Search Results (5,813)

Obesity is a systemic metabolic disorder characterized by chronic low-grade inflammation and insulin resistance, with growing evidence indicating that the brain represents a primary and particularly vulnerable target organ. Beyond peripheral metabolic consequences, obesity induces region-specific structural, functional, and biochemical alterations within the central nervous system, contributing to cognitive impairment, dysregulated energy homeostasis, and increased susceptibility to neurodegenerative diseases. This narrative review examines key neurometabolic and neuroinflammatory mechanisms underlying obesity-related brain vulnerability, including downstream neuroinflammation, impaired insulin signaling, mitochondrial dysfunction, oxidative stress, blood–brain barrier disruption, and impaired brain clearance mechanisms. These processes preferentially affect frontal and limbic networks involved in executive control, reward processing, salience detection, and appetite regulation. Advanced neuroimaging has substantially refined our understanding of these mechanisms. Magnetic resonance spectroscopy provides unique in vivo insight into early neurometabolic alterations that may precede irreversible structural damage and is complemented by diffusion imaging, volumetric MRI, functional MRI, cerebral perfusion imaging, and positron emission tomography. Together, these complementary modalities reveal microstructural, network-level, structural, hemodynamic, and molecular alterations associated with obesity-related brain vulnerability and support the concept that such brain dysfunction is dynamic and potentially modifiable. Integrating neurometabolic and multimodal neuroimaging biomarkers with metabolic and clinical profiling may improve early risk stratification and guide preventive and therapeutic strategies aimed at preserving long-term brain health in obesity. Full article

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in childhood, characterized by persistent inattention, hyperactivity, and impulsivity. This narrative review aims to synthesize and critically evaluate recent large-scale magnetic resonance imaging (MRI) studies to clarify the neuroanatomical and functional brain alterations associated with ADHD in children. By addressing current gaps in understanding, this work seeks to identify reliable neurobiological markers that could improve diagnostic accuracy and guide personalized interventions. The literature reveals that large-scale structural MRI studies consistently report abnormal development in total cortical volume and surface area, prefrontal cortex volume, and basal ganglia volume in children with ADHD. Moreover, gray matter alterations show significant age-dependent effects, with the degree of impairment potentially serving as neurobiological markers. Diffusion magnetic resonance imaging studies reveal disrupted white matter microstructures in regions such as the left uncinate fasciculus, superior and inferior longitudinal fasciculi, corpus callosum, cingulum, and internal capsule. Importantly, these white matter abnormalities often persist into adulthood, highlighting their clinical relevance. Functional MRI findings indicate reduced global connectivity within core hubs of the default mode network in children with ADHD. Furthermore, deficits in inhibitory control identified via fMRI may represent one of the neurofunctional signatures that differentiates ADHD from typically developing controls. By consolidating evidence from large-scale multimodal MRI studies, this review provides a comprehensive understanding of the neurodevelopmental alterations in ADHD and underscores their potential utility for improving diagnosis and treatment. Full article

This work presents the design, fabrication, and experimental characterization of microelectromechanical system (MEMS) ultrasonic transducers engineered for multi-frequency operation in photoacoustic imaging (PAI). The proposed devices integrate multiple resonant geometries, including circular diaphragms, floated crosses, anchored cross membranes, and cantilever arrays, within compact footprints to overcome the inherently narrow frequency response of conventional MEMS transducers. All devices were fabricated using the PiezoMUMPs commercial microfabrication process, with finite element simulations guiding modal optimization and laser Doppler vibrometry used for experimental validation in air. The circular diaphragm exhibited a narrowband response with a dominant resonance at 1.69 MHz and a quality factor (Q) of 268, confirming the bandwidth limitations of traditional geometries. In contrast, complex designs such as the floated cross and cantilever arrays achieved significantly broader spectral responses, with resonances spanning from 275 kHz to beyond 7.5 MHz. The cantilever array, with systematically varied arm lengths, achieved the highest modal density through asynchronous activation across the spectrum. Results demonstrate that structurally diverse MEMS devices can overcome the bandwidth constraints of traditional piezoelectric transducers. The integration of heterogeneous MEMS geometries offers a viable approach for broadband sensitivity in PAI, enabling improved spatial resolution and depth selectivity without compromising miniaturization or manufacturability. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

