Search Results (202)

Objectives: This multicenter study aimed to clarify the recurrence patterns; treatment outcomes; and prognostic factors of thymic carcinoma, a rare cancer. Methods: We analyzed 101 patients with thymic carcinoma who underwent multidisciplinary treatment, including radiotherapy. The median age was 62 years, with 27 patients in stage I–II; 44 in stage III; and 30 in stage IV by the TNM classification. Seventy-two patients underwent surgery with radiotherapy; and 29 patients underwent definitive radiotherapy. Image-guided radiotherapy (IGRT) and elective nodal irradiation (ENI) were used for 35 and 23 patients, respectively. Local recurrence-free survival (LRFS); progression-free survival (PFS); and overall survival (OS) were calculated, and univariate and multivariate analyses were performed. Results: With a median follow-up of 68 months, we observed 17 local recurrences; 27 regional recurrences; and 35 distant metastases. The 5-year LRFS; PFS; and OS were 82%, 41%, and 76%, respectively. Multivariate analysis revealed that stage was the only factor associated with LRFS; PFS; and OS (p = 0.040; p < 0.0001; and p = 0.048, respectively), while treatment modality was associated with only LRFS (p = 0.015). IGRT and ENI were also associated with LRFS (p = 0.002 and 0.013, respectively). PFS and OS of stage IV patients were comparable between the surgery with radiotherapy and definitive radiotherapy groups (p = 0.99 and 0.98, respectively). Conclusions: Our results suggest the importance of stage-specific treatment strategies rather than resectability, especially for stage IV patients. These results should be validated in a prospective study. Our results also suggest that radiotherapy methods influence recurrence Full article

Background/Objectives: To develop a DL-based model predicting recurrence risk in HER2-low breast cancer patients and to compare performance of the MRI-alone, clinicopathologic-alone, and combined models. Methods: We analyzed 453 patients with HER2-low breast cancer who underwent surgery and preoperative breast MRI between May 2018 and April 2022. Patients were randomly assigned to either a training cohort (n = 331) or a test cohort (n = 122). Imaging features were extracted from DCE-MRI and ADC maps, with regions of interest manually annotated by radiologists. Clinicopathological features included tumor size, nodal status, histological grade, and hormone receptor status. Three DL prediction models were developed: a CNN-based MRI-alone model, a clinicopathologic-alone model based on a multi-layer perceptron (MLP) and a combined model integrating CNN-extracted MRI features with clinicopathological data via MLP. Model performance was evaluated using AUC, sensitivity, specificity, and F1-score. Results: The MRI-alone model achieved an AUC of 0.69 (95% CI, 0.68–0.69), with a sensitivity of 37.6% (95% CI, 35.7–39.4), specificity of 87.5% (95% CI, 86.9–88.2), and F1-score of 0.34 (95% CI, 0.33–0.35). The clinicopathologic-alone model yielded the highest AUC of 0.92 (95% CI, 0.92–0.92) and sensitivity of 93.6% (95% CI, 93.4–93.8), but showed the lowest specificity (72.3%, 95% CI, 71.8–72.8) and F1-score of 0.50 (95% CI, 0.49–0.50). The combined model demonstrated the most balanced performance, achieving an AUC of 0.90 (95% CI, 0.89–0.91), sensitivity of 80.0% (95% CI, 78.7–81.3), specificity of 83.2% (95% CI: 82.7–83.6), and the highest F1-score of 0.55 (95% CI, 0.54–0.57). Conclusions: The DL-based model combining MRI and clinicopathological features showed superior performance in predicting recurrence in HER2-low breast cancer. This multimodal approach offers a framework for individualized risk assessment and may aid in refining follow-up strategies. Full article

