Search Results (6,483)

Cerebral ischemia/reperfusion (I/R) injury remains a significant contributor to adult neurological morbidity, primarily due to exacerbated neuroinflammation and cell apoptosis. These processes amplify brain damage through the release of various pro-inflammatory cytokines and pro-apoptotic mediators. Although long non-coding RNAs (lncRNAs) are increasingly recognized for their involvement in regulating diverse biological pathways, their precise role in cerebral I/R injury has not been fully elucidated. In the current study, transcriptomic profiling was conducted using a rat model of focal cerebral I/R, leading to the identification of lncRNA-1810026B05Rik—also referred to as CHASERR—as a novel lncRNA responsive to ischemic conditions. The elevated expression of this lncRNA was observed in mouse brain tissues subjected to middle cerebral artery occlusion followed by reperfusion (MCAO/R), as well as in primary cortical neurons derived from rats exposed to oxygen-glucose deprivation and subsequent reoxygenation (OGD/R). The results suggested that lncRNA-1810026B05RiK mediates the activation of the nuclear factor-kappaB (NF-κB) signaling pathway by physically binding to NF-kappa-B inhibitor alpha (IκBα) and promoting its phosphorylation, thus leading to neuroinflammation and neuronal apoptosis during cerebral ischemia/reperfusion. In addition, lncRNA-1810026B05Rik knockdown acts as an NF-κB inhibitor in the OGD/R and MCAO/R pathological processes, suggesting that lncRNA-1810026B05Rik downregulation exerts a protective effect on cerebral I/R injury. In summary, the lncRNA-1810026B05Rik has been identified as a critical regulator of neuronal apoptosis and inflammation through the activation of the NF-κB signaling cascade. This discovery uncovers a previously unrecognized role of 1810026B05Rik in the molecular mechanisms underlying ischemic stroke, offering valuable insights into disease pathology. Moreover, its involvement highlights its potential as a novel therapeutic target, paving the way for innovative treatment strategies for stroke patients. Full article

Occluded person re-identification (Re-ID) faces significant challenges, mainly due to the interference of occlusion noise and the scarcity of realistic occluded training data. Although data augmentation is a commonly used solution, the current occlusion augmentation methods suffer from the problem of dual inconsistencies: intra-sample inconsistency is caused by misaligned synthetic occluders (an augmentation operation for simulating real occlusion situations); i.e., randomly pasted occluders ignore spatial prior information and style differences, resulting in unrealistic artifacts that mislead feature learning; inter-sample inconsistency stems from information loss during random cropping (an augmentation operation for simulating occlusion-induced information loss); i.e., single-scale cropping strategies discard discriminative regions, weakening the robustness of the model. To address the aforementioned dual inconsistencies, this study proposes the unified Multi-Aligned and Multi-Scale Augmentation (MA–MSA) framework based on the core principle of ”synthetic data should resemble real-world data”. First, the Frequency–Style–Position Data Augmentation (FSPDA) module is designed: it ensures consistency in three aspects (frequency, style, and position) by constructing an occluder library that conforms to real-world distribution, achieving style alignment via adaptive instance normalization and optimizing the placement of occluders using hierarchical position rules. Second, the Multi-Scale Crop Data Augmentation (MSCDA) strategy is proposed. It eliminates the problem of information loss through multi-scale cropping with non-overlapping ratios and dynamic view fusion. In addition, different from the traditional serial augmentation method, MA–MSA integrates FSPDA and MSCDA in a parallel manner to achieve the collaborative resolution of dual inconsistencies. Extensive experiments on Occluded-Duke and Occluded-REID show that MA–MSA achieves state-leading performance of 73.3% Rank-1 (+1.5%) and 62.9% mAP on Occluded-Duke, and 87.3% Rank-1 (+2.0%) and 82.1% mAP on Occluded-REID, demonstrating superior robustness without auxiliary models. Full article

This in vitro study aimed to quantitatively evaluate the accuracy of reproducing attachments for clear aligner therapy (CAT) using a standardized in-house fabrication protocol and to analyze discrepancies across maxillary tooth types. A custom attachment was designed on a symmetrical master model, and 30 experimental models were fabricated by three-dimensional (3D) printing, template construction, and bonding. Following scanning and superimposition, dimensional, angular, and positional deviations were quantified and statistically analyzed (p < 0.05). Results showed minor mean discrepancies but a consistent pattern of under-reproduction, most evident in the mesial and distal wall angles, as well as in the gingival bevel angle and attachment height. A significant trend was observed in the occlusal bevel, demonstrating marked extrusion in the anterior region that decreased posteriorly. Positional errors were minimal mesiodistally but substantial in the lingual and occlusal directions, with magnitudes varying by tooth type. In conclusion, this study identified consistent, predictable inaccuracies in a simulated in-house attachment reproduction protocol. These findings indicate that similar deviations may occur clinically, potentially affecting the predictability of CAT. Full article

