Integration of machine learning and metaanalysis identifies the transcriptomic biosignature of mastitis disease in cattle

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of ‘-omics’ data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software’s limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs’ protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS , GJA1 , NTRK2 , CTNND1 , and SP3 , are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1 , ASB16-AS1 , and LINC01094 ) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p , hsa-miR-200a-3p , hsa-miR-20a-5p , hsa-miR-22-3p , hsa-miR-23a-3p , hsa-miR-27a-3p , hsa-miR-29b-3p , hsa-miR-29c-3p , hsa-miR-34a-5p ) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the “MAPK signaling pathway”, “Kaposi sarcoma-associated herpesvirus infection”, “Human immunodeficiency virus one infection”, “Lipid and atherosclerosis”, and “Amphetamine addiction”. Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS

Optimized Production Assessment, Compartmental Modeling and Dosimetric Evaluation of 177Lu- PSMA-617 for Clinical Trials

177Lu-PSMA-617 was prepared at the optimized conditions (95°C, 15-18 µg peptide, 35-40 min; solid phase purification) using 177Lu obtained from 176Lu(n, γ)177Lu reaction(>98%, ITLC, HPLC, S.A. 22-24 TBq/mM) followed by stability (up to 48 h), biodistribution studies (up to 168 h), planar imaging, compartmental modeling and dosimetry estimations via Sparks’s extrapolation method in human organs. Kidney is the critical organ with the dose of 0.067 mGy/MBq and the radiopharmaceutical can be safely used in trials considering the human dose

Global systematic review of primary immunodeficiency registries

Introduction During the last 4 decades, registration of patients with primary immunodeficiencies (PID) has played an essential role in different aspects of these diseases worldwide including epidemiological indexes, policymaking, quality controls of care/life, facilitation of genetic studies and clinical trials as well as improving our understanding about the natural history of the disease and the immune system function. However, due to the limitation of sustainable resources supporting these registries, inconsistency in diagnostic criteria and lack of molecular diagnosis as well as difficulties in the documentation and designing any universal platform, the global perspective of these diseases remains unclear. Areas covered Published and unpublished studies from January 1981 to June 2020 were systematically reviewed on PubMed, Web of Science and Scopus. Additionally, the reference list of all studies was hand-searched for additional studies. This effort identified a total of 104614 registered patients and suggests identification of at least 10590 additional PID patients, mainly from countries located in Asia and Africa. Molecular defects in genes known to cause PID were identified and reported in 13852 (13.2% of all registered) patients. Expert opinion Although these data suggest some progress in the identification and documentation of PID patients worldwide, achieving the basic requirement for the global PID burden estimation and registration of undiagnosed patients will require more reinforcement of the progress, involving both improved diagnostic facilities and neonatal screening.Peer reviewe

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Designing the Electrode Geometry and Electrolyte to Enhance the Product Selectivity and Activity in Carbon Dioxide Electroreduction

