Repurposing Tranexamic Acid as an Anticancer Agent

Tranexamic Acid (TA) is a clinically used antifibrinolytic agent that acts as a Lys mimetic to block binding of Plasminogen with Plasminogen activators, preventing conversion of Plasminogen to its proteolytically activated form, Plasmin. Previous studies suggested that TA may exhibit anticancer activity by blockade of extracellular Plasmin formation. Plasmin-mediated cleavage of the CDCP1 protein may increase its oncogenic functions through several downstream pathways. Results presented herein demonstrate that TA blocks Plasmin-mediated excision of the extracellular domain of the oncoprotein CDCP1. In vitro studies indicate that TA reduces the viability of a broad array of human and murine cancer cell lines, and breast tumor growth studies demonstrate that TA reduces cancer growth in vivo. Based on the ability of TA to mimic Lys and Arg, we hypothesized that TA may perturb multiple processes that involve Lys/Arg-rich protein sequences, and that TA may alter intracellular signaling pathways in addition to blocking extracellular Plasmin production. Indeed, TA-mediated suppression of tumor cell viability is associated with multiple biochemical actions, including inhibition of protein synthesis, reduced activating phosphorylation of STAT3 and S6K1, decreased expression of the MYC oncoprotein, and suppression of Lys acetylation. Further, TA inhibited uptake of Lys and Arg by cancer cells. These findings suggest that TA or TA analogs may serve as lead compounds and inspire the production of new classes of anticancer agents that function by mimicking Lys and Arg.

Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates

Understanding the biodiversity and genetics of the gut microbiome has important implications for host physiology. One underexplored and elusive group is ciliated protozoa, which play crucial roles in regulating gut microbial interactions. Integrating single-cell sequencing and an assembly-and-identification pipeline, we acquired 52 high-quality ciliate genomes of 22 rumen morphospecies for all major abundant clades. With these genomes, we firstly resolved the taxonomic and phylogenetic framework that reclassified them into 19 species spanning 13 genera and reassigned the genus Dasytricha from Isotrichidae to a new family Dasytrichidae. Via extensive horizontal gene transfer and gene family expansion, rumen ciliates possess a broad array of enzymes to synergistically degrade plant and microbial carbohydrates. In particular, ~80% of the degrading enzymes in Diplodiniinae and Ophryoscolecinae act on plant cell wall, and the high activities of their cellulase, xylanase and lysozyme reflect the potential of ciliate enzymes for biomass-conversion. Additionally, the new ciliate dataset greatly facilitated the rumen metagenomic analyses by allowing ~12% of reads to be classified.

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Artificial intelligence techniques are at the centre of a major shift in business today. They have a very broad array of applications within businesses, including that of optimisation for risk reduction in civil engineering projects. This is an active area of research, which has started to see real-world applications over the last few decades. It is still hindered by the extreme complexity of civil engineering problems and the computing power necessary to tackle these, but the economic and other benefits of these emerging technologies are too important to ignore. With that in mind, this chapter reviews the current state of research and real-world practice of optimisation techniques and artificial intelligence in risk reduction in this field. It also examines related promising techniques and their future potential.

Introduction to the Special Section on Justice-Centered Computing Education, Part 1

The ideas we offer below for considering justice-centered computing education point to a broad array of problem-spaces, contexts, and communities that scholars, educators, technologists, and activists might engage with. In exploring and deepening the conversation around this project, the seven articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to intersectionality, transformational justice, intercultural computing, ethnocomputing, translanguaging, socially responsible computing, and institutional theory. Across them, rather than consensus on a narrow set of issues, we see the possibilities of a pluralistic and wide-ranging conversation about how we might constitute the meanings of “justice-centered” within computing education, the tools that might be used to produce such meanings, and the actions that might address them.

Burnout Among Healthcare Professionals in Bosnia and Herzegovina

Burnout, a state of stress-induced emotional, physical, and mental exhaustion, continues to be a topic of interest across a broad array of sciences. It is because burnout not only causes psychosomatic problems, but also has an effect on job performance, which is vital in high-stake professions. Therefore, exploring the level of healthcare professionals’ burnout, and understanding which work-place factors are correlated with it, is of outmost importance. For this purpose, the present correlational study explored this issue in a convenience sample of 209 MDs and nurses from primary healthcare institutions in Bosnia and Herzegovina, including, for the first time, a comprehensive number of psychosocial factors at work. Interestingly, the majority of healthcare professionals scored low on burnout measures of MBI. Nevertheless, one fifth of participants had potential early warning signs of burnout. Compared to their colleagues in other European countries, Bosnian-Herzegovinian doctors experience lower yet comparable levels of emotional exhaustion, lower depersonalization, and higher sense of personal achievement. Furthermore, nurses show an even more beneficial trend on all three burnout dimensions. In addition, the study established some significant positive predictors of burnout dimensions pertaining to the work environment. More precisely, quantitative workload and decision-making demands were found to be positive predictors of emotional exhaustion, while the strongest predictors of depersonalization were work-place support (from colleagues and superiors) and self-esteem. Significant predictors of personal accomplishment were perception of mastery and work centrality. This suggests that burnout among healthcare professionals arises both from the immediate workplace factors and individual ones, therefore implicating institutions in its prevention and reduction.

