Search Results (1,881)

Background/Objectives: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men globally. Radiation oncologists often find PCa tumor characterization and outcome prediction challenging. Therefore, the potential for artificial intelligence (AI) implementation in radiation oncology has increased in recent years. This systematic review aims to evaluate the efficacy of AI algorithms in characterizing PCa tumors and predicting post-therapy outcomes. Methods: A total of 2055 studies were identified through a comprehensive search across PubMed and Scopus, then exported to Covidence. Inclusion criteria focused on prospective and retrospective cohort studies as well as randomized clinical trials (RCTs) published between 2015 and 2024 that explored the implementation of AI in tumor characterization and outcome prediction of PCa. Two independent reviewers evaluated each paper, and evaluation metrics such as specificity, sensitivity, accuracy, and area under the curve (AUC) were analyzed. The Risk of Bias in Non-randomized Studies of Interventions, Version 2 (ROBINS-I V2) tool was used to assess the risk of bias (ROB). Results: Across the 19 studies analyzed, there was no significant difference in model performance between machine learning (ML) and deep learning (DL) models. AI models using multi-input strategies (e.g., radiomics with clinical markers) generally performed better than single-input models. Of the imaging modalities used for radiomic feature extraction, multiparametric MRI (mpMRI)-trained AI models consistently achieved the highest performance. Conclusions: AI displays considerable potential for integration into clinical workflows for PCa management. However, further studies utilizing larger datasets and external cohorts independent of the sample population are needed to validate clinical utility and improve model transparency for reliable implementation. Full article

Objectives: This study aimed to examine the association between the dangling choroid plexus sign and fetal structural, chromosomal, and genetic abnormalities, as well as to define the normal range of lateral ventricular width and the ratio of choroid plexus width to lateral ventricular width at 14–17 weeks of gestation. Methods: This retrospective study analyzed ultrasound images from early fetal anatomy scans performed between January 2018 and July 2025 at two tertiary fetal medicine centres. In centre A, 6063 singleton pregnancies underwent routine scans at 11–13 and 14–17 weeks. In centre B, 776 fetuses with suspected abnormalities or increased nuchal translucency at 11–13 weeks were reassessed at 14–17 weeks. Additionally, 400 fetuses without obvious abnormalities at 14–17 weeks were used to determine normal ventricular measurements. Results: In normal fetuses, the mean lateral ventricular width was 6.90 mm (95% CI 6.81–6.99) and the mean choroid plexus-to-ventricle ratio was 0.85 (95% CI 0.84–0.86). A dangling choroid plexus was identified in 38 fetuses (0.16% in routine and 3.6% in high-risk populations). Out of 38 cases of dangling choroid plexus, 37 were associated with additional structural defects, chromosomal abnormalities, or single-gene disorders. Chromosomal abnormalities were found in 11/30 tested cases, most commonly trisomy 21. The most common defects observed on initial or subsequent scans were ventriculomegaly, cardiac defects, and abnormal posterior fossa. Conclusions: A dangling choroid plexus at 14–17 weeks is a sonographic marker associated with major fetal abnormalities and should prompt detailed anatomical assessment and consideration of genetic testing. Full article

