Search Results (152)

Background/Objectives: Breast cancer is the most commonly diagnosed cancer worldwide, with high rates of distant metastasis. While circulating tumor cells (CTCs) are the disseminatory units of metastasis and are indicative of a poor prognosis, CTC heterogeneity within individual patients, among breast cancer subtypes, and between primary and metastatic tumors within a patient obscures the relationship between CTCs and disease progression. EpCAM, its homolog Trop2, and a pan-Cytokeratin marker were evaluated to determine their contributions to CTC presence and clustering over the study period. We conducted a systematic longitudinal analysis of 51 breast cancer patients during the course of their treatment to deepen our understanding of CTC contributions to breast cancer progression. Methods: 272 total blood samples from 51 metastatic breast cancer (mBC) patients were included in the study. Patients received diverse treatment schedules based on discretion of the practicing oncologist. Patients were monitored from July 2020 to March 2023, with blood samples collected at scheduled care appointments. Nucleated cells were isolated, imaged, and analyzed using Rarecyte^® technology, and statistical analysis was performed in R using the lmerTest and lme4 packages, as well as in Graphpad Prism version 10.4.1. Results: Both classical CTCs (DAPI+, EpCAM+, CK+, CD45– cells) and Trop2+ CTCs were detected in the blood of breast cancer patients. A high degree of correlation was found between CTC biomarkers, and CTC expression of EpCAM, Trop2, and the presence of CD45+ cells, all predicted cluster size, while Pan-CK did not. Furthermore, while analyses of biomarkers by receptor status revealed no significant differences among HR+, HER2+, and TNBC patients, longitudinal analysis found evidence for discrete trajectories of EpCAM, Trop2, and clustering between HR+ and HER2+ cancers after diagnosis of metastasis. Conclusions: Correlation and longitudinal analysis revealed that EpCAM+, Trop2+, and CD45+ cells were predictive of CTC cluster presence and size, and highlighted distinct trajectories of biomarker change over time between HR+ and HER2+ cancers following metastatic diagnosis. Full article

This study utilized the North American PhenoCam network to evaluate phenological characteristics and their relationships with geographic and climatic factors across deciduous broadleaf (n = 39) and evergreen needleleaf (n = 13) forests over the past decade. Using high temporal resolution near-surface imagery, key phenological indicators including the start, end, and length of growing season were derived and analyzed using linear regression and structural equation modeling. The results revealed substantial spatial variation; the evergreen needleleaf sites exhibited earlier starts to the growing season (112 vs. 130 Julian date), later ends to the growing season (286 vs. 264 Julian date), and longer lengths for the growing season (172 vs. 131 days) compared with the deciduous broadleaf sites. Latitude was significantly related to the start of the growing season and the length of the growing season at the deciduous broadleaf sites (R² = 0.28–0.41, p < 0.01), while these relationships were weaker at the evergreen needleleaf sites, and elevation had mixed effects. The mean annual temperature strongly influenced the phenology for both forest types (R² = 0.18–0.76, p < 0.01), whereas longitude, distance to the coast, and precipitation had negligible effects. Temporal trends in the phenological indicators were sporadic across both the deciduous broadleaf and evergreen needleleaf sites. Structural equation modeling revealed distinct causal pathways for each forest type, highlighting complex interactions among the geographical and climatic variables. At the deciduous broadleaf sites, geographical factors (latitude, elevation, and distance to the nearest coast) predominated the mean annual temperature, which in turn significantly affected phenological development (χ² = 2.171, p = 0.975). At the evergreen needleleaf sites, geographical variables had more complex effects on the climatic factors, start of the growing season, and end of the growing season, with the end of the growing season emerging as the primary determinant of growing season length (χ² = 0.486, p = 0.784). The PhenoCam network provides valuable fine-scale phenological dynamics, offering great insights for forest management, biodiversity conservation, and understanding carbon cycling under climate change. Full article

