Superoxide Dismutase (SOD) Enzyme Activity Assay in Fasciola spp. Parasites and Liver Tissue Extract

Background: The purpose of this comparative study was to detect superoxide dismutase (SOD) activities in Fasciola hepatica, F. gigantica parasites, infected and healthy liver tissues in order to determine of species effects and liver infection on SODs activity level.Methods: Fasciola spp. parasites and sheep liver tissues (healthy and infected liver tissues), 10 samples for each, were collected, homogenized and investigated for protein measurement, protein detection and SOD enzyme activity assay. Protein concentration was measured by Bradford method and SODs band protein was detected on SDS-PAGE. SODs activity was determined by iodonitrotetrazolium chloride, INT, and xanthine substrates. Independent samples t-test was conducted for analysis of SODs activities difference.Results: Protein concentration means were detected for F. hepatica 1.3 mg/ ml, F. gigantica 2.9 mg/ml, healthy liver tissue 5.5 mg/ml and infected liver tissue 1.6 mg/ml (with similar weight sample mass). Specific enzyme activities in the samples were obtained 0.58, 0.57, 0.51, 1.43 U/mg for F. hepatica, F. gigantica, healthy liver and infected liver respectively. Gel electrophoresis of Fasciola spp. and sheep liver tissue extracts revealed a band protein with MW of 60 kDa. The statistical analysis revealed significant difference between SOD activities of Fasciola species and also between SOD activity of liver tissues (P<.05).Conclusion: Fasciola species and liver infection are effective causes on SOD enzyme activity level

How to Enable Uncertainty Estimation in Proximal Policy Optimization

While deep reinforcement learning (RL) agents have showcased strong results across many domains, a major concern is their inherent opaqueness and the safety of such systems in real-world use cases. To overcome these issues, we need agents that can quantify their uncertainty and detect out-of-distribution (OOD) states. Existing uncertainty estimation techniques, like Monte-Carlo Dropout or Deep Ensembles, have not seen widespread adoption in on-policy deep RL. We posit that this is due to two reasons: concepts like uncertainty and OOD states are not well defined compared to supervised learning, especially for on-policy RL methods. Secondly, available implementations and comparative studies for uncertainty estimation methods in RL have been limited. To overcome the first gap, we propose definitions of uncertainty and OOD for Actor-Critic RL algorithms, namely, proximal policy optimization (PPO), and present possible applicable measures. In particular, we discuss the concepts of value and policy uncertainty. The second point is addressed by implementing different uncertainty estimation methods and comparing them across a number of environments. The OOD detection performance is evaluated via a custom evaluation benchmark of in-distribution (ID) and OOD states for various RL environments. We identify a trade-off between reward and OOD detection performance. To overcome this, we formulate a Pareto optimization problem in which we simultaneously optimize for reward and OOD detection performance. We show experimentally that the recently proposed method of Masksembles strikes a favourable balance among the survey methods, enabling high-quality uncertainty estimation and OOD detection while matching the performance of original RL agents

Patient safety in developing countries: retrospective estimation of scale and nature of harm to patients in hospital

OBJECTIVE: To assess the frequency and nature of adverse events to patients in selected hospitals in developing or transitional economies. DESIGN: Retrospective medical record review of hospital admissions during 2005 in eight countries. SETTING: Ministries of Health of Egypt, Jordan, Kenya, Morocco, Tunisia, Sudan, South Africa and Yemen; the World Health Organisation (WHO) Eastern Mediterranean and African Regions (EMRO and AFRO), and WHO Patient Safety. PARTICIPANTS: Convenience sample of 26 hospitals from which 15,548 patient records were randomly sampled. MAIN OUTCOME MEASURES: Two stage screening. Initial screening based on 18 explicit criteria. Records that screened positive were then reviewed by a senior physician for determination of adverse event, its preventability, and the resulting disability. RESULTS: Of the 15,548 records reviewed, 8.2% showed at least one adverse event, with a range of 2.5% to 18.4% per country. Of these events, 83% were judged to be preventable, while about 30% were associated with death of the patient. About 34% adverse events were from therapeutic errors in relatively non-complex clinical situations. Inadequate training and supervision of clinical staff or the failure to follow policies or protocols contributed to most events. CONCLUSIONS: Unsafe patient care represents a serious and considerable danger to patients in the hospitals that were studied, and hence should be a high priority public health problem. Many other developing and transitional economies will probably share similar rates of harm and similar contributory factors. The convenience sampling of hospitals might limit the interpretation of results, but the identified adverse event rates show an estimate that should stimulate and facilitate the urgent institution of appropriate remedial action and also to trigger more research. Prevention of these adverse events will be complex and involves improving basic clinical processes and does not simply depend on the provision of more resources