Background: Glioblastoma (GBM) exhibits marked cellular heterogeneity and resistance to therapy. Calcium (Ca²⁺) signaling at endoplasmic reticulum (ER)–mitochondria contact sites has emerged as a key regulator of mitochondrial function and cell fate; however, its lineage-specific role and therapeutic relevance in GBM remain unclear. Methods: ITPR1 expression was analyzed using single-cell and bulk RNA sequencing (RNA-seq) datasets and validated by immunohistochemistry and survival analyses. Functional studies were conducted using genetic silencing or CRISPR-mediated activation of ITPR1, combined with DRP1 knockdown, Ca²⁺ imaging, transmission electron microscopy, co-immunoprecipitation, mitochondrial fractionation, and mitochondrial functional assays. Therapeutic efficacy was evaluated in orthotopic GBM xenograft models treated with 2-aminoethoxydiphenyl borate (2-APB), temozolomide (TMZ), or their combination. Results: ITPR1 was enriched in mesenchymal-like malignant cell states and associated with higher tumor grade, recurrence, and poor prognosis. ITPR1 knockdown suppressed GBM cell proliferation and tumor growth while promoting intrinsic apoptosis. Mechanistically, loss of ITPR1 impaired ER-to-mitochondria Ca²⁺ transfer, disrupted ER–mitochondria contacts, and altered mitochondrial ultrastructure. This was accompanied by reduced DRP1 Ser616 phosphorylation and mitochondrial recruitment, as well as decreased autophagy and mitophagy activity. Consequently, ITPR1 knockdown led to mitochondrial depolarization, increased mitochondrial reactive oxygen species (ROS) accumulation, and activation of mitochondria-dependent apoptosis. Conversely, DRP1 knockdown attenuated the mitochondrial and pro-survival effects induced by ITPR1 overexpression. In vivo, combined treatment with 2-APB and TMZ resulted in greater tumor suppression and prolonged survival compared with either treatment alone, accompanied by increased apoptosis and reduced proliferation in tumor tissues. Conclusions: ITPR1 promotes GBM progression by sustaining ER–mitochondria Ca²⁺ coupling and DRP1-dependent mitochondrial quality control, thereby maintaining mitochondrial homeostasis and cell survival. Targeting inositol 1,4,5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling with 2-APB enhances the therapeutic efficacy of TMZ, suggesting that ITPR1-centered Ca²⁺ signaling may represent a potential therapeutic vulnerability in aggressive GBM. Full article

Advances in organoid and other three-dimensional culture systems, single-cell and spatial transcriptomics, multi-omics, and high-resolution imaging are reshaping our understanding of the cellular origins and evolutionary trajectories of glioblastoma. When integrated with modern data science approaches, these technologies enable the construction of increasingly detailed molecular biographies of normal neural stem and progenitor cells as well as malignant stem-like cellular states. Such molecular biographies illuminate how developmental programs, cellular plasticity, and microenvironmental cues are co-opted during gliomagenesis. At the same time, progress in machine learning, immunotherapy, and precision molecular targeting is beginning to translate these biological insights into therapeutic strategies that specifically disrupt glioblastoma stem-like states. Together, these converging approaches provide a conceptual and technological framework for improved tumor modeling, earlier detection, and increasingly personalized therapies for malignant gliomas. Full article

Neuronal activity and cerebral blood flow are tightly coupled to support the high metabolic demands of the brain. Disruption of neurovascular coupling is a defining feature of many neurodegenerative disorders such as Alzheimer’s disease, stroke, small vessel disease, Parkinson’s disease, and aging. Progress in understanding the mechanisms underlying neurovascular coupling requires experimental approaches that can simultaneously measure neuronal activity and vascular dynamics with high spatial and temporal resolution, while also enabling targeted perturbations of the system. Here, we present a methodological framework that combines chronic electrophysiological recordings with two-photon imaging of cerebral blood flow and optogenetic manipulation of the vasculature in vivo. Using a chronically implanted flexible electrode array, we obtain measurements of the single- and multi-unit spiking activity, as well as local field potentials. Concurrently, two-photon microscopy enables high-resolution measurements of vessel diameter and blood flow within individual vascular segments. In addition, optogenetic control of vascular smooth muscle cells allows for rapid and reversible manipulation of the vessel diameter through the same cranial window while simultaneously recording the neural and vascular activity. We provide detailed protocols for surgical implantation, data acquisition, and analysis, and discuss experimental considerations and limitations. This combined platform offers a powerful tool for mechanistic studies of neurovascular coupling and its dysfunction in disease models. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