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting. Full article

Background/objectives: Despite advancements in early diagnosis and clinical practices guided by standardized care protocols, Merkel cell carcinoma (MCC) is marked by an unfavorable prognosis with a 5-year relative survival rate of 65%, based primarily on data collected prior to the introduction of immunotherapy. Regional nodal metastases affect 40–50% of MCC patients, while approximately 33% experience distant dissemination. Among these, bone and bone marrow metastases are particularly notable, although the characteristics and clinical implications of this metastatic disease in MCC remain poorly understood. Methods: A comprehensive review was conducted using the Medline database (via PubMed) up to January 2025. The search strategy included the string “(Merkel cell carcinoma AND (bone OR marrow))”. Results: A total of 1133 (69.3% male and 30.7% female) patients diagnosed with advanced MCC were collected. The median (IQR) age at diagnosis was 67.5 (12.65) years old. Overall, 201 (20.8%) cases of bone and/or bone marrow metastases were identified and linked to a primary known MCC in 75.7% of cases. Bone metastases (BMs) appear as the third most common metastatic site, following the liver (second) and lymph nodes (first). They show mixed biological and radiological behavior, with a marked preference for the axial skeleton over the appendicular one. Addressing the characteristics of metastatic bone disease, neurological symptoms were the most documented, whereas bone marrow involvement and leukemic spread seemed to be primarily related to immunosuppression. Multimodal treatment strategies, including platinum-based chemotherapy and radiotherapy, were the primary approaches adopted, reflecting therapeutic practices from the pre-immunotherapy era. Conclusions: The pattern of metastatic spread in MCC differs among studies, with the bones resulting as the third most common site of distant spread. Excluding head and neck MCC, which seems to be more regularly associated with liver metastases, the relationship between the primary tumor site and the development of bone or bone marrow metastases appears inconsistent. Overall, BMs mostly correlated with advanced MCC stages and poorer survival outcomes, with a median overall survival (OS) of 8 months (range 12.75–4). The integration of international guidelines, evolving evidence from clinical trials, and the expanding role of immune checkpoint inhibitors (ICIs) will contribute to improving systemic disease control and enhance patient care. Full article

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article

Carbon transfer embodies the spatial redistribution of carbon emissions resulting from interregional economic activities and trade. In recent years, accelerated regional integration and deepening specialization within industrial chains have rendered traditional bilateral analytical frameworks inadequate for capturing the complexity of interregional carbon transfer networks. This evolving context necessitates the incorporation of spatial interaction effects to elucidate the multi-nodal and multi-pathway characteristics inherent in contemporary carbon transfer patterns. Based on the spatial interaction theoretical framework and a multiregional input–output (MRIO) model, we analyze the spatial dependence characteristics of interregional carbon transfer in China. The results reveal that interregional carbon transfer in China exhibited an upward trend from 2012 to 2017, demonstrating statistically significant positive origin dependence, destination dependence, and network dependence. The distance between regions exerts a significantly negative influence on interregional carbon transfer. Interregional carbon transfer is not merely a bilateral phenomenon; its fundamental nature is characterized as a network phenomenon. Our study demonstrates that precise regulation of the allocation of industrial land and transportation infrastructure land, strengthening the decisive role of market mechanisms in resource allocation for regional low-carbon development, and establishing interregional collaboration mechanisms for low-carbon exchange can effectively reduce the occurrence of interregional carbon transfer. These findings provide policymakers with more precise information to achieve equitable carbon emissions distribution across regions. Full article

The Andalusian Black Cattle (Negra Andaluza) represents a genetic lineage linked to the ancient Eurasian aurochs, shaped by domestication events in the Near East and later introgressions from Italian and North African wild cattle. This study investigates the breed’s anthropological and historical origins, geographical distribution, and genetic structure. Key influences include historical use as draft animals, regional breeding preferences, and gene flow via transhumant routes. The genetic analysis reveals that Córdoba is the principal nucleus, accounting for 448 identified ancestors, compared to 252 in Huelva and 193 in Seville. In Córdoba, contributions of nodal ancestors through inbreeding loops reached a maximum of 0.0447, while mean inbreeding ($\overline{\mathrm{F}}$) was 0.000949 and mean coancestry ($\overline{\mathrm{C}}$) was 0.000475, indicating moderate but geographically structured genetic drift. In contrast, areas with better connectivity showed higher heterogeneity and lower inbreeding contributions. Canonical discriminant analysis (CDA) revealed that the first discriminant function (F1) explained 79.72% of the variation among groups, primarily driven by nodal ancestors and inbreeding loops. Despite these signs of inbreeding, historical transhumance has helped preserve overall genetic diversity. These findings offer essential insights for conservation programs aimed at maintaining both the genetic integrity and adaptive potential of this historically and culturally important breed. Full article