Objectives: This article introduces a digital technique for virtual articulator mounting by employing the scan of a facebow worn by the patient as a virtual reference. Methods: The digital technique enables the transfer of the maxillary arch orientation relative to the cranial base into a CAD-CAM environment (Ceramill Mind; AmannGirrbach), without the need for ionizing radiation or identification of facial landmarks. By digitally aligning the intraoral scans of the dental arches (Trios 4; 3Shape) with a 3D facial scan and the scanned facebow in position (Artex; AmannGirrbach), clinicians can reproduce the cranium-to-maxilla spatial relationship accurately and intuitively. Results: This radiation-free protocol provides virtual cross-mounting and allows for the use of a semi-adjustable articulator within common CAD-CAM software. Conclusions: Given that intraoral scanners, facial scanners, and design software with articulator simulation are becoming more available in modern clinical workflows, this method introduced here could be a viable radiation-free and easy-to-use alternative. However, larger cohorts and standardized testing protocols are needed to determine its clinical reproducibility and reliability. Full article

Background and Objectives: Diabetes is a known risk factor for postoperative delirium (POD); however, the relationship between the markers of glycemic control and the occurrence of POD in noncardiac surgery is not established. We initiated this pilot study to determine any possible associations between preoperative HbA1c levels and POD development; this will allow for larger, definitive studies to be designed and preliminary effect sizes to be established for future research. Materials and Methods: This retrospective pilot study included 78 patients with diabetes who underwent elective noncardiac surgery under general anesthesia between July 2020 and January 2021. We obtained the patients’ demographic data, medical history, surgical parameters, and preoperative HbA1c levels to determine the occurrence of POD (using CAM-ICU). Univariate and multivariate regression analyses were applied to check the leading associations for the development of POD. Results: POD was observed in seven patients (9.0%). The results of the preliminary multivariate analysis suggested that HbA1c may be associated with POD (OR, 2.96; 95% CI [1.34–6.52], p = 0.007); fasting blood glucose (OR, 1.04; 95% CI [1.01–1.07], p = 0.013); and duration of anesthesia (OR, 1.02; 95% CI [1.00–1.04], p = 0.019). The ROC analysis of HbA1c showed an optimal threshold of 7.4%, with a sensitivity of 91.5%, and a specificity of 85.7% in terms of predicting POD (AUC = 0.91, p < 0.001). Conclusions: Through this pilot study, we have provided evidence that leads to the assumption that preoperative HbA1c at, or above, 7.4% can result in an increased risk of delirium in diabetic patients who undergo noncardiac surgery. The findings of this study allow for the implementation of the proposed methodology and the collection of critical data necessary for the design of appropriately powered definitive trials. Full article

Introduction: Obesity is a rapidly increasing condition that leads to serious health issues. The sense of smell, one of the oldest senses related to energy metabolism, has been increasingly studied in relation to obesity. Objective: This study investigates the impact of childhood obesity on the volumes of the olfactory bulb and pituitary gland, exploring the relationship between body mass index and these brain structures. Method: This study included 146 participants aged 6–18 years with different body mass indices between 2021 and 2024 at Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey. Participants were classified into normal weight, obese, and morbidly obese groups, and olfactory bulb and pituitary gland volumes were retrospectively analyzed. MRI scans were performed to exclude intracranial pathologies due to headache complaints, and patients with cranial pathologies were excluded from the study. Results: This study examined the olfactory bulb and pituitary gland volumes among normal weight, obese, and morbidly obese groups aged 6–18 years. In the morbidly obese group, right olfactory bulb area and right olfactory bulb volume were significantly higher compared to the other groups, while left olfactory bulb area was higher in both the obese and morbidly obese groups. Additionally, in the morbidly obese group, pituitary height was significantly lower than the other groups, and pituitary volume was also found to be reduced in morbid obesity. Conclusions: This study demonstrated that childhood obesity is linked to significant changes in the volumes of the olfactory bulb and pituitary gland. In morbidly obese children, an increase in pituitary volume and alterations in olfactory bulb volume suggest possible neuroanatomical adaptations. Full article

