Search Results (1,907)

Background/Objectives: The KRAS rs61764370 T>G single-nucleotide polymorphism (SNP), located in a let-7 microRNA binding site within the 3′ untranslated region (3′UTR) of the KRAS gene, may modulate tumor aggressiveness by altering post-transcriptional gene regulation. This study evaluated its association with adverse histopathological features in colorectal cancer (CRC). Methods: A preliminary study on 83 CRC patients carrying either the TT (wild-type, n = 64) or TG (heterozygous, n = 19) genotype was analyzed. Clinicopathological variables included patient sex, tumor location, American Joint Committee on Cancer (AJCC) staging system, histological grade, perineural invasion (PNI), and lymphovascular invasion (LVI). A composite “tumor aggressiveness” score was defined based on the presence of Grade 3 differentiation, LVI, and/or PNI. Group comparisons were performed using the Chi-square test or Fisher’s exact test, as appropriate. Results: No statistically significant differences were observed in sex (p = 0.689), tumor location (p = 0.781), or stage at diagnosis (p = 0.812). Poorly differentiated tumors (Grade 3) were present in 20.3% of TT patients and absent in TG carriers (p = 0.06), while low-grade tumors (Grade 1) were more prevalent among TG patients (47.4%) compared to TT (29.7%). The composite high-aggressiveness score was lower in TG (36.8%) than in TT (48.4%), while co-occurrence of PNI and LVI was similar in both groups (~26%). Conclusions: Although no significant associations were identified, TG carriers showed a tendency toward lower-grade, less aggressive tumors. Given the limited sample size, these findings should be interpreted with caution, necessitating larger cohorts in order to validate results. Full article

Body composition, specifically the quantification of skeletal muscle and adipose tissue using preoperative computed tomography (CT) imaging, is a clinically significant predictor of postoperative complications after lung cancer surgery. The main features of CT-derived body composition analysis are: skeletal muscle index, muscle density, adipose tissue quantification and automated or semi-automated segmentation. Low skeletal muscle mass (sarcopenia) independently increases the risk of perioperative complications, including respiratory complications, and is associated with longer hospital length of stay and worse long-term survival. Sarcopenic obesity—characterized by low muscle mass in the context of high adiposity—further elevates complication risk and prolongs recovery. CT-derived measures such as muscle cross-sectional area, muscle density, and adipose tissue distribution (visceral, subcutaneous, and intramuscular) provide more precise risk stratification than BMI alone. Skeletal muscle area and density are inversely correlated with postoperative complications and recurrence risk; patients with lower muscle mass and density experience more adverse outcomes. In men, age and reduced skeletal muscle area are particularly strong predictors of complications after pneumonectomy. Obesity, when not accompanied by sarcopenia or myosteatosis, may confer a survival advantage—the so-called “obesity paradox”—but this protective effect is lost in patients with low muscle mass or poor muscle quality. Systemic inflammation and nutritional status further modulate the impact of body composition on surgical risk. This review highlights the critical role of CT-derived body composition analysis in predicting postoperative outcomes following lung cancer surgery. Full article

Background: Accurate detection of cervical lymph node metastases is a critical determinant of prognosis and treatment planning in head and neck squamous cell carcinoma (HNSCC). Although ultrasound-guided fine-needle aspiration biopsy (USgFNAB) is widely used as a minimally invasive diagnostic tool, its sensitivity for detecting occult metastases remains limited. Current preoperative staging modalities are further constrained by operator dependency and suboptimal specificity in early-stage disease. Integration of molecular diagnostics, particularly the analysis of long non-coding RNAs (lncRNAs), represents a promising strategy to enhance diagnostic accuracy. Objective: This review synthesizes the current evidence on lncRNA expression profiles in HNSCC, with an emphasis on their association with lymph node metastasis and potential application in FNAB-derived material for pre-treatment staging. Methods: A structured literature search was conducted, focusing on studies evaluating lncRNA expression profiles in HNSCC and their relevance to lymph node metastasis, with a particular focus on the feasibility of analysis of USgFNAB samples. Results: Multiple lncRNAs, including HOTAIR, MALAT1, UCA1, TUG1, AFAP1-AS1, H19, MEG3, and ADAMTS9-AS2, have been implicated in metastatic progression through their involvement in diverse mechanisms such as epithelial-to-mesenchymal transition, chromatin remodeling, angiogenesis, and pre-metastatic niche formation. Elevated expression of several of these transcripts correlates with adverse clinicopathological features, including advanced tumor stage, extranodal extension, and reduced survival. However, no studies have profiled lncRNA expression in matched primary tumors and metastatic lymph nodes, and transcriptomic analysis of FNAB samples remains largely unexplored in HNSCC. Conclusions: lncRNAs represent promising molecular biomarkers for enhancing the sensitivity and specificity of USgFNAB in detecting occult cervical metastases. Future research should prioritize paired tumor–node lncRNA profiling, validation of FNAB-based molecular assays, and integration of multi-omics data for predictive modeling. Overall, integrating lncRNA analysis into ultrasound-guided fine-needle aspiration biopsy may enhance the detection of occult nodal metastases in head and neck squamous cell carcinoma and support more accurate nodal staging in clinically node-negative patients. Full article

