91341 research outputs found
Sort by
Postcard from Unknown to [Milton Wright], from Paris, France (La Conciergerie)
Get PDFAn undated Paris, France postcard featuring La Conciergerie. Collected by Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1083/thumbnail.jp
Postcard from Unknown to [Milton Wright], from Indianapolis, Indiana (State Capitol Building)
Get PDFAn undated Indianapolis, Indiana postcard featuring the State Capitol Building. Collected by Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1061/thumbnail.jp
Postcard from Unknown to [Milton Wright], from Knightstown, Indiana (Main Street, Looking East)
Get PDFAn undated Knightstown, Indiana postcard featuring Knightstown\u27s Main Street. Collected by Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1066/thumbnail.jp
Faculty Senate Meeting Agenda and Minutes, February 24, 2025
Get PDFAgenda and minutes from the Wright State University Faculty Senate Meeting held on, February 24, 2025
Accelerating Knowledge Graph and Ontology Engineering With Large Language Models
No full textLarge Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area of research, and argue that modular approaches to ontologies will be of central importance
Extraction of Patient Subtypes using LLM Generated Knowledge Graphs Integrated With a Transformer Architecture
No full textExtracting patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria) from unstructured medical notes is an ongoing challenge due to the variability of clinical language and the complex nature of patient conditions. We demonstrate a pipeline that combines named entity recognition (NER), transformer embeddings, guided dimensionality reduction, and LLM-mediated knowledge graph integration to enhance patient extraction. The approach begins with NER using the UMLS metathesaurus [1] to extract clinical terms, followed by transformation into vector embeddings using a biomedical transformer. These embeddings are augmented with structured knowledge graph representations generated through an LLM-driven extraction process and graph embeddings via TransE [2]. To improve the separation of key semantic features, we apply autoencoder-based dimensionality reduction before concatenating term embeddings with their graph-based counterparts. A feedforward neural network with an attention layer classifies extracted embeddings to determine patient subgroup membership. We evaluate the pipeline on multiple datasets, including extracting a subpopulation taken from Dayton Childrens\u27 Hospital, with experiments demonstrating improvements over baseline BERT-only and keyword-based methods in classifying medical reports by specialty and behavioral health relevance. Our results show that incorporating knowledge graphs and dimensionality reduction enhances precision and interpretability while maintaining adaptability for different research queries
Modernizing Preclinical Drug Development: The Role of New Approach Methodologies
No full textOver 90% of investigational drugs fail during clinical development, largely due to poor translation of pharmacokinetic, efficacy, and toxicity data from preclinical to clinical settings. The high costs and ethical concerns associated with translational failures highlight the need for more efficient and reliable preclinical tools. Human-relevant new approach methodologies (NAMs), including advanced in vitro systems, in silico mechanistic models, and computational techniques like artificial intelligence and machine learning, can improve translational success, as evident by several literature examples. Case studies on physiologically based pharmacokinetic modeling and quantitative systems pharmacology applications demonstrate the potential of NAMs in improving translational accuracy, reducing reliance on animal studies. Additionally, mechanistic modeling approaches for drug-induced liver injury and tumor microenvironment models have provided critical insights into drug safety and efficacy. We propose a structured and iterative a priori in silico workflow that integrates NAM components to actively guide preclinical study designa step toward more predictive and resource-efficient drug development. The proposed workflow can enable in vivo predictions to guide the design of reduced and optimal preclinical studies. The findings froM.T.hese preclinical studies can then be used to refine computational models to enhance the accuracy of human predictions or guide additional preclinical studies, as needed. To conclude, integrating computational and in vitro NAM approaches can optimize preclinical drug development, improving translational accuracy and reducing clinical trial failures. This paradigm shift is further supported by global regulations, such as the FDA Modernization Act 2.0 and EMA directive 2010/63/EU, underscoring the regulatory momentuM.T.oward adopting human-relevant NAMs as the new standard in preclinical drug development
Radiotherapy Enhancing and Radioprotective Properties of Berberine
No full textBackground: Natural compounds such as Berberine (Ber) have been considered due to favorable anticancer properties, low side effects, and availability along with chemotherapy treatments. Objectives: This study aimed to investigate the radiosensitizing and radioprotective properties of Ber. Methods: In this systematic review that was performed according to PRISMA 2020 guidelines, we searched the publications before 25 Sep 2023 in Web of Science, PubMed, Scopus, Embase, and Cochrane Library databases. After determining inclusion and exclusion criteria, data were extracted and imported into an Excel form, and the results of the studies were reviewed. Results: Ber by reducing the levels of reactive oxygen species (ROS), malondialdehyde (MDA), tumor necrosis factor-alpha (TNF-α), transforming growth factor-beta 1 (TGF-β1), and increasing interleukin 10 (IL-10) levels, showed its antioxidant and anti-inflammatory properties against ionizing radiation. Reducing cell cytotoxicity and apoptosis were other radioprotective properties of Ber. Conversely, in cancer cells, Ber, via inducing oxidative stress and accumulation ROS in tumor tissues, inducing DNA damage, mitochondrial dysfunction and hyperpolarization, inducing apoptosis, and cell cycle arrest, inhibits the up-regulation of hypoxia-inducible factor-1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF) revealed radiosensitizing properties. Conclusion: Ber, via various mechanisms, showed favorable radioprotective and radiosensitizing properties in clinical and experimental studies. However, more clinical studies are needed in this field