Orthognathic surgery (OS) is a complex procedure commonly used to treat dentofacial deformities (DFDs). These conditions, related to jaw position or size and often involving malocclusion, affect approximately 15% of the population. Due to the complexity of OS, accurate planning is essential. Digital assessment using computer-aided design (CAD) and computer-aided manufacturing (CAM) tools enhances surgical predictability. However, limitations in soft tissue simulation often require surgeon input to optimize aesthetic results and minimize surgical impact. This study aimed to evaluate the accuracy of virtual surgery planning (VSP) by analyzing the relationship between planning deviations and surgical satisfaction. A single-center, retrospective study was conducted on 16 patients who underwent OS at San Pedro University Hospital of La Rioja. VSP was based on CT scans using Dolphin Imaging software (v12.0, Patterson Dental, St. Paul, MN, USA) and surgeries were guided by VSP-designed occlusal splints. Outcomes were assessed using the Orthognathic Quality of Life (OQOL) questionnaire and deviations were measured through pre- and postoperative imaging. The results showed high satisfaction scores and good overall outcomes, despite moderate deviations from the virtual plan in many cases, particularly among Class II patients. A total of 63% of patients required VSP modifications due to poor soft tissue fitting, with 72% of these being Class II DFDs. Most deviations involved less maxillary advancement than planned, while maintaining optimal occlusion. This suggests that VSP may overestimate advancement needs, especially in Class II cases. No significant differences in satisfaction were observed between patients with low (<2 mm) and high (>2 mm) deviations. These findings support the use of VSP as a valuable planning tool for OS. However, surgeon experience remains essential, especially in managing soft tissue behavior. Improvements in soft tissue prediction are needed to enhance accuracy, particularly for Class II DFDs. Full article

With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial location information into consideration. There are also limitations in extracting various time-scale features. To address these issues, a fault diagnosis method on the basis of a dual-path mixed convolutional attention-BiGRU network (DMCA-BiGRUN) is proposed for industrial processes. Firstly, a dual-path mixed CNN (DMCNN) is designed to capture features at multiple scales while effectively reducing the parameter count. Secondly, a coordinate attention mechanism (CAM) is designed to help the network to concentrate on main features more effectively during feature extraction by combining the channel relationship and position information. Finally, a bidirectional gated recurrent unit (BiGRU) is introduced to process sequences in both directions, which can effectively learn the long-range temporal dependencies of sequence data. To verify the fault diagnosis performance of the proposed method, simulation experiments are implemented on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets. Some deep learning methods are compared in the experiments, and the results confirm the feasibility and superiority of DMCA-BiGRUN. Full article

The tip-of-the-tongue (TOT) phenomenon is characterized by a temporary inability to retrieve a word despite a strong sense of familiarity. While extensive research has linked phonological processing to TOT, the exact nature of this relationship remains debated. The “blocking hypothesis” suggests that the retrieval of target words is interfered with by phonological neighbors, whereas the “transmission deficit hypothesis” posits that TOT arises from insufficient phonological activation of the target words. This study revisited this issue by examining the relationship between the microstructural integrity of the phonological processing brain network and TOT, utilizing graph-theoretical analyses of neuroimaging data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), which included diffusion tensor imaging (DTI) data from 576 participants aged 18–87. The results revealed that global efficiency and mean degree centrality of the phonological processing network positively predicted TOT rates. At the nodal level, the nodal efficiency of the bilateral posterior superior temporal gyrus and the clustering coefficient of the left premotor cortex positively predicted TOT rates, while the degree centrality of the left dorsal superior temporal gyrus (dSTG) and the clustering coefficient of the left posterior supramarginal gyrus (pSMG) negatively predicted TOT rates. Overall, these findings suggest that individuals with a more enriched network of phonological representations tend to experience more TOTs, supporting the blocking hypothesis. Additionally, this study highlights the roles of the left dSTG and pSMG in facilitating word retrieval, potentially reducing the occurrence of TOTs. Full article

Lunar crater detection plays a crucial role in geological analysis and the advancement of lunar exploration. Accurate identification of craters is also essential for constructing high-resolution topographic maps and supporting mission planning in future lunar exploration efforts. However, lunar craters often suffer from insufficient feature representation due to their small size and blurred boundaries. In addition, the visual similarity between craters and surrounding terrain further exacerbates background confusion. These challenges significantly hinder detection performance in remote sensing imagery and underscore the necessity of enhancing both local feature representation and global semantic reasoning. In this paper, we propose a novel Spatial Channel Fusion and Context-Aware YOLO (SCCA-YOLO) model built upon the YOLO11 framework. Specifically, the Context-Aware Module (CAM) employs a multi-branch dilated convolutional structure to enhance feature richness and expand the local receptive field, thereby strengthening the feature extraction capability. The Joint Spatial and Channel Fusion Module (SCFM) is utilized to fuse spatial and channel information to model the global relationships between craters and the background, effectively suppressing background noise and reinforcing feature discrimination. In addition, the improved Channel Attention Concatenation (CAC) strategy adaptively learns channel-wise importance weights during feature concatenation, further optimizing multi-scale semantic feature fusion and enhancing the model’s sensitivity to critical crater features. The proposed method is validated on a self-constructed Chang’e 6 dataset, covering the landing site and its surrounding areas. Experimental results demonstrate that our model achieves an $mA{P}_{0.5}$ of 96.5% and an $mA{P}_{0.5:0.95}$ of 81.5%, outperforming other mainstream detection models including the YOLO family of algorithms. These findings highlight the potential of SCCA-YOLO for high-precision lunar crater detection and provide valuable insights into future lunar surface analysis. Full article