Image blur is a major factor that degrades object detection in agricultural applications, particularly in orchards where crop occlusion, leaf movement, and camera shake frequently reduce image quality. This study proposed a lightweight generative adversarial network, AGG-DeblurGAN, to address non-uniform motion blur in citrus tree images. The model integrates the GhostNet backbone, attention-enhanced Ghost modules, and a Gated Half Instance Normalization Module. A blur detection mechanism enabled dynamic routing, reducing computation on sharp images. Experiments on a citrus dataset showed that AGG-DeblurGAN maintained restoration quality while improving efficiency. For object detection, restored citrus images achieved an 86.4% improvement in mAP@0.5:0.95, a 76.9% gain in recall, and a 40.1% increase in F1 score compared to blurred images, while the false negative rate dropped by 63.9%. These results indicate that AGG-DeblurGAN can serve as a reference for improving image preprocessing and detection performance in agricultural vision systems. Full article

Obstructive coronary artery disease (CAD) is common in patients undergoing transcatheter aortic valve implantation (TAVI). While invasive coronary angiography (ICA) is the gold standard for coronary evaluation, coronary computed tomography angiography (cCTA) is gaining interest for its potential to exclude obstructive CAD during pre-procedural imaging. This study aimed to assess the diagnostic accuracy of cCTA in ruling out significant CAD in TAVI candidates. We retrospectively analyzed 95 TAVI candidates (mean age 77.7 ± 8.5 years) who underwent both cCTA and ICA. Diagnostic performance of cCTA—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy—was assessed using ICA as the reference, in both patient- and vessel-based models. Obstructive CAD was defined as ≥50% luminal stenosis or occlusion of a stent/bypass graft. ICA detected obstructive CAD in 27 patients (28.4%). Excluding non-evaluable cases, cCTA showed a negative predictive value (NPV) of 97% (patient-level) and 95% (vessel-level), with a diagnostic accuracy of 85% and 87%, respectively. Including all patients, regardless of scan quality, the NPV remained high (97%), although overall accuracy dropped to 67% (patient-level) and 66% (vessel-level). cCTA demonstrated high accuracy in excluding significant CAD, with a stable NPV of 95–97%. The relatively high rate of non-diagnostic scans and the single-center, retrospective design suggest that its role should be considered complementary to ICA, potentially reducing—but not replacing—the need for ICA in selected TAVI candidates. Full article

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8,250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Lane detection is a fundamental component of perception systems in autonomous driving. Despite significant progress in this area, existing methods still face challenges in complex scenarios such as abnormal weather, occlusions, and curved roads. These situations typically demand the integration of both the global semantic context and local visual features to predict the lane position and shape. This paper presents ORANet, an enhanced lane detection framework built upon the baseline CLRNet. ORANet incorporates two novel modules: Enhanced Coordinate Attention (EnCA) and Channel–Spatial Shuffle Attention (CSSA). EnCA models long-range lane structures while effectively capturing global semantic information, whereas CSSA strengthens the precise extraction of local features and provides optimized inputs for EnCA. These components operate in hierarchical synergy, collectively establishing a complete enhancement pathway from refined local feature extraction to efficient global feature fusion. The experimental results demonstrate that ORANet achieves greater performance stability than CLRNet in complex roadway scenarios. Notably, under shadow conditions, ORANet achieves an F1 score improvement of nearly 3% over CLRNet. These results highlight the potential of ORANet for reliable lane detection in real-world autonomous driving environments. Full article

