Search Results (22,595)

Background/Objectives: The detection of ovarian cancer remains challenging due to the lack of reliable serum biomarkers that reflect malignant transformation rather than mere tumor presence. We developed a novel biotest using an immortalized human fallopian tube epithelial cell line (TY), which exhibits anchorage-independent growth (AIG) in response to cancer-associated serum factors. Methods: Sera from ovarian and breast cancer patients, non-cancer controls, and ID8 ovarian cancer-bearing mice were tested for AIG-promoting activity in TY cells. Results: TY cells (passage 96) effectively distinguished cancer sera from controls (68.50 ± 2.12 vs. 17.50 ± 3.54 colonies, p < 0.01) and correlated with serum CA125 levels (r = 0.73, p = 0.03) in ovarian cancer patients. Receiver operating characteristic (ROC) analysis showed high diagnostic accuracy (AUC = 0.85, cutoff: 23.75 colonies). The AIG-promoting activity was mediated by HGF/c-MET and IGF/IGF-1R signaling, as inhibition of these pathways reduced phosphorylation and AIG. In an ID8 mouse ovarian cancer model, TY-AIG colonies strongly correlated with tumor burden (r = 0.95, p < 0.01). Conclusions: Our findings demonstrate that the TY cell-based AIG assay is a sensitive and specific biotest for detecting ovarian cancer and potentially other malignancies, leveraging the fundamental hallmark of malignant transformation. Full article

Hypothermia-related deaths present significant diagnostic challenges due to non-specific and often inconsistent autopsy findings. This study investigated the histological and immunohistochemical alterations associated with primary and secondary hypothermia in an experimental Rattus norvegicus model, focusing on the effects of benzodiazepine and alcohol ingestion. Twenty-one male rats were divided into three groups: control (K), benzodiazepine-treated (B), and alcohol-treated (A). After two weeks of substance administration, hypothermia was induced and multiple organ samples were analyzed. Histologically, renal tissue showed hydropic and vacuolar degeneration, congestion, and acute tubular injury across all groups, with no significant differences in E-cadherin expression. Lung samples revealed congestion, emphysema, and hemorrhage, with more pronounced vascular congestion in the alcohol and benzodiazepine groups. Cardiac tissue exhibited vacuolar degeneration and protein denaturation, particularly in substance-exposed animals. The spleen showed preserved architecture but increased erythrocyte infiltration and significantly elevated myeloperoxidase (MPO)-positive granulocytes in the intoxicated groups. Liver samples demonstrated congestion, focal necrosis, and subcapsular hemorrhage, especially in the alcohol group. Immunohistochemical analysis revealed statistically significant differences in MPO expression in both lung and spleen tissues, with the highest levels observed in the benzodiazepine group. Similarly, CK7 and CK20 expression in the gastroesophageal junction was significantly elevated in both alcohol- and benzodiazepine-treated animals compared to the controls. In contrast, E-cadherin expression in the kidney did not differ significantly among the groups. These findings suggest that specific histological and immunohistochemical patterns, particularly involving pulmonary, cardiac, hepatic, and splenic tissues, may help differentiate primary hypothermia from substance-related secondary hypothermia. The study underscores the value of integrating toxicological, histological, and molecular analyses to enhance the forensic assessment of hypothermia-related fatalities. Future research should aim to validate these markers in human autopsy series and explore additional molecular indicators to refine diagnostic accuracy in forensic pathology. Full article

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article

Background/Objectives: Non-coding microRNA-34a (miR-34a) regulates the expression of key factors involved in several cellular processes, such as differentiation, apoptosis, proliferation, cell cycle, and senescence. Deregulation of the expression of these factors is implicated in the onset and progression of several human diseases, including cancer, neurodegenerative disorders, and pathologies associated with viral infections and inflammation. Despite numerous studies, the molecular mechanisms regulated by miR-34a remain to be fully understood. The present study aimed to generate miR-34a knockout cell lines to identify novel genes potentially regulated by its expression. Methods: We employed the CRISPR-Cas9 gene editing system to knock out the hsa-miR-34a gene in HeLa and 293T cell lines, two widely used models for studying molecular and cellular mechanisms. We compared proliferation rates and gene expression profiles via RNA-seq and qPCR analyses between the wild-type and miR-34a KO cell lines. Results: Knockout of miR-34a resulted in a decreased proliferation rate in both cell lines. Noteworthy, the ablation of miR-34a resulted in increased expression of the long non-coding RNA MALAT1. Additionally, miR-34a-5p silencing in the A375 melanoma cell line led to MALAT1 overexpression. Conclusions: Our findings support the role of the miR-34a/MALAT1 axis in regulating proliferation processes. Full article

