Modulators for palmitoylation of proteins and small molecules.

As an essential form of lipid modification for maintaining vital cellular functions, palmitoylation plays an important role in in the regulation of various physiological processes, serving as a promising therapeutic target for diseases like cancer and neurological disorders. Ongoing research has revealed that palmitoylation can be categorized into three distinct types: N-palmitoylation, O-palmitoylation and S-palmitoylation. Herein this paper provides an overview of the regulatory enzymes involved in palmitoylation, including palmitoyltransferases and depalmitoylases, and discusses the currently available broad-spectrum and selective inhibitors for these enzymes.

Design, synthesis, antitumor activity, and molecular dynamics simulations of novel sphingosine kinase 2 inhibitors.

Targeting sphingosine kinase 2 (SphK2) has become a novel strategy for the treatment of cancer. However, potent and selective SphK2 inhibitors are rare. In our work, a series of novel SphK2 inhibitors were innovatively designed, synthesized and screened. Compound 12e showed the best inhibitory activity. Molecular dynamics simulations were carried out to analyze the detailed interactions between the SphK2 and its inhibitors. Moreover, 12e exhibited anti-proliferative activity in various cancer cells, and inhibited the migration of human breast cancer cells MCF-7.

Prior Distribution Estimation of Monitored Information in the Intensive Care Unit with the Hidden Markov Model and Decision Tree Methods.

In the intensive care unit, the monitored variables collected from sensors may have different behaviors among patients with different clinical basic information. Giving prior information of the monitored variables based on their specific basic information as soon as the patient is admitted will support the clinicians with better decisions during the surgery. Instead of black box models, the explainable hidden Markov model is proposed, which can estimate the possible distribution parameters of the monitored variables under different clinical basic information. A Student's t-test or correlation test is conducted further to test whether the parameters have a significant relationship with the basic variables. The specific relationship is explored by using a conditional inference tree, which is an explainable model giving deciding rules. Instead of point estimation, interval forecast is chosen as the performance metrics including coverage rate and relative interval width, which provide more reliable results. By applying the methods to an intensive care unit data set with more than 20 thousand patients, the model has good performance with an area under the ROC Curve value of 0.75, which means the hidden states can generally be correctly labelled. The significant test shows that only a few combinations of the basic and monitored variables are not significant under the 0.01 significant level. The tree model based on different quantile intervals provides different coverage and width combination choices. A coverage rate around 0.8 is suggested, which has a relative interval width of 0.77.