Background/Objectives: The management of localized prostate cancer with regional lymph node involvement (N1M0) presents significant clinical challenges. While once considered indicative of systemic disease, improved imaging and evolving treatment paradigms have redefined node-positive disease as potentially curable. This systematic review aims to assess current evidence regarding treatment modalities and outcomes for patients with localized N1M0 prostate cancer. Methods: A systematic review was conducted to identify studies evaluating therapeutic strategies for N1M0 prostate cancer. Eligible studies included randomized controlled trials, retrospective analyses, and consensus guidelines. Treatment approaches reviewed included radical prostatectomy (RP) with pelvic lymph node dissection (PLND), whole pelvic radiotherapy (WPRT), prostate-only radiotherapy (PORT), androgen deprivation therapy (ADT), and metastasis-directed therapy (MDT), including stereotactic body radiotherapy (SBRT). Key outcomes included overall survival (OS), biochemical recurrence-free survival (bRFS), disease-free survival (DFS), and treatment-related toxicity. Results: Multimodal approaches—particularly the combination of ADT with WPRT or adjuvant radiotherapy following RP—were associated with improved survival outcomes. Patients with limited nodal burden and undetectable postoperative prostate-specific antigen (PSA) levels derived the most benefit. The use of prostate-specific antigen membrane positron-emission tomography/computed tomography (PSMA PET/CT) enhanced detection and guided MDT in oligorecurrent disease. SBRT, simultaneous integrated boost (SIB), and hypofractionated regimens demonstrated promising efficacy with acceptable toxicity profiles. Conclusions: Node-positive localized prostate cancer is optimally managed with individualized, multidisciplinary strategies. Combining systemic and locoregional treatments improves outcomes in selected patients. Ongoing prospective studies are warranted to refine patient selection, optimize treatment sequencing, and integrate novel imaging and systemic agents. Full article

Conventional approaches for distributed generation (DG) planning often fall short in addressing operational demands and regional control requirements within distribution networks. To overcome these limitations, this paper introduces a cluster-oriented DG planning method. In terms of cluster partitioning, this study breaks through the limitations of traditional methods that solely focus on electrical parameters or single functions. Innovatively, it partitions the distribution network by comprehensively considering multiple critical factors such as system grid structure, nodal load characteristics, electrical coupling strength, and power balance, thereby establishing a unique multi-level grid structure of **distribution network—cluster—node**. This partitioning approach not only effectively reduces inter-cluster reactive power transmission and enhances regional power self-balancing capabilities but also lays a solid foundation for the precise planning of subsequent distributed energy resources. It represents a functional expansion that existing cluster partitioning methods have not fully achieved. In the construction of the planning model, a two-layer coordinated siting and sizing planning model for distributed photovoltaics (DPV) and energy storage systems (ESS) is proposed based on cluster partitioning. In contrast to traditional models, this model for the first time considers the interaction between power source planning and system operation across different time scales. The upper layer aims to minimize the annual comprehensive cost by optimizing the capacity and power allocation of DPV and ESS in each cluster. The lower layer focuses on minimizing system network losses to precisely determine the PV connection capacity of each node within the cluster and the grid connection locations of ESS, achieving comprehensive optimization from macro to micro levels. For the solution algorithm, a two-layer iterative hybrid particle swarm algorithm (HPSO) embedded with power flow calculation is designed. Compared to traditional single particle swarm algorithms, HPSO integrates power flow calculations, allowing for a more accurate consideration of the actual operating conditions of the power grid and avoiding the issue in traditional methods where the current and voltage distribution are often neglected in the optimization process. Additionally, HPSO, through its two-layer iterative approach, is able to better balance global and local search, effectively improving the solution efficiency and accuracy. This algorithm integrates the advantages of the particle swarm optimization algorithm and the binary particle swarm optimization algorithm, achieving iterative solutions through efficient information exchange between the two layers of particle swarms. Compared with conventional particle swarm algorithms and other related algorithms, it represents a qualitative leap in computational efficiency and accuracy, enabling faster and more accurate handling of complex planning problems. Case studies on a real 10 kV distribution network validate the practicality of the proposed framework and the robustness of the solution technique. Full article

