Search Results (14,635)

Transarterial chemoembolization (TACE) is the standard treatment for patients with intermediate-stage hepatocellular carcinoma (HCC), yet nearly half of treated patients fail to achieve durable benefit, and reliable biomarkers enabling early therapeutic stratification are still lacking. Treatment response is typically assessed by imaging one month after TACE and at three-month intervals, potentially delaying timely access to alternative therapies in non-responding patients. Circulating microRNAs (miRNAs) represent promising biomarkers due to their stability in body fluids and ease of detection. Here, we evaluated circulating miR-22 as an early predictor of TACE non-responder status and as a mechanistically relevant therapeutic target. Circulating miR-22 levels were measured by microarray and quantitative RT–PCR in three independent cohorts of early-to-intermediate-stage HCC patients undergoing TACE. Circulating miR-22 increased significantly in non-responders as early as 48 h after treatment, and fold changes consistently predicted treatment failure across two independent validation cohorts. Mechanistically, we identified the G2/M checkpoint kinase WEE1 as a direct functional target of miR-22. Modulation of the miR-22/WEE1 axis affected cell-cycle progression, proliferation, apoptosis, and DNA damage response in HCC cell lines and xenograft models. Under hypoxia-mimicking conditions combined with doxorubicin exposure, pharmacological inhibition of WEE1 induced mitotic catastrophe in highly proliferative miR-22-silenced cells. Collectively, these findings identify early post-TACE elevation of circulating miR-22 as a biomarker of non-response and highlight the miR-22/WEE1 axis as a potential target for precision treatment strategies in HCC. Full article

High-resolution remote sensing image (HRRSI) scene classification faces challenges such as significant target scale variations, complex background interference, and the difficult spatial parsing of dense objects (such as tightly packed buildings in dense residential areas or scattered aircraft on aprons), while existing models struggle to balance computational efficiency and classification accuracy. To address these issues, this paper proposes a lightweight Multi-Scale Selective Dilated Attention Residual Network (MS-DARNet). The model utilizes a Multi-branch Dilated Feature Extraction (MDFE) module, employing parallel convolutional branches with varying dilation rates to dynamically expand the receptive field and collaboratively extract multi-scale features without increasing parameter counts. Furthermore, a Context-Position Aware Attention (CPAA) module is introduced, combining a large kernel decomposition strategy to suppress irrelevant background noise with direction-aware feature aggregation to retain precise spatial coordinates for dense objects. Extensive experiments on the AID, NWPU-RESISC45, and RSD-WHU46 datasets show that MS-DARNet achieves superior classification accuracies of 97.78%, 94.53%, and 94.55%, respectively. Concurrently, it maintains a significantly low complexity of just 2.50 M parameters and 0.5940 GMACs. These findings demonstrate that MS-DARNet effectively achieves an optimal balance between lightweight architecture and exceptional classification performance for complex remote sensing scenes. Full article

Adversarial examples reveal critical vulnerabilities in deep neural networks, posing significant risks in real-world deployment. In black-box settings, transferable targeted attacks rely on surrogate models but often suffer from low success rates. We argue that this limitation arises not only from surrogate-boundary overfitting but also from insufficient alignment with the target semantic space, which restricts the ability of adversarial examples to encode target-specific characteristics. To address this issue, we propose Cosine-Based Logit Alignment (CBLA), a unified framework for transferable targeted attacks. CBLA replaces the conventional cross-entropy loss with a cosine similarity objective to enhance directional alignment in logit space and alleviate gradient saturation. In addition, a semantic-invariant transformation strategy is introduced to improve structural consistency and cross-model generalization. Experiments on the ImageNet validation set demonstrate that CBLA consistently improves targeted attack success rates, achieving an average gain of 4.55% over strong baselines across multiple architectures. Full article

