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Mechanisms of change in a community-based alcohol prevention framework among adolescents
Staphylococcus aureus derived extracellular vesicles modulate osteoblast-like cell immune responses independently of vesicle internalization
Enhanced crystallinity and electrical properties through homoepitaxial growth on reduced graphene oxide templates
Abstract Graphene oxide (GO), which can be synthesized inexpensively and in large quantities, is regarded as a promising starting material for electronic device applications due to its ability to recover electrical conductivity through reduction. However, oxygen-containing functional groups and structural defects introduced during the oxidation and reduction process significantly impair the electrical performance of reduced graphene oxide (rGO), posing a major challenge for practical implementation. In this study, we demonstrate that high-temperature thermal reduction in the presence of a carbonaceous gas not only facilitates the repair of vacancies in rGO thin films but also induces the homoepitaxial growth of two-dimensional graphene islands, guided by the underlying rGO template. By precisely controlling the growth driving force of the carbonaceous gas, epitaxial graphene islands were successfully formed, resulting in a significant improvement in electrical performance, with Hall mobilities reaching up to 365 cm 2 /V·s. These results suggest that the homoepitaxial growth of graphene islands plays a crucial role in enhancing both the crystallinity and electrical properties of rGO thin films.
Mechanical properties of steel fiber concrete modified with nano-TiC and nano-SiO2
Comparing the effects of ischemic compression and kinesiology taping techniques in patients with chronic low back pain
Extracellular vesicle proteomics in staphylococcus aureus-treated blood and sepsis reveals coordinated complement, acute phase, neutrophil, and exocytosis responses
Abstract Sepsis accounts for nearly 20% of global mortality, with antibiotic resistance worsening clinical outcomes. Rapid antibiotic administration and accurate pathogen identification remain crucial. It is well now known that extracellular vesicles (EVs) from human cells and bacterial membrane vesicles (bMVs) play a central role in the interaction between host and pathogen and represent promising biomarkers for early infections. This study investigated how antibiotic exposure alters EV responses in Staphylococcus aureus (SA)spiked blood and compared these findings with EV proteome profiles from bacteremia patients. In an in vitro model, whole blood from healthy donors was spiked with SA at a multiplicity of infection (MOI) of 0.001, treated with clinically relevant concentrations of piperacillin–tazobactam, vancomycin, or moxifloxacin, and plasma was subsequently isolated for EV analysis. EVs were isolated using the Miltenyi Pan EV Kit and analyzed by bead-based flow cytometry and high-resolution LC–MS/MS. In parallel, serum EVs from healthy controls ( n = 6) and bacteremia patients ( n = 12; 6 blood culture–positive and 6 culture-negative) were analyzed using the same workflow. Flow cytometry revealed increased levels of CMO⁺ CD45⁺ PanEV⁺ SA⁺ vesicles in SA-spiked samples, particularly following low-dose piperacillin–tazobactam and high-dose vancomycin treatment, despite minimal changes in vesicle size and total particle counts. Proteomic analysis of plasma EVs showed significant alterations in protein composition, including increased abundance of the SA-derived ribosomal protein rplU and host defense–associated proteins. Functional enrichment highlighted pathways related to neutrophil degranulation, vesicle-mediated transport, and antibacterial responses. In patient samples, serum EVs were enriched in acute-phase and immune-related proteins, including SERPINA1, SERPINA3, CRP, and SAA2, along with canonical EV markers such as CD81 and syntenin-1, irrespective of blood culture status. Antibiotic exposure and SA infection are associated with measurable changes in the human EV proteome, characterized by enrichment of immune and host defense–related proteins despite stable vesicle numbers. Similar EV-associated protein patterns were observed in both blood culture–positive and –negative patient samples, reflecting shared features of the systemic host response to infection and highlighting the potential of EV profiling to capture infection-associated biological signals.