Objectives: The current study aimed to summarize the current evidence on vertical control provided by the bonded rapid palatal expander (BRPE) in pediatric patient populations within 6 months after expansion. Methods: Relevant studies were screened independently by two researchers from the eight databases MEDLINE (PubMed), Web of Science, SCOPUS, Embase, Cochrane, LILACS (Latin American and Caribbean Health Sciences Literature), LIVIVO and Google Scholar, and supplemented by a manual search of the reference lists from studies selected for full-manuscript reading. Relevant data from lateral cephalograms taken pre- and post-expansion was extracted. A meta-analysis was performed with RStudio and a risk of bias assessment of the included articles was completed. Results: Ten relevant studies were included for data extraction, although most had a high risk of bias. The meta-analysis revealed that within 6-month retention after BRPE treatment, there were (1) slight increases in total (0.83 mm), upper (0.57 mm), and lower (0.70 mm) facial height; (2) minimum change in the palatal plane angulation (−0.01°); (3) minimum change in the occlusal angulation (−0.04°); and (4) minimal mandibular plane angulation changes with 0.01° increase in SN-GoGn angle, 0.71° increase in SN-MP angle, 0.17° increase in FMA, and 0.82° increase in PP-GoGn angle. Conclusion: Current evidence indicates that BRPEs may not control or reduce the vertical dimension significantly within 6 months after expansion. Further high-quality studies, particularly on hyperdivergent patients, are needed to clarify whether bonded expanders offer advantages over traditional banded expanders in management of the vertical dimension. Full article

Malignant cerebral infarction (MCI) is rare but often fatal. Early identification helps guide monitoring and decompressive surgery. This study evaluated whether serum biomarkers add predictive value beyond clinical and imaging data in severe stroke patients with anterior circulation large vessel occlusion (LVO). In this prospective study, 73 acute severe LVO stroke patients underwent whole-brain CT perfusion (CTP) with rCBV-based core measurement at admission and follow-up MRI at 24 ± 12 h for infarct and edema volume assessment. Serum biomarkers (s100b, NSE, VEGF, ICAM1) were sampled a median of 20.5 h after baseline imaging. Logistic regression models predicted MCI using baseline variables (NIHSS, ASPECTS, rCBV < 30%), adding treatment data (rtPA, mTICI, NIHSS posttreatment), and adding serum biomarkers. Performance was assessed by AUC, accuracy, F1, and cross-validated R². MCI occurred in 18/73 (24%) patients. Baseline models showed an AUC of 0.72; adding treatment improved the AUC to 0.88. Biomarkers slightly increased the AUC (0.90) but did not improve F1. Higher s100b was associated with more severe injury but did not enhance the prediction of MCI. Models with baseline imaging and treatment best explained infarct (R² ≈ 0.27) and edema (R² ≈ 0.58). In conclusion, admission severity, CTP, and early treatment response are the main predictors of MCI and aid early risk stratification of patients. Despite their pathophysiologic relevance, serum biomarkers do not add substantial predictive value. Full article

Background and Clinical Significance: Moyamoya disease (MMD) is a progressive intracranial vasculopathy characterized by stenosis or occlusion of the terminal internal carotid arteries and the development of fragile collateral networks. It predisposes patients to ischemic and hemorrhagic strokes. Although both direct and indirect revascularization procedures are recommended to restore cerebral blood flow, recurrent cerebrovascular events may still occur, and delayed hemorrhage following revascularization is particularly uncommon. Case Description: We report the case of a 42-year-old woman who presented with seizure, syncope, and aphasia. Cranial computed tomography (CT) revealed a large left temporal–insular intraparenchymal hematoma with a midline shift. Computed tomography angiography (CTA) demonstrated bilateral internal carotid artery narrowing and collateral vessel proliferation, without aneurysm. Her history indicated a hemorrhagic stroke 15 years earlier, at which time MMD was diagnosed by magnetic resonance angiography (MRA) and managed with multiple burr hole surgeries. She remained free of cerebrovascular events until the current presentation. The patient underwent emergent hematoma evacuation, followed by intensive care management. Postoperatively, she demonstrated neurological improvement, though with residual motor aphasia and right-sided weakness, and was discharged for rehabilitation. Conclusions: This case underscores the rare occurrence of delayed intracerebral hemorrhage 15 years after indirect revascularization in MMD. Although revascularization surgery remains the standard therapeutic approach, this report highlights the importance of sustained long-term surveillance, strict risk factor management, and careful postoperative follow-up. The key point is that late hemorrhagic complications, though uncommon, must be considered in the long-term care of MMD patients following revascularization. Full article