Background: Metastatic melanoma is a highly aggressive malignancy with poor prognoses and frequent resistance to conventional chemotherapy. Approximately 40% of melanoma cases carry the BRAF^V600E mutation, for which vemurafenib, a selective BRAF^V600E inhibitor, is approved. Despite initial clinical benefits, vemurafenib often leads to drug resistance and relapse, highlighting the need for improved therapeutic strategies. Objectives, methods: In this study, we designed, synthesized, and characterized five novel vemurafenib analogs—RF-86A, RF-87A, RF-94A, RF-94B, and RF-96B—with the aim of enhancing anti-proliferative and anti-metastatic effects against human melanoma cells. Results: All compounds induced apoptosis in BRAF^V600E-mutated A375 cells, with RF-86A displaying the lowest IC₅₀ value among the series, comparable to that of vemurafenib. Moreover, RF-86A exhibited the highest selectivity index, as determined using HEK293T cells as a non-tumorigenic control. Additionally, migration assays and gelatin zymography demonstrated that the analogs, unlike vemurafenib, significantly inhibited matrix metalloproteinases MMP-2 and MMP-9, key enzymes involved in tumor invasion and metastasis. Conclusions: These findings suggest that structural modifications to the vemurafenib scaffold may improve therapeutic efficacy and offer a promising strategy to overcome acquired resistance. Full article

Understanding the utilization patterns of nuclear medicine (NM) services is essential for optimizing resource allocation and service provision. This study aimed to address the regional evidence gap by reporting the demand for NM services by sex and age at a public hospital in Jamaica. This was a non-experimental, retrospective study of NM scans that were completed at the University Hospital of the West Indies from 1 June 2022 to 31 May 2024. While all scans were reported in the descriptive totals, for patients with multiple scans during the study period, only the data from the first visit was used in the inferential statistical analysis. This was performed with the IBM SPSS (version 29.0) software and involved the use of chi-square goodness of fit and multinomial logistic regression. A total of 1135 NM scans for 1098 patients were completed (37 patients had more than one scan); 596 (54.3%) were female and 502 (45.7%) were male, with the ages ranging from 3 days to 94 years old. Among the female patients, there was a greater demand in the ≥60 years age group for cardiac amyloid scans (χ² = 6.40, p < 0.05), while females 18–59 years had a greater demand for thyroid scans (χ² = 7.714, p < 0.05) and bone scans (χ² = 3.904, p < 0.05). On the other hand, significantly more males in the ≥60 age group presented for cardiac amyloid (χ² = 4.167; p < 0.05) and bone scans (χ² = 145.79, p < 0.01). Males were significantly less likely to undergo a thyroid scan than females (p < 0.01, OR = 0.072, 95% CI: 0.021, 0.243) while individuals aged 18–59 years were more likely to undergo this scan than patients aged 60 or older (p = 0.02, OR = 3.565, 95% CI: 1.258, 10.104). Males were more likely to do a cardiac amyloid scan (p < 0.05, OR = 2.237, 95% CI: 1.023, 4.891) but less likely to undergo a cardiac rest/stress test than females (p = 0.02, OR = 0.307, 95% CI: 0.114, 0.828). Prolonged life expectancy and an aging population have the potential to impact NM utilization, thus requiring planning for infrastructure, equipment, work force, and supplies. Cancer-related and cardiovascular indications are a top priority at this facility; hence, age- and sex-specific analysis are useful in establishing models for policy makers with regard to the allocation of economic and human resources for the sustainability of this specialized service. Full article

This paper introduces a novel system that utilizes OpenPose for skeleton estimation to enable a tabletop robot to interact with humans proactively. By accurately recognizing upper-body poses based on the skeleton information, the robot autonomously approaches individuals and initiates conversations. The contributions of this paper can be summarized into three main features. Firstly, we conducted a comprehensive data collection process, capturing five different table-front poses: looking down, looking at the screen, looking at the robot, resting the head on hands, and stretching both hands. These poses were selected to represent common interaction scenarios. Secondly, we designed the robot’s dialog content and movement patterns to correspond with the identified table-front poses. By aligning the robot’s responses with the specific pose, we aimed to create a more engaging and intuitive interaction experience for users. Finally, we performed an extensive evaluation by exploring the performance of three classification models—non-linear Support Vector Machine (SVM), Artificial Neural Network (ANN), and convolutional neural network (CNN)—for accurately recognizing table-front poses. We used an Asus Zenbo Junior robot to acquire images and leveraged OpenPose to extract 12 upper-body skeleton points as input for training the classification models. The experimental results indicate that the ANN model outperformed the other models, demonstrating its effectiveness in pose recognition. Overall, the proposed system not only showcases the potential of utilizing OpenPose for proactive human–robot interaction but also demonstrates its real-world applicability. By combining advanced pose recognition techniques with carefully designed dialog and movement patterns, the tabletop robot successfully engages with humans in a proactive manner. Full article

