Search Results (159)

Alzheimer’s disease (AD) is a leading cause of dementia, characterized by progressive cognitive and language impairments that significantly impact communication and quality of life. Neuromodulation techniques, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS), have emerged as promising interventions. This study employs bibliometric analysis to evaluate global research trends in neuromodulation treatments for AD-related language impairments. A total of 88 publications from the Web of Science Core Collection (2006–2024) were analyzed using bibliometric methods. Key indicators such as publication trends, citation patterns, collaboration networks, and research themes were examined to map the intellectual landscape of this field. The analysis identified 580 authors across 65 journals, with an average of 34.82 citations per article. Nearly half of the publications were produced after 2021, indicating rapid recent growth. The findings highlight a predominant focus on non-invasive neuromodulation methods, particularly rTMS and tDCS, within neurosciences and neurology. While research activity is increasing, significant challenges persist, including ethical concerns, operational constraints, and the translational gap between research and clinical applications. This study provides insights into the current research landscape and future directions for neuromodulation in AD-related language impairments. The results emphasize the need for novel neuromodulation techniques and interdisciplinary collaboration to enhance therapeutic efficacy and clinical integration. Full article

Background: Invasive and non-invasive neuromodulation in psychiatry represents a burgeoning field that leverages advanced neuromodulation techniques to address substance use disorders (SUDs). Aims: This narrative review synthesizes findings from multiple reviews to evaluate the efficacy of neuromodulation in treating SUDs. Methods: A comprehensive literature search was conducted between December 2024 and April 2025, focusing on systematic reviews and meta-analyses that examined various neuromodulation modalities, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS). The selected reviews were analyzed to identify common themes, outcomes, and gaps in the current understanding of these treatments for SUDs. Results: 11 reviews met the final inclusion criteria; 5 focused on non-invasive neuromodulation (rTMS, tDCS) and 6 on invasive neuromodulation (DBS). Non-invasive neurostimulation was associated with modest improvements in craving and cognitive dysfunction in individuals with SUDs. Similarly, invasive neuromodulation (DBS), through high-frequency stimulation of the bilateral nucleus accumbens, appeared to reduce cravings and improve comorbid psychiatric symptoms in both preclinical and human studies. Importantly, small sample sizes, heterogeneity in targets and stimulation protocols, and short follow-up periods significantly limit the generalizability of current findings from both non-invasive and invasive neuromodulation studies. Conclusions: As novel and more effective therapies for the treatment of SUD are desperately needed, procedural interventional psychiatry holds promise. However, despite encouraging results, existing evidence is still preliminary, and larger, rigorously designed studies are warranted to further establish the safety and efficacy of neuromodulatory interventions for SUD treatment. Full article

The success of adhesive restorations largely depends on the optimal bond strength between the tooth structure and the restorative material. The aim of this study was to evaluate the shear bond strength (SBS) of four different adhesives applied to mandibular molars on deep and superficial dentin. The total of 56 teeth used in the study were randomly divided into 2 subgroups of superficial dentin and deep dentin participants (n = 28). Superficial and deep dentin groups were randomly divided into 4 subgroups (n = 7) for application with different adhesive agents. We formed the following groups: Group 1 (G1)—deep dentin and GC bonding agent (G-Premio BOND); Group 2 (G2)—superficial dentin and GC bonding agent; Group 3 (G3)—deep dentin and Clearfil S3 bond bonding agent (Clearfil TM S3 BOND); Group 4 (G4)—superficial dentin and Clearfil S3 bond bonding agent; Group 5 (G5)—deep dentin and KerrOptibond bonding agent (KerrOptibond^TM Universal); Group 6 (G6)—superficial dentin and Kerr Optibond bonding agent; Group 7 (G7)—deep dentin and 3M-ESPE universal bonding agent (3M ESPE); Group 8 (G8)—superficial dentin and 3M-ESPE universal bonding agent. The silicone block with a diameter of 3 mm and a thickness of 1 mm was placed in the middle of the occlusal surface and the test composite was loaded. All prepared specimens were aged in thermal cycles at 5–55 °C for 5000 cycles. The teeth were subjected to SBS (shear bond strength) tests at a crosshead speed of 1 mm/min in a universal testing machine. In all adhesive systems, deep dentin showed a higher bond strength than superficial dentin and the bond strength value was statistically significant (p = 0.05). The bond strength in all tested adhesive systems was observed to be significantly higher in deep dentin than in superficial dentin. Full article

