Search Results (3,280)

Background: Angiosarcomas (ASs) represent a heterogeneous and highly aggressive subset of tumors that respond poorly to systemic treatments and are associated with short progression-free survival (PFS) and overall survival (OS). The aim of this study was to develop and validate an immune-related prognostic model—termed the AS score—using data from two independent sarcoma cohorts. Methods: A prognostic model was developed using a previously characterized cohort of 25 angiosarcoma samples. Candidate genes were identified via the Maxstat algorithm (Maxstat v0.7-25 for R), combined with log-rank testing. The AS score was then computed by weighing normalized gene expression levels according to Cox regression coefficients. For external validation, transcriptomic data from TCGA Sarcoma cohort (n = 253) were analyzed. The Immunoscore—which reflects the tumor immune microenvironment—was inferred using the ESTIMATE package (v1.0.13) in R. All statistical analyses were performed in RStudio (v 4.0.3). Results: Four genes—IGF1R, MAP2K1, SERPINE1, and TCF12—were ultimately selected to construct the prognostic model. The resulting AS score enabled the classification of angiosarcoma cases into two prognostically distinct groups (p = 0.00012). Cases with high AS score values, which included both cutaneous and non-cutaneous forms, exhibited significantly poorer outcomes, whereas cases with low AS scores were predominantly cutaneous. A significant association was observed between the AS score and the Immunoscore (p = 0.025), with higher Immunoscore values found in high-AS score tumors. Validation using TCGA sarcoma cohort confirmed the prognostic value of both the AS score (p = 0.0066) and the Immunoscore (p = 0.0029), with a strong correlation between their continuous values (p = 2.9 × 10⁻⁸). Further survival analysis, integrating categorized scores into four groups, demonstrated robust prognostic significance (p = 0.00021). Notably, in tumors with a low Immunoscore, AS score stratification was not prognostic. In contrast, among cases with a high Immunoscore, the AS score effectively distinguished outcomes (p < 0.0001), identifying a subgroup with poor prognosis but potential sensitivity to immunotherapy. Conclusions: This combined classification using the AS score and Immunoscore has prognostic relevance in sarcoma, suggesting that angiosarcomas with an immunologically active microenvironment (high Immunoscore) and poor prognosis (high AS score) may be prime candidates for immunotherapy and this approach warrants prospective validation. Full article

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article

Background: Colorectal cancer (CRC) is a prevalent gastrointestinal malignancy, ranking third in incidence and second in cancer-related mortality. Despite therapeutic advances, challenges such as chemotherapy toxicity and drug resistance persist. Thus, there is an urgent need for novel CRC treatments. However, developing new drugs is time-consuming and resource-intensive. As a more efficient approach, drug repurposing offers a promising alternative for discovering new therapies. Methods: In this study, we screened 1068 small molecular compounds from an FDA-approved drug library in CRC cells. Menadione was selected for further study based on its activity profile. Mechanistic analysis included a cell death pathway PCR array, differential gene expression, enrichment, and network analysis. Gene expressions were validated by RT-qPCR. Results: We identified menadione as a potent anti-tumor drug. Menadione induced three programmed cell death (PCD) signaling pathways: necroptosis, apoptosis, and autophagy. Furthermore, we found that the anti-tumor effect induced by menadione in CRC cells was mediated through a key gene: MAPK8. Conclusions: By employing methods of cell biology, molecular biology, and bioinformatics, we conclude that menadione can induce multiple forms of PCD in CRC cells by activating MAPK8, providing a foundation for repurposing the “new use” of the “old drug” menadione in CRC treatment. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

The development of the Great Wall National Cultural Park has unleashed the potential for integrating cultural and tourism development along the Great Wall. However, ICH along the Great Wall, a key part of its cultural identity, suffers from low recognition and a mismatch between protection and development efforts. This study analyzes provincial-level and above ICH along Hebei’s Great Wall using geospatial tools and the Geographical Detector model to explore distribution patterns and influencing factors, while Geographically Weighted Regression is utilized to reveal spatial heterogeneity. It tests two hypotheses: (H1) ICH shows a clustered pattern; (H2) economic factors have a greater impact than cultural and natural factors. Key findings show: (1) ICH distribution is numerically balanced north–south but spatially uneven, with dense clusters in the south and scattered patterns in the north. (2) ICH and crafts cluster significantly, while dramatic balladry spreads evenly, and other categories are random. (3) Average annual temperature and precipitation have the greatest impact on ICH distribution, with the factors ranked as: natural > cultural > economic. Multidimensional interactions show significant enhancement effects. (4) Influencing factors vary spatially. Population density, transport, temperature, and traditional villages are positively related to ICH. Elevation, precipitation, tourism, and cultural institutions show mixed effects across regions. These insights support targeted ICH conservation and sustainable development in the Great Wall cultural corridor. Full article

