Search Results (10,077)

Background: Renal cell carcinoma (RCC) is a common malignancy with a rising global incidence. While cytoreductive nephrectomy (CRN) was historically a cornerstone in the management of metastatic RCC (mRCC), its role has been questioned following pivotal trials such as CARMENA and SURTIME. With the advent of immune checkpoint inhibitors (ICIs) and targeted therapies, the contemporary relevance of CRN coupled with first-line immunotherapy and targeted therapy combination regimens warrants re-evaluation. Methods: This retrospective cohort study utilized the TriNetX research network to identify patients aged 18–90 years diagnosed with mRCC between 2005 and 2024 who received first-line systemic therapies. Patients were stratified into two cohorts based on receipt of CRN status within one year of diagnosis. Propensity score matching (1:1) was done to adjust baseline characteristics. Kaplan–Meier survival analysis and Cox proportional hazards modeling were used to compare five-year overall survival between the groups. Results: Among 5960 eligible patients, 1776 (888 CRN matched to 888 who did not) formed the cohort of analysis. The CRN group demonstrated significantly higher five-year survival (57.7% vs. 45.0%, p < 0.0001) with a hazard ratio of 1.56 (95% CI: 1.33–1.83). Subgroup analyses showed consistent survival benefits across all four NCCN-recommended first-line regimens—Axitinib + Pembrolizumab: 64.0% (CRN) vs. 53.3% (no CRN), p = 0.01; Cabozantinib + Nivolumab: 50.1% vs. 40.4%, p = 0.004; Lenvatinib + Pembrolizumab: 37.4% vs. 22.8%, p = 0.012; Nivolumab + Ipilimumab: 56.4% vs. 46.1%, p = 0.005. Conclusions: In the era of modern immunotherapy and targeted agents, CRN remains associated with improved survival in patients with mRCC receiving NCCN-recommended first-line regimens. These findings support the continued evaluation of CRN as a component of multimodal therapy, particularly in patients with favorable risk profiles. Full article

This research advances the integration of terrestrial laser scanning (TLS) in heritage documentation, targeting the development of holistic and practical guidance for practitioners to adopt the technology effectively. Acknowledging the pivotal role of TLS in capturing detailed and accurate representations of cultural heritage, the study emerges against a backdrop of technological progression and the evolving needs of heritage conservation. Through a comprehensive literature review, critical case studies of heritage sites in the U.S., expert interviews, and the development of a TLS for Heritage Documentation Best Practice Guide (the guide), the paper addresses the existing gaps in streamlined practices in the domain of TLS’s applications in heritage documentation. While recognizing and building upon foundational efforts such as international guidelines developed over the past decades, this study contributes a practice-oriented perspective grounded in field experience and case-based analysis. The developed guide seeks to equip practitioners with structured methods and practical tools to optimize the use of TLS, ultimately enhancing the quality and accessibility of heritage documentation. It also sets a foundation for integrating TLS datasets with other technologies, such as Building Information Modeling (BIM), virtual reality (VR), and augmented reality (AR) for heritage preservation, tourism, education, and interpretation, ultimately enhancing access to and engagement with cultural heritage sites. The paper also critically situates this guidance within the evolving theoretical discourse on digital heritage practices, highlighting its alignment with and divergence from existing methodologies. Full article

Globally, endometriosis affects almost 10% of reproductive-aged women, leading to chronic pain and discomfort. Endocrine-disrupting compounds (EDCs) seem to play a pivotal role as a causal factor. The current manuscript aims to explain potential molecular pathways, synthesize current evidence regarding EDCs as causative agents of endometriosis, and highlight implications in the general population and clinical work. A thorough review of experimental, epidemiologic, and mechanistic research studies was conducted to explain the association between EDCs and endometriosis. Among the primary EDCs under investigation are polychlorinated biphenyls, dioxins, phthalates, and bisphenol A (BPA). Despite methodological heterogeneity and some discrepancies, epidemiologic evidence supports a positive association between some increased levels of BPA, phthalates, and dioxins in urine or in blood, and endometriosis. Experiments support some effect of EDCs on endometrial cells and causing endometriosis. EDCs function as xenoestrogens, alter immune function, induce oxidative stress, and disrupt progesterone signaling. Epigenetic reprogramming may play a role in mediating EDC-induced endometriosis. Endocrine, immunological, and epigenetic pathways link EDCs and endometriosis. Prevention techniques require deeper comprehension of those factors. Causal linkages and possible treatment targets should be based on longitudinal studies and multi-omics techniques. Restriction of EDCs could be beneficial for endometriosis prevalence limitation. Full article

Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification. Full article

Fructose-1,6-bisphosphatase (FBP) is a crucial regulatory enzyme in sucrose synthesis and photosynthetic carbon assimilation, functioning through two distinct isoforms: cytosolic FBP (cyFBP) and chloroplastic FBP (cpFBP). However, the identification and functional characterization of FBP genes in Saccharum remains limited. In this study, we conducted a systematic identification and comparative genomics analyses of FBPs in three Saccharum species. We further examined their expression patterns across leaf developmental zones, spatiotemporal profiles, and responses to diurnal rhythms and hormonal treatments. Our analysis identified 95 FBP genes, including 44 cyFBPs and 51 cpFBPs. Comparative analyses revealed significant divergence in physicochemical properties, gene structures, and motif compositions between the two isoforms. Expression profiling indicated that both cyFBPs and cpFBPs were predominantly expressed in leaves, particularly in maturing and mature zones. During diurnal cycles, their expression peaked around the night–day transition, with cpFBPs exhibiting earlier peaks than cyFBPs. FBP genes in Saccharum spontaneum displayed greater diurnal sensitivity than those in Saccharum officinarum. Hormonal treatments further revealed significant regulatory divergence in FBP genes, both between isoforms and across species. Notably, cyFBP_2 and cpFBP_2 members consistently exhibited higher expression levels across all datasets, suggesting their pivotal roles in sugarcane physiology. These findings not only identify potential target genes for enhancing sucrose accumulation, but also highlight the breeding value of S. spontaneum and S. officinarum in sugarcane breeding. Full article

Astragalus sinicus, a key leguminous green manure widely cultivated in Southern China’s rice-based cropping systems, plays a pivotal role in sustainable agriculture by enhancing soil organic matter sequestration, improving rice yield, and elevating grain quality. The symbiotic nitrogen-fixing association between A. sinicus and its matching rhizobia is fundamental to its agronomic value; however, suboptimal inoculant efficiency and field application methodologies constrain its full potential. To address these limitations, we conducted a multi-phase study involving (1) rhizobial strain screening under controlled greenhouse conditions, (2) an optimized lyophilization protocol evaluating cryoprotectant (trehalose, skimmed milk powder and others), and (3) seed pelleting trails with rhizobial viability and nodulation assessments over different storage periods. Our results demonstrate that Mesorhizobium huakuii CCBAU 33470 exhibits a superior nitrogen-fixing efficacy, significantly enhancing key traits in A. sinicus, including leaf chlorophyll content, tiller number, and aboveground biomass. Lyophilized inoculants prepared with cryoprotectants (20% trehalose or 20% skimmed milk powder) maintained >90% bacterial viability for 60 days and markedly improved nodulation capacity relative to unprotected formulations. The optimized seed pellets sustained high rhizobial loads (5.5 × 10³ cells/seed) with an undiminished viability after 15 days of storage and nodulation ability after 40 days of storage. This integrated approach of rhizobial selection, inoculant formulation, and seed coating overcomes cultivation bottlenecks, boosting symbiotic nitrogen fixation for A. sinicus cultivation. Full article

Chronic low-grade inflammation is a hallmark of both metabolic and neurodegenerative diseases. In recent years, several studies have highlighted the pivotal role of systemic metabolic dysfunction, particularly insulin resistance, in shaping neuroinflammatory processes and contributing to impaired cognitive performance. Among metabolic disorders, type 2 diabetes mellitus has emerged as a major risk factor for the development of age-related neurodegenerative conditions, suggesting a complex and bidirectional crosstalk between peripheral metabolic imbalance and central nervous system function. This review aims to explore the cellular and molecular mechanisms underlying the interaction between metabolic dysregulation and brain inflammation. By integrating current findings from endocrinology, immunology, and neuroscience, this work provides a comprehensive overview of how chronic metabolic inflammation may contribute to the onset and progression of neurodegenerative conditions. This interdisciplinary approach could offer novel insights into potential therapeutic strategies targeting both metabolic and neuroinflammatory pathways. Full article

Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) are aggressive malignancies characterized by a poor prognosis and resistance to conventional therapies. Mounting evidence suggests the pivotal role of epithelial–mesenchymal transition (EMT) in tumor progression, metastasis, and therapeutic resistance in these cancers. Protein induced by vitamin K absence II (PIVKA-II)—a valuable HCC detector—has ultimately emerged as a potentially relevant biomarker in PDAC, serving as both a serum biomarker and a prognostic indicator. This study investigates the putative link between PIVKA-II expression and the EMT process in HCC and PDAC. Using a Western blot analysis and electrochemiluminescence immunoassay (ECLIA), we quantified PIVKA-II serum levels alongside two canonical EMT markers—Vimentin and E-cadherin—in selected cohorts. Emerging data suggest a dual, context-dependent role for PIVKA-II. Beyond its diagnostic value in both malignancies, its co-expression with EMT markers points to a potential mechanistic involvement in tumor invasiveness and phenotypic plasticity. Notably, the selective detection of E-cadherin in HCC implies limited EMT activation and a preservation of the epithelial phenotype, whereas the higher expression of Vimentin in PDAC reflects a more substantial shift toward EMT. We provide a comprehensive analysis of key molecular markers, their involvement in EMT-driven pathophysiological mechanisms, and their potential as novel diagnostic tools. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Chemotherapy remains a cornerstone in the treatment of esophageal cancer (EC), yet chemoresistance remains a critical challenge, leading to poor outcomes and limited therapeutic success. Mitochondrial DNA (mtDNA) has emerged as a pivotal player in mediating these responses, influencing cellular metabolism, oxidative stress regulation, and apoptotic pathways. This review provides a comprehensive overview of the mechanisms by which mtDNA alterations, including mutations and copy number variations, drive chemoresistance in EC. Specific focus is given to the role of mtDNA in metabolic reprogramming, including its contribution to the Warburg effect and lipid metabolism, as well as its impact on epithelial–mesenchymal transition (EMT) and mitochondrial bioenergetics. Recent advances in targeting mitochondrial pathways through novel therapeutic agents, such as metformin and mitoquinone, and innovative approaches like CRISPR/Cas9 gene editing, are also discussed. These interventions highlight the potential for overcoming chemoresistance and improving patient outcomes. By integrating mitochondrial diagnostics with personalized treatment strategies, we propose a roadmap for future research that bridges basic mitochondrial biology with translational applications in oncology. The insights offered in this review emphasize the critical need for continued exploration of mtDNA-targeted therapies to address the unmet needs in EC management and other diseases associated with mitochondria. Full article

The living roots of woody plants in forests play a crucial role in sustaining the soil temperature equilibrium. However, there is limited research investigating the effects of soil temperature balance disruption, influenced by living roots, on soil microarthropods, especially in the context of global climate change. To address this knowledge gap, we conducted a three-year in situ simulation experiment involving either experimental warming or root trenching treatments to mimic environmental changes and their impacts on soil microarthropod communities in a temperate forest ecosystem in Northeast China. Statistical analysis focused on assessing the abundance and family richness of Collembola and Acari. Warming increased soil temperature, while root trenching had contrasting effects. In the absence of root trenching, warming positively influenced Collembola but negatively affected Acari. Conversely, when combined with root trenching, warming had a diminished impact on both Collembola and Acari. Our findings demonstrate that the interactive effects of warming on soil microarthropod communities vary depending on the presence or absence of root trenching. Specifically, within the context of root trenching treatment compared to no-root trenching treatment, warming exhibited a comparatively attenuated influence on soil microarthropod communities. Overall, living roots play a pivotal role in mediating soil temperature conditions, which significantly impact soil microarthropod communities in the context of global climate change. Full article

Industrial catalysts are accelerating the global transition toward renewable energy, serving as enablers for innovative technologies that enhance efficiency, lower costs, and improve environmental sustainability. This review explores the pivotal roles of industrial catalysts in hydrogen production, biofuel generation, and biomass conversion, highlighting their transformative impact on renewable energy systems. Precious-metal-based electrocatalysts such as ruthenium (Ru), iridium (Ir), and platinum (Pt) demonstrate high efficiency but face challenges due to their cost and stability. Alternatives like nickel-cobalt oxide (NiCo₂O₄) and Ti₃C₂ MXene materials show promise in addressing these limitations, enabling cost-effective and scalable hydrogen production. Additionally, nickel-based catalysts supported on alumina optimize SMR, reducing coke formation and improving efficiency. In biofuel production, heterogeneous catalysts play a crucial role in converting biomass into valuable fuels. Co-based bimetallic catalysts enhance hydrodeoxygenation (HDO) processes, improving the yield of biofuels like dimethylfuran (DMF) and γ-valerolactone (GVL). Innovative materials such as biochar, red mud, and metal–organic frameworks (MOFs) facilitate sustainable waste-to-fuel conversion and biodiesel production, offering environmental and economic benefits. Power-to-X technologies, which convert renewable electricity into chemical energy carriers like hydrogen and synthetic fuels, rely on advanced catalysts to improve reaction rates, selectivity, and energy efficiency. Innovations in non-precious metal catalysts, nanostructured materials, and defect-engineered catalysts provide solutions for sustainable energy systems. These advancements promise to enhance efficiency, reduce environmental footprints, and ensure the viability of renewable energy technologies. Full article

