USMLE WORLD STEP 1 CORRELATION FULL

Traction forces, thus related to various cell functions, are generated by the activity of nonmuscle myosin II and actin filaments that determine cellular contractility 2, 10, 11, 12.

In response to the mechanical cues, cells often adjust their cytoskeletal tension such thatВ many of the mechanical information are translated into a level of inherent cellular traction forces, and in turn into intracellular signals regulating the related functions 3, 7, 8, 9. There is now growing evidence showing that cells sense mechanical cues in the surrounding microenvironment to regulate their functions such as proliferation, differentiation, apoptosis, and pro-inflammation 1, 2, 3, 4, 5, 6.

Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is much simpler method compared to the TFM experiment. After sufficient training, the network is utilized to predict the cellular forces just from the input images. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. Second, the wrinkle positions are extracted from the microscope images. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously.

USMLE WORLD STEP 1 CORRELATION FULL

The full process can be divided into three steps. Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images.

Usmle world step 1 correlation