Present state and future perspectives of using pluripotent stem cells in toxicology research

The use of novel drugs and chemicals requires reliable data on their potential toxic effects on humans. Current test systems are mainly based on animals or in vitro–cultured animal-derived cells and do not or not sufficiently mirror the situation in humans. Therefore, in vitro models based on human pluripotent stem cells (hPSCs) have become an attractive alternative. The article summarizes the characteristics of pluripotent stem cells, including embryonic carcinoma and embryonic germ cells, and discusses the potential of pluripotent stem cells for safety pharmacology and toxicology. Special attention is directed to the potential application of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) for the assessment of developmental toxicology as well as cardio- and hepatotoxicology. With respect to embryotoxicology, recent achievements of the embryonic stem cell test (EST) are described and current limitations as well as prospects of embryotoxicity studies using pluripotent stem cells are discussed. Furthermore, recent efforts to establish hPSC-based cell models for testing cardio- and hepatotoxicity are presented. In this context, methods for differentiation and selection of cardiac and hepatic cells from hPSCs are summarized, requirements and implications with respect to the use of these cells in safety pharmacology and toxicology are presented, and future challenges and perspectives of using hPSCs are discussed

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Immunoglobulin G Antibody Seroprevalence Among Truck Drivers and Assistants in Kenya.

In October 2020, anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G seroprevalence among truck drivers and their assistants (TDA) in Kenya was 42.3%, higher than among healthcare workers and blood donors. Truck drivers and their assistants transport essential supplies during the coronavirus disease 2019 pandemic, placing them at increased risk of being infected and of transmitting SARS-CoV-2 over a wide geographical area

The Effects of Step-by-Step Self-regulation on Controlling Study Behavior, Attitude to Study and Academic Achievement

Abstract: Using personal potential is one of the key elements in behavior modification and the purpose of this research was to study the effects of a new method in changing human attitude and behavior in school context. Following this goal, in current research the authors have tried to examine the role of Step-by-Step Self-Regulation on studying behavior control, attitude to study and students' academic achievement. The total sample size was 120 high school male students in Hamedan. The subjects were divided into two groups: experimental group and control group. Then step-by-step self-regulation method were taught and carried out for eight sessions over the experimental group. Researcher made questionnaires were used for gathering data on study behavior control and students' attitude to study and for evaluating students' academic achievement their scholastic scores were used. In testing research hypotheses, a multivariate three-way ANOVA and independent and paired t tests were used. Comparing experimental and control groups data show that applying step-by-step self-regulation improves the personal ability to control study behavior while it does not have any meaningful effect on attitudes to study and students' academic achievement

Resilience of IOTA Consensus

Blockchains are appealing technologies with various applications ranging from banking to networking. IOTA blockchain is one of the most prominent blockchain specifically designed for IoT environments. In this paper we investigate the convergence of IOTA consensus algorithms: Fast Probabilistic Consensus and Cellular Consensus, when run on top of various topologies. Furthermore, we investigate their resilience to various types of adversaries. Our extensive simulations show that both Cellular Consensus and Fast Probabilistic Consensus have poor convergence rates even under low power adversaries and have poor scaling performances except for the case of Watts Strogatz topologies. Our study points out that the design of IOTs dedicated blockchains is still an open research problem and gives hints design

Resilience of IOTA Consensus

Blockchains are appealing technologies with various applications ranging from banking to networking. IOTA blockchain is one of the most prominent blockchain specifically designed for IoT environments. In this paper we investigate the convergence of IOTA consensus algorithms: Fast Probabilistic Consensus and Cellular Consensus, when run on top of various topologies. Furthermore, we investigate their resilience to various types of adversaries. Our extensive simulations show that both Cellular Consensus and Fast Probabilistic Consensus have poor convergence rates even under low power adversaries and have poor scaling performances except for the case of Watts Strogatz topologies. Our study points out that the design of IOTs dedicated blockchains is still an open research problem and gives hints design

Evaluation des performances du consensus IOTA sous des hypothèses d'implémentation réalistes

International audienceLes registres distribués sont des technologies attractives ayant des applications variées allant du monde financier au monde des télécommunications. Dans le paysage des blockchains IOTA est le seul registre partagé dédié à l'internet des objets. Afin d'assurer des propriétés fortes de cohérence la version initiale d'IOTA proposait l'utilisation d'un contrôleur permettant d'assurer un ordre total sur les transactions insérées dans le système. Plus récemment, IOTA a proposé de remplacer le contrôleur par un algorithme de consensus. Pour cela, deux algorithmes de consensus ont été proposés par la fondation IOTA, décrits dans le framework Coordicide : Fast Probabilistic Consensus et Cellular Consensus. Au moment de leur publication, ces algorithmes étaient publicités comme étant la nouvelle brique de consensus de IOTA. Nous avons évalué les performances de ces algorithmes en utilisant des hypothèses d'implémentation réalistes. De plus, nous avons évalué la convergence des ces algorithmes en variant les topologies du réseau sous-jacent. Nos simulations montrent des taux de convergence faible, même sous des adversaires de faible puissance. De plus, nous avons observé de mauvaises performances de passage à l'échelle sauf lors des tests avec des topologies Watts Strogatz. Nos résultats indiquent que la conception de registres distribués dédiés aux IoT reste un problème ouvert et proposent des directions de recherche potentielle. Depuis l'apparition de nos résultats, la fondation IOTA a annoncé la publication imminente d'une nouvelle version de sa brique de consensus