Background: Additive manufacturing provides a rapid and flexible alternative to conventional micromolding for producing microneedle systems. This study evaluates the potential of a cost-effective LCD 3D printer for fabricating microneedle arrays (MNAs) and investigates how the geometry of MNAs and the formulation of hydrogel influence the performance of lidocaine-coated arrays. Methods: Conical and pyramidal MNAs, along with a reservoir plate, were designed and manufactured. Lidocaine-loaded and placebo hydrogels with two different polymer concentrations were prepared for dip-coating using both single and multilayer applications. Mechanical resistance and insertion efficiency were evaluated under controlled compression. The physicochemical behavior of the hydrogels were characterized, including pH, spreadability, adhesiveness, and rheological behavior. The uniformity of the coating was analyzed using 3D confocal microscopy. Drug loading was quantified by HPLC, drug release was studied using Franz diffusion cells, and skin penetration was confirmed by 3D confocal imaging and Raman mapping. Results: Conical microneedles exhibited high mechanical integrity, showing only a 2% reduction in height compared to 4% for pyramidal MNAs. Stronger drug signals were achieved in deeper skin layers with the conical geometry, indicating enhanced penetration, while pyramidal MNAs provided slightly higher lidocaine loading due to their larger lateral surface. Hydrogels with higher polymer content produced more stable, uniform coatings, particularly when applied in three layers. Rapid drug release was observed, with over 70% of the drug delivered within minutes. Conclusions: LCD 3D printing offers a cost-effective approach for fabricating MNAs with suitable structural stability and sharpness. The optimized hydrogel formulation ensured uniform coverage, as well as maximal and consistence penetration, making this platform a promising candidate for the dermal delivery of other potent drugs. Full article

Although renal disease in Syrian hamsters (SHs) has been reported, imaging studies of normal kidneys in this commonly used pet and laboratory species are lacking, despite the key role of imaging in diagnosis. This study aimed to examine kidneys of Syrian hamsters using radiographic and anatomical methods, focusing on kidney location, visibility, size, and its ratio to the second lumbar vertebra, along with the effects of sex, body size, side, and recumbency. Abdominal radiographs were obtained from 29 clinically healthy adult Syrian hamsters of both sexes to assess kidney visibility, position, and size as well as the length of second lumbar vertebral body on lateral and ventrodorsal (VD) views, followed by an in situ anatomical study for comparative analysis. The kidneys were typically located opposite the first to third lumbar vertebrae. On VD views, the left kidney was generally visible, whereas the right was identified in only 28%. The mean values of radiographic RKL, LKL, and 2LVL were 15.2 mm, 12.44 mm, and 14.27 mm, respectively, and the KL/2LVL ratio ranged from 2.66 to 4.00. No significant sex differences were observed in KL or the KL/2LVL ratio in either anatomical or radiographic measurements (p > 0.05). Sex had a significant effect on both radiographic and anatomical 2LVL measurements, with females generally showing higher values than males. Unlike the anatomical measurements, no significant differences between sides were found in radiographic KL and the KL/2LVL ratio. The radiographic RKL, LKL, and 2LVL were significantly larger than those obtained from anatomical measurements. No significant correlation was found between KL, 2LVL, or the KL/2LVL ratio and body length or body weight in either radiographic or anatomical measurements, except for a correlation between body weight and anatomical KL. Right and left kidneys were symmetrically placed, as in rats, but differed from rabbits and guinea pigs. Kidney visibility on VD views was similar to that reported in rabbits. Radiographic RKL, LKL, and 2LVL values differed from those of rodents and rabbits. The radiographic ratio was larger than the values reported in rats, chinchillas, guinea pigs, and rabbits. A single KL-to-2LVL ratio reference range applies to both kidneys and sexes, simplifying clinical assessment. Full article