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

This study develops a domain-adaptive multimodal RAG (Retrieval-Augmented Generation) system to improve the accuracy and efficiency of technical question answering based on large-scale structured manuals. Using Hyundai Staria maintenance documents as a case study, we extracted text and images from PDF manuals and constructed QA, RAG, and Multi-Turn datasets to reflect realistic troubleshooting scenarios. To overcome limitations of baseline RAG models, we proposed an enhanced architecture that incorporates sentence-level similarity annotations and parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) using the bLLossom-8B language model and BAAI-bge-m3 embedding model. Experimental results show that the proposed system achieved improvements of 3.0%p in BERTScore, 3.0%p in cosine similarity, and 18.0%p in ROUGE-L compared to existing RAG systems, with notable gains in image-guided response accuracy. A qualitative evaluation by 20 domain experts yielded an average satisfaction score of 4.4 out of 5. This study presents a practical and extensible AI framework for multimodal document understanding, with broad applicability across automotive, industrial, and defense-related technical documentation. Full article

The impact of five different nets—conventional black, grey, white, and photoselective red and yellow—on the performance of “Gala Redlum” apples was evaluated over a five-year period (2020–2024) and compared to an uncovered control. The cumulative production over this period, ranked from highest to lowest, was as follows: white net (182.4 t/ha), grey net (178.5 t/ha), yellow net (175.8 t/ha), black net (175.5 t/ha), red net (169.5 t/ha), and uncovered control (138.8 t/ha). Vegetative growth results were inconsistent among the studied years. The cumulative photosynthetic rate (An) was slightly higher under the white net (57.9 µmol m⁻² s⁻¹). Fv/Fm values remained closest to optimal levels under the black and grey nets. Netting effectively protected fruits from elevated temperatures, particularly under the grey net, and reduced sunburn damage, with the grey, black, and yellow nets performing best in this regard. Overall profitability was increased by netting: the black net provided the highest cumulative income per hectare over a five-year period (EUR 72,315) alongside the second-lowest sunburn loss (0.69%), while the yellow net also showed strong economic performance (€64,742) with a moderate sunburn loss (1.26%) compared to the red net. Fruit dry matter and soluble solids content (SSC) were generally higher in the uncovered control, whereas °Hue values tended to be higher under the red and yellow nets. In summary, the black and yellow nets provided more balanced microclimatic conditions that enhanced tree performance, particularly under heat stress, leading to improved yield and profitability. However, the economic feasibility of each net type should be evaluated in relation to its installation and maintenance costs. Full article

The environmental detriments of the growing global production and overconsumption of beef, including greenhouse gas emissions, deforestation, and biodiversity loss, are well-documented. However, public awareness of how dietary choices affect the environment remains limited. This study examines sustainability views on beef consumption and the potential for behavioral change as a step toward more sustainable intake levels. An observational web-based survey was conducted (n = 1367) to assess respondents’ current beef intake frequency, views on beef consumption related to planetary health, tropical deforestation, greenhouse gas emissions, and climate change, and willingness to modify beef consumption behavior. Chi-square tests were used for group comparisons, and weighted average scores were applied to rank levels of resistance to reducing beef intake. Environmental concern related to beef consumption was associated with greater beef cutback intentions and lower long-term intake reduction resistance amongst beef eaters. Beef eaters who strongly agreed that global beef consumption negatively impacts the environment were considerably more likely to express intentions to reduce their long-term beef intake compared to those who strongly disagreed (94.4% vs. 19.6%). Overall, 76.6% of beef eaters indicated wanting to eat less beef or phase it out entirely (30.7% reduce, 29.4% minimize, 16.6% stop), with only 23.4% of them intending to keep their consumption unchanged. Compelling messages that help translate awareness into action, such as the #NoBeefWeek concept explored in this study, may support individuals in adopting more sustainable food choices. These cross-national findings provide evidence for a ‘knowledge–intent’ gap in sustainable diet research, with relevance for health communicators and policymakers. Future research could examine the factors and motivations influencing decisions to modify beef consumption, including the barriers to achieving sustainable consumption levels and the role of suitable alternatives in facilitating this transition. Full article

