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Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.Coronavirus disease 2019 (COVID-19) has had a substantial impact on the incidence of cardiac arrest and survival. The challenge is to find the correct balance between the risk to the rescuer when undertaking cardiopulmonary resuscitation (CPR) on a person with possible COVID-19 and the risk to that person if CPR is delayed. These guidelines focus specifically on patients with suspected or confirmed COVID-19. The guidelines include the delivery of basic and advanced life support in adults and children and recommendations for delivering training during the pandemic. Where uncertainty exists treatment should be informed by a dynamic risk assessment which may consider current COVID-19 prevalence, the person's presentation (e.g. history of COVID-19 contact, COVID-19 symptoms), likelihood that treatment will be effective, availability of personal protective equipment (PPE) and personal risks for those providing treatment. These guidelines will be subject to evolving knowledge and experience of COVID-19. As countries are at different stages of the pandemic, there may some international variation in practice.Giardia duodenalis is one of main causative agents of diarrhea that affects the health of millions of people on a global scale per year. It has been clear that attachment of G. duodenalis trophozoites to intestinal epithelium cells (IECs) can induce cell death, while the underlying cellular and molecular mechanisms remain to be explored. It was shown in this study that treatment of Caco-2 cells with Giardia trophozoites could result in reduced cell viability. RNA sequencing analysis demonstrated that expressions of many apoptosis-related genes and some deubiquitinase genes displayed marked changes in trophozoite-treated cells. Trophozoites activated the death-signaling receptor TNFR1 on the IEC surface and caspase-3/8 (CASP3/8) signaling pathways in Caco-2 cells. K63 ubiquitination level of RIP1 was reduced upon stimulation with trophozoites, in parallel, the expressions of deubiquitinases CYLD and A20 were increased. The caspase inhibitor Q-VD-OPH could rescue trophozoite-induced cell apoptosis. Likewise, TNFR1, CYLD, and A20 silencing decreased the levels of cleaved CASP3/8 in trophozoite-treated cells and reversed the pro-apoptosis induction effect of trophozoites. These data suggest that Giardia trophozoite stimulation can activate CASP3/8 signaling pathways via activation of TNFR1 and K63 de-ubiquitination of RIP1 caused by up-regulated expressions of CYLD and A20, and promote Caco-2 cell apoptosis. The present study deepens our understanding of the mechanism of interaction between Giardia and IECs.Data analytics is routinely used to support biomedical research in all areas, with particular focus on the most relevant clinical conditions, such as cancer. Bioinformatics approaches, in particular, have been used to characterize the molecular aspects of diseases. In recent years, numerous studies have been performed on cancer based upon single and multi-omics data. For example, Single-omics-based studies have employed a diverse set of data, such as gene expression, DNA methylation, or miRNA, to name only a few instances. Despite that, a significant part of literature reports studies on gene expression with microarray datasets. JW74 ic50 Single-omics data have high numbers of attributes and very low sample counts. This characteristic makes them paradigmatic of an under-sampled, small-n large-p machine learning problem. An important goal of single-omics data analysis is to find the most relevant genes, in terms of their potential use in clinics and research, in the batch of available data. This problem has been addressed in gene selection as one of the pre-processing steps in data mining. An analysis that use only one type of data (single-omics) often miss the complexity of the landscape of molecular phenomena underlying the disease. As a result, they provide limited and sometimes poorly reliable information about the disease mechanisms. Therefore, in recent years, researchers have been eager to build models that are more complex, obtaining more reliable results using multi-omics data. However, to achieve this, the most important challenge is data integration. In this paper, we provide a comprehensive overview of the challenges in single and multi-omics data analysis of cancer data, focusing on gene selection and data integration methods.Prostate cancer (PCa) incidence is surging in United States and other parts of the world. Synthetic and natural compounds have been explored as potential modulators of PI3K/Akt signaling that is known to drive PCa growth. Here, we evaluated the efficacy of a series of triphenyltin (IV) carboxylate derivatives against PCa. From this library, triphenylstannyl 2-(benzylcarbamoyl)benzoate (Ch-319) resulted in reduced viability and induction of cell cycle arrest in PTEN-/- PC3M and PTEN+/- DU145 cells. In parallel, downregulation of PI3K p85/p110 subunits, dephosphorylation of Akt-1 and increase in FOXO3a expression were observed. In silico studies indicated binding interactions of Ch-319 within the ATP binding site of Akt-1 at Met281, Phe442 and Glu234 residues. Elevated po-pulation of apoptotic cells, activation of Bax and reduced Bcl2 expression indicated apoptosis by Ch-319. Pre-sensitization of PCa cells with Ch-319 augmented the effect of cabazitaxel, a commonly used taxane in patients with castration-resistant PCa.