Early detection is a key tool for mitigating the devastating effects of wildfires. Single-frame detection methods that do not consider inter-frame dependencies often fail to detect smoke plumes at the earliest stage and at greater distances, or produce excessive false alarms. Biological vision is particularly sensitive to motion cues, and this translates well to automated systems. Recent temporal-memory approaches have demonstrated improved performance over purely spatial methods, but typically rely on complex, computationally heavy multi-stage architectures. This study investigates the possibility of encoding temporal and contextual information into additional image channels as a basis for compiling data models with increased information content. Seven distinct data models were proposed, and corresponding datasets were generated to train standard YOLO architectures without modifications to the network structure. The datasets were compiled from real wildfire footage collected from an operational wildfire surveillance system in Croatia, comprising 333 annotated sequences of real fires recorded between 2018 and 2024. Experimental evaluation compared the performance of YOLO models trained on the information-enriched datasets with those trained on standard RGB images. Based on the results, the best data model for early wildfire smoke detection, combining original RGB channels with short-term and long-term temporal memory, was selected. Comparative evaluation demonstrated improved detection accuracy, achieving up to 5 percent higher true-positive detection rate for models trained on spatio-temporal data compared to standard RGB images, while maintaining low inference latency. The proposed approach shifts the focus to the structure and information content of the data while preserving the efficiency of standard convolutional neural network architectures. This approach could be applied to other problems requiring high efficiency and real-time operation, where temporal and contextual information can improve detection performance. Full article

Automated fruit harvesting is crucial for alleviating labor shortages and enhancing agricultural productivity. In this context, it is crucial to obtain information on fruit poses before picking in order to avoid damaging the fruit and/or the plant. However, the complex and unstructured orchard environment poses significant challenges regarding the pose estimation task. In this study, a dragon fruit pose estimation (DFPE) framework using a single RGB image is proposed for dragon fruit automated harvesting, which includes three key components: dataset annotation processing, keypoint detection, and geometric pose estimation. First, a multi-source dataset consisting of 8467 images is constructed to enhance the estimation model’s generalizability. A pseudo four-keypoint annotation strategy is designed to fit the annotation rules of mainstream single-class keypoint detection models and mitigate the inherent limitations of multi-target keypoint detection in agricultural scenarios. This strategy implicitly encodes the fruit’s orientation using bounding box group IDs, while preserving geometric information for pose inference. Then, the fruit body and its two core keypoints (navel and stem) are detected via a real-time keypoint detection model. Notably, the proposed DFPE framework is detector-agnostic: other mainstream keypoint detection models can also be plugged into the subsequent geometric pose inference stage, which guarantees the generality and scalability of the framework. Finally, a dragon fruit pose estimation algorithm based on customized geometric constraints is designed, which takes the detected pose information as the input and outputs the posture of dragon fruit. The results of experiments conducted in natural orchard and laboratory environments demonstrate that the ellipses fitted using the proposed DFPE framework closely aligned with fruit contours, even under foliage occlusion conditions. In the laboratory environment, roll errors reached a maximum of 14.8°, whereas yaw errors peaked at 13.4°. Crucially, all roll and yaw errors remained consistently below 15°, which is well within the tolerance threshold required for non-destructive picking operations using a harvesting robot. In summary, this work presents a low-cost solution for dragon fruit pose estimation from a single RGB image, which can potentially be extended to other ellipsoid crops and is suitable for implementation in harvesting robots operating in orchards. Full article