Background/Objectives: The insula is a deep, functionally heterogeneous region involved in various pathological conditions. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising therapeutic avenue for neuromodulation, yet very few studies have directly investigated its effects on insular activity. Moreover, empirical evidence of target engagement of this region remains scarce. This study aimed to stimulate the insula with rTMS and assess blood oxygen level-dependent (BOLD) signal modulation using concurrent functional magnetic resonance imaging (fMRI). Methods: Ten participants were recruited, six of whom underwent a single session of 5 Hz high-frequency rTMS over the right insular cortex inside the MRI scanner. Stimulation was delivered using a compatible MRI-B91 TMS coil. Stimulation consisted of 10 trains of 10 s each, with a 50 s interval between trains. Frameless stereotactic neuronavigation ensured precise targeting. Paired t-tests were used to compare the mean BOLD signal obtained between stimulation trains with resting-state fMRI acquired before the rTMS stimulation session. A significant cluster threshold of q < 0.01 (False Discovery Rate; FDR) with a minimum cluster size of 10 voxels was applied. Results: Concurrent rTMS-fMRI revealed the significant modulation of BOLD activity within insular subregions. Increased activity was observed in the anterior, middle, and middle-inferior insula, while decreased activity was identified in the ventral anterior and posterior insula. Additionally, two participants reported transient dysgeusia following stimulation, which provides further evidence of insular modulation. Conclusions: These findings provide preliminary evidence that rTMS can modulate distinct subregions of the insular cortex. The combination of region-specific BOLD responses and stimulation-induced dysgeusia supports the feasibility of using rTMS to modulate insular activity. Full article

The internal growth patterns and surface micromorphology of diamonds provide a record of their multi-stage evolution, from initial formation within the mantle to their eventual ascent to the Earth’s surface via deeply derived kimberlite magmas. In this study, gemological microscopic examination, Diamond View^TM, Raman spectroscopy, and electron probe analysis were employed to analyze the surface features, internal patterns, and inclusions of the Tancheng alluvial diamonds in Shandong Province, China. The results show that surface features of octahedra with triangular and sharp edges, thick steps with irregular contours or rounded edges, and thin triangular or serrated layers are developed on diamonds during deep-mantle storage, as well as during the growth process of diamonds, when they are not subjected to intense dissolution. The rounding of octahedral and cubic diamond edges and their transformation into tetrahedral (THH) shapes are attributed to resorption in kimberlitic magma. These characteristics indicate that the Tancheng diamonds were commonly resorbed by carbonate–silicate melts during mantle storage. Abnormal birefringence phenomena, including irregular extinction patterns, petaloid and radial extinction patterns, and banded birefringence, were formed during the diamond growth stage. In contrast, fine grid extinction patterns and composite superimposed extinction patterns are related to later plastic deformation. The studied diamonds mainly contain P-type inclusions of olivine and graphite, with a minority of E-type inclusions, including coesite and omphacite. The pressure of entrapment of olivine inclusions within the Tancheng diamonds ranges from 4.3 to 5.9 GPa, which is consistent with that of coesite inclusions, which yield pressure ranging from 5.2 to 5.5 GPa, and a temperature range of 1083–1264 °C. Overall, the evidence suggests that Tancheng diamonds probably originated from hybrid mantle sources metasomatized by the subduction of ancient oceanic lithosphere. Full article

Background/Objectives: Transcranial magnetic stimulation (TMS) is a non-invasive and well-tolerated treatment, offering an effective alternative for elderly patients with depression, especially when side effects or comorbidities limit medication. Methods: This scoping review analyzes 16 studies published over the past seven years, to evaluate the efficacy, safety, and clinical applications of TMS in older adults with depression. Results: The review examines various TMS modalities, including repetitive TMS (rTMS), deep TMS, and theta burst stimulation (TBS), with most protocols targeting the dorsolateral prefrontal cortex (DLPFC). Adverse effects were rare, mild, and transient, supporting the treatment’s safety profile. Pharmacological co-treatment was common but not essential for clinical improvement, highlighting TMS’s potential as a standalone therapy. A subset of studies used neuroplasticity (SICI, ICF, CSP) or neuroimaging measures (MRI and MRI-based neuronavigation), revealing that age-related cortical inhibition may limit plasticity rather than depression itself. Conclusions: Overall, TMS demonstrates promising effectiveness and tolerability in managing late-life depression. Across studies, remission rates varied from 20% to 63%, with higher efficacy generally observed in bilateral stimulation or high-frequency protocols. Standardization of protocols and further research into individualized targeting and long-term outcomes are warranted to support broader clinical adoption. Full article

