STUDY QUESTION Can an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy? SUMMARY ANSWER We have combined computer vision image processing methods and deep learning techniques to create the non-invasive Life Whisperer AI model for robust prediction of embryo viability, as measured by clinical pregnancy outcome, using single static images of Day 5 blastocysts obtained from standard optical light microscope systems. WHAT IS KNOWN ALREADY Embryo selection following IVF is a critical factor in determining the success of ensuing pregnancy. Traditional morphokinetic grading by trained embryologists can be subjective and variable, and other complementary techniques, such as time-lapse imaging, require costly equipment and have not reliably demonstrated predictive ability for the endpoint of clinical pregnancy. AI methods are being investigated as a promising means for improving embryo selection and predicting implantation and pregnancy outcomes. STUDY DESIGN, SIZE, DURATION These studies involved analysis of retrospectively collected data including standard optical light microscope images and clinical outcomes of 8886 embryos from 11 different IVF clinics, across three different countries, between 2011 and 2018. PARTICIPANTS/MATERIALS, SETTING, METHODS The AI-based model was trained using static two-dimensional optical light microscope images with known clinical pregnancy outcome as measured by fetal heartbeat to provide a confidence score for prediction of pregnancy. Predictive accuracy was determined by evaluating sensitivity, specificity and overall weighted accuracy, and was visualized using histograms of the distributions of predictions. Comparison to embryologists’ predictive accuracy was performed using a binary classification approach and a 5-band ranking comparison. MAIN RESULTS AND THE ROLE OF CHANCE The Life Whisperer AI model showed a sensitivity of 70.1% for viable embryos while maintaining a specificity of 60.5% for non-viable embryos across three independent blind test sets from different clinics. The weighted overall accuracy in each blind test set was >63%, with a combined accuracy of 64.3% across both viable and non-viable embryos, demonstrating model robustness and generalizability beyond the result expected from chance. Distributions of predictions showed clear separation of correctly and incorrectly classified embryos. Binary comparison of viable/non-viable embryo classification demonstrated an improvement of 24.7% over embryologists’ accuracy (P = 0.047, n = 2, Student’s t test), and 5-band ranking comparison demonstrated an improvement of 42.0% over embryologists (P = 0.028, n = 2, Student’s t test). LIMITATIONS, REASONS FOR CAUTION The AI model developed here is limited to analysis of Day 5 embryos; therefore, further evaluation or modification of the model is needed to incorporate information from different time points. The endpoint described is clinical pregnancy as measured by fetal heartbeat, and this does not indicate the probability of live birth. The current investigation was performed with retrospectively collected data, and hence it will be of importance to collect data prospectively to assess real-world use of the AI model. WIDER IMPLICATIONS OF THE FINDINGS These studies demonstrated an improved predictive ability for evaluation of embryo viability when compared with embryologists’ traditional morphokinetic grading methods. The superior accuracy of the Life Whisperer AI model could lead to improved pregnancy success rates in IVF when used in a clinical setting. It could also potentially assist in standardization of embryo selection methods across multiple clinical environments, while eliminating the need for complex time-lapse imaging equipment. Finally, the cloud-based software application used to apply the Life Whisperer AI model in clinical practice makes it broadly applicable and globally scalable to IVF clinics worldwide. STUDY FUNDING/COMPETING INTEREST(S) Life Whisperer Diagnostics, Pty Ltd is a wholly owned subsidiary of the parent company, Presagen Pty Ltd. Funding for the study was provided by Presagen with grant funding received from the South Australian Government: Research, Commercialisation and Startup Fund (RCSF). ‘In kind’ support and embryology expertise to guide algorithm development were provided by Ovation Fertility. J.M.M.H., D.P. and M.P. are co-owners of Life Whisperer and Presagen. Presagen has filed a provisional patent for the technology described in this manuscript (52985P pending). A.P.M. owns stock in Life Whisperer, and S.M.D., A.J., T.N. and A.P.M. are employees of Life Whisperer.

The mechanisms by which cells control their growth and behavioral identities are complex and require adaptability to environmental changes. Transcription factors act as master controllers of many of these pivotal points through their ability to influence the expression of many thousands of downstream genes, and increasingly research is showing that transcription factor regulation of target genes can change in response to environmental stimuli and cell type such that their function is not prescribed but rather context-dependent. Kr€ uppel like factor 5 (KLF5) is an example of such a transcription factor, where evidence of disparate effects on cell growth and differentiation in normal and transformed tissue are clear. Here we present and discuss the literature covering the differential roles of KLF5 in particular tissues and cancer states, and the mechanisms by which these differences are effected through the regulation of KLF5 protein function in response to different cellular states and the direct effect on target gene expression.

Hypoxia-inducible factor (HIF)-1α accumulation promotes hematopoietic stem cells' quiescence and is necessary to maintain their self-renewal. However, the role of HIF-2α in hematopoietic cells is less clear. We investigated the role of HIF-2α in leukemia and lymphoma cells. HIF-2α expression was high in subsets of human and mouse leukemia and lymphoma cells, whereas it was low in normal bone marrow leukocytes. To investigate the role of HIF-2α, we transduced human HIF-2α cDNA in mouse syngeneic models of myeloid preleukemia and a transgenic model of B lymphoma. Ectopic expression of HIF-2α accelerated leukemia cell proliferation in vitro. Mice transplanted with cells transduced with HIF-2α died significantly faster of leukemia or B lymphoma than control mice transplanted with empty vector-transduced cells. Conversely, HIF-2α knockdown in human myeloid leukemia HL60 cells decreased proliferation in vitro and significantly prolonged animal survival following transplantation. In human acute myeloid leukemia (AML), HIF-2α mRNA was significantly elevated in several subsets such as the t(15;17), inv(16), complex karyotype and favorable cytogenetic groups. However, patients with high HIF-2α expression had a trend to higher disease-free survival in univariate analysis. The different effects of HIF-2α overexpression in mouse models of leukemia and human AML illustrates the complexity of this mutliclonal disease.