A new scheme for knee joint CT image segmentation: A max-flow and watershed method driving semi-automatic CT knee bone segmentation
Abstract Knee joint segmentation from CT images is critical for diagnosing and treating arthritis and other knee disorders. Manual segmentation is time-consuming and expert-dependent, highlighting the need for automated, accurate, and efficient methods. This study proposes a scheme for 3D CT knee-joint segmentation that integrates an adaptive weighted continuous max-flow algorithm with a graphical user interface (GUI). Input CT volumes are preprocessed to normalize intensities and improve robustness. Segmentation employs a continuous max-flow formulation with an adaptive weighting function to better capture weak or ill-defined edges in knee CT data. The watershed algorithm is used to resolve adhesions and separate contiguous structures. The approach supports semi-supervised interaction, allowing limited manual guidance when necessary. A graphical user interface (GUI) facilitates data input, interactive refinement, and result export. Performance was assessed on 18 real datasets using precision, sensitivity, and specificity, and was compared against four open-source segmentation tools. Across the 18 datasets, the proposed method achieved higher precision, sensitivity, and specificity than the evaluated open-source tools, demonstrating improved segmentation accuracy and robustness in knee CT images. The proposed semi-automated scheme yields high-precision, efficient knee joint segmentation from 3D CT, reducing manual effort and streamlining the clinical workflow. The improved accuracy and robustness have the potential to enhance diagnostic and treatment planning in orthopedic applications.
Bioengineered Zn2TiO4 nanomaterial and its effective electrochemical, antibacterial and photocatalytic responses
Insights into the antibacterial, antifungal, and antiparasitic activities of functionalized ZnO quantum dots
Adaptation and validation of the Chinese version of the Service Quality Questionnaire for Internet hospital services in China
PromptSE: drug side effect prediction with LLM-derived pharmacological representations
Abstract Predicting drug-side effect associations is vital for drug discovery and patient safety. Accurate prediction requires high-quality representations of both drugs and side effects. While drug representations have advanced due to rich structured data, side effect information is mostly unstructured, symptom-oriented and heterogeneous texts, making it hard to capture underlying pharmacological mechanisms. We propose prompt-based side effect prediction (PromptSE), a hybrid framework that combines the reasoning power of large language models with the predictive capability of deep learning. PromptSE employs stepwise prompting tailored to the characteristics of side effect texts, moving beyond simple encoding or conventional prompting, to generate pharmacologically relevant representations. These representations are then fed into a deep learning module for drug-side effect prediction. Building on this framework, PromptSE+ extends the prediction module by integrating multi-modal drug information. Rare entity representations are further refined via a custom graph neural network module. Experiments show that PromptSE outperforms non-drug-informed baselines by 9.26% in AUPR, confirming the effectiveness of our representations. PromptSE+ enhances state-of-the-art drug-side effect prediction methods across all metrics, including a 1.81% AUPR gain, demonstrating its compatibility with advanced methods and potential to support safer drug development and more reliable pharmacological research.
The influence of model orientation on achieved accuracy in multi jet fusion additive manufacturing
Thermal processing optimization and modeling for nutritional quality retention in anchote (Coccinia abyssinica) tuber flour
Sleep spindle density increases in patients with severe obstructive sleep apnea after 6 months of treatment: a longitudinal study
AI image generation technology for visual communication design
Comparative evaluation of CNN models for nasopharyngeal carcinoma classification on pathology data
Will participating in entrepreneurship competitions enhance college students’ employability? — evidence from Sichuan Province, China
PKM2-driven glycolysis mediates rotenone neurotoxicity via MG-Hs in Parkinson’s disease
Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder lacking disease-modifying therapies. Rotenone (Rot) is widely used to model PD, but its neurotoxicity is not fully understood beyond mitochondrial complex I inhibition. Here, we identify a glycolytic mechanism that contributes to Rot-induced neuronal damage downstream of complex I inhibition. Our in vitro data demonstrate that Rot enhances glycolytic flux, leading to accumulation of methylglyoxal-derived hydroimidazolones (MG-Hs), which drive irreversible cellular damage. Shikonin effectively attenuates Rot-induced apoptosis by inhibiting PKM2, thereby suppressing glycolysis and reducing MG-Hs formation. In a rat model, shikonin robustly improves motor function and preserves nigrostriatal dopaminergic neurons. Collectively, our findings reveal a previously unrecognized glycolytic-mediated pathway involving PKM2-driven glycolysis and MG-Hs accumulation that contributes to rotenone neurotoxicity alongside mitochondrial dysfunction, and highlight shikonin as a promising neuroprotective agent for Parkinson’s disease intervention.