As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, which was divided into temperate meadow steppe (TMS), temperate typical steppe (TTS), temperate desert steppe (TDS), temperate steppe desert (TSD) and temperate desert (TD). Multi-source features, including multispectral reflectance, vegetation growth, topography, water bodies, meteorological data, and soil characteristics, were selected based on their distinct physical properties and remote sensing variations. Then, we applied deep neural network (DNN) models to classify them, achieving an accuracy of 79.4% in the 1980s and 81.1% in the 2000s. Additionally, validation in the 2010s through field reconnaissance demonstrated an accuracy of 72.7%, which was acceptable, confirming that DNN is an effective method for classifying temperate grasslands. The results revealed that TTS had the highest proportion in the study area (39%), while TMS and TSD had the lowest (8.2% and 8.1%, respectively). Grassland types have the distribution law of aggregation; according to statistics, 61.1% of the grassland area remained unchanged, and the transition zone between adjacent grassland classes was highly easy to change. The area variation mainly came from TTS, TDS, and TSD, but not TD. The mutual transformation of different grassland types occurred mainly in adjacent areas between them. This study demonstrates the potential of DNN for long-term grassland mapping and provides the most comprehensive classification maps of Inner Mongolia grasslands to date, which are invaluable for grassland research and conservation efforts in the area. Full article

Viruses exhibit significantly greater diversity than cellular organisms, posing a complex challenge to their taxonomic classification. While primary sequences may diverge considerably, protein functional domains can maintain conserved 3D structures throughout evolution. Consequently, structural homology of viral proteins can reveal deep taxonomic relationships, overcoming limitations inherent in sequence-based methods. In this work, we introduce MPACT (Multimetric Pairwise Comparison Tool), an integrated tool that utilizes both sequence- and structure-based metrics. The program incorporates five metrics: sequence identity, similarity, maximum likelihood distance, TM-score, and 3Di-character similarity. MPACT generates heatmaps and distance trees to visualize viral relationships across multiple levels, enabling users to substantiate viral taxa demarcation. Taxa delineation can be achieved by specifying appropriate score cutoffs for each metric, facilitating the definition of viral groups, and storing their corresponding sequence data. By analyzing diverse viral datasets spanning various levels of divergence, we demonstrate MPACT’s capability to reveal viral relationships, even among distantly related taxa. This tool provides a comprehensive approach to assist viral classification, exceeding the current methods by integrating multiple metrics and uncovering deeper evolutionary connections. Full article

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture. Full article

Alzheimer’s disease (AD), the most prevalent form of dementia, is marked by progressive cognitive decline, affecting memory, language, orientation, and behavior. Pathological hallmarks include extracellular amyloid plaques and intracellular tau tangles, which disrupt synaptic function and connectivity. Neural oscillations, the rhythmic synchronization of neuronal activity across frequency bands, are integral to cognitive processes but become dysregulated in AD, contributing to network dysfunction and memory impairments. Targeting these oscillations has emerged as a promising therapeutic strategy. Preclinical studies have demonstrated that specific frequency modulations can restore oscillatory balance, improve synaptic plasticity, and reduce amyloid and tau pathology. In animal models, interventions, such as gamma entrainment using sensory stimulation and transcranial alternating current stimulation (tACS), have shown efficacy in enhancing memory function and modulating neuroinflammatory responses. Clinical trials have reported promising cognitive improvements with repetitive transcranial magnetic stimulation (rTMS) and deep brain stimulation (DBS), particularly when targeting key hubs in memory-related networks, such as the default mode network (DMN) and frontal–parietal network. Moreover, gamma-tACS has been linked to increased cholinergic activity and enhanced network connectivity, which are correlated with improved cognitive outcomes in AD patients. Despite these advancements, challenges remain in optimizing stimulation parameters, individualizing treatment protocols, and understanding long-term effects. Emerging approaches, including transcranial pulse stimulation (TPS) and closed-loop adaptive neuromodulation, hold promise for refining therapeutic strategies. Integrating neuromodulation with pharmacological and lifestyle interventions may maximize cognitive benefits. Continued interdisciplinary efforts are essential to refine these approaches and translate them into clinical practice, advancing the potential for neural oscillation-based therapies in AD. Full article