Background: Left bundle branch block (LBBB) and QRS prolongation are markers of electrical dyssynchrony in heart failure, but they do not fully reflect its mechanical consequences. Myocardial work (MW)-derived indices may provide a more comprehensive assessment of left ventricular (LV) mechanical dyssynchrony. We evaluated associations between LV MW parameters, QRS duration, and LBBB in patients with heart failure with reduced ejection fraction (HFrEF) referred for ICD/CRT implantation. Methods: In this single-centre observational cross-sectional study, 96 consecutive patients referred for ICD or CRT implantation were screened. All patients underwent standardized baseline comprehensive echocardiography followed by advanced MW analysis. Myocardial work index (MWI) dispersion was assessed using two complementary methods. MWI dispersion (SD) was calculated as the standard deviation of segmental MWI values across all LV segments, and MWI dispersion (IQR) was defined as the interquartile range (IQR) of segmental MWI values. We evaluated the associations between QRS duration and MW-derived dyssynchrony parameters (individual and composite), as well as their discriminative performance for LBBB. Seven patients were excluded from further analysis due to inadequate echocardiography image quality. Results: The final study group comprised 89 patients with HFrEF (median age 65.5 years), of whom 67.4% were assigned to CRT. LBBB was present in 41.6%, and the median QRS duration was 142 ms (112–162). All analyzed LV MW indices were significantly associated with QRS duration (all q < 0.01). The strongest correlations were observed for MWI dispersion (IQR) (r = 0.58), peak strain dispersion (PSD) (r = 0.54), lateral–septal work asymmetry (r = 0.53), and MWI dispersion (SD) (r = 0.52) (all q < 0.0001). All MW indices differed significantly between patients with and without LBBB (all q ≤ 0.0001). MWI dispersion (IQR) showed the best single-marker discrimination of LBBB (AUC = 0.852). Composite indices achieved AUC = 0.84 but did not significantly improve discrimination versus MWI dispersion (IQR) alone. Conclusions: Myocardial work-derived indices of left ventricular dyssynchrony are strongly associated with QRS duration and the presence of LBBB in patients with HFrEF. Among them, MWI dispersion (IQR) was shown to be the best-performing MW marker for identifying LBBB. These findings suggest that MW dispersion may serve as a robust echocardiographic marker of mechanical dyssynchrony and warrants further investigation as a potential tool for predicting CRT response. Full article

Accurate brain tumor segmentation in magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and therapy monitoring. Conventional deep learning models often struggle with large variations in tumor shape, size, and contrast, as well as severe foreground–background imbalance. To address these challenges, this study presents Deep-MiSR, an enhanced encoder–decoder framework built upon DeepLabV3+ with a MobileNetV2 backbone, tailored for single-modality contrast-enhanced T1-weighted (T1CE) MRI segmentation. Three complementary components are integrated into the architecture: mixed depthwise convolution (MixConv) with heterogeneous kernels within the atrous spatial pyramid pooling module for multi-scale feature aggregation, a squeeze-and-excitation block for adaptive channel recalibration, and R-Drop regularization that enforces prediction consistency via symmetric Kullback–Leibler divergence. The model was evaluated on 3064 T1CE slices from 233 patients drawn from the publicly available Nanfang Hospital brain MRI dataset. Deep-MiSR achieved a Dice similarity coefficient of 0.9281, a mean intersection-over-union of 0.8738, a precision of 0.8839, and a 95th-percentile Hausdorff distance of 7.69 mm, demonstrating consistent improvements over both the DeepLabV3+ baseline and all prior methods evaluated on the same data. Ablation studies confirmed that each component contributes independently, with R-Drop providing the largest individual gain. These findings demonstrate that combining multi-scale convolution, channel attention, and consistency regularization constitutes an effective and computationally practical strategy for robust single-modality brain tumor segmentation. Full article

Childhood obesity is a growing global health problem with significant biomechanical and psychosocial consequences. While many studies have examined these domains separately, few integrate postural abnormalities, psychophysical functioning, and lifestyle factors within a single framework. This narrative review synthesises the literature published between 2005 and 2025 to summarise current evidence and identify research gaps. The findings indicate that overweight and obesity increase the risk of musculoskeletal deviations such as genu valgum, flat feet, and increased lumbar lordosis, as well as altered gait biomechanics and reduced motor competence. Excess body weight is also associated with lower self-esteem, negative body image, depressive symptoms, and reduced health-related quality of life in children and adolescents. These outcomes appear to be influenced by modifiable lifestyle factors, including parental health behaviours, sleep patterns, and screen time, although reported associations remain inconsistent. Notably, few studies address biomechanical, psychological, and environmental factors simultaneously, which limits the understanding of their interactions. To address this gap, a prospective observational study of 250–300 children aged 7–17 years is proposed. The study will combine objective postural assessments, validated psychometric tools, and lifestyle analyses at baseline and after a 12–14-month follow-up. This integrated approach aims to identify postural compensation patterns, psychosocial risk trajectories, and modifiable behavioural predictors associated with childhood obesity, supporting the development of early preventive and interdisciplinary interventions. Full article

