In metastatic pancreatic cancer patients non eligible to surgery, signal-targeted therapies so far failed to show a significant amelioration of survival. These therapeutic options were tested in Phase II/III clinical trials mostly in combination with the reference treatment Gemcitabine. These innovative therapies aim at annihilating the oncogene dependency; they also aim at renormalizing the tumoral stroma to allow immune cell function or re-vascularisation. Transcriptomics and genomics large scale analysis show the great heterogeneity of pancreatic cancers and failed to clearly delineate specific oncogene dependency besides oncogenic Kras. In this review, we will describe the most recent proteomic data in pancreatic tumors and its metastasis, which could help at identifying their major signalling dependencies, as well as explain why they are intrinsically resistant to signal-targeted therapies. We will also discuss why PI3K signalling, as a paradigm of pro-tumorigenic cell signalling and of tumoral adaptative resistance to drugs, is a relevant target in this context.

In metastatic pancreatic cancer patients non eligible to surgery, signal-targeted therapies so far failed to show a significant amelioration of survival. These therapeutic options were tested in Phase II/III clinical trials mostly in combination with the reference treatment Gemcitabine. These innovative therapies aim at annihilating the oncogene dependency; they also aim at renormalizing the tumoral stroma to allow immune cell function or re-vascularisation. Transcriptomics and genomics large scale analysis show the great heterogeneity of pancreatic cancers and failed to clearly delineate specific oncogene dependency besides oncogenic Kras. In this review, we will describe the most recent proteomic data in pancreatic tumors and its metastasis, which could help at identifying their major signalling dependencies, as well as explain why they are intrinsically resistant to signal-targeted therapies. We will also discuss why PI3K signalling, as a paradigm of pro-tumorigenic cell signalling and of tumoral adaptative resistance to drugs, is a relevant target in this context.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand, therefore the need to optimise water resources in this area is crucial, and climate change could worsen this condition. In citrus cultivation, which is one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are im-plemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies constitutes valuable information for the stakeholders. Therefore, the objective of this study was to determine the prob-ability of presence of citrus in Sicily at changing of the percentage of water deficit application, in order to analyse change in the surface area and localisation of the crop. The methodology was based on the application of Species Distribution Models and Geographic Information Systems to the case study of the Province of Syracuse in Sicily. Different geostatistical and machine learning models were applied, based on 3-decades bioclimatic variables, DTM and irrigation; assessment of the outcomes was carried by using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that affected most the probability of presence. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory and a decrease of citrus pres-ence in southern areas of the considered territory, where climate conditions are less favourable in terms of temperature and precipitations, thus providing useful information for decision support tools in agriculture and land use policy.

This study develops a predictor-corrector algorithm for the numerical simulation of IVPs involving singular generalized fractional derivatives with Mittag-Leffler kernels. The proposed algorithm converts the considered IVP into a Volterra-type integral equation and then uses Trapezoidal rule to obtain approximate solutions. Numerical approximate solutions of some singular generalized fractional derivative with Mittag-Leffler kernels models have been presented to demonstrate the efficiency and accuracy of the proposed algorithm. The algorithm describes the influence of the fractional derivative parameters on the dynamics of the studied models. The suggested method is expected to be effectively employed in the field of simulating generalized fractional derivative models

One third of a person’s life is spent on sleep, therefore the quality and habit of sleep affects health. A single case study indicated that perspiration could serve as a prognostic marker. Diagnosing nocturnal perspiration is common clinical practice, since this serves as a major symptom in many pathologies. Till this day no specific evidence-based approach for diagnosing nocturnal perspiration exists. By introducing the Q-strip, a device which quantitatively measures nocturnal perspiration, this could be acquired. The Q-strip could serve a purpose in diagnosing nocturnal perspiration more efficient without being intrusive. In addition to its health sensing potentials, the Q-strip makes it possible to visualise perspiration patterns. This introduces the possibility to examine the quality of sleep. Future research is recommended to investigate this.