This paper presents a study on open energy data in Spain and its contribution to sustainability, analyzing its content and its reuse potential. Since energy plays an important role in the sustainability and economic development of a country or region, energy strategies must be managed through public policies that promote the development of this sector. In this sense, open data is relevant for decision-making in the energy sector, especially in areas such as energy consumption and renewable energy policies. Our research aims to analyze the work of Spain’s autonomous communities in the field of energy information by conducting a population analysis of all datasets tagged in the energy category. After compiling the information and eliminating irrelevant datasets (those that are mislabeled, obsolete, or have a scope less than the level of the autonomous community), it can be seen that the supply is very scarce and that this category is one of the least populated among all existing categories. The typological analysis indicates that information on consumption is the one offering the most datasets, followed, at a short distance, by heterogeneous and difficult-to-classify information and by the set related to energy certificates or audits (the most recurrent, as it is offered only once by the autonomous communities). One of the main findings of the research is the heterogeneity of the initiatives and the significant differences in scores on an indicator created for this purpose. The ranking has taken into account both the existence of information and the quality of reuse, with Catalonia, the Basque Country, and Cantabria being the leaders (with Castilla y León, the performance reaches 60%, so the three remaining communities do not reach 40%). The research concludes with recommendations based on the gaps detected: more data should be published that can drive economic development and environmental sustainability, reduce heterogeneity, and facilitate the use of these data for greater applicability, which will increase the chances that open energy data can contribute more to sustainability. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that primarily affects motor neurons, leading to loss of muscle control, and, ultimately, respiratory failure and death. Despite some advances in recent years, the underlying genetic and molecular mechanisms of ALS remain largely elusive. In this respect, a better understanding of these mechanisms is needed to identify new and biologically relevant therapeutic targets that could be developed into treatments that are truly disease-modifying, in that they address the underlying causes rather than the symptoms of ALS. In this study, we used two approaches to model multi-omics data in order to map and elucidate the genetic and molecular mechanisms involved in ALS, i.e., the molecular landscape building approach and the Patrimony platform. These two methods are complementary because they rely upon different omics data sets, analytic methods, and scoring systems to identify and rank therapeutic target candidates. The orthogonal combination of the two modeling approaches led to significant convergences, as well as some complementarity, both for validating existing therapeutic targets and identifying novel targets. As for validating existing targets, we found that, out of 217 different targets that have been or are being investigated for drug development, 10 have high scores in both the landscape and Patrimony models, suggesting that they are highly relevant for ALS. Moreover, through both models, we identified or corroborated novel putative drug targets for ALS. A notable example of such a target is MATR3, a protein that has strong genetic, molecular, and functional links with ALS pathology. In conclusion, by using two distinct and highly complementary disease modeling approaches, this study enhances our understanding of ALS pathogenesis and provides a framework for prioritizing new therapeutic targets. Moreover, our findings underscore the potential of leveraging multi-omics analyses to improve target discovery and accelerate the development of effective treatments for ALS, and potentially other related complex human diseases. Full article

Face recognition technology is a biometric technology, which is based on the identification or verification of facial features. Automatic face recognition is an active research field in the context of computer vision and artificial intelligence (AI) that is fundamental for a variety of real-time applications. In this research, the design and implementation of a face verification and identification system of a flexible, modular, secure, and scalable architecture is proposed. The proposed system incorporates several and various types of system components: (i) portable capabilities (mobile application and mixed reality [MR] glasses), (ii) enhanced monitoring and visualization via a user-friendly Web-based user interface (UI), and (iii) information sharing via middleware to other external systems. The experiments showed that such interconnected and complementary system components were able to perform robust and real-time results related to face identification and verification. Furthermore, to identify a proper model of high accuracy, robustness, and performance speed for face identification and verification tasks, a comprehensive evaluation of multiple face recognition pre-trained models (FaceNet, ArcFace, Dlib, and MobileNetV2) on a curated version of the ID vs. Spot dataset was performed. Among the models used, FaceNet emerged as a preferable choice for real-time tasks due to its balance between accuracy and inference speed for both face identification and verification tasks achieving AUC of 0.99, Rank-1 of 91.8%, Rank-5 of 95.8%, FNR of 2% and FAR of 0.1%, accuracy of 98.6%, and inference speed of 52 ms. Full article