Background: Persistent pain following orthopaedic trauma is common, often disproportionate to structural healing, and increasingly interpreted as reflecting centrally mediated pain mechanisms. However, the mechanisms, clinical features, diagnostic approaches, prognostic indicators, and management strategies relevant to trauma-related central sensitisation (CS) remain poorly understood. Objective: To map and synthesise existing evidence on CS following orthopaedic trauma, addressing mechanistic pathways, clinical manifestations, epidemiology, assessment methods, management approaches, and health system implications. Methods: A scoping review was conducted in accordance with PRISMA-ScR. Twenty-one studies met the eligibility criteria, comprising nine primary trauma cohorts and 12 contextual mechanistic or review studies relevant to trauma-associated CS. Data were charted across six prespecified domains of mechanistic processes, clinical presentation and diagnostic features, epidemiology and prognosis, assessment tools and outcome measures, interventions, and health system and care delivery considerations. Results: Mechanistic studies demonstrated trauma-induced neuroimmune activation, altered cortical and spinal excitability, and molecular pathways consistent with sensitisation. Clinical studies have identified neuropathic features, widespread pain, and heightened sensory responsiveness following fractures and other injuries. Neurophysiological evidence has indicated early cortical disinhibition following upper limb trauma, whereas epidemiological cohorts have reported persistent pain and disability years after major trauma. Measurement studies have highlighted the limited reliability and specificity of current tools in trauma populations, including quantitative sensory testing and self-report instruments. Early predictors of adverse trajectories include severe acute pain, neuropathic descriptors, psychological distress, and opioid-dominant analgesia. Evidence regarding early intervention, rehabilitation strategies, and system-level screening pathways remains limited. Conclusions: Central sensitisation (CS)–consistent mechanisms after orthopaedic trauma are suggested by convergent mechanistic, neurophysiological, and clinical findings. However, trauma-specific diagnostic criteria, prognostic models, and management frameworks remain underdeveloped. High-quality longitudinal research is needed to clarify early trajectories, refine assessment methods, and establish targeted interventions to reduce long-term pain and disability. Full article

Background/Objectives: Cribriform architecture is an adverse histopathologic feature in prostate cancer and has been associated with poor oncologic outcomes. Emerging evidence suggests that cribriform-positive tumors may behave as a biologically non-localized disease, raising the possibility of early occult dissemination. Lymphovascular invasion (LVI) is a key pathological marker of metastatic potential, but its relationship with cribriform architecture has not been evaluated. We examined the association between cribriform morphology and LVI to provide biological context for the aggressive clinical course of cribriform-positive prostate cancer. Methods: We performed a retrospective analysis of patients with prostate adenocarcinoma who underwent radical prostatectomy and had available clinicopathologic data. Cribriform architecture was determined by a centralized pathology review, and LVI status was obtained from original pathology reports. Unadjusted associations were evaluated using contingency tables. Multivariable logistic regression was used to assess whether cribriform architecture was independently associated with LVI after adjustments for Gleason score, tumor stage, and nodal status. Results: Among 338 patients, 28 (8.3%) had LVI and 123 (36.4%) had cribriform architecture. LVI was more common in cribriform-positive than cribriform-negative tumors (17.9% vs. 2.8%; p < 0.001), corresponding to a crude odds ratio (OR) of 7.6 (95% CI, 3.0–19.3). Cribriform architecture remained independently associated with LVI after adjustment (adjusted OR, 5.20; 95% CI, 2.12–1.40; p < 0.001). Conclusions: Cribriform architecture is strongly and independently associated with LVI, supporting a biological link between cribriform morphology and early metastatic dissemination. These findings support the design of prospective, biomarker-driven studies to evaluate treatment intensification strategies in this high-risk subgroup. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

