Search Results (171)

Background/Objectives: Lateral patellar dislocation (LPD) is a common pathology of the adolescent knee and a major predisposing factor for patellofemoral instability (PFI). The pathogenesis of PFI involves a combination of anatomical and biomechanical contributors, with increasing evidence pointing to sex-specific differences in knee morphology. Despite this, the developmental course of these parameters and their variation between sexes remain insufficiently characterized. This study aims to investigate sex-related differences in patellofemoral joint geometry among skeletally immature patients with a history of PFI, focusing on how these anatomical variations evolve with increasing knee size, as represented by femoral condylar width. Methods: A total of 315 knee MRIs from patients under 18 years with documented PFI were retrospectively analyzed. Trochlear morphology, patellar tilt, axial positioning, and sagittal alignment were assessed using established MRI-based parameters. All measurements were normalized to bicondylar width to account for individual knee size. Sex-specific comparisons were performed using independent t-tests and linear regression analysis. Results: Females exhibited significantly smaller femoral widths, shallower trochlear depth (TD), shorter tibial tubercle–posterior cruciate ligament (TTPCL) distances, and lower patellar trochlear index (PTI) values compared to males (p < 0.05). In males, increasing femoral width was associated with progressive normalization of patellar tilt and sagittal alignment parameters. In contrast, these alignment parameters in females remained largely unchanged or worsened across different femoral sizes. Additionally, patellar inclination angle and PTI were significantly influenced by knee size in males (p < 0.05), whereas no such relationship was identified in females. Conclusions: Sex-specific morphological differences in patellofemoral geometry are evident early in development and evolve distinctly with growth. These differences may contribute to the higher prevalence of PFI in females and underscore the importance of considering sex and knee size in anatomical assessments. Full article

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

The concept of “platinum sensitivity” has long guided prognostic assessment and treatment selection in recurrent ovarian cancer. However, the emergence of targeted agents, such as bevacizumab and poly (ADP-ribose) polymerase inhibitors, has complicated its clinical utility. In contrast, emerging evidence suggests that platinum sensitivity may also be applicable to recurrent endometrial cancer. As in ovarian cancer, a prolonged platinum-free interval (PFI) in recurrent endometrial cancer is associated with an improved efficacy of subsequent platinum-based chemotherapy. The PFI is linearly correlated with the response rate to platinum re-administration, progression-free survival, and overall survival. Patients are typically classified as having platinum-resistant or platinum-sensitive disease based on a PFI cutoff of 6 or 12 months. However, unlike in ovarian cancer—where the duration of response to second-line platinum-based chemotherapy rarely exceeds the prior PFI (~3%)—approximately 30% of patients with recurrent endometrial cancer exhibit a sustained response to platinum rechallenge that extends beyond their preceding PFI. Despite the incorporation of immune checkpoint inhibitors into the treatment landscape of endometrial cancer, the role of platinum sensitivity in clinical decision-making—particularly regarding treatment sequencing and drug selection—remains a critical and unresolved issue. Further research is warranted to elucidate the mechanisms underlying platinum resistance and to guide optimal therapeutic strategies. Full article

An increasing global population, rising energy demands, and the shift toward a circular bioeconomy are driving the need for more resource-efficient waste management. The increase in the world population—now exceeding 8 billion as of 2024—results in an increased need for alternative proteins, both human and feed grade proteins, as well as for biopolymers and bioenergy. As such, agricultural, forest, and marine waste biomass represent a valuable feedstock for production of food and feed ingredients, biopolymers, and bioenergy. However, the lack of integrated and efficient valorization strategies for these diverse biomass sources remains a major challenge. This literature review aims to give a systematic approach on the recent research status of agricultural, forest, and marine waste biomass valorization, focusing on cascade processing (a sequential combination of processes such as pretreatment, extraction, and conversion methods). Potential products will be identified that create the most economic value over multiple lifetimes, to maximize resource efficiency. It highlights the challenges associated with cascade processing of waste biomass and proposes technological synergies for waste biomass valorization. Moreover, this review will provide a comprehensive understanding of the potential of waste biomass valorization in the context of sustainable and circular bioeconomy. Full article

