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The objective of this study is to elucidate osteoarthritis (OA) progression in the temporomandibular joint (TMJ) in two genetic mouse models by assessing the expression of an identified inflammatory marker associated with OA, viz., Tgf-ß1.
We sought to determine the reproducibility of two murine models by assessing clinical severity and local immune cell response 24 hours after septic insult.
To follow up the above evidence of an anxiolytic effect in animal models, we decided to investigate the Gelsemium s. mechanism of action in neuronal models by assessing the drug effects on whole genome expression changes.
Moreover, it offers the possibility to compare a large variety of models by assessing through parametric bootstrapping their respective ability to reproduce a given characteristic of interest, measured on a real data set.
In our previous study, we demonstrated that increased ROS production is one of the major contributors to the pathogenesis of cisplatin ototoxicity in both in vitro and in vivo models by assessing DNA damage.
In this study we explored the role of COX-2 in these models by assessing tumour expression of COX-2 in tumour that have progressed while on sunitinib and studying the effects of the combination of sunitinib and the selective COX-2 inhibitor, celecoxib.
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Quantum chemical calculations and molecular dynamics simulation of VIBDGE were employed to get insight to the adsorption model by assessing the molecular configuration and electronic orbital parameters.
Here we test this hypothesis in a mouse model by assessing the chemotherapeutic activity of p97-drug conjugates within the brain in comparison with unconjugated drugs.
Future studies will address this aspect of the model by assessing participants at several time points.
We only assessed fit of the adapted model by assessing overall incidence of cervical cancer and did not assess age-specific data.
We evaluated the validity of the network model by assessing three important characteristics of the model: between trial heterogeneity, goodness of fit, and consistency.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com