Lung cancer remains a major public health challenge due to high incidence and mortality. The chemokine receptor CXCR4 and its ligand CXCL12 (SDF-1) constitute a critical axis in tumor biology, influencing tumor cell proliferation, invasion, angiogenesis, and immune evasion. Aberrant CXCR4 expression is frequently observed in lung cancer and is closely associated with adverse prognosis, enhanced metastatic potential, and therapeutic resistance. Mechanistically, CXCR4 activates signaling pathways including PI3K/AKT, MAPK/ERK, JAK/STAT, and FAK/Src, promoting epithelial–mesenchymal transition, stemness, and survival. The CXCL12/CXCR4 axis also orchestrates interactions with the tumor microenvironment, facilitating chemotaxis toward CXCL12-rich niches (e.g., bone marrow and brain) and modulating anti-tumor immunity via regulatory cells. Regulation of CXCR4 occurs at transcriptional, epigenetic, and post-transcriptional levels, with modulation by hypoxia, inflammatory signals, microRNAs, and post-translational modifications. Clinically, high CXCR4 expression correlates with metastasis, poor prognosis, and reduced response to certain therapies, underscoring its potential as a prognostic biomarker and therapeutic target. Therapeutic strategies targeting CXCR4 include small-molecule antagonists (e.g., AMD3100/plerixafor; balixafortide), anti-CXCR4 antibodies, and CXCL12 decoys, as well as imaging probes for patient selection and response monitoring (e.g., 68Ga-pentixafor PET). Preclinical and early clinical studies suggest that CXCR4 blockade can impair tumor growth, limit metastatic spread, and enhance chemotherapy and immunotherapy efficacy, although hematopoietic side effects and infection risk necessitate careful therapeutic design. This review synthesizes the molecular features, regulatory networks, and translational potential of CXCR4 in lung cancer and discusses future directions for precision therapy and biomarker-guided intervention. Full article

Accurate quantification of field-grown rice seedlings is essential for evaluating yield potential and guiding precision field management. Unmanned aerial vehicle (UAV)-based remote sensing, with its high spatial resolution and broad coverage, provides a robust basis for accurate seedling detection and population density estimation. However, in previous studies, UAVs were typically employed at relatively low altitudes, which provided high-resolution imagery and facilitated seedling recognition but limited efficiency. To enable large-area monitoring, higher flight altitudes are required, which reduces image resolution and adversely affects rice seedling recognition accuracy. In this study, UAVs were flown at a height of 30 m, and the resulting lower-resolution imagery, combined with the small size of seedlings, their dense spatial distribution, and the complex field background, necessitated algorithmic improvements for accurate detection. To address these challenges, we propose an enhanced You Only Look Once version 8 nano (YOLOv8n)-based detection model specifically designed to improve seedling recognition under high-altitude UAV imagery. The model incorporates an improved Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature fusion and small-object detection, a Global-to-Local Spatial Aggregation (GLSA) module for enriched spatial context modeling, and a Content-Guided Attention Fusion (CGAFusion) module to enhance discriminative feature learning. Experiments on high-altitude UAV imagery demonstrate that the proposed model achieves an mAP@0.5 of 94.7%, a precision of 91.0%, and a recall of 91.2%, representing a 2.3% improvement over the original YOLOv8n. These results highlight the model’s innovation in handling high-altitude UAV imagery for large-area rice seedling detection, demonstrating its effectiveness and practical potential under complex field conditions. Full article