Background: Within the solar ultraviolet (UV) spectrum, ultraviolet A rays (UVA, 320–400 nm), although less energetic than ultraviolet B rays (UVB, 280–320 nm), constitute at least 95% of solar UV radiation that penetrates deep into the skin The UV rays are associated with both epidermal and dermal damage resulting from the generation of reactive oxygen species (ROS). Among them, the longest UVA wavelengths (UVA1, 340–400 nm) can represent up to 75% of the total UV energy. Therefore, UVA radiation is linked to various acute and chronic conditions, including increased skin pigmentation and photoaging. Despite many advances in the skin photoprotection category, there is still a growing demand for natural daily photoprotection active ingredients that offer broad protection against skin damage caused by UVA exposure. In our quest to discover new, disruptive, next generation of photoprotective ingredients, we were drawn to pomegranate, based on its diverse polyphenolic profile. We investigated the pericarp of the fruit, so far considered as byproducts of the pomegranate supply chain, to design a novel patented extract “POMAOX” with a desired spectrum of phenolic components comprising of αβ-punicalagins, αβ-punicalins and ellagic acid. Methods: Antioxidant properties of POMAOX were measured using in-tubo standard tests capable of revealing a battery of radical oxygen species (ROS): peroxyl radical (ORAC), singlet oxygen (SOAC), superoxide anion (SORAC), peroxynitrite (NORAC), and hydroxyl radical (HORAC). In vitro, confirmation of antioxidant properties was first performed by evaluating protection against UVA-induced lipid peroxidation in human dermal fibroblasts (HDF), via the release of 8 iso-prostanes. The protection offered by POMAOX was further validated in a 3D in vitro reconstructed T-Skin^TM model, by analyzing tissue viability/morphology and measuring the release of Matrix Metallopeptidase 1 (MMP-1) & pro-inflammatory mediators (IL-1α, IL-1ra, IL-6, IL-8, GM-CSF, and TNF-α) after UVA1 exposure. Results: POMAOX displayed strong antioxidant activity against peroxynitrite (NORAC) at 1.0–3.0 ppm, comparable to the reference vitaminC, as well as singlet oxygen (SOAC) at 220 ppm, and superoxide radicals with a SORAC value of 500 ppm. Additionally, POMAOX demonstrated strong photoprotection benefit at 0.001% concentration, offering up to 74% protection against UVA-induced lipid peroxidation on HDF, in a similar range as the positive reference, Vitamin E at 0.002% (50 µM), and with higher efficacy than ellagic acid alone at 5 µM. Moreover, our pomegranate-derived extract delivered photoprotection at 0.001%, mitigating dermal damages induced by UVA1, through inhibition of MMP-1 and significant inhibition of pro-inflammatory mediators release (including IL-1α, IL-1ra, IL-6, IL-8, GM-CSF, and TNFα) on an in vitro reconstructed full-thickness human skin model with a similar level of protection to that of Vitamin C tested at 0.035% (200 µM). Conclusions: Overall, the novel pomegranate-derived extract “POMAOX” significantly reduced the impact of UVA on human skin, due to its broad-spectrum antioxidant profile. These findings suggest that POMAOX could offer enhanced protection against the detrimental effects of UV exposure, addressing the growing consumer demand for strong photoprotection with skincare benefits. Full article

Research on the multi-field coupling effects in rocks has been ongoing for several decades, encompassing studies on single physical fields as well as two-field (TH, TM, HM) and three-field (THM) couplings. However, the environmental conditions of rock masses in deep resource extraction and underground space development are highly complex. In such settings, rocks are put through thermal-hydrological-mechanical-chemical (THMC) coupling effects under peak temperatures, strong osmotic pressures, extreme stress, and chemically reactive environments. The interaction between these fields is not a simple additive process but rather a dynamic interplay where each field influences the others. This paper provides a comprehensive analysis of fragmentation evolution, deformation mechanics, mechanical constitutive models, and the construction of coupling models under multi-field interactions. Based on rock strength theory, the constitutive models for both multi-field coupling and creep behavior in rocks are developed. The research focus on multi-field coupling varies across industries, reflecting the diverse needs of sectors such as mineral resource extraction, oil and gas production, geothermal energy, water conservancy, hydropower engineering, permafrost engineering, subsurface construction, nuclear waste disposal, and deep energy storage. The coupling of intense stress, fluid flow, temperature, and chemical factors not only triggers interactions between these fields but also alters the physical and mechanical properties of the rocks themselves. Investigating the mechanical behavior of rocks under these conditions is essential for averting accidents and assuring the soundness of engineering projects. Eventually, we discuss vital challenges and future directions in multi-field coupling research, providing valuable insights for engineering applications and addressing allied issues. Full article