Pet ownership and wildlife tourism are two prominent ways people interact with non-human animals in contemporary contexts. Despite this, there is a need for further exploration of the interconnections between pets, wildlife, and visitors. Utilizing an ecological-phenomenological framework, this study examines how these multispecies interactions contribute to experiences that extend beyond the human domain. This research is based on a quantitative survey of 1422 participants at the Panda Center that were analyzed using inferential statistical methods to assess differences in visitor experiences. The statistical results reveal that pet ownership and wildlife encounters mediate the environmental affordances and constraints encountered by visitors, creating a dynamic and intricate nexus among pets, wildlife, and tourists. Specifically, pet ownership is shown to enhance both the richness and frequency of wildlife encounters. Nonetheless, the study highlights that human dominance over non-human animals remains a central environmental constraint in multispecies interactions despite the adoption of a more humane approach to animal management through tourism activities. Full article

Background: Artificial non-nutritive sweeteners (NNSs), such as sucralose, have been associated with gut microbiota (GM) alterations. However, the impact of rebaudioside A (reb A), a natural NNS, on GM has received limited scrutiny. Objective: The objective of this study was to examine the response of GM composition to sucralose and reb A in rats under two dietary conditions. Methods: Male Wistar rats (150–200 g) fed with a normal diet (ND) or a high-fat diet (HFD) were randomly assigned to receive sucralose (SCL), reb A (REB), glucose (GLU, control), or sucrose (SUC). The NNS interventions were administered in water at doses equivalent to the acceptable daily intake (ADI). After eight weeks, the GM composition in fecal samples was analyzed through 16S ribosomal RNA gene sequencing. Results: The NNSs did not modify the diversity, structure, phylum-level composition, or Firmicutes/Bacteroidetes (F/B) ratio of the GM in rats under ND or HFD. However, REB with HFD decreased Bacilli and increased Faecalibacterium abundance at the class level. SCL and REB in rats receiving ND reduced the genera Romboutsia and Lactobacillus. Conclusions: Our study suggests that when sucralose or reb A is consumed at recommended doses, there is no alteration in the diversity or the composition of the GM at the phylum level. The clinical relevance of these findings lies in the potential modifications of the GM at specific taxonomic levels by the consumption of these NNSs. Further research involving humans and including a broader range of microbial analyses is warranted. Full article