Ensuring sustained driver attention is critical for intelligent transportation safety systems; however, the performance of data-driven driver distraction detection models is often limited by the high cost of large-scale manual annotation. To address this challenge, this paper proposes an adaptive semi-supervised driver distraction detection framework based on teacher–student learning and deformable pyramid feature fusion. The framework leverages a limited amount of labeled data together with abundant unlabeled samples to achieve robust and scalable distraction detection. An adaptive pseudo-label optimization strategy is introduced, incorporating category-aware pseudo-label thresholding, delayed pseudo-label scheduling, and a confidence-weighted pseudo-label loss to dynamically balance pseudo-label quality and training stability. To enhance fine-grained perception of subtle driver behaviors, a Deformable Pyramid Sparse Transformer (DPST) module is integrated into a lightweight YOLOv11 detector, enabling precise multi-scale feature alignment and efficient cross-scale semantic fusion. Furthermore, a teacher-guided feature consistency distillation mechanism is employed to promote semantic alignment between teacher and student models at the feature level, mitigating the adverse effects of noisy pseudo-labels. Extensive experiments conducted on the Roboflow Distracted Driving Dataset demonstrate that the proposed method outperforms representative fully supervised baselines in terms of mAP@0.5 and mAP@0.5:0.95 while maintaining a balanced trade-off between precision and recall. These results indicate that the proposed framework provides an effective and practical solution for real-world driver monitoring systems under limited annotation conditions. Full article

This structured narrative review compared the efficacy, durability, and safety of anti-vascular endothelial growth factor (anti-VEGF) agents and intravitreal corticosteroids for the treatment of diabetic macular edema (DME), with the aim of identifying patient- and disease-specific factors to guide individualize therapy. A comprehensive search of PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov was conducted for studies published between January 2009 and November 2025, including randomized controlled trials, meta-analyses, and large observational cohorts with at least six months of follow-up. Visual acuity, anatomical outcomes, treatment burden, durability, and safety were extracted, and evidence quality was assessed using the GRADE framework. Eleven studies encompassing 1341 eyes were included. Anti-VEGF therapy consistently produced greater improvements in best-corrected visual acuity, particularly in treatment-naïve eyes and in patients with worse baseline vision, whereas corticosteroids achieved larger reductions in central macular thickness and significantly reduced injection burden because of longer durability. However, corticosteroid therapy was associated with higher rates of intraocular pressure elevation and cataract progression. In pseudophakic patients and in chronic or refractory DME, functional and anatomical outcomes were generally comparable between the two therapeutic classes. Combination therapy resulted in the greatest anatomical improvement but at the cost of increased ocular adverse events. Overall, anti-VEGF agents remain the preferred first-line treatment for most patients with DME owing to superior visual outcomes and a more favorable safety profile, while corticosteroids represent valuable alternatives in pseudophakic eyes, chronic or anti-VEGF–refractory DME, and cases with prominent inflammatory features, provided that careful monitoring for ocular adverse events is maintained. Full article

Efficient cooling systems are crucial for achieving high efficiency and power density in electric vehicle motors. To enhance motor cooling performance, a novel oil spray cooling system was developed, referred to as the centripetal-inclined oil spray (CIOS) cooling system. The CIOS cooling system features axial oil channels evenly distributed on the surface of the stator core, with each channel connected at both ends to stepped oil channels. This configuration allows for direct oil spraying towards the center at specific inclined angles without the need for additional components such as nozzles, oil spray rings, and oil spray tubes, which reduces costs, minimizes the risk of oil leakage, and enhances motor reliability. Electromagnetic and computational fluid dynamic simulations were conducted on the motor with the CIOS cooling system. The results indicated that the CIOS cooling system adversely impacted core losses and torque, while these effects were minimized after optimization, with losses increasing by up to 0.29% and torque decreasing by up to 0.45%. The CIOS cooling system achieved stable oil spraying, forming oil films on the end-winding with a maximum formation rate of 49.4% and an average thickness of 1.56 mm. Compared to the motor with oil spray rings, the motor with the CIOS cooling system exhibited lower temperatures across all components and more uniform cooling. Finally, the cooling performance of the CIOS cooling system was verified through experiments, and the results showed that the measured temperature closely matched the simulated results, with a maximum error of 5.9%. The findings in this study are expected to provide new insights for optimizing oil cooling systems in electric vehicle motors. Full article

