PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia

Targeting polo-like kinase 1 to treat kidney diseases

Globally, ∼850 million individuals suffer from some form of kidney disease. This staggering figure underscores the importance of continued research and innovation in the field of nephrology to develop effective treatments and improve overall global kidney health. In current research, the polo-like kinase (Plk) family has emerged as a group of highly conserved enzyme kinases vital for proper cell cycle regulation. Plks are defined by their N-terminal kinase domain and C-terminal polo-box domain, which regulate their catalytic activity, subcellular localization, and substrate recognition. Among the Plk family members, Plk1 has garnered significant attention due to its pivotal role in regulating multiple mitotic processes, particularly in the kidneys. It is a crucial serine-threonine (Ser-Thr) kinase involved in cell division and genomic stability. In this review, we delve into the types and functions of Plks, focusing on Plk1's significance in processes such as cell proliferation, spindle assembly, and DNA damage repair. The review also underscores Plk1's vital contributions to maintaining kidney homeostasis, elucidating its involvement in nuclear envelope breakdown, anaphase-promoting complex/cyclosome activation, and the regulation of mRNA translation machinery. Furthermore, the review discusses how Plk1 contributes to the development and progression of kidney diseases, emphasizing its overexpression in conditions such as acute kidney injury, chronic kidney disease, and so forth. It also highlights the importance of exploring Plk1 modulators as targeted therapies for kidney diseases in future. This review will help in understanding the role of Plk1 in kidney disease development, paving the way for the discovery and development of novel therapeutic approaches to manage kidney diseases effectively.

AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data

Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.