Obstructive sleep apnea (OSA) is characterized by recurrent partial or complete upper airway collapse during sleep. Accurate assessment of airway anatomy is crucial for risk stratification, diagnosis, and treatment planning. While polysomnography (PSG) is considered the gold standard for OSA diagnosis, it provides limited anatomical insights. Cone-beam computed tomography (CBCT) has emerged as a valuable tool with lower radiation dose for three-dimensional (3D) assessment of the upper airway space and craniofacial structures. CBCT enables precise measurement of critical airway parameters including total airway volume and length, minimum cross-sectional area, linear dimensions of anteroposterior and lateral diameters, as well as soft tissue structures such as tongue, tonsils, and adenoids. This review aims to explore and comprehensively review the role of CBCT, primarily in upper airway assessment for OSA, with an emphasis on airway measurement parameters, anatomical reference landmarks, and the variabilities, in addition to its clinical applications in treatment planning and simulation and post-treatment efficacy evaluation. This review also highlights the technical considerations such image acquisition protocols, machine specifications and software algorithm, and patient positioning, which may affect measurement reliability and diagnostic accuracy. CBCT serves as a powerful adjunct in OSA diagnosis and management, enabling comprehensive assessment of the airway space and hard and soft tissue structures. It complements PSG by guiding personalized interventions such as maxillomandibular advancement or CPAP optimization. Standardized imaging protocols and consideration of patient positioning can further improve its clinical utility. Full article

Plasmonics and metasurfaces (MSs) have emerged as two of the most influential platforms for manipulating light at the nanoscale, each offering complementary strengths that challenge the limits of conventional optical design. Plasmonics enables extreme subwavelength field confinement, ultrafast light–matter interaction, and strong optical nonlinearities, while MSs provide versatile and compact control over phase, amplitude, polarization, and dispersion through planar, nanostructured interfaces. Recent advances in materials, nanofabrication, and device engineering are increasingly enabling these technologies to be combined within unified planar and hybrid optical platforms. This review surveys the physical principles, material strategies, and device architectures that underpin plasmonic, MS, and hybrid plasmonic–dielectric systems, with an emphasis on interface-mediated optical functionality rather than long-range guided-wave propagation. Key developments in modulators, detectors, nanolasers, metalenses, beam steering devices, and programmable optical surfaces are discussed, highlighting how hybrid designs can leverage strong field localization alongside low-loss wavefront control. System-level challenges including optical loss, thermal management, dispersion engineering, and large-area fabrication are critically examined. Looking forward, plasmonic and MS technologies are poised to define a new generation of flat, multifunctional, and programmable optical systems. Applications spanning imaging, sensing, communications, augmented and virtual reality, and optical information processing illustrate the transformative potential of these platforms. By consolidating recent progress and outlining future directions, this review provides a coherent perspective on how plasmonics and MSs are reshaping the design space of next-generation planar optical hardware. Full article

We report the case of an 80 yo female patient with cardiovascular risk factors and previous diagnosis of Tako-Tsubo syndrome, who was referred to our institution one year after a previous diagnosis, due to symptoms suggestive of acute coronary syndrome (SCA) after severe emotional stress. After ruling out suspected CAD by cardiac computed tomography (CCT) and subsequent invasive coronary angiography (ICA) confirming no significant stenosis but presence of vulnerable plaque, the patient underwent further investigation by cardiac magnetic resonance (CMR) that confirmed a clinical picture compatible with recurrence of Tako-Tsubo syndrome. Our case underlines the importance of multimodality imaging to guide diagnosis and treatment in this specific clinical scenario. Full article

The integration of artificial intelligence (AI) into industrial processes is a promising method for enhancing operational efficiency and quality control. In particular, contact lens manufacturing requires specialized artificial intelligence technologies owing to stringent safety requirements. This study introduces a novel approach that employs polar coordinate transformation and a customized deep learning model, the Polar Sequence-guided Attention Network (PSgANet), to improve the accuracy of defect detection in the rim-connected zone (RCZ) of contact lenses. PSgANet is specifically designed to process polar coordinate-transformed image data by integrating sequence learning and attention mechanisms to maximise the capability for detecting and classifying defective patterns. This model converts irregularities along the edges of contact lenses into linear arrays via polar coordinate transformation, enabling a clearer and more consistent identification of defective regions. To achieve this, we applied sequence learning architectures such as GRU, LSTM, and Transformer within PSgANet and compared their performances with those of conventional models, including GoogleNetv4, EfficientNet, and Vision Transformer. The experimental results demonstrated that the PSgANet models outperformed the existing CNN-based models. In particular, the LSTM-based PSgANet achieved the highest accuracy and balanced precision and recall metrics, showing up to a 7.75% improvement in accuracy compared with the traditional GoogleNetv4 model. These results suggest that the proposed method is an effective tool for detecting and classifying defects within the RCZ during contact lens manufacturing processes. Full article