Background/Objectives: Neurodegenerative diseases such as Parkinson’s disease (PD) are becoming increasingly prevalent, necessitating diverse treatment options to manage symptoms. The effectiveness of these treatments depends on accurate and sensitive diagnostic methods. This exploratory pilot study explores the use of eye tracking and compares it to neuropsychological tests on patients treated with deep transcranial magnetic stimulation (dTMS). Methods: We used the HTC Vive Pro Eye VR headset with Tobii eye tracker to measure eye movements in 10 Parkinson syndrome patients while viewing three 360-degree scenes. Eye movements were recorded pre- and post-dTMS, focusing on Fixation Duration, Longest Fixation Period, Saccade Rate, and Total Fixations. Neuropsychological assessments (MoCA, TUG, BDI) were conducted before and after stimulation. dTMS was performed using the Brainsway device with the H5 helmet, targeting the motor cortex (1 Hz) and the prefrontal cortex (10 Hz) for 7–12 sessions. Results: ROC analysis indicated a moderate ability to differentiate between states using eye movement parameters. Significant correlations were found between changes in the longest fixation period and MoCA scores (r = 0.65, p = 0.025), and between fixation durations and BDI scores (r = −0.55, p = 0.043). Paired t-tests showed no significant differences in eye movement parameters, but BDI scores significantly reduced post-dTMS (t(5) = 2.57, p = 0.049). Conclusions: Eye-tracking parameters, particularly the Longest Fixation Duration and Saccade Rate, could serve as sensitive and feasible biomarkers for cognitive changes in Parkinson’s Syndrome, offering a quick alternative to traditional methods. Traditional neuropsychological tests showed a significant improvement in depressive symptoms after dTMS. Further research with larger sample sizes is necessary to validate these findings and explore the diagnostic utility of eye tracking. Full article

As the focus on green chemistry intensifies, researchers are progressively looking to incorporate biodegradable and environmentally friendly solvents. Given the prevalent use of inorganic solvents in conventional methods for detecting selenium content, this study utilized a mixture design approach to create four deep eutectic solvents (DESs). The elements of the DESs consisted of six different compounds: guanidine hydrochloride, fructose, glycerol, citric acid, proline, and choline chloride. The synthesized deep eutectic solvents (DESs) exhibited a uniform and transparent appearance. The ideal ratios for each DES were established based on their density and viscosity measurements, leading to the formulations of DES1 (34% guanidine hydrochloride, 21% fructose, 45% water), DES2 (23% guanidine hydrochloride, 32% glycerol, 45% water), DES3 (27.5% citric acid, 27.5% proline, 45% water), and DES4 (30% choline chloride, 25% citric acid, 45% water). The characterization of the deep eutectic solvents (DESs) was performed using nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy, which confirmed the molecular formation of each DES. Following this, the DESs were applied as extraction solvents in a process involving ultrasonic-assisted microextraction (UAE) combined with inductively coupled plasma mass spectrometry (ICP-MS) to assess the selenium levels in selenium-rich rice. The results were benchmarked against traditional microwave-assisted acid digestion (TM-AD), revealing selenium recovery rates ranging from 85.5% to 106.7%. These results indicate that UAE is an effective method for extracting selenium from selenium-rich rice, thereby establishing a solid data foundation for the environmentally friendly analysis of selenium content in rice. Full article

With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses challenges. For instance, some characteristics (e.g., high-frequency, noisy and non-stationary) exacerbate the data processing and model training procedures, and the heterogeneity in household consumption patterns causes difficulties for models with the generalization capability. Further, the real-time data processing requirement calls for both the high forecasting accuracy and improved computational efficiency. Thus, we propose a diffusion–attention-enhanced temporal (DATE-TM) model with multi-feature fusion to address the above issues. First, the DATE-TM model could integrate residents’ electricity consumption patterns with climatic factors. Then, it extracts the temporal feature using an encoder and meanwhile models the data uncertainty through a diffusion model. Finally, the decoder, enhanced with the attention mechanism, creates the precise prediction for the household load forecasting. Experimental results reveal that DATE-TM significantly surpasses classical neural networks such as BiLSTM and DeepAR, especially in handling the data uncertainty and long-term dependency. Full article