EMT inhibitory property of an EGFR inhibitor, Gefitinib.

<p>T2 plate image and CDR dose response profile of Gefitinib, against EGF- (A) HGF- (B) and IGF-1- (C) induced EMT.</p

EMT spot migration screening assay overview.

<p>(A) Schematic of the spot migration screening assay to identify EMT inhibitory compounds. EMT can be initiated and maintained in epithelial cells via growth factor signaling. This assay measures the dispersion of cells in the presence of a test compound and an EMT inducer (EGF, HGF or IGF-1). The prevention of cell dispersion directly correlates to the propensity of a test compound to block an induced EMT signaling pathway. (B) Screening assay image acquisition workflow. Robot-assisted plating of H2B-mcherry transfected NBT-II cells into the well centers of 96-well plates. The initial plate image acquired at T1 served as the baseline reference for calculating the CCR and CDR values for each well. The cells were treated with test compounds overnight and further incubated for 24 h with a growth factor to induce EMT. (C) Final plate image acquired at T2 depicted the dispersion response of cells 24 h after addition of the compounds and growth factor treatment. In the example shown, columns 2–11 were treated with 80 different test compounds at 6.67 µM and EGF. Column-1 served as negative controls treated with 0.67% DMSO and EGF, while column-12 served as positive controls treated with 6.67 µM AG1478 and EGF. (D) Magnified images of selected wells acquired at T1 and T2. Wells C12, H03 and D02 are examples of cell colonies treated by compounds that inhibited EGF-initiated cell dispersion and did not inhibit cell growth. Well C01 is a cell colony undergoing EGF-induced EMT without any dispersion inhibition. Well E09 is a cell colony treated by a growth inhibitory or toxic compound.</p

Image processing procedure to determine the cell count and dispersion values of a well.

<p>(A) Colony nuclei image of each well was obtained by stitching four adjacent, non-overlapping fields together. The example here shows a primary cell colony surrounded by several cell outliers. (B) Nuclei segmentation, which consists of a wavelet transform and watershed algorithm steps, was applied to identify all nuclei in the well. (C) The nuclei segmentation mask was then dilated to generate merging region areas where distinct cell clusters could be isolated. In general, the largest region (yellow), representing the cell colony of interest, and other smaller regions (other colors), representing outlier cell clusters, were identified. (D) Nuclei within the colony of interest were kept for measurement. Cell count was determined by the total nuclei count within the colony. Cell dispersion was determined by applying the spreading coefficient formula. The blue arrow represents a vector centered on the colony center with distance equal to the spreading coefficient.</p

Cell dispersion ratio (CDR) vs. cell count ratio (CCR) plots.

<p>The graphs illustrate the behavior of NBT-II cells treated with different test compounds and growth factors in the spot migration assay. CDR threshold was set at 50% CDR between positive control CDR and negative control CDR. CCR threshold was set at 1.5 growth rate. We assessed compounds that inhibit cell dispersion (i.e. less than CDR threshold) and do not severely inhibit cell growth (i.e. more than CCR threshold). To further refine our hits, the test compounds were run at a low and high concentration (1.67 and 6.67 µM, respectively). Hit compounds (crossed squares) were classified as test compounds that satisfy the CDR and CCR threshold criteria at both concentrations.</p

EMT inhibitory property of an ALK5 inhibitor, A83-01.

<p>T2 plate image and CDR dose response profile of A83-01, against EGF- (A) HGF- (B) and IGF-1- (C) induced EMT.</p

Summary of EMT inhibitor CDR IC₅₀ values against EGF-, HGF- and IGF-1-induced EMT.

a<p>Clinical trials information: ClinicalTrials.gov</p

Image1_Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.jpg

IntroductionThe importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.MethodsA total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebrae and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. Results were validated on an external independent group of CT scans.ResultsThe algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets.ConclusionsOur deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.</p