This study proposes a deep learning-based, non-contact method for detecting elevated intraocular pressure (IOP) by integrating Scheimpflug images with corneal biomechanical features. Glaucoma, the leading cause of irreversible blindness worldwide, requires accurate IOP monitoring for early diagnosis and effective treatment. Traditional IOP measurements are often influenced by corneal biomechanical variability, leading to inaccurate readings. To address these limitations, we present a multi-modal framework incorporating CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and the Kolmogorov–Arnold Network (KAN) for efficient fusion of heterogeneous data. KAN approximates complex nonlinear relationships with fewer parameters, making it effective in small-sample scenarios with intricate variable dependencies. A diverse dataset was constructed and augmented to alleviate data scarcity and class imbalance. By combining Scheimpflug imaging with clinical parameters, the model effectively integrates multi-source information to improve high IOP prediction accuracy. Experiments on a real-world private hospital dataset show that the model achieves a diagnostic accuracy of 0.91, outperforming traditional approaches. Grad-CAM visualizations identify critical anatomical regions, such as corneal thickness and anterior chamber depth, that correlate with IOP changes. These findings underscore the role of corneal structure in IOP regulation and suggest new directions for non-invasive, biomechanics-informed IOP screening. Full article

Non-active current in the time domain is considered for application to the diagnostics and classification of loads in power grids based on waveform-distortion characteristics, taking as a working example several recordings of the pantograph current in an AC railway system. Data are processed with a deep autoencoder for feature extraction and then clustered via k-means to allow identification of patterns in the latent space. Clustering enables the evaluation of the relationship between the physical meaning and operation of the system and the distortion phenomena emerging in the waveforms during operation. Euclidean distance (ED) is used to measure the diversity and pertinence of observations within pattern groups and to identify anomalies (abnormal distortion, transients, …). This approach allows the classification of new data by assigning data to clusters based on proximity to centroids. This unsupervised method exploiting non-active current is novel and has proven useful for providing data with labels for later supervised learning performed with the 1D-CNN, which achieved a balanced accuracy of 96.46% under normal conditions. ED and 1D-CNN methods were tested on an additional unlabeled dataset and achieved 89.56% agreement in identifying normal states. Additionally, Grad-CAM, when applied to the 1D-CNN, quantitatively identifies the waveform parts that influence the model predictions, significantly enhancing the interpretability of the classification results. This is particularly useful for obtaining a better understanding of load operation, including anomalies that affect grid stability and energy efficiency. Finally, the method has been also successfully further validated for general applicability with data from a different scenario (charging of electric vehicles). The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency. Full article

This study presents an integrated experimental and theoretical investigation of two pharmacologically significant neolignans—magnolol and honokiol—with the aim of characterizing their structural and spectroscopic properties in detail. Experimental Fourier-transform infrared (FT-IR), ultraviolet–visible (UV-Vis), and nuclear magnetic resonance (¹H NMR) spectra were recorded and analyzed. To support and interpret these findings, a series of density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were conducted using several hybrid and long-range corrected functionals (B3LYP, CAM-B3LYP, M06X, PW6B95D3, and ωB97XD). Implicit solvation effects were modeled using the CPCM approach across a variety of solvents. The theoretical spectra were systematically compared to experimental data to determine the most reliable computational approaches. Additionally, natural bond orbital (NBO) analysis, molecular electrostatic potential (MEP) mapping, and frontier molecular orbital (FMO) visualization were performed to explore electronic properties and reactivity descriptors. The results provide valuable insight into the structure–spectrum relationships of magnolol and honokiol and establish a computational benchmark for further studies on neolignan analogues. Full article

