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Abstract Oncolytic viruses (OVs) preferentially infect and replicate within tumors, induce immunogenic cell death, and remodel immune-poor microenvironments to support T-cell infiltration. These features provide a rationale for combining OVs with immune checkpoint blockade, adoptive cellular therapies, bispecific T-cell engagers, cancer vaccines, and innate immune agonists, and for engineering viruses to express immunostimulatory cytokines. Clinical outcomes, however, have been inconsistent.
We believe that neither physics nor machine learning alone can compete against a unified hybrid approach. Integrating physics and machine learning combines the best of the two worlds, resulting in higher accuracy, better scalability, and cost efficiency. In short, hybrid models will be invaluable in the future of modeling in the industry. Flow simulations facilitate production optimization by monitoring and planning the flow of oil and gas in wells and pipelines.
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