Cylinder-to-cylinder variation (CTCV) is a prevalent issue for natural gas (NG) premixed engines with port fuel injection (PFI), which significantly impacts the engine’s power performance, fuel economy, and reliability. Focusing on this issue, this study established a three-dimensional simulation platform based on a six-cylinder natural gas premixed engine. Quantitative analysis was conducted to discuss the differences in the main boundaries, combustion process, and engine power between cylinders. Additionally, influencing factors of CTCV were explored in terms of mixture uniformity and distribution uniformity. The results indicate that, for the NG premixed engine, many parameters vary significantly between cylinders even under the economical operating condition of 1200 rpm. For example, the difference rate in the peak cylinder pressure and peak phase between cylinder 3 and cylinder 2 can reach 23.5% and 24.3%, respectively. Through the design of simulation cases, it was found that improving the mixture uniformity had a more significant impact on CTCV than improving the distribution uniformity. For example, the relative standard deviation (RSD) of peak pressure decreased by 2.15% through mixture uniformity improvement, while it only decreased by 0.39% through distribution uniformity improvement. At a high speed of 1800 rpm, the influence of distribution uniformity on CTCV increased notably, but the influence of mixture uniformity still remained greater than that of distribution uniformity. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, necessitating the identification of novel biomarkers for improved prognosis and diagnosis. This study investigates the role of epoxide hydrolase 4 (EPHX4), a member of the epoxide hydrolase family, in LUAD. Using data sourced from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, which were subsequently validated by the Gene Expression Omnibus (GEO), we analyzed levels of EPHX4 expression, mutation, and methylation in tumors versus normal tissues. Our findings revealed a significant upregulation of EPHX4 in LUAD tissues compared to normal lung tissues (p < 0.001), correlating with poorer overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Furthermore, EPHX4 exhibited considerable diagnostic potential, as demonstrated by an area under the curve (AUC) of 0.854 in a Receiver Operating Characteristic (ROC) analysis. Notably, EPHX4 expression was associated with immune infiltration, specifically Th2 cells, neutrophils, and macrophages, along with immune checkpoint molecules including PD-L1, PD-L2, and TIM-3. Additionally, EPHX4 was involved in pivotal tumor-associated pathways, particularly cell cycle regulation. In conclusion, an elevated EPHX4 expression is indicative of poorer prognosis in LUAD and may play a role in immune evasion and cell cycle dysregulation, highlighting its potential as a promising biomarker for the diagnosis and prognostic prediction of LUAD. Full article

The real driving emissions of gasoline and diesel vehicles are significantly influenced by altitude, temperature, and starting conditions. In this study, the real driving emissions (RDEs) of gasoline and diesel vehicles compliant with China V standards were investigated under various conditions. The adaptability of RDE testing in China was evaluated by analyzing vehicle emissions at different altitudes, ambient temperatures, and starting conditions. The results show that, with increasing altitude, CO, NOx, and PN emissions generally exhibit a downward trend, particularly for gasoline vehicles, whose conformity factors remain well below the China VI limit. However, for China V diesel vehicles relying solely on EGR technology, NOx emissions significantly exceed China VI standards, indicating that EGR alone is insufficient to meet regulatory requirements. Temperature variations have little effect on the emissions of China V PFI gasoline vehicles, while diesel vehicles continue to exhibit excessive NOx emissions under varying temperatures. Although the cold-start phase generates substantial pollutant emissions, the EMROAD evaluation method excludes this phase, resulting in limited differences between cold- and hot-start emission results. Nevertheless, the inclusion of cold-start emissions should be considered in future RDE assessments. Full article