Background: To investigate the predictive value of cardiac magnetic resonance (CMR) feature parameters for major adverse cardiovascular events (MACEs) in patients with nondilated left ventricular cardiomyopathy and reduced left ventricular ejection fraction (NDLVC-rLVEF). Methods: This single-center retrospective study enrolled patients with NDLVC-rLVEF who underwent CMR between January 2015 and May 2025. MACEs included cardiovascular death, implantable cardioverter–defibrillator (ICD) discharge, and hospitalization due to heart failure or arrhythmia. Multivariable Cox regression analysis was used to identify independent risk factors for MACEs. Results: A total of 160 patients were included (mean age: 50.83 ± 15.81 years; 114 males, 46 females), with a median follow-up time of 53.00 months (IQR: 32.25–82.00). During this period, 41 patients (25.63%) experienced MACEs, including 10 cases of cardiovascular death, 1 case of ICD discharge, and 30 cases of rehospitalization due to heart failure or arrhythmia. Multivariable Cox regression analysis revealed that right ventricular ejection fraction (RVEF) and left ventricular global radial strain (LVGRS) were independent predictors of MACEs in patients with NDLVC-rLVEF. Kaplan–Meier analysis further demonstrated that patients with RVEF < 37% or LVGRS < 13% had a significantly higher incidence of MACEs (p < 0.05). Conclusions: Multimodal CMR parameters (RVEF and LVGRS) have significant predictive value for adverse prognosis in patients with NDLVC-rLVEF, facilitating early risk stratification and clinical intervention. Full article

Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness for diverse site applications. We introduce the Effective Inlier Count ($N_{eff}$) as a lightweight gating mechanism to evaluate the spatial quality of feature points and dynamically weigh sensor modalities in real-time. By employing a $20\times 16$ grid-based spatial filtering algorithm, the system effectively suppresses the influence of geometric burstiness without significant computational overhead on server-side processing. Validation experiments across various real-world scenarios demonstrate that the proposed method maintains high geometric registration accuracy where traditional RGB-only methods fail. In texture-less and specular conditions, the system consistently maintained an average Intersection over Union (IoU) above 0.72, while the baseline suffered from complete tracking loss or significant drift. These results confirm that thermal-RGB integration ensures operational availability and improves long-term stability by mitigating modality-specific noise. This approach offers a reliable solution for various drone-based AEC tasks, particularly in GPS-denied or adverse environments. Full article

Text-to-video retrieval offers an intelligent solution for Internet of Vehicles (IoV) users to access desired content on demand. However, the constrained communication channels in IoV, characterized by low signal-to-noise ratios (SNR), pose significant obstacles to retrieval performance. To tackle these issues, this study presents SemTVR, a semantic communication framework dedicated to achieving superior robustness in text-to-video retrieval tasks in low-SNR IoV environments. By integrating the semantic communication paradigm with edge–cloud collaboration, our architecture leverages roadside unit (RSU) features and cloud resources to enable collaborative retrieval. We introduce a multi-semantic interactive reliable transmission mechanism that utilizes historical search records to enhance semantic recovery accuracy under adverse channel conditions. Furthermore, we devise a cross-modal fine-grained matching strategy assigning differentiated weights to video content and query sentences. Experimental results conducted on authoritative datasets demonstrate that SemTVR significantly outperforms baseline methods in terms of search accuracy, particularly in low SNR scenarios, validating its effectiveness for future IoV applications. Full article

Improving city crossroads safety is a critical problem for modern smart transportation systems (STS). This article presents the results of developing, upgrading, and comprehensively experimentally testing an acoustic monitoring system prototype designed for rapid accident detection. Unlike conventional camera- or lidar-based approaches, the proposed solution uses passive sound source localization to operate effectively with no direct visibility and in adverse weather conditions, addressing a key limitation of camera- or lidar-based systems. Generalized Cross-Correlation with Phase Transform (GCC-PHAT) algorithms were used to develop a hardware–software complex featuring four microphones, a multichannel audio interface, and a computation module. This study focuses on the gradual upgrading of the algorithm to reduce the mean localization error in real-life urban conditions. Laboratory and complex field tests were conducted on an open-air testing ground of a university campus. During these tests, the system demonstrated that it can accurately determine the coordinates of a sound source imitating accidents (sirens, collisions). The analysis confirmed that the system satisfies the V2X infrastructure integration response time requirement (<200 ms). The results suggest that the system can be used as part of smart transportation systems. Full article