The brain age gap (BAG), the divergence of an individual’s neurobiologically predicted brain age from their chronological age, is a key indicator of brain health. While BAG can be derived from diverse brain metrics, its interpretation often polarizes between early-life trait influences and current state-dependent factors like cognitive decline. Here, we propose an integrative framework that moves beyond single summary statistics by considering the full distribution of brain metrics across regions or time. We distinguish between a neural system’s “baseline” (typical values, e.g., mean) and its “capacity” (extreme values, e.g., maximum) within these distributions. To test this, we analyzed resting-state magnetoencephalography (MEG) from the Cam-CAN adult cohort, focusing on cross-frequency coupling (CFC) within functional MRI-defined networks. We derived network-specific CFC baseline (mean) and capacity (maximum) measures. Separate brain age prediction models were trained for each measure. The resulting BAGs (baseline-BAG and capacity-BAG) for each network were then correlated with cognitive performance on a picture priming task. Both baseline-BAG and capacity-BAG profiles showed associations with cognitive scores, with younger predicted brain age correlating with better performance. However, capacity-BAG exhibited more conclusive relationships, suggesting that metrics reflecting a neural system’s peak operational ability (capacity) may better capture an individual’s current cognitive state. These findings indicate that brain age models emphasizing neural capacity, rather than just baseline activity, could offer a more sensitive lens for understanding the state-dependent aspects of brain ageing. Full article

Background: Despite the success of transcatheter aortic valve repair (TAVR) over the past years, its impact on global and cerebral hemodynamics remains largely unexplored. Changes in cerebral blood flow may be associated with delirium, which may occur in 26 to 29% of cases. We aimed to examine the relationships between global hemodynamic parameters and cerebral parameters in patients who underwent TAVR and their impact on postinterventional delirium. Methods: Patients scheduled for TAVR were enrolled after obtaining written informed consent. Patients received light sedation according to standard procedures. Cerebral blood flow (CBF) was measured with a noninvasive near-infrared spectroscopy-based method using intravenous indocyanine green injection. CBF measurements were taken at the beginning of the TAVR procedure and after the valve was in place. Patients were screened for delirium using CAM-ICU and NuDESC tests before and after intervention. Results: A total of 52 of 60 patients remained for analysis. Thirteen patients (25%) developed delirium. Mean arterial pressure (MAP) remained unchanged, while cardiac output increased after TAVR by 44%. CBF also increased after TAVR. No significant difference was observed in CBF changes between the groups with and without delirium. A linear mixed model analysis revealed a linear relationship between CO and CBF but not between MAP and CBF. In an exploratory analysis, decreased cerebral oxygenation and increased deoxygenated hemoglobin, as measured by NIRS after TAVR, were associated with delirium. Conclusions: The results confirm that CO is an independent factor in CBF, while CBF changes per se are not linked to delirium. However, we found a mismatch between CBF and regional cerebral parameters, which may reflect cerebral metabolism and its relation to the development of delirium. This remains to be confirmed by further studies. Full article

Urban image semantic segmentation faces challenges including the coexistence of multi-scale objects, blurred semantic relationships between complex structures, and dynamic occlusion interference. Existing methods often struggle to balance global contextual understanding of large scenes and fine-grained details of small objects due to insufficient granularity in multi-scale feature extraction and rigid fusion strategies. To address these issues, this paper proposes an Adaptive Multi-scale Feature Fusion Network (AMFFNet). The network primarily consists of four modules: a Multi-scale Feature Extraction Module (MFEM), an Adaptive Fusion Module (AFM), an Efficient Channel Attention (ECA) module, and an auxiliary supervision head. Firstly, the MFEM utilizes multiple depthwise strip convolutions to capture features at various scales, effectively leveraging contextual information. Then, the AFM employs a dynamic weight assignment strategy to harmonize multi-level features, enhancing the network’s ability to model complex urban scene structures. Additionally, the ECA attention mechanism introduces cross-channel interactions and nonlinear transformations to mitigate the issue of small-object segmentation omissions. Finally, the auxiliary supervision head enables shallow features to directly affect the final segmentation results. Experimental evaluations on the CamVid and Cityscapes datasets demonstrate that the proposed network achieves superior mean Intersection over Union (mIoU) scores of 77.8% and 81.9%, respectively, outperforming existing methods. The results confirm that AMFFNet has a stronger ability to understand complex urban scenes. Full article