Tumor oxygenation is a key determinant of cancer biology and treatment response, correlating with angiogenesis, recurrence, and malignant progression. Hypoxia is a defining feature of glioblastoma (GBM) and adult diffuse gliomas, generating low-oxygen niches that promote invasion, stem-like states, immune suppression, and resistance to radiotherapy and temozolomide, contributing to poor outcomes. Measuring tissue partial pressure of oxygen (pO₂) and mapping its spatial heterogeneity can, therefore, inform mechanistic understanding and therapeutic development, including hypoxia-activated prodrugs, hypoxia-responsive gene therapy, and optimized radiotherapy planning. Although direct pO₂ assessment is challenging, invasive probes and multimodal imaging can characterize regional hypoxia pre-operatively, support patient stratification, monitor treatment effects, and improve outcome prediction. This review summarizes oxygen dynamics in GBM; analyzes causes of hypoxia (rapid growth outpacing supply, diffusion-limited hypoxia, and abnormal/chaotic vasculature); compares methods to quantify oxygenation from direct measurements to noninvasive imaging surrogates; and evaluates preclinical and clinical strategies that target hypoxia to enhance standard therapy, including barriers to translation. We further integrate oxygenation with cell signaling and redox biology: oxygen gradients are transduced via hypoxia-inducible factor programs and redox-sensitive pathways (NRF2/KEAP1, NOX-derived ROS, nitric oxide/S-nitrosylation, and sulfur metabolic routes), shaping mesenchymal-like transitions and cell-death programs such as ferroptosis. Framing oxygenation as both a microenvironmental and redox-signaling variable positions oxygen imaging as an entry point to biomarker-guided therapies that exploit oxidative vulnerabilities. Full article

Smart feedlots are increasingly adopting Precision Livestock Farming technologies to enable continuous, individual-animal monitoring and more proactive management in intensive beef production systems. This narrative review synthesises evidence from approximately 350 academic publications, of which 117 are formally cited, complemented by industry deployments and the authors’ experience in smart feedlot system development. We cover enabling digital infrastructure (power, sensing networks, wireless connectivity, and gateways), animal identification and sensing (RFID, automated weighing, wearables, and pen-side sensors), machine vision (RGB, thermal, and multispectral imaging from fixed and mobile platforms), and AI-based analytics and decision support for health, welfare, performance, and environmental management. Across the literature, key components have progressed beyond proof-of-concept toward operation under commercial constraints. Reported outcomes include reduced reliance on routine pen-rider observation and yard handling, earlier triage of emerging morbidity risk and behavioural change, and more standardised welfare auditing. Vision-based methods are repeatedly validated against trained human scorers in both on-farm and abattoir contexts, while automated weighing and image-based liveweight estimation support higher-frequency growth monitoring with low single-digit percentage error in representative studies. Precision feeding and targeted supplementation are associated with improved feed utilisation and reduced resource wastage, although effectiveness and adoption vary across animal classes and production stages. We identify priorities for robust, scalable deployment: resilient communications in harsh environments, appropriate edge–cloud partitioning under intermittent connectivity, and interoperable multi-sensor data fusion to deliver trustworthy alerts and actionable insights. Persistent barriers remain cost, durability, maintenance burden, integration and interoperability, data governance, and workforce capability. Full article

Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs . The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. Full article

In ultra-wideband (UWB) synthetic aperture radar (SAR) imaging, in-band antenna standing waves (SW) can generate range ghosts, degrading image quality. To address this issue, an image-domain suppression method is proposed, leveraging the phase symmetry property (PSP) between the SW signal and its mirror SW (MSW) signal. Based on PSP, the MSW signal is rapidly constructed from the SW signal, ensuring that both share the same target region but exhibit different ghost regions. PSP is further extended to the image domain. Specifically, the SW-induced phase is extracted in the wavenumber domain. Based on the PSP, this phase is then used to construct the MSW signal, which exhibits a phase spectrum that is symmetric to that of the SW signal with respect to the origin. The MSW image is subsequently fused with the original SAR image, thereby effectively suppressing SW-induced ghosts. The experimental results demonstrate that the proposed method significantly mitigates ghosting while preserving the amplitude and structural integrity of the main signal, thereby enhancing overall imaging quality. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