Skeleton-based action recognition is an important subject in deep learning. Graph Convolutional Networks (GCNs) have demonstrated strong performance by modeling the human skeleton as a natural topological graph, representing the connections between joints. However, most existing methods rely on non-adaptive topologies or insufficiently expressive representations. To address these limitations, we propose a Multi-view Topology Refinement Graph Convolutional Network (MTR-GCN), which is efficient, lightweight, and delivers high performance. Specifically: (1) We propose a new spatial topology modeling approach that incorporates two views. A dynamic view fuses joint information from dual streams in a pairwise manner, while a static view encodes the shortest static paths between joints, preserving the original connectivity relationships. (2) We propose a new MultiScale Temporal Convolutional Network (MSTC), which is efficient and lightweight. (3) Furthermore, we introduce a new temporal topology strategy by modeling temporal frames as a graph, which strengthens the extraction of temporal features. By modeling the human skeleton as both a spatial and a temporal graph, we reveal a topological symmetry between space and time within the unified spatio-temporal framework. The proposed model achieves state-of-the-art performance on several benchmark datasets, including NTU RGB + D (XSub: 92.8%, XView: 96.8%), NTU RGB + D 120 (XSub: 89.6%, XSet: 90.8%), and NW-UCLA (95.7%), demonstrating the effectiveness of our GCN module, TCN module, and overall architecture. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Background: The aim of this review is to summarize and evaluate the properties of antibacterial polysaccharides for application in dental implantology to identify knowledge gaps and provide new research ideas. Methods: The electronic databases PubMed, Medline, ProQuest, and Google Scholar were used to search for peer-reviewed scientific publications published between 2018 and 2025 that provide insights to answer research questions on the role of antibacterial polysaccharides in combating pathogens in dental implantology without triggering immune reactions and inflammation. Further research questions relate to the efficacy against various dental pathogens and the understanding of the antibacterial mechanism, which may enable the development of functionalized polysaccharides with long-term antibacterial activity. Results: Biomedical implants have revolutionized medicine but also increased the risk of infections. Implant infections are a major problem in implantology and lead to implant failure and replacement. An antibacterial coating could be an excellent strategy to extend the lifespan of implants and improve the quality of the patient’s life. Bacterial resistance to antibiotics poses significant challenges for researchers, forcing them to search for new ways to prevent bacterial infections in implantology. Antibacterial natural polymers have recently received considerable research attention due to their long-term antibacterial activity. Polysaccharides from marine sources, such as chitosan and alginate, or pectin, xanthan, etc., from various plants, appear to be promising biopolymers for such applications in implantology due to their antibacterial activity, biocompatibility, and osteogenic properties. The antibacterial activity of these natural biopolymers depends on their chemical and physical properties. Nanopolysaccharides exhibit higher antibacterial activity than conventional polysaccharides, but their toxicity to human cells must be considered. Their antibacterial activity is based on the disruption of bacterial DNA or RNA synthesis, increased cell wall permeability, membrane disruption, and cytoplasmic leakage. Conclusions: Polysaccharides are a class of natural polymers with a broad spectrum of biological activities. They exhibit antioxidant, immunomodulatory, anticoagulant, anticancer, anti-inflammatory, antibacterial, and antiviral activity. Furthermore, polysaccharides are non-cytotoxic and exhibit good biocompatibility with osteogenic cells. Bactericidal polysaccharides are attractive new antibacterial materials against implant infections and open up new perspectives in implantology. Full article

Cardiovascular disease (CVD) is the leading cause of death worldwide, with pathophysiological mechanisms often involving platelet activation and chronic inflammation. While antiplatelet agents targeting adenosine diphosphate (ADP)-mediated pathways are well established in CVD management, less is known about drug interactions with the platelet-activating factor (PAF) pathway, a key mediator of inflammation. This study aimed to evaluate the effects of several commonly used cardiovascular and anti-inflammatory drug classes—including clopidogrel, non-steroidal anti-inflammatory drugs (NSAIDs), angiotensin II receptor blockers (ARBs), β-blockers, and analgesics—on platelet function via both the ADP and PAF pathways. Using human platelet-rich plasma (hPRP) from healthy donors, we assessed platelet aggregation in response to these two agonists in the absence and presence of graded concentrations of each of these drugs or of their usually prescribed combinations. The study identified differential drug effects on platelet aggregation, with some agents showing pathway-specific activity. Clopidogrel and NSAIDs demonstrated expected antiplatelet effects, while some (not all) antihypertensives exhibited additional anti-inflammatory potential. These findings highlight the relevance of evaluating pharmacological activity beyond traditional targets, particularly in relation to PAF-mediated inflammation and thrombosis. This dual-pathway analysis may contribute to a broader understanding of drug mechanisms and inform the development of more comprehensive therapeutic strategies for the prevention and treatment of cardiovascular, hypertension, and inflammation-driven diseases. Full article

Efficient planning of train crew rosters is a key factor in ensuring operational reliability and promoting long-term sustainability in railway transport, both economically and socially. This article presents a systematic approach to developing a crew rostering model in passenger rail transport, with a focus on the operational setting of the train crew depot in Česká Třebová, a city in the Czech Republic. The seven-step methodology includes identifying available train shifts, defining scheduling constraints, creating roster variants, and calculating personnel and time requirements for each option. The proposed roster reduced staffing needs by two employees, increased the average shift duration to 9 h and 42 min, and decreased non-productive time by 384 h annually. These improvements enhance sustainability by optimizing human resource use, lowering unnecessary energy consumption, and improving employees’ work–life balance. The model also provides a quantitative assessment of operational feasibility and economic efficiency. Compared to existing rosters, the proposed model offers clear advantages and remains applicable even in settings with limited technological support. The findings show that a well-designed rostering system can contribute not only to cost savings and personnel stabilization, but also to broader objectives in sustainable public transport, supporting resilient and resource-efficient rail operations. Full article