Metformin (MET), 1,1-dimethylbiguanide hydrochloride, is a biguanide drug used as the first-line medication in the treatment of type 2 diabetes. The recent years have brought many observations showing metformin in its new role. The drug, commonly used in the therapy of diabetes, may also find application in the therapy of a vast variety of tumors. Its effectiveness has been demonstrated in colon, breast, prostate, pancreatic cancer, leukemia, melanoma, lung and endometrial carcinoma, as well as in gliomas. This is especially important in light of the poor options offered to patients in the case of high-grade gliomas, which include glioblastoma (GBM). A thorough understanding of the mechanism of action of metformin can make it possible to discover new drugs that could be used in neoplasm therapy.

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The aim of this study was to compare neural network topology of 30 patients with first episode schizophrenia (FES) and 30 multiepisode schizophrenia (mean number of psychotic relapses =4 years, duration of illness >5 years) patients, who were assessed with graph theory methods. This comparison was designed to identify network differences, which might be assigned to the burden of a mental disease. To estimate functional connectivity, we applied the phase lag index algorithm and the minimum spanning tree (MST) for the characterization of network topology. Group comparison revealed significant between-group differences of maximal betweenness centrality and tree hierarchy in the beta-band and hierarchy in the gamma-band. MST results showed that in the beta-band the network of patients with longer duration of illness (LDI) was characterized by more centralized network, while subjects with short duration of illness (FES) showed more decentralized topology. Furthermore, in the gamma-band, our results suggest that illness duration can disturb the balance between overload prevention and large-scale integration in the brain network. A qualitative analysis proved that the topological displacement of hubs also differentiated the FES and LDI groups. Our findings suggest that the duration of illness significantly affects the topology of resting-state functional network, supporting the “disconnectivity hypothesis’ in schizophrenia.

Since the beginning of SESN protein development, they have attracted highly progressive attention due to their regulatory role in multiple signalling pathways. Through their antioxidant activity and autophagy regulation implication, they can function as powerful antioxidants to reduce oxidative stress in cells. SESN proteins received special attention in the field of regulation of reactive oxygen species level in the cell and its interplay with signalling pathways determining energy and nutrient homeostasis. Since perturbations in these pathways are implicated in cancer onset and development, SESNs might constitute potential novel therapeutic targets of broad interest. In this review, we discuss the impact of SESN proteins on anti-cancer therapy based on naturally occurring compounds and conventionally used drugs that influence oxidative stress and autophagy-induced cellular signalling pathways. The significant changes in reactive oxygen species level and nutrient status in cancer cells generate subsequent biological effect through the regulation of SESN-dependent pathways. Thus, SESN may serve as the key molecule for regulating anti-cancer drugs’ induced cellular response.