Objective: Pulmonary arterial hypertension (PAH) is a progressive and life-threatening disease. Adverse events (AEs) related to its drug treatment seriously damaged the patient’s health. This study aims to clarify the causal relationship between PAH drugs and these AEs by combining pharmacovigilance signal detection with the Bayesian causal network model. Methods: Patient data were obtained from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), covering reports from 2013 to 2023. In accordance with standard pharmacovigilance methodologies, disproportionality analysis was performed to detect signals. Target drugs were selected based on the following criteria: number of reports (a) ≥ 3, proportional reporting ratio (PRR) ≥ 2, and chi-square (χ²) ≥ 4. Bayesian causal network models were then constructed to estimate causal relationships. The do-calculus and adjustment formula were applied to calculate the causal effects between drugs and AEs. Results: Signal detection revealed that Ambrisentan, Bosentan, and Iloprost were associated with serious AEs, including death, dyspnea, pneumonia, and edema. For Ambrisentan, the top-ranked adverse drug events (ADEs) based on average causal effect (ACE) were peripheral swelling (ACE = 0.032) and anemia (ACE = 0.021). For Iloprost, the most prominent ADE was hyperthyroidism (ACE = 0.048). Conclusions: This study quantifies causal drug–event relationships in PAH using Bayesian causal networks. The findings offer valuable evidence regarding the clinical safety of PAH medications, thereby improving patient health outcomes. Full article

The global impact of the COVID-19 crisis has underscored the need for novel therapeutic candidates capable of efficiently targeting essential viral proteins. Existing therapeutic strategies continue to encounter limitations such as reduced efficacy against emerging variants, safety concerns, and suboptimal pharmacodynamics, which emphasize the potential of natural-origin compounds as supportive agents with immunomodulatory, anti-inflammatory, and antioxidant benefits. The present study significantly advances prior molecular docking research through comprehensive virtual screening of structurally related analogs derived from antiviral phytochemicals. These compounds were evaluated specifically against the SARS-CoV-2 main protease (3CLpro) and papain-like protease (PLpro). Utilizing chemical similarity algorithms via the ChEMBL database, over 600 candidate molecules were retrieved and subjected to automated docking, interaction pattern analysis, and comprehensive ADMET profiling. Several analogs showed enhanced binding scores relative to their parent scaffolds, with CHEMBL1720210 (a shogaol-derived analog) demonstrating strong interaction with PLpro (−9.34 kcal/mol), and CHEMBL1495225 (a 6-gingerol derivative) showing high affinity for 3CLpro (−8.04 kcal/mol). Molecular interaction analysis revealed that CHEMBL1720210 forms hydrogen bonds with key PLpro residues including GLY163, LEU162, GLN269, TYR265, and TYR273, complemented by hydrophobic interactions with TYR268 and PRO248. CHEMBL1495225 establishes multiple hydrogen bonds with the 3CLpro residues ASP197, ARG131, TYR239, LEU272, and GLY195, along with hydrophobic contacts with LEU287. Gene expression predictions via DIGEP-Pred indicated that the top-ranked compounds could influence biological pathways linked to inflammation and oxidative stress, processes implicated in COVID-19’s pathology. Notably, CHEMBL4069090 emerged as a lead compound with favorable drug-likeness and predicted binding to PLpro. Overall, the applied in silico framework facilitated the rational prioritization of bioactive analogs with promising pharmacological profiles, supporting their advancement toward experimental validation and therapeutic exploration against SARS-CoV-2. Full article

Background: The biological mediators for the epidemiologic overlap between osteoporosis and dementia are unclear. We undertook a scoping review of clinical studies to identify genetic and biological factors linked with these degenerative conditions, exploring the mechanisms and pathways connecting both conditions. Methods: Studies selected (1) involved clinical research investigating genetic factors or biomarkers associated with dementia or osteoporosis, and (2) were published in English in a peer-reviewed journal between July 1993 and March 2025. We searched Medline Ovid, Embase, PsycINFO, the Cochrane Library, the Web of Science databases, Google Scholar, and the reference lists of studies following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR). Results: Twenty-three studies were included in this review. These explored the role of the APOE polymorphism (n = 2) and the APOE4 allele (n = 13), associations between TREM2 mutation and late onset AD (n = 1), and associations between amyloid beta and bone remodeling (n = 1); bone-related biomarkers like DKK1, OPG, and TRAIL as predictors of cognitive change (n = 2); extracellular vesicles as bone–brain communication pathways (1); and the role of dementia-related genes (n = 1), AD-related CSF biomarkers (n = 1), and parathyroid hormone (PTH) (n = 1) in osteoporosis–dementia pathophysiology. Conclusions: Bone-related biomarkers active in the Wnt/β-Catenin pathway (Dkk1 and sclerostin) and the RANKL/RANK/OPG pathway (OPG/TRAIL ratio) present consistent evidence of involvement in AD and osteoporosis development. Reports proposing APOE4 as a causal genetic link for both osteoporosis and AD in women are not corroborated by newer observational studies. The role of Aβ toxicity in osteoporosis development is unverified in a large clinical study. Full article