AAZ2 induces mitochondrial-dependent apoptosis by targeting PDK1 in gastric cancer

Drastic surges in intracellular reactive oxygen species(ROS)induce cell apoptosis,while most chemotherapy drugs lead to the accumulation of ROS.Here,we constructed an organic compound,arsenical N-(4-(1,3,2-dithiarsinan-2-yl)phenyl)acrylamide(AAZ2),which could prompt the ROS to trigger mitochondrial-dependent apoptosis in gastric cancer(GC).Mechanistically,by targeting pyruvate dehydrogenase kinase 1(PDK1),AAZ2 caused metabolism alteration and the imbalance of redox homeostasis,followed by the inhibition of phosphoinositide-3-kinase(PI3K)/protein kinase B(AKT)/mammalian target of rapamycin(mTOR)pathway and leading to the activation of B-cell lymphoma 2(Bcl2)/Bcl2-associated X(Bax)/caspase-9(Cas9)/Cas3 cascades.Importantly,our in vivo data demonstrated that AAZ2 could inhibit the growth of GC xenograft.Overall,our data suggested that AAZ2 could contribute to metabolic abnormalities,leading to mitochondrial-dependent apoptosis by targeting PDK1 in GC.

Electropolymerization of cobalt porphyrins and corroles for the oxygen evolution reaction

Developing large-scale electrocatalysts using molecular complexes for the oxygen evolution reaction(OER)is of great importance. Herein, four cobalt porphyrins and corroles are deposited on electrode substrates using a simple and fast electropolymerization method. Our results showed that Co-1-P@CC, formed by electropolymerizing Co tetrakis(p-N-pyrrolylphenyl)porphyrin(Co-1-P) on carbon cloth(CC), is the most active OER catalyst in the examined Co porphyrins and corroles in alkaline aqueous solutions by displaying an onset overpotential of 380 m V. Long-term electrolysis tests confirmed the stability of these electropolymerized films by functioning as OER electrocatalysts.

Toward a data analytics -based approach for computing and predicting context- sensitive teaching effectiveness

The traditional student-oriented course evaluation has been the major assessment method on teaching effectiveness worldwide.Useful as it is,it has been widely and continuously criticized for not being a fair,accurate,and reliable measurement.In search of a more objective assessment method on teaching effectiveness that also reflects the impacts of context-based learning,we propose a theoretical approach from a unique perspective that recognizes teaching effectiveness as a result of the interplays between teacher,student,and context.The approach can be used to compute as well as to predict teaching effectiveness using machine and deep learning technologies,which brings strategical benefits to institutional management.In addition,we install into the approach a mechanism using tokens as incentives to assure the quality of subjective data input.The application framework for the approach is proposed leveraging blockchain.Each implementation of the framework by an establishment is a decentralized application that runs on its chosen blockchain.It is envisioned that the implementations together will form a collective ecology on context-based relative teaching effectiveness,which has the potential to fundamentally impact other academic practices besides teaching effectiveness measurement.The theoretical approach provides a common language to delineate teaching effectiveness from the context-based relative perspective and is customizable during implementation.The teaching effectiveness assessment using the approach downplays the roles played by bias(subjectity)and hence is more objective than that by traditional student-oriented course evaluation.