Background: Frequent nocturnal arousals are a core feature of comorbid insomnia and obstructive sleep apnea (COMISA), yet the underlying central mechanisms remain unclear. Identifying brain functional correlates of nocturnal awakenings may help clarify arousal-related mechanisms and inform potential interventional targets. Methods: A total of 99 participants (COMISA, insomnia alone, OSA alone, and healthy controls) underwent clinical assessments, polysomnography, and brain magnetic resonance imaging (MRI). MRI metrics were compared across groups, followed by correlation and regression analyses with the arousal index, adjusting for respiratory events and insomnia-related factors. Results: Patients with COMISA exhibited more severe insomnia symptoms, greater daytime dysfunction, and more frequent nocturnal awakenings than those with insomnia alone, although their arousal index did not differ from that of the OSA group. Patients with COMISA exhibited altered activity in the right cerebellar lobule VIII (Cerebelum_8_R), left middle temporal gyrus, and right inferior frontal gyrus, opercular part. Lower fractional amplitude of low-frequency fluctuations (fALFF) in the Cerebelum_8_R was associated with a higher arousal index. This association remained significant after controlling for insomnia severity and sleep efficiency but was attenuated after adjustment for AHI. Conclusions: Reduced functional activity in the Cerebelum_8_R was independently associated with sleep fragmentation in COMISA, independent of insomnia severity but potentially mediated by respiratory events. These findings suggest this region may be involved in arousal-related neural regulation and could represent a therapeutic target for the complex symptoms of COMISA. Trial Registration: Chinese Clinical Trial Registry, ChiCTR2500095809. Full article

Aphanomyces root rot is a major threat to legume production worldwide, mainly in pea and lentil, crops on which extensive research programs are targeting the management of the disease. However, other legumes such as common vetch, although known to be severely affected by the disease, remain largely unexplored. This study aimed to identify sources of resistance within V. sativa subsp. sativa accessions. A total of 211 genetically diverse accessions were screened under controlled conditions following inoculation with isolate RB84. Disease progression was monitored through periodic foliar assessments and final root symptom evaluation. To assess resistance stability, a subset of 13 accessions representing contrasting response levels was further inoculated with three additional isolates (Aph-1, AE11, and AE12). In this multi-isolate assay, disease severity was quantified, shoot biomass was recorded, and root system architecture traits were determined using WinRHIZO image analysis. A high correlation between foliar and root symptoms at 20 days indicated that foliar symptom assessment provides a reliable, non-destructive indicator of root health. Considerable variation in disease response was detected, with several genotypes maintaining consistently low symptom levels and three exhibiting near-complete resistance across all isolates. Root architectural traits further corroborated visual disease assessments, showing patterns consistent with resistance and susceptibility responses. Overall, this study demonstrates the presence of genetic variability in the response of V. sativa to A. euteiches, with a subset of accessions showing resistance to the four isolates tested. This resistance potential can be directly used in breeding programs focused on improving tolerance to root rot. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Stress fractures (SFs) are a common overuse injury in athletes and represent the severe end of the bone stress injury (BSI) continuum. They result from repetitive mechanical loading exceeding the bone’s capacity for adaptation and are associated with impaired performance, prolonged time away from sport, and risk of recurrence if not appropriately managed. This narrative review provides a clinically oriented synthesis of current evidence on the epidemiology, pathophysiology, risk factors, diagnosis, management, and prevention of SFs in athletes. Particular emphasis is placed on modifiable contributors, including training load errors, neuromuscular fatigue, and low energy availability within the framework of Relative Energy Deficiency in Sport (RED-S). Diagnostic evaluation is discussed using a stepwise clinical approach integrating history, physical examination, targeted laboratory assessment, and imaging, with magnetic resonance imaging (MRI) as the reference standard for early detection and severity grading. Management is presented through a risk-based framework combining MRI severity and anatomical site classification to guide treatment decisions and return-to-sport pathways. While most low-risk SFs respond to conservative strategies, high-risk lesions require closer monitoring and, in selected cases, early surgical consideration. This review proposes a practical clinical framework to support decision-making in athletes with suspected or confirmed SFs, aiming to improve early diagnosis, optimize management, and reduce recurrence risk in sports medicine practice. Full article