Dental developmental anomalies are well documented in clinical veterinary medicine but remain rarely reported in archeological dogs. This study presents a radiologically confirmed case of an unerupted left maxillary canine associated with the absence of an alveolus for the left maxillary first molar and incisors in a dog skull from early medieval Wolin. This study aimed to determine whether the observed absence of teeth resulted from congenital agenesis, developmental arrest, ante-mortem loss, or post-depositional processes. Radiographic examination revealed a fully formed but unerupted canine, while the M¹ region exhibited a smooth bony surface without reactive remodeling, periapical radiolucencies, or signs of ante-mortem tooth loss. Differential diagnosis did not support canine agenesis, ante-mortem loss, or taphonomic damage as primary explanations. The findings most strongly support a congenital or very early developmental origin of the observed alterations. The estimated age of the individual (7–10 years) and the absence of secondary pathological changes suggest that these anomalies did not significantly impair masticatory function. Owing to the single-case nature of the material, broader population-level inferences cannot be made. This case underscores the methodological importance of radiographic imaging in archeological dental research and suggests that alveolar absence should not be automatically equated with impaired survival or poor health in this individual. Full article

The automatic extraction of seismic reflection events is fundamental to seismic interpretation and structural identification in oil and gas exploration, particularly for large-scale regional surveys and preliminary basin-scale assessments. Although the B-COSFIRE (Bar-Combination of Shifted Filter Responses) method has demonstrated strong capability in detecting ridge-like structures, its application in large-scale seismic processing is limited by high computational cost and complex filter bank configuration. Conventional edge detectors such as the Canny operator are computationally efficient but often produce fragmented and noise-sensitive results in low signal-to-noise ratio (SNR) seismic data because they rely solely on local gradient information and ignore the spatial continuity of geological horizons. To overcome these limitations, this study proposes a lightweight and computationally efficient framework for rapid seismic event extraction. The method simplifies the B-COSFIRE architecture by replacing its configurable filter bank with a Difference-of-Gaussian (DoG) operator, which enhances ridge-like reflection features while suppressing background interference through a center–surround mechanism. Furthermore, a Spatial Consistency Constraint (SCC) module is introduced to enforce lateral continuity using directional morphological closing operations. This strategy reconstructs disrupted reflection segments and converts isolated detection responses into spatially coherent linear structures. Adaptive thresholding and skeletonization are then applied to obtain single-pixel-wide reflection contours suitable for geological interpretation and regional structural analysis. The proposed method was evaluated using both synthetic seismic models (Ricker wavelet convolution with Gaussian noise, σ = 0.15) and real post-stack seismic profiles characterized by low SNR conditions. Experimental results demonstrate that the proposed method achieves a Precision of 0.9527, Recall of 1.0000, and F1-score of 0.9758 on synthetic data, outperforming both the standard Canny detector (F1: 0.8972) and B-COSFIRE (F1: 0.7311). The Continuity Index reaches 261.00 pixels, substantially higher than Canny (223.67 pixels) and B-COSFIRE (66.86 pixels). Notably, B-COSFIRE exhibits a severely imbalanced detection profile (Precision: 0.5762, Recall: 1.000), indicating excessive false positives that undermine its practical utility. The proposed method additionally achieves the lowest runtime (0.024 s per profile), representing a 44× speedup over B-COSFIRE (1.039 s), while requiring no training data. Overall, the proposed framework provides a practical and efficient solution for automated seismic event extraction. With only a small number of geologically interpretable parameters and strong robustness across different datasets, the method is well-suited for large-scale seismic data processing and preliminary structural assessment in underexplored regions, enabling rapid first-pass evaluation of extensive survey areas before detailed interpretation and reservoir characterization. These characteristics make the method particularly suitable for computer-assisted interpretation workflows in industrial oil and gas exploration. Unlike prior approaches that treat seismic event extraction as a generic edge detection problem, the proposed framework explicitly encodes geological prior knowledge—specifically, the lateral continuity of stratigraphic interfaces—as a morphological constraint, bridging the gap between image processing methodology and geophysical interpretation requirements. Full article