To study the mechanical properties of asymmetric K-shaped square tubular joints with built-in stiffening rib lap joints, axial tensile tests were carried out on one K-shaped joint without built-in stiffening ribs and four K-shaped joints with built-in stiffening ribs using an electro-hydraulic servo structural testing system. The effects of the addition of stiffening ribs and the welding method of the stiffening ribs on the mechanical properties were studied comparatively. The failure mode of the K-shaped joint was obtained, and the strain distribution and peak displacement reaction force in the nodal region were analyzed. A finite element analysis of the K-shaped joint was carried out, and the finite element results were compared with the experimental results. The results showed that the addition of transverse reinforcement ribs and more complete welds shared the squeezing effect of the brace on the chord. Arranging more reinforcing ribs in the fittings makes the chord more uniformly stressed and absorbs more energy while increasing the flexural load capacity of the fittings’ side plates. The presence of a weld gives a short-lived temperature increase in the area around the crack, and the buckling of the structure causes the surface temperature in the buckling area to continue to increase for some time. The temperature change successfully localized where the structure was deforming and creating cracks. The addition of the reinforcing ribs resulted in a change in the deformation pattern of the model, and the difference occurred because the flexural capacity of the brace with the added reinforcing ribs was greater than that of the side plate buckling. Full article

Preliminary tunnel surveys are essential for identifying geological hazards such as aquifers, faults, and karstic zones. While first-arrival tomography is effective for imaging shallow anomalies, traditional seismic sources face significant limitations in forested mountainous regions due to mobility, cost, and environmental impact. To address this, we deployed a seismic source delivered by an unmanned aerial vehicle (UAV) for a highway tunnel survey in Lijiang, China. The UAV system, paired with nodal geophones, enabled rapid, low-impact, and high-resolution data acquisition in rugged terrain. To enhance the weak far-offset refractions affected by near-surface attenuation, we applied supervirtual refraction interferometry (SVI), which significantly improved the signal-to-noise ratio and expanded the usable first-arrival dataset. The combined use of UAV excitation and SVI processing produced a high-precision P-wave velocity model through traveltime tomography, aligned well with borehole data. This model revealed the spatial distribution of weathered zones and bedrock interfaces, and allowed us to infer potential fracture zones. The results offer critical guidance for tunnel alignment and hazard mitigation in complex geological settings. Full article

Background: Extramammary Paget’s disease (EMPD) is a rare cutaneous carcinoma that typically affects the elderly and is frequently observed in genital and perianal regions. We analyzed the outcomes and prognostic factors for EMPD after radiation therapy (RT). Methods: We analyzed data from 81 patients with non-metastatic EMPD who received either RT alone or in combination with surgery and/or chemotherapy. The median radiation dose was 56 Gy in 28 fractions. Local control (LC), progression-free survival (PFS), and overall survival (OS) rates were calculated using the Kaplan–Meier method. Multivariate analyses were performed using the Cox proportional hazards model. Late adverse events were evaluated by NCI-CTCAE version 5.0. Results: The median age was 78 years, and the median follow-up period was 36 months. The three-year LC, PFS, and OS rates were 75%, 52%, and 80%, respectively. Multivariate analyses identified the presence of lymph node (LN) metastasis, the absence of surgery, and the omission of elective nodal irradiation (i.e., local irradiation only) as significant factors for unfavorable LC (p = 0.01, 0.02, and 0.006) and PFS (p = 0.001, 0.04, and 0.03). LN metastasis was also a significant factor for unfavorable OS (p = 0.005). One patient developed grade 2 skin infection, and another developed grade 3 lymphedema; no grade 4 or higher toxicity was observed. Conclusions: The present results revealed prognostic factors for EMPD after RT and suggest that the absence of surgery and omission of elective nodal irradiation worsened outcomes. A prospective study is needed to establish an optimal treatment strategy for this rare malignancy, which is common in the elderly. Full article

Background: The role of CDK4/6 inhibitors (CDK4/6i) has expanded from the treatment of advanced breast cancer to early-stage disease, as recent studies have demonstrated their therapeutic benefits. However, evidence regarding the safety of combining CDK4/6i with adjuvant radiation therapy (RT) in a curative setting remains limited. This study aims to present clinical experiences of pulmonary toxicity following the combined use of adjuvant RT and CDK4/6i. Case presentation: We report a case of an Asian female with left breast cancer who underwent a modified radical mastectomy followed by adjuvant chemotherapy, RT, endocrine therapy, and CDK4/6i (abemaciclib) treatment. Cancer therapy-induced grade 2 pneumonitis was impressed by clinical signs and image findings. A 57-year-old postmenopausal woman was diagnosed with left breast invasive lobular carcinoma, hormone receptor–positive, human epidermal growth factor receptor 2–negative (HR+/HER2−), K67 index of 5–10%, and classified as pT3N3aM0 (stage IIIC). She received adjuvant chemotherapy with FEC followed by docetaxel, endocrine therapy with letrozole, and adjuvant RT of 50.4 Gy in 28 fractions to the left chest wall and regional nodal irradiation. Abemaciclib was initiated after completing RT. Treatment-related pneumonitis developed five months after RT and abemaciclib use. Conclusions: In breast cancer patients receiving a combination of RT and CDK4/6i as curative adjuvant treatment, pulmonary toxicity is a concern and requires careful monitoring, particularly in Asian populations. Full article

