Enriching the Arsenal of Pharmacological Tools against MICAL2.

Molecule interacting with CasL 2 (MICAL2), a cytoskeleton dynamics regulator, are strongly expressed in several human cancer types, especially at the invasive front, in metastasizing cancer cells and in the neo-angiogenic vasculature. Although a plethora of data exist and stress a growing relevance of MICAL2 to human cancer, it is worth noting that only one small-molecule inhibitor, named CCG-1423 (1), is known to date. Herein, with the aim to develop novel MICAL2 inhibitors, starting from CCG-1423 (1), a small library of new compounds was synthetized and biologically evaluated on human dermal microvascular endothelial cells (HMEC-1) and on renal cell adenocarcinoma (786-O) cells. Among the novel compounds, 10 and 7 gave interesting results in terms of reduction in cell proliferation and/or motility, whereas no effects were observed in MICAL2-knocked down cells. Aside from the interesting biological activities, this work provides the first structure-activity relationships (SARs) of CCG-1423 (1), thus providing precious information for the discovery of new MICAL2 inhibitors.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas.

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

On the extreme hydrologic events determinants by means of Beta-Singh-Maddala reparameterization.

In previous studies, beta-k distribution and distribution functions strongly related to that, have played important roles in representing extreme events. Among these distributions, the Beta-Singh-Maddala turned out to be adequate for modelling hydrological extreme events. Starting from this distribution, the aim of the paper is to express the model as a function of indexes of hydrological interest and simultaneously investigate on their dependence with a set of explanatory variables in such a way to explore on possible determinants of extreme hydrologic events. Finally, an application to a real hydrologic dataset is considered in order to show the potentiality of the proposed model in describing data and in understanding effects of covariates on frequently adopted hydrological indicators.