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Experiments in the late 19th century sought to define the host specificity of the causative agents of tuberculosis in mammals.
We now define the host reproductive number R0hj as the total number of host infections resulting from a single infected host with normal destination j.
With advances in gene expression profiling and application of microarray technology to the field of infectious diseases, we can now more clearly define the host's immune response to a pathogen and identify a unique biosignature that is not limited by traditional diagnostic or microbiologic techniques [16] [18].
Further studies are necessary to precisely define the host cytokines that are critical to the CCL19-mediated antitumour response.
Furthermore, by applying specific phagocyte ablation strategies, we have begun to define the host cell populations responsible for S. aureus infection progression.
In this analysis, we attempt to more clearly define the host risk factors and disease course to refine prevention and treatment efforts.
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Such molecular interactions that define the host-microbe interface are generally non-covalent in nature and frequently involve extensive intermolecular interfaces and multivalent binding.
New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition.
Collectively, these data suggest that the experimental injury paradigm is a critical factor in defining the host niche post-trauma.
Defining the host response to influenza (H5N1) in natural infection is urgently needed to better understand the basis of protection and subsequent development of efficacious vaccines.
In pathogenic bacteria, SNPs serve as evolutionary markers and those present in virulence factors may aid in defining the host specificity at molecular level [ 18].
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