The inherent semantic symmetry and cross-modal alignment between textual prompts and generated images have fueled the success of text-to-image (T2I) generation. However, this strong correlation also introduces security vulnerabilities, specifically prompt stealing attacks, where valuable prompts are reverse-engineered from images. In this paper, we address the challenge of information asymmetry in black-box attack scenarios and propose PromptTrace, a fine-grained prompt stealing framework via Contrastive Language-Image Pre-training (CLIP)-guidedbeam search. Unlike existing methods that rely on single-stage generation, PromptTrace structurally decomposes prompt reconstruction into subject generation, modifier extraction, and iterative search optimization to effectively restore the visual–textual correspondence. By leveraging a CLIP-guided beam search strategy, our method progressively optimizes candidate prompts based on image–text similarity feedback, ensuring the stolen prompt achieves high fidelity in both semantic intent and stylistic representation. Extensive evaluations across multiple datasets and T2I models demonstrate that PromptTrace outperforms existing methods, highlighting the feasibility of exploiting cross-modal symmetry for attacks and underscoring the urgent need for defense mechanisms in the T2I ecosystem. Full article

Background/Objectives: Fibroepithelial tumors (FETs) of the breast, including fibroadenomas (FAs) and phyllodes tumors (PTs), are among the most common breast masses encountered by breast radiologists and pathologists. Differentiating FAs from benign or borderline PTs can be challenging, especially on core biopsy specimens where sampling limitations obscure key histologic features. Although imaging techniques provide useful diagnostic context, their predictive accuracy for pathologic classification remains limited. Methods: We conducted a single-institution pilot study to assess whether tumor growth rate (TGR) derived from serial imaging could serve as a noninvasive correlate of histopathologic outcomes in FETs. Thirty-two patients with serial imaging and subsequent surgical excision (January 2020–May 2025) were analyzed. TGR, expressed as percentage volume increase per month, was calculated from diameter-based volumetrics. Results: The cohort included conventional FA (n = 10), cellular FA (n = 4), benign PT (n = 8), borderline PT (n = 6), and malignant PT (n = 4). Malignant PTs demonstrated significantly higher median TGRs (180.4%/month) and shorter imaging intervals (1.1 months) compared with other groups (p = 0.0357 and p = 0.005, respectively). These large effect-size differences suggest clinically meaningful growth dynamics. Conclusions: As a pilot, this study establishes foundational variance and effect-size estimates for powering a multicenter trial. If validated, TGR may provide an objective, noninvasive metric to enhance preoperative risk stratification and guide management of breast FETs. Full article

To maintain reliability, safety, and sustainability in power transmission, insulator defect detection has become a critical task in power line inspection. Due to the complex backgrounds and small defect sizes encountered in insulator defect images, issues such as false detections and missed detections often occur. The existing You Only Look Once (YOLO) object detection algorithm is currently the mainstream method for image-based insulator defect detection in power lines. However, existing models suffer from low detection accuracy. To address this issue, this paper presents an improved YOLOv5-based MC-YOLO insulator detection algorithm. To effectively extract multi-scale information and enhance the model’s ability to represent feature information, a multi-scale attention convolutional fusion (MACF) module incorporating an attention mechanism is proposed. This module utilises parallel convolutions with different kernel sizes to effectively extract features at various scales and highlights the feature representation of key targets through the attention mechanism, thereby improving the detection accuracy. Additionally, a cross-context feature fusion module (CCFM) is designed, where shallow features gain partial deep semantic supplementation and deep features absorb shallow spatial information, achieving bidirectional information flow. Furthermore, the Spatial-Channel Dual Attention Module (SCDAM) is introduced into CCFM. By incorporating a dynamic attention-guided bidirectional cross-fusion mechanism, it effectively resolves the feature deviation between shallow details and deep semantics during multi-scale feature fusion. The experimental results show that the MC-YOLO algorithm achieves an mAP@0.5 of 67.4% on the dataset used in this study, which is a 4.1% improvement over the original YOLOv5. Although the FPS is slightly reduced compared to the original model, it remains practical and capable of rapidly and accurately detecting insulator defects. Full article

Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI) serves as a visual biofeedback tool, providing real-time imaging to enhance PFM training, motor learning, and treatment adherence. Aim: This narrative review evaluates the role and efficacy of RUSI in pelvic floor rehabilitation. Method: A comprehensive search of PubMed, Cochrane, and MEDLINE was conducted using keywords related to pelvic floor rehabilitation, ultrasound, and biofeedback, limited to English-language publications up to July 2025. Systematic reviews, meta-analyses, and clinical trials were prioritized. Results: Transperineal and transabdominal ultrasound improve PFM function across diverse populations. In post-prostatectomy men, transperineal ultrasound-guided training enhanced PFM contraction and reduced urinary leakage. In postpartum women with pelvic girdle pain, transabdominal ultrasound-guided biofeedback combined with exercises decreased pain and improved function. Ultrasound-guided pelvic floor muscle contraction demonstrated superior performance compared to verbal instruction. Notably, 57% of participants who were unable to contract the pelvic floor muscles with verbal cues achieved a correct contraction with ultrasound biofeedback, and this approach also resulted in more sustained improvements in PFM strength. Compared to other biofeedback modalities, RUSI demonstrated outcomes that are comparable to or superior to those of alternative methods. However, evidence is limited by a lack of standardized protocols and randomized controlled trials comparing RUSI with other modalities. Conclusions: RUSI is an effective visual biofeedback tool that enhances outcomes of PFM training in pelvic floor rehabilitation. It supports clinical decision-making and patient engagement, particularly in cases where traditional assessments are challenging. Further research, including the development of standardized protocols and comparative trials, is necessary to optimize the clinical integration of this method and confirm its superiority over other biofeedback methods. Full article

Prostate cancer is one of the most common malignancies in men, and advanced or metastatic disease remains associated with substantial morbidity and mortality. Therapeutic progress in recent years has been driven by the introduction of targeted treatment strategies, notably poly (ADP-ribose) polymerase (PARP) inhibitors, prostate-specific membrane antigen (PSMA)–directed radioligand therapy (RLT), and androgen receptor pathway inhibitors (ARPIs). This review summarizes evidence from phase II and III clinical trials, meta-analyses, and real-world studies evaluating the efficacy, safety, and clinical integration of olaparib, lutetium (¹⁷⁷Lu) vipivotide tetraxetan, and abiraterone in advanced prostate cancer. Emphasis is placed on the practical clinical application of these agents, including patient selection, treatment sequencing, and combination strategies. PARP inhibition with olaparib has demonstrated clear benefits in metastatic castration-resistant prostate cancer (mCRPC) with homologous recombination repair (HRR) mutations, particularly BRCA1/2 alterations. PSMA-directed RLT offers a survival advantage in PSMA-positive mCRPC following AR pathway inhibition, with distinct toxicity considerations that influence patient selection. Abiraterone remains a cornerstone therapy across disease stages and plays an important role both as monotherapy and as a combination partner. Emerging data suggest a potential synergy between PARP inhibitors and AR-targeted agents, while also highlighting the limitations of biomarker-unselected approaches. We conclude that the optimal use of PARP inhibitors, PSMA-targeted RLT, and ARPIs requires a personalized strategy guided by molecular profiling, functional imaging, prior treatment exposure, and safety considerations. This clinically focused overview aims to support evidence-based decision-making in an increasingly complex treatment landscape. Full article

This study presents a texture-aware image synthesis framework designed to generate material-consistent façades using adversarial learning. The proposed architecture incorporates a mask-guided channel-wise attention mechanism that adaptively merges segmentation information with texture statistics to reconcile structural guiding with textural fidelity. A thorough comparative analysis was performed utilizing three internal variants—Vanilla GAN, Wasserstein GAN (WGAN), and WGAN-GP—against leading baselines, including TextureGAN and Pix2Pix. The assessment utilized a comprehensive multi-metric framework that included SSIM, FID, KID, LPIPS, and DISTS, in conjunction with a VGG-19 based perceptual loss. Experimental results indicate a notable divergence between pixel-wise accuracy and perceptual realism; although established baselines attained elevated PSNR values, the suggested Vanilla GAN and WGAN models exhibited enhanced perceptual fidelity, achieving the lowest LPIPS and DISTS scores. The WGAN-GP model, although theoretically stable, produced smoother but less complex textures due to the regularization enforced by the gradient penalty term. Ablation investigations further validated that the attention mechanism consistently enhanced structural alignment and texture sharpness across all topologies. Thus, the study suggests that Vanilla GAN and WGAN architectures, enhanced by attention-based fusion, offer an optimal balance between realism and structural fidelity for high-frequency texture creation applications. Full article