Insulin resistance (IR) plays a pivotal role in the pathogenesis of several dermatological diseases, including psoriasis, acne, acanthosis nigricans, and hidradenitis suppurativa (HS). These conditions are characterized by chronic inflammation, oxidative stress, and metabolic dysfunction, which are exacerbated by IR. This narrative review examines the emerging role of nutraceutical insulin-sensitizing agents (ISAs), including myo-inositol, alpha-lipoic acid, vitamin D, vitamin C, and folic acid, in managing IR-related dermatological disorders. A comprehensive literature search was conducted across Cochrane Library and MEDLINE (1965–May 2025), focusing on clinical trials involving nutraceutical ISAs in dermatological conditions associated with IR. Only human studies published in English were included. Evidence from randomized controlled trials (RCTs) and observational studies suggests that ISAs improve glycemic control, reduce oxidative stress, and modulate inflammatory pathways in IR-related dermatoses. Notably, myo-inositol combined with magnesium and folic acid has demonstrated significant reductions in acne severity, hirsutism, and quality-of-life impairments in women with polycystic ovary syndrome. Similar benefits have been observed in psoriasis and HS, though data remain limited. Nutraceutical ISAs offer a promising adjunctive approach for the management of IR-associated dermatological diseases, potentially addressing both metabolic dysfunction and skin inflammation. However, robust RCTs with long-term follow-up are needed to confirm these preliminary findings and to establish optimal treatment regimens. Full article

This study presented a comprehensive investigation into the performance of object detection models tailored for edge devices, particularly in the context of Unmanned Aerial Vehicle swarms. Object detection plays a pivotal role in enhancing autonomous navigation, situational awareness, and target tracking capabilities within UAV swarms, where computing resources are constrained by the onboard low-cost computers. Initially, a thorough review of the existing literature was conducted to identify state-of-the-art object detection models suitable for deployment on edge devices. These models are evaluated based on their speed, accuracy, and efficiency, with a focus on real-time inference capabilities crucial for Unmanned Aerial Vehicle applications. Following the literature review, selected models undergo empirical validation through custom training using the Vision Meets Drone dataset, a widely recognized dataset for Unmanned Aerial Vehicle-based object detection tasks. Performance metrics such as mean average precision, inference speed, and resource utilization were measured and compared across different models. Lastly, the study extended its analysis beyond traditional object detection to explore the efficacy of instance segmentation and proposed an optimization to an object tracking technique within the context of unmanned Aerial Vehicles. Instance segmentation offers finer-grained object delineation, enabling more precise target or landmark identification and tracking, albeit at higher resource usage and higher inference time. Full article

The dispersion behavior of ceramic particles in aluminum alloys during rapid solidification critically affects the resulting microstructure and mechanical performance. In this study, we investigated the nucleation and growth of Al₃(Sc,Zr) on TiB₂ surfaces in a 2TiB₂/Al–8Ni–0.6Sc–0.1Zr alloy, fabricated via wedge-shaped copper mold casting and laser surface remelting. Thermodynamic calculations were employed to optimize alloy composition, ensuring sufficient nucleation driving force under rapid solidification conditions. The results show that the formation of Al₃(Sc,Zr)/TiB₂ composite interfaces is highly dependent on cooling rate and plays a pivotal role in promoting uniform TiB₂ dispersion. At an optimal cooling rate (~1200 °C/s), Al₃(Sc,Zr) nucleates heterogeneously on TiB₂, forming core–shell structures and enhancing particle engulfment into the α-Al matrix. Orientation relationship analysis reveals a preferred (111)α-Al//(0001)TiB₂ alignment in Sc/Zr-containing samples. A classical nucleation model quantitatively explains the observed trends and reveals the critical cooling-rate window for composite interface formation. This work provides a mechanistic foundation for designing high-performance aluminum-based composites with uniformly dispersed reinforcements for additive manufacturing applications. Full article