CONTESTING BOUNDARIES AND PRODUCING THE NORM: GENDER-RELATED ISSUES IN ISLAMIC THEORY AND PRACTICE

Particularly in the last decades, due to the rapid development of theoretical perspectives across disciplines, the notion of gender has evolved into an umbrella term that today refers to a broad array of themes: from gender-defined social behaviour to sexual and reproductive rights and to matters related to control over one’s body. Following major trends in the humanities and social sciences, also the field of Islamic studies has been contributing to the ongoing scholarly discussion on gender. Two recent volumes that focus specifically on gender issues in Muslim societies (Duderija et al., 2020; Howe, 2020) have marked the expansion of the field, away from dealing primarily with topics related to Muslim women and femininity to a cautious but firm engagement with a broader spectrum of gender-related themes.

Assessing data accuracy in a large multi-institutional quality improvement registry: an update from the Pediatric Cardiac Critical Care Consortium (PC4)

Background: The Pediatric Cardiac Critical Care Consortium (PC4) is a multi-institutional quality improvement registry focused on the care delivered in the cardiac ICU for patients with CHD and acquired heart disease. To assess data quality, a rigorous procedure of data auditing has been in place since the inception of the consortium. Materials and methods: This report describes the data auditing process and quantifies the audit results for the initial 39 audits that took place after the transition from version one to version two of the registry’s database. Results: In total, 2219 total encounters were audited for an average of 57 encounters per site. The overall data accuracy rate across all sites was 99.4%, with a major discrepancy rate of 0.52%. A passing score is based on an overall accuracy of >97% (achieved by all sites) and a major discrepancy rate of <1.5% (achieved by 38 of 39 sites, with 35 of 39 sites having a major discrepancy rate of <1%). Fields with the highest discrepancy rates included arrhythmia type, cardiac arrest count, and current surgical status. Conclusions: The extensive PC4 auditing process, including initial and routinely scheduled follow-up audits of every participating site, demonstrates an extremely high level of accuracy across a broad array of audited fields and supports the continued use of consortium data to identify best practices in paediatric cardiac critical care.

Targeting the Ghrelin Receptor as a Novel Therapeutic Option for Epilepsy

Epilepsy is a neurological disease affecting more than 50 million individuals worldwide. Notwithstanding the availability of a broad array of antiseizure drugs (ASDs), 30% of patients suffer from pharmacoresistant epilepsy. This highlights the urgent need for novel therapeutic options, preferably with an emphasis on new targets, since “me too” drugs have been shown to be of no avail. One of the appealing novel targets for ASDs is the ghrelin receptor (ghrelin-R). In epilepsy patients, alterations in the plasma levels of its endogenous ligand, ghrelin, have been described, and various ghrelin-R ligands are anticonvulsant in preclinical seizure and epilepsy models. Up until now, the exact mechanism-of-action of ghrelin-R-mediated anticonvulsant effects has remained poorly understood and is further complicated by multiple downstream signaling pathways and the heteromerization properties of the receptor. This review compiles current knowledge, and discusses the potential mechanisms-of-action of the anticonvulsant effects mediated by the ghrelin-R.

mRNA Therapeutic Modalities Design, Formulation and Manufacturing under Pharma 4.0 Principles

In the quest for a formidable weapon against the SARS-CoV-2 pandemic, mRNA therapeutics have stolen the spotlight. mRNA vaccines are a prime example of the benefits of mRNA approaches towards a broad array of clinical entities and druggable targets. Amongst these benefits is the rapid cycle “from design to production” of an mRNA product compared to their peptide counterparts, the mutability of the production line should another target be chosen, the side-stepping of safety issues posed by DNA therapeutics being permanently integrated into the transfected cell’s genome and the controlled precision over the translated peptides. Furthermore, mRNA applications are versatile: apart from vaccines it can be used as a replacement therapy, even to create chimeric antigen receptor T-cells or reprogram somatic cells. Still, the sudden global demand for mRNA has highlighted the shortcomings in its industrial production as well as its formulation, efficacy and applicability. Continuous, smart mRNA manufacturing 4.0 technologies have been recently proposed to address such challenges. In this work, we examine the lab and upscaled production of mRNA therapeutics, the mRNA modifications proposed that increase its efficacy and lower its immunogenicity, the vectors available for delivery and the stability considerations concerning long-term storage.

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.