This work aimed to quantify axial deformations of a human premolar during occlusion with its antagonist and to compare them with the same premolar restored with a ceramic crown. The deformations were put under stress using a mechanical press with a force ranging from 1 to 100 Newtons. These deformations were quantified using the optical interferometry technique with a laser source (633 nm, 0.95 mW). Using a CMOS camera, interference fringes were obtained, stored, and subsequently processed. The premolars were restored with Cerasmart GC ceramic, using the CAD-CAM system. The average deformations of healthy premolars were found to be in a range of 0.69 to 1.74 µm, while the restored ones were deformed in a range of 0.53 to 1.10 µm. The results of this work showed that the Cerasmart ceramic material had similar properties to those of the natural tooth for small forces. However, for higher forces, the ceramics increased the coronal stiffness of the tooth. This modified the optimal combination of stiffness, strength, and resilience between the enamel and dentin, causing a decrease in the tooth’s ability to dissipate energy; therefore, the tooth could receive more stress. The observed mechanical properties lead to the conclusion that the Cerasmart material can be indicated for the restoration of anterior and premolar teeth in most cases where a fixed prosthesis is required. Full article

This in vitro study evaluated the fracture resistance of metal–ceramic crowns repaired with milled hybrid resin, printed hybrid resin, lithium disilicate, and direct composite resin. One hundred crowns were fabricated, fractured under controlled loading, and 80 with standardized defects were randomly assigned to four groups (n = 20). Repairs were performed using CAD/CAM onlays or direct composite, followed by compressive testing until fracture. Mean fracture resistance values ranged from 1858.95 N to 1997 N across all groups, exceeding typical posterior occlusal forces (700–900 N). No statistically significant differences were found among groups (p = 0.200). Most failures were cohesive. These results indicate that both digital (milled and printed) and direct techniques offer sufficient strength to serve as minimally invasive and cost-effective alternatives to full crown replacement. Although limited by the in vitro design, this study supports the applicability of modern repair approaches in daily practice. Full article

Face recognition systems typically face actual challenges like facial pose, illumination, occlusion, and ageing that significantly impact the recognition accuracy. In this paper, a robust face recognition system that uses Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and face alignment with an enhanced FaceNet for facial embedding extraction is presented. The enhanced FaceNet uses attention mechanisms to achieve more discriminative facial embeddings, especially in challenging scenarios. In addition, an Adaptive Feature Fusion module synthetically combines identity-specific embeddings with context information such as pose, lighting, and presence of masks, hence enhancing robustness and accuracy. Training takes place using the CelebA dataset, and the test is conducted independently on LFW and IJB-C to enable subject-disjoint evaluation. CelebA has over 200,000 faces of 10,177 individuals, LFW consists of 13,000+ faces of 5749 individuals in unconstrained conditions, and IJB-C has 31,000 faces and 117,000 video frames with extreme pose and occlusion changes. The system introduced here achieves 99.6% on CelebA, 94.2% on LFW, and 91.5% on IJB-C and outperforms baselines such as simple MTCNN-FaceNet, AFF-Net, and state-of-the-art models such as ArcFace, CosFace, and AdaCos. These findings demonstrate that the proposed framework generalizes effectively between datasets and is resilient in real-world scenarios. Full article

Background: Ischemia with no obstructive coronary artery disease (INOCA) is characterized by myocardial ischemia in the absence of significant coronary artery stenosis. Despite the lack of major obstructive lesions, patients often present with chest pain, making diagnosis and management a significant challenge. Materials and Methods: A comprehensive search strategy of electronic databases (2000 to 2024) was used to identify studies assessing pathophysiology, diagnosis, surgical treatments, interventions, and outcomes in INOCA. Clinical trials, observational studies, case-control studies, and cohort studies were included. Results: Emerging surgical treatments may have a role in certain subgroups of INOCA patients, particularly those with severe and persistent symptoms or underlying pathophysiological factors that do not respond adequately to pharmacological therapies. Transmyocardial revascularization (TMR) and sympathetic denervation procedures reduce coronary vasospasm in refractory angina. Trials have shown promise for coronary sinus occlusion. Autologous stem cell therapy is an innovative surgical approach that has shown promise in early trials but remains investigational. Selective surgical cardiac vein retroperfusion remains largely experimental, with limited clinical data. Conclusions: This review highlights the need for ongoing research and clinical trials to assess the effectiveness of surgical and nonsurgical options in INOCA. Although current data on surgical interventions is limited, these treatments may offer hope for patients with refractory symptoms. A personalized and multidisciplinary approach to management is essential for optimal patient outcomes. Full article