider gate-tunable asymmetric quantum dots (AQDs) on graphene heterostructures and exhibiting a $C_{3v}$ and/or $C_{6v}$ symmetry. By employing a tight-binding model, we explicitly reveal a pure valley confinement and valley signal in AQDs by streaming the valley local density, leading to valley-charge separation in real space. The confinement of the valley quasi-bound states is sensitive to the locally induced SOCs and to the spatial distribution of the induced AQDs; it is also robust against on-site disorder. The adopted process of attaining a pure valley-Hall conductivity and confinement with zero charge currents is expected to provide more options towards valley-dependent electron optics. Full article

Background/Objectives: Tourette Syndrome (TS), Obsessive Compulsive Disorder (OCD), and Body-Focused Repetitive Behaviors (BFRB) are three disorders that share many similarities in terms of phenomenology, neuroanatomy, and functionality. However, despite the literature pointing toward a plausible spectrum of these disorders, only a few studies have compared them. Studying the neurocognitive processes using Event-Related Potentials (ERPs) offers the advantage of assessing brain activity with excellent temporal resolution. The ERP components can then reflect specific processes known to be potentially affected by these disorders. Our first goal is to characterize ‘when’ in the processing stream group differences are the most prominent. The second goal is to identify ‘where’ in the brain the group discrepancies could be. Methods: Participants with TS (n = 24), OCD (n = 18), and BFRB (n = 16) were matched to a control group (n = 59) and were recorded with 58 EEG electrodes during a visual counting oddball task. Three ERP components were extracted (i.e., P200, N200, and P300), and generating sources were modelized with Standardized Low-Resolution Electromagnetic Tomography. Results: We showed no group differences for the P200 and N200 when controlling for anxiety and depressive symptoms, suggesting that the early cognitive processes reflected by these components are relatively intact in these populations. Our results also showed a decrease in the later anterior P300 oddball effect for the TS and OCD groups, whereas an intact oddball effect was observed for the BFRB group. Source localization analyses with sLORETA revealed activations in the lingual and middle occipital gyrus for the OCD group, distinguishing it from the other two clinical groups and the controls. Conclusions: It seems that both TS and OCD groups share deficits in anterior P300 activation but reflect distinct brain-generating source activations. Full article