Predicting self‐declared movie watching behavior using Facebook data and information‐fusion sensitivity analysis

The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self-reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self-declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self-reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information-fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time- and frequency-based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self-reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie-watchers and market their movies more efficiently

PAX4 Enhances Beta-Cell Differentiation of Human Embryonic Stem Cells

Background Human embryonic stem cells (HESC) readily differentiate into an apparently haphazard array of cell types, corresponding to all three germ layers, when their culture conditions are altered, for example by growth in suspension as aggregates known as embryoid bodies (EBs). However, this diversity of differentiation means that the efficiency of producing any one particular cell type is inevitably low. Although pancreatic differentiation has been reported from HESC, practicable applications for the use of β-cells derived from HESC to treat diabetes will only be possible once techniques are developed to promote efficient differentiation along the pancreatic lineages. Methods and Findings Here, we have tested whether the transcription factor, Pax4 can be used to drive the differentiation of HESC to a β-cell fate in vitro. We constitutively over-expressed Pax4 in HESCs by stable transfection, and used Q-PCR analysis, immunocytochemistry, ELISA, Ca2+ microfluorimetry and cell imaging to assess the role of Pax4 in the differentiation and intracellular Ca2+ homeostasis of β-cells developing in embryoid bodies produced from such HESC. Cells expressing key β-cell markers were isolated by fluorescence-activated cell sorting after staining for high zinc content using the vital dye, Newport Green. Conclusion Constitutive expression of Pax4 in HESC substantially enhances their propensity to form putative β-cells. Our findings provide a novel foundation to study the mechanism of pancreatic β-cells differentiation during early human development and to help evaluate strategies for the generation of purified β-cells for future clinical applications

Endothelial Cells in Co-culture Enhance Embryonic Stem Cell Differentiation to Pancreatic Progenitors and Insulin-Producing Cells through BMP Signaling

Endothelial cells (ECs) represent the major component of the embryonic pancreatic niche and play a key role in the differentiation of insulin-producing β cells in vivo. However, it is unknown if ECs promote such differentiation in vitro. We investigated whether interaction of ECs with mouse embryoid bodies (EBs) in culture promotes differentiation of pancreatic progenitors and insulin-producing cells and the mechanisms involved. We developed a co-culture system of mouse EBs and human microvascular ECs (HMECs). An increase in the expression of the pancreatic markers PDX-1, Ngn3, Nkx6.1, proinsulin, GLUT-2, and Ptf1a was observed at the interface between EBs and ECs (EB-EC). No expression of these markers was found at the periphery of EBs cultured without ECs or those co-cultured with mouse embryonic fibroblasts (MEFs). At EB-EC interface, proinsulin and Nkx6.1 positive cells co-expressed phospho-Smad1/5/8 (pSmad1/5/8). Therefore, EBs were treated with HMEC conditioned media (HMEC-CM) suspecting soluble factors involved in bone morphogenetic protein (BMP) pathway activation. Upregulation of PDX-1, Ngn3, Nkx6.1, insulin-1, insulin-2, amylin, SUR1, GKS, and amylase as well as down-regulation of SST were detected in treated EBs. In addition, higher expression of BMP-2/-4 and their receptor (BMPR1A) were also found in these EBs. Recombinant human BMP-2 (rhBMP-2) mimicked the effects of the HMEC-CM on EBs. Noggin (NOG), a BMP antagonist, partially inhibited these effects. These results indicate that the differentiation of EBs to pancreatic progenitors and insulin-producing cells can be enhanced by ECs in vitro and that BMP pathway activation is central to this process

Embryonic Stem Cells: New Possible Therapy for Degenerative Diseases That Affect Elderly People

The capacity of embryonic stem (ES) cells for virtually unlimited self renewal and differentiation has opened up the prospect of widespread applications in biomedical research and regenerative medicine. The use of these cells would overcome the problems of donor tissue shortage and implant rejection, if the cells are made immunocompatible with the recipient. Since the derivation in 1998 of human ES cell lines from preimplantation embryos, considerable research is centered on their biology, on how differentiation can be encouraged toward particular cell lineages, and also on the means to enrich and purify derivative cell types. In addition, ES cells may be used as an in vitro system not only to study cell differentiation but also to evaluate the effects of new drugs and the identification of genes as potential therapeutic targets. This review will summarize what is known about animal and human ES cells with particular emphasis on their application in four animal models of human diseases. Present studies of mouse ES cell transplantation reveal encouraging results but also technical barriers that have to be overcome before clinical trials can be considered