Kdm7a expression is spatiotemporally regulated in developing Xenopus laevis embryos, and its overexpression influences late retinal development

Background: Post-translational histone modifications are among the most common epigenetic modifications that orchestrate gene expression, playing a pivotal role during embryonic development and in various pathological conditions. Among histone lysine demethylases, KDM7A, also known as KIAA1718 or JHDM1D, catalyzes the demethylation of H3K9me1/2 and H3K27me1/2, leading to transcriptional regulation. Previous data suggest that KDM7A plays a central role in several biological processes, including cell proliferation, commitment, differentiation, apoptosis, and maintenance. However, information on the expression pattern of KDM7A in whole organisms is limited, and its functional role is still unclear. Results: In Xenopus development, kdm7a is expressed early, undergoing spatiotemporal regulation in various organs and tissues, including the central nervous system and the eye. Focusing on retinal development, we found that kdm7a overexpression does not affect the expression of genes critically involved in early neural development and eye-field specification, whereas unbalances the distribution of neural cell subtypes in the mature retina by disfavoring the development of ganglion cells while promoting that of horizontal cells. Conclusions: Kdm7a is dynamically expressed during embryonic development, and its overexpression influences late retinal development, suggesting a potential involvement in the molecular machinery regulating the spatiotemporally ordered generation of retinal neuronal subtypes

Implication of metabolism in the polarization of tumor-associated-macrophages: the mass spectrometry-based point of view

Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally contrasting subtypes, namely classical activated M1 macrophages and alternatively activated M2 macrophages. TAMs showed different activation states that can be represent by the two extremes of the complex profile of macrophages biology, the M1-like phenotype (pro-inflammatory activity) and the M2-like phenotype (anti-inflammatory activity). Based on the tumor type, and grades, TAMs can acquire different functions and properties; usually, the M1-like phenotype is typical of early tumor stages and is associated to an anti-tumor activity, while M2-like phenotype has a pro-inflammatory activity and is related to a poor patients’ prognosis. The classification of macrophages into M1/M2 groups based on well-defined stimuli does not model the infinitely more complex tissue milieu where macrophages (potentially of different origin) would be exposed to multiple signals in different sequential order. This review aims to summarize the recent mass spectrometry-based (MS-based) metabolomics findings about the modifications of metabolism in TAMs polarization in different tumors. The published data shows that MS-based metabolomics is a promising tool to help better understanding TAMs metabolic phenotypes, although it is still poorly applied for TAMs metabolism. The knowledge of key metabolic alterations in TAMs is an essential step for discovering TAMs polarization novel biomarkers and developing novel therapeutic approaches targeting TAM metabolism to repolarize TAMs towards their anti-tumor phenotype

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0

Gefitinib in Non Small Cell Lung Cancer

Gefitinib is an oral, reversible, tyrosine kinase inhibitor of epidermal growth factor receptor (EGFR) that plays a key role in the biology of non small cell lung cancer (NSCLC). Phase I studies indicated that the recommended dose of gefitinib was 250 mg/day. Rash, diarrhea, and nausea were the most common adverse events. The positive results obtained in early phase 2 clinical trials with gefitinib were not confirmed in large phase 3 trials in unselected patients with advanced NSCLC. The subsequent discovery that the presence of somatic mutations in the kinase domain of EGFR strongly correlates with increased responsiveness to EGFR tyrosine kinase inhibitors prompted phase 2 and 3 trials with gefitinib in the first line-treatment of EGFR-mutated NSCLC. The results of these trials have demonstrated the efficacy of gefitinib that can be now considered as the standard first-line treatment of patients with advanced NSCLC harbouring activating EGFR mutations