Extracting nodal features is crucial for analyzing rock structure stability and plays a significant role in designing engineering projects. This study presents an enhanced version of the FraSegNet algorithm, focusing on improving its ability to identify nodal features in images. The updated FraSegNet incorporates the ResNet101 backbone and integrates the Squeeze-and-Excitation (SE) attention mechanism, enabling better concentration on key nodal characteristics. The primary improvements are as follows: (1) Multi-scale feature extraction: Leveraging the ResNet101 architecture for the effective extraction of detailed information from nodal images. (2) Better attention mechanisms: The SE module focuses on nodal regions, resulting in clearer and more refined feature representations. (3) Dynamic learning strategies: I incorporation of cosine annealing and warm-up techniques to optimize training efficiency. The algorithm was validated with the Barton–Bandis model and Hoek–Brown criterion. The experimental results demonstrate its superior performance, achieving 97.1% accuracy in nodal feature detection with an average error of only 1.5% compared to the rock mass parameter. This small error proves the model works well. FraSegNet offers accurate segmentation and precise geometric parameter extraction, making it a valuable tool for advancing rock stability analysis and practical applications in rock mechanics. Full article

Background: The Satyrinae subfamily represents a taxonomically critical group within Nymphalidae, characterized by its remarkable species diversity. Despite its evolutionary significance, the phylogenetic relationships among tribal and subtribal lineages remain poorly resolved. Although mitochondrial genomes have become crucial molecular markers in Lepidoptera phylogenetics, their potential remains underutilized in the systematics of Satyrinae. Notably, Amathusiini exhibits a particular paucity, with only two congeneric representatives having been comprehensively sequenced to date. Methods: We employed high-throughput sequencing to assemble the complete mitochondrial genomes of two Amathusiini species, Discophora sondaica and Aemona amathusia. Our study revealed novel evolutionary insights through comparative genomics, which encompassed all available Satyrinae mitochondrial genomes. Additionally, we conducted phylogenetic reconstruction using maximum likelihood and Bayesian inference approaches, utilizing the most extensive dataset to date. Results: The closed, circular mitochondrial genomes measure 15,333 bp for D. sondaica and 15,423 bp for A. amathusia, maintaining the ancestral lepidopteran architecture: 13 protein-coding genes (PCGs), 22 tRNAs, 2 rRNAs, and an AT-rich control region. Comparative analyses of 71 mitochondrial genomes revealed strong evolutionary conservation across multiple parameters: nucleotide composition (AT content range: 77.9–81.8%), codon usage bias (ENC = 30.83–37.55), tRNA secondary structures, and control region organization. All PCGs showed purifying selection signals (Ka/Ks < 1.0), with atp8 exhibiting the highest evolutionary rate (Ka/Ks = 0.277). Phylogenetic reconstructions yielded congruent tribal-level topologies with strong nodal support: ((Satyrini + Melanitini) + (Amathusiini + Elymniini) + Zetherini), confirming a sister relationship between Amathusiini and Elymniini. Within Satyrini, five subtribes formed monophyletic groups: Ypthimina, Erebiina, Maniolina, Satyrina, and Melanargiina, arranged as ((Ypthimina + (Erebiina + Maniolina)) + (Satyrina + Melanargiina)). Mycalesina, Lethina, and Parargina comprised a well-supported clade (BS = 100%; PP = 1.0), though internal relationships required further resolution due to Lethina’s polyphyly. Conclusions: This study provides novel insights into mitochondrial genomic evolution within the Satyrinae subfamily while elucidating the efficacy of mitogenomic data for resolving deep phylogenetic relationships within this ecologically significant subfamily. Our findings establish critical genome baselines for further systematic research and underscore essential pathways for refining subtribal-level taxonomy through integrative molecular approaches. Full article