Background/Objectives: Human glioblastoma (GBM) is the most aggressive brain cancer in adults and a highly treatment-refractory malignancy. The overall prognosis for the GBM is extremely poor, with a median survival of 12–14 months after initial diagnosis. Many GBM patients initially respond to the DNA alkylating agent temozolomide (TMZ), but patients often become therapy-resistant, and tumors recur. We previously reported that treatment with PFI-3, which is a small molecule inhibitor of the bromodomain of the BRG1 subunit of the SW1/SNF chromatin remodeling complex, enhanced the sensitivity of GBM cells to TMZ in vitro and in vivo GBM animal models. Our general objective was to perform an SAR study of new diphenyl PFI-3 analogs. Methods: New structural analogs of PFI-3 were developed, synthesized, and tested for their ability to enhance TMZ-induced GBM cell death by ELISA. Results: Following on the enhanced activity of compounds 2a and 2b, new diphenyl PFI-3 analogs with specific structural adjustments were made to better understand the structural requirements to optimize function. Additionally, several new structurally different candidates (e.g., 4a, 4b, and 5) showed much better efficacy in sensitizing GBM cells to TMZ-induced GBM cell death. Conclusions: Four series of PFI-3 analogs (2, 3, 4, and 5) were designed, synthesized, and tested for the ability to sensitize GBM cells to TMZ-induced cell death. Series 2 optimized the A-ring and R-isomer chirality. Series 3 used a 5-membered linker with weak activity. Series 4’s di-phenyl urea compounds showed better bromodomain inhibition. Series 5’s methoxyphenyl-B-ring analogs were exceptionally strong inhibitors. Full article

Breakthrough innovations present both opportunities and challenges for firms navigating unpredictable technological and market dynamics, which is significant to firms’ sustainable development. However, the impact of strategic alliances on breakthrough innovations remains contested, and the underlying mechanisms are yet to be fully clarified. Drawing on ambidexterity theory and profiting from innovation (PFI) theory, this study investigates the relationships among ambidextrous (exploratory vs. exploitative) alliances, complementary assets, and firms’ breakthrough innovations. By analyzing 279 questionnaire responses from Chinese firms, we demonstrate that both exploratory and exploitative alliances, alongside complementary assets, significantly enhance breakthrough innovations. Furthermore, complementary assets play a complete mediating role between exploratory alliances and breakthrough market innovations. In addition, complementary assets also play a partial mediating role between exploratory alliances and breakthrough technological innovations, as well as between exploitative alliances and both breakthrough technological and market innovations. These findings advance ambidextrous theory by delineating the ambidextrous roles of alliances, extend PFI theory through the integration of the mediating roles of complementary assets, and offer actionable insights for managers seeking to leverage ambidextrous alliances for breakthrough innovation success. Full article

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research. Full article

Kármán vortex streets are quintessential phenomena in fluid dynamics, manifested by the periodic shedding of vortices as airflow interacts with obstacles. The genesis and characteristics of these vortex structures are significantly influenced by various atmospheric parameters, including temperature, humidity, pressure, and wind velocities, which collectively dictate their formation conditions, spatial arrangement, and dynamic behavior. Although deep learning methodologies have advanced the automated detection of Kármán vortex streets in remote sensing imagery, existing approaches largely emphasize visual feature extraction without adequately incorporating critical atmospheric variables. To overcome this limitation, this study presents an innovative auxiliary network framework that integrates essential atmospheric physical parameters to bolster the detection performance of Kármán vortex streets. Utilizing reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5), representative atmospheric features are extracted and subjected to feature permutation importance (PFI) analysis to quantitatively evaluate the influence of each parameter on the detection task. This analysis identifies five pivotal variables: geopotential, specific humidity, temperature, horizontal wind speed, and vertical air velocity, which are subsequently employed as inputs for the auxiliary task. Building upon the YOLOv8s object detection model, the proposed auxiliary network systematically examines the impact of various atmospheric variable combinations on detection efficacy. Experimental results demonstrate that the integration of horizontal wind speed and vertical air velocity achieves the highest detection metrics (precision of 0.838, recall of 0.797, mAP50 of 0.865, and mAP50-95 of 0.413) in precision-critical scenarios, outperforming traditional image-only detection method (precision of 0.745, recall of 0.745, mAP50 of 0.759, and mAP50-95 of 0.372). The optimized selection of atmospheric parameters markedly improves the detection metrics and reliability of Kármán vortex streets, underscoring the efficacy and practicality of the proposed methodological framework. This advancement paves the way for more robust automated analysis of atmospheric fluid dynamics phenomena. Full article