Background: Magnetic resonance imaging (MRI) is the reference standard for evaluating temporomandibular joint (TMJ) disorders, particularly for assessing disc position, joint effusion, and degenerative changes. With increasing imaging demands and advances in deep learning, artificial intelligence (AI) has emerged as a potential adjunct to expert interpretation. This systematic review aimed to compare the diagnostic performance of AI-based models with that of human experts in TMJ MRI analysis. Methods: This review was conducted in accordance with the PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420251174127). A systematic search of PubMed/MEDLINE, ScienceDirect, Wiley Online Library, and Springer Nature Link was performed for studies published between 2020 and 2026. Eligible studies included human participants undergoing TMJ MRI and evaluated AI, machine learning, or deep learning models against human expert interpretation. Extracted outcomes included sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and agreement metrics. Risk of bias was assessed using QUADAS-2. Due to substantial heterogeneity, a narrative synthesis was conducted. Results: Five retrospective diagnostic accuracy studies were included, comprising sample sizes ranging from 118 to 1474 patients. Target conditions included anterior disc displacement, joint effusion, osteoarthritis, and disc perforation. AI models demonstrated strong discriminative performance, with reported AUC values ranging from 0.79 to 0.98. In direct comparisons, AI achieved diagnostic accuracy comparable to experienced radiologists. AI systems frequently demonstrated higher specificity and similar overall accuracy, whereas human experts often showed higher sensitivity. In osteoarthritis assessment, AI performance approached expert level and exceeded that of less experienced readers. All studies were retrospective and predominantly single-center, with heterogeneous reference standards and limited external validation. Conclusions: AI achieves diagnostic performance comparable to experienced clinicians in TMJ MRI interpretation and shows promise as a decision-support tool. Nevertheless, it should be regarded as complementary to, rather than a replacement for, expert radiological assessment pending further rigorous validation. Full article

Background: Intravascular lithotripsy (IVL) has emerged as a novel vessel preparation device for patients with peripheral artery disease undergoing angioplasty. The IVL catheter includes an integrated balloon, which emits high pressure and transient sonic waves. The release of shockwaves results in cracking of intimal and medial calcium within the vessel wall improving lumen patency. Objectives: The aim of this prospective observational cohort study is to evaluate the morphological and imaging changes in atherosclerotic plaque in patients with PAD undergoing IVL as a vessel preparation technique, followed by angioplasty with drug-coated balloon (DCB) or stent placement if required. Secondary endpoint is to evaluate the efficacy of IVL in the perfusion of the lower extremities, by calculating the ankle–brachial index (ABI) and toe–brachial index (TBI) post-angioplasty, as well as adverse events within 30 days. Methods: Consecutive adult (≥18 years of age) patients with symptomatic femoropopliteal artery disease selected to undergo IVL will be included in the study. Computed tomography angiography (CTA) of the lower limbs will be performed pre- and postoperatively. Intraoperatively, an intravascular ultrasound (IVUS) will be used before and immediately post-angioplasty, for real-time evaluation of the morphological and quantitative changes in the atherosclerotic plaque. All participants will be clinically re-evaluated in 30 days postoperatively and a color Duplex ultrasound of the lower extremity arteries will be performed. The perfusion of the peripheral arteries will be assessed using ABI and TBI post-procedurally. Outcomes: The primary outcome is the quantitative assessment of changes in plaque morphology and volume within the index target lesion, based on pre- and post-procedural computed tomography angiography using TeraRecon™ (Durham, NC, USA) plaque analysis module, reflecting plaque modification and redistribution, in the context of IVL-based vessel preparation. Secondary outcomes include improvement of peripheral arterial perfusion and freedom from clinically driven target lesion revascularization (CD-TLR) and major adverse events. Full article

This paper presents a novel adversarial defense framework that strategically exploits the non-transferability of adversarial attacks across multi-modal foundation models. While Contrastive Language–Image Pre-training (CLIP) models demonstrate remarkable zero-shot capabilities, they remain vulnerable to adversarial samples. Adversarial fine-tuning is widely adopted as a standard defense, yet the resulting robustness against sophisticated white-box attacks is often insufficient. To address this limitation, we aim to boost the robustness of an adversarially fine-tuned model by utilizing a pre-trained auxiliary model to leverage attack non-transferability. Specifically, we construct a common embedding space and introduce a detection scheme that identifies the attack target based on feature distances. By adaptively switching the prediction output, we effectively mitigate attacks. Experimental results demonstrate that our approach outperforms state-of-the-art adversarial fine-tuning methods in terms of adversarial robustness. Full article