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Background: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem. Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research. Materials and Methods: Published literature databases were searched to retrieve OMOP CDM and cancer-related English language articles published between January 2010 and December 2023. A charting form was developed for two main themes, i.e., clinically focused data analysis studies and infrastructure development studies in the cancer domain. Results: In total, 50 unique articles were included, with 30 for the data analysis theme and 23 for the infrastructure theme, with 3 articles belonging to both themes. The topics covered by the existing body of research was depicted. Conclusion: Through depicting the status quo of research efforts to improve or leverage the potential of the OMOP CDM ecosystem for advancing cancer research, we identify challenges and opportunities surrounding data analysis and infrastructure including data quality, advanced analytics methodology adoption, in-depth phenotypic data inclusion through NLP, and multisite evaluation.

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BACKGROUND: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. OBJECTIVE: This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. METHODS: We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. RESULTS: The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. CONCLUSIONS: This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

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Microbial electrosynthesis (MES) or electro-fermentation (EF) is a promising microbial electrochemical technology for the synthesis of valuable chemicals or high-value fuels with aid of microbial cells as catalysts. By introducing electrical energy (current), fermentation environments can be altered or controlled in which the microbial cells are affected. The key role for electrical energy is to supply electrons to microbial metabolism. To realize electricity utility, a process termed inward extracellular electron transfer (EET) is necessary, and its efficiency is crucial to bioelectrochemical systems. The use of electron mediators was one of the main ways to realize electron transfer and improve EET efficiency. To break through some limitation of exogenous electron mediators, we introduced the phenazine-1-carboxylic acid (PCA) pathway from Pseudomonas aeruginosa PAO1 into Escherichia coli. The engineered E. coli facilitated reduction of fumarate by using PCA as endogenous electron mediator driven by electricity. Furthermore, the heterologously expressed PCA pathway in E. coli led to better EET efficiency and a strong metabolic shift to greater production of reduced metabolites, but lower biomass in the system. Then, we found that synthesis of adenosine triphosphate (ATP), as the "energy currency" in metabolism, was also affected. The reduction of menaquinon was demonstrated as one of the key reactions in self-excreted PCA-mediated succinate electrosynthesis. This study demonstrates the feasibility of electron transfer between the electrode and E. coli cells using heterologous self-excreted PCA as an electron transfer mediator in a bioelectrochemical system and lays a foundation for subsequent optimization.

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Artemisia selengensis straw is an agricultural residue with great potential as a renewable resource because it is rich in lignocellulose. In this study, A. selengensis straw was used as a source of hemicelluloses (ASH) and cellulose nanocrystals (ASCNC) to produce biodegradable films. Different content levels of ASCNC were used as additives to improve composite films with ASH and polyvinyl alcohol (PVA). Various mechanical and hydrophobic properties of the films were analyzed. The composite films enhanced by ASCNC exhibited greater strength and were more effective as a barrier to water vapor when compared to that of the control ASH/PVA film. The tensile strength of the composite film was increased 80.1% to 36.21 MPa with ASCNC loading exceeding 9%, and the water vapor transmission rate decreased 15.45% when 12% ASCNC was added. Furthermore, the ASCNC-enhanced ASH/PVA composite film reduced a greater amount of light transmission than the control film.

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Research on the biocathode-based bioelectrochemical system (BES) has attracted extensive attention because of its ability to increase the electricity-driven production of high-value fuels or chemicals by relying on microbial cells as catalysts. An extracellular electron transfer (EET) that makes electrical connections to microorganisms plays a key role in the BES. Compared with the better understanding of the EET-to-anode connection, the understanding of the mechanism and elements involved in inward EET from cathodes to microbes remains limited. Additionally, the low capability of the EET limits its applications in BESs for producing chemicals. Here, we introduced the Mtr pathway into Escherichia coli cells by expressing ccmABCDEFGH from E. coli and mtrABC from Shewanella oneidensis. Through selection by electrochemical pressure, the evolved E. coli could use electricity to increase the production of succinate in direct BES and enhance the electroactivity. In addition, in studying the mechanism of inward EET, menaquinone was found to be one of the key components of inward EET, and it is essential for the fumarate reduction reaction. Lastly, the intracellular NADH and ATP levels showed that there were differences in the energy conservation coupling between the electron transfer routes of the inward Mtr pathway and the electron mediator.

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Metal-organic frameworks (MOFs) for enzyme encapsulation-induced biomimetic mineralization under mild reaction conditions are commonly microporous and hydrophobic, which result in a rather high mass transfer resistance of the reactants and restrain the enzyme catalytic activity. Herein, we prepared a type of hierarchical porous and hydrophilic MOF through the biomimetic mineralization of enzymes, zinc ions, 2-methylimidazole, and lithocholic acid. The hierarchical porous structure accelerated the diffusion process of the reactants and the increased hydrophilicity conferred interfacial activity and increased the enzyme catalytic activity. The immobilized enzyme retained higher catalytic activity than the free enzyme and exhibited enhanced resistance to alkaline, organic, and high-temperature conditions. The nanobiocatalyst was reusable and showed long-term storage stability.

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Artificial metalloenzymes that combine the advantages of natural enzymes and metal catalysts have been getting more attention in research. As a proof of concept, an artificial nanometalloenzyme (CALB-Shvo@MiMBN) was prepared by co-encapsulation of metallo-organic catalyst and enzyme in a soft nanocomposite consisting of 2-methylimidazole, metal ions, and biosurfactant in mild reaction conditions using a one-pot self-assembly method. The artificial nanometalloenzyme with lipase acted as the core, and the metallo-organic catalyst embedded in micropore exhibited a spherical structure of 30-50 nm in diameter. The artificial nanometalloenzyme showed high catalytic efficiency in the dynamic kinetic resolution of racemic primary amines or secondary alcohols compared to the one-pot catalytic reaction of immobilized lipase and free metallo-organic catalyst. This artificial nanometalloenzyme holds great promise for integrated enzymatic and heterogeneous catalysis.