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In the present study, we investigated whether treatment with AR inhibitors could prevent alterations in the cytokine and chemokine levels in a cellular model using human SAEC and a clinically relevant mice model which mimics allergic airway inflammatory conditions in humans.
We previously established an ex vivo cellular model using synovial tissue (ST -derived inflammatory cellST -derivedproduced pannus-linflammatoryrowth and osteocellsic activity in vitro.
We previously established an ex vivo cellular model using the ST-derived inflammatory cells, which reproduced pannus-like tissue growth and osteoclastic activity in vitro.
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We have previously characterized the cellular model used in this study [7], [10] and found that Snail1 overexpression promoted the acquisition of a more stellate cell morphology (Figure 1B), the repression of the epithelial markers E-cadherin, Occludin and Claudin-7, and the induction of the mesenchymal protein Lef-1 (Figure 1C).
The cellular model used in this study consisted of primary cultures of human keratinocytes (HPK) isolated from infant foreskin.
As expected in the transformed SaOs-2 cellular model used in these data, few apoptotic cells have been noted following FTI treatment.
The major problem is that most of the cellular model used (IMR-90, WI38, MEF cells, and so on) are adapted to grow with this high level of glucose, and decreasing this level can affect cell growth.
Toward this end, in the last few decades, numerous articles have been written to analyze various cellular models using information theoretic argument to gain insight into the implications on the performance of the system parameters.
Quite unexpectedly, generating mouse models using conditional approaches as well as GAA-based mouse models (Puccio et al., 2001; Miranda et al., 2002; Al-Mahdawi et al., 2006) was more successful than the design of stable cellular models using disease-relevant cell types.
One strategy to improve the rate of success of new cancer drugs transitioning into the clinic would be to more closely align the cellular models used in the early lead discovery with pre-clinical animal models and patient tumors.
Furthermore, it is well known that cellular models used in building network models affect and can dictate the network output.
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