The integrity of p53 machinery is crucial for platinum activity, while p53 mutation is frequent in high-grade serous ovarian cancer (HGS-OC). This study aimed to evaluate the link between p53 mutations, platinum sensitivity (PS), and the platinum-free interval (PFI) in patients with HGS-OC. We prospectively analyzed 159 consecutive women with ovarian cancer who underwent surgery. The somatic mutational status of BRCA, HRD, and TP53 (according to structural, hotspot, and functional classification) was evaluated. Among enrolled patients, 82.4% of cases were TP53-mutated (MT), and 27.8% were BRCA-MT. The distribution of TP53 mutation categories did not differ significantly between the BRCA-MT and wild-type (WT) cases. In the entire population, the proportion of PS patients was significantly lower in TP53-MT compared to TP53-WT (p = 0.0208), in nonsense/frameshift/splicing compared to missense (p = 0.0319), and in loss-of-function (LOF) compared to GOF (p = 0.0048) MT cases. For the BRCA-MT patients, structural and functional TP53 mutations were not significantly different between the PS and PR patients. Conversely, for the BRCA WT patients, the distribution of structural and functional TP53 mutations significantly differed between PS and PR patients. In a multivariate regression analysis, LOF mutations were found to be independent negative predictors of PS (HR: 0.1717; 95% CI: 0.0661–0.4461; p-value: 0.0003). Kaplan–Meier curves showed a significantly lower PFI in cases with LOF mutations in the overall population (log-rank p = 0.0020) and in BRCA-WT patients (log-rank p = 0.0140). Via multivariate COX testing, it was found that LOF mutations were independently associated with a decreased PFI (p = 0.0036). In conclusion, our data show that HGS-OC harboring p53 LOF mutations is the poorest prognostic subgroup regarding PS and the PFI. Further studies are needed to confirm our findings. Full article

Background/Objectives: The diagnostic guidelines for pediatric patellofemoral instability (PFI) remain incomplete. PFI remains a challenging issue as it affects the biomechanics of the knee joint, triggers anterior knee pain, and is linked to the development of early-onset osteoarthritis. The diagnostic process is complicated by numerous anatomical factors that must be considered. This review aims to consolidate current knowledge presented in the literature on radiological diagnostics for PFI in pediatric populations, with the application of all imaging techniques—including ultrasonography (US), magnetic resonance imaging (MRI), computed tomography (CT), and radiography (RTG)—which enable the evaluation of anatomical risk factors critical for the diagnosis, prevention, and treatment of PFI. Methods: A search of the PubMed/MEDLINE database was conducted to identify relevant studies from 1975 to 2024. The search terms were as follows: (patellar or patella) and (instability or displacement or dislocation) and (diagnostic or diagnosis or imaging or radiographic). A total of 2743 articles were retrieved, which were screened to yield 29 studies for further review. These studies were then divided into seven categories regarding the diagnostic methods: risk factors, tibial tubercle trochlear groove (TT-TG)/tibial tubercle posterior cruciate ligament (TT-PCL), MPFL injury and cartilage damage, patella and trochlear dysplasia, torsional abnormalities, coronal plane alignment, and genetics. Results: The methods presented statistically significant differences, with those most commonly used for the diagnosis of patella dislocation being TT-TG index, MPFL rapture, and trochlear dysplasia. Conclusions: In summary, multiple diagnostic tools, including MRI, CT, X-ray, and physical examination, are available for the assessment of PFI, each contributing to treatment decisions. Although MRI remains the primary diagnostic tool, further research is needed to establish more precise diagnostic criteria. Full article