Excessive utilization of the fossil fuels due to the rapid growth of the global population has resulted in a dramatic increase in the carbon dioxide (CO2) level in the atmosphere which is the main reason for global warming and climate change. Therefore, green technologies are in high demand to develop carbon-neutral energy cycles. In this regard, CO2 electroreduction (CO2ER) has been proposed as a promising approach for CO2 utilization. CO2ER can mitigate the CO2 level in the atmosphere as well as produce value-added chemicals and fuels at ambient conditions. Despite the benefits of CO2 electroreduction, the low energy efficiency and poor product selectivity in CO2ER have retarded large-scale application of this process. Numerous strategies have been proposed to control the selectivity and enhance the catalytic activity in CO2ER. However, the electrode geometry and electrolyte composition in the aqueous electrolytes have been less studied in CO2ER compared to other factors such as catalyst materials and catalyst morphology. In the first part of this work, the effect of electrode geometry on CO2ER was examined for both polycrystalline Cu and Ag metals. For this purpose, CO2ER was performed on three different electrode shapes, flag (2-D), foil coil (3-D), and wire coil (3-D), in 0.1 M potassium bicarbonate (KHCO3). In addition to the experimental study, COMSOL Multiphysics was also used to predict the current, potential, and electric field distribution. Results showed that both foil coil and wire coil have a higher CO2ER catalytic activity in relation to the flag electrodesregardless of the electrode material (Cu or Ag). By changing the electrode geometry from flag to foil coil and wire coil, a 69% and 76% increase, respectively, in faradaic efficiency (FE) for C2 products were observed. However, the FE for methane increased only on Cu foil coil (104% increase compared to Cu flag), and the Cu wire coil showed a lower FEmethanecompared to other electrode shapes. The shape of the electrode also affected the CO selectivity and activity on Ag electrodes. Ag foil coil and Ag wire coil had a 20% and 5% increase in FE for CO compared to Ag flag at -1.12 V. The observed superior performance on foil coil and wire coil electrodes can be explained by the high electric fields around them due to the larger amount ofsharp and high curvature points on the surface compared to the flag electrode. Enhanced electric field at the interface causes more cations to adsorb to the surface and stabilize the intermediates such as CO2 •− radicals which are needed for CO2ER. In the second part of this study, the effect of anion and cation in ionic additives on the product selectivity and activity of the Cu catalyst in CO2ER was investigated. For the anion study, 10 mM of an ionic liquid (IL) with the same cation 1-butyl-3-methylimidazolium [BMIM]+ and various anions: bis(trifluoromethylsulfonyl)imide [NTF2]–, triflate [OTF]–, acetate [Ac]–, chloride [Cl]–, and dicyanamide[DCA]– was used. The results showed that although imidazolium-based ILs have a potential to enhance CO2ER due to the interaction of CO2 with imidazolium ring, the anion of IL also plays an important role in CO2ER. It was found that there is a relationship between the hydrophobicity of the anion and CO2ER activity. Higher CO2ER activity was found for more hydrophobic ILs such as [BMIM][NTF2]. In all ILs except for [BMIM][DCA], the formate FE% increased by adding the ILs to the electrolyte. The maximum increase in formate (38.7% FE) was observed for [BMIM][NTF2] at -0.92 V which has the highest hydrophobicity compared to other ILs. However, [BMIM][DCA] which has a high hydrophilicity and a low CO2 affinity shut off the CO2ER and enhanced HER at all potentials. This observation is attributed to the surface poisoning due to the strong adsorption of [BMIM][DCA] which was confirmed by X-ray photoelectron spectroscopy (XPS). Changing the cation from [BMIM]+ to sodium (Na+) and potassium (K+) with [NTF2]– and [DCA]– anions showed that the cation of the additive also plays a role in CO2ER especially for [NTF2]–-based additives. Results showed that all [NTF2]–-based additives increased the FE for formate compared to the additive-free electrolyte (9% FE). Among [NTF2]–-baased additives, [BMIM][NTF2] had a higher FE for formate (38.7%) compared to K[NTF2] (23.2%) and Na[NTF2] (18.5%) at -0.92 V probably due to the presence of imidazolium cation which can further stabilize the intermediates on the surface and enhance CO2ER. However, the FE for C2products (ethylene and ethanol) at high negative potentials were lower for [BMIM][NTF2] and K[NTF2] compared to the additive-free and Na[NTF2] electrolytes. This observation can be due to the presence of [BMIM]+ and hydrated K+ cations on the surface and inhibiting the *CO dimerization which is needed for the formation of C2 products. Electrolytes containing [DCA]–-based additives had a very high HER activity and low CO2ER activity regardless of the cation nature. This is due to the strong adsorption of [DCA]– anions on the surface which poisons the surface for CO2ER

A Multi-Product Inventory Model for Selecting the First and Second Layers of Suppliers in a Supply Chain

In recent years, Supplier evaluation and selection, an important element in supply chain management, has been gaining attention in both academic literature and industrial practice. The Mixed integer multi-Objective non-Linear programming model (MIMONLP) presented in this paper aimed to evaluate and select the appropriate set of suppliers considering quantitative and qualitative criteria and in addition to selecting the first layer's suppliers which relate directly to the organization, analyses the characteristics of second-layers suppliers, and design a network to determine the flow rate of products and materials between buyers and best suppliers in both layers. Another important feature of this model is considering holding costs of different products over the planning horizon and quantity discounts for the first layer's suppliers at the same time. Finally, the model is solved by using goal programming approach and numerical examples are presented to test the performance of proposed model

Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of ‘-omics’ data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.This article is published as Sharifi, Somayeh, Abbas Pakdel, Mansour Ebrahimi, James M. Reecy, Samaneh Fazeli Farsani, and Esmaeil Ebrahimie. "Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle." PloS one 13, no. 2 (2018): e0191227. doi: 10.1371/journal.pone.0191227.</p

Synthesis and Pharmacological Evaluation of New 2-Substituted-5-{2-[(2-halobenzyl)thio)phenyl}-1,3,4-oxadiazoles as Anticonvulsant Agents

A new series of 2-substituted-5-{2-[(2-halobenzyl)thio)phenyl}-1,3,4-oxadiazoles was designed, synthesized and investigated for anticonvulsant activities. The designed compounds contain the main essential pharmacophore for binding to the benzodiazepine receptors. Conformational analysis and superimposition of energy minima conformers of designed molecules on estazolam, a known benzodiazepine receptor agonist, revealed that the main characteristics of the proposed benzodiazepine pharmacophore were well matched. Electroshock and pentylenetetrazole-induced lethal convulsion tests showed that some of the synthesized compounds had significant anticonvulsant activity. The structure-activity relationship study of these compounds indicated that the introduction of an amino group at position 2 of 1,3,4-oxadiazole ring and a fluoro substituent at the ortho position of the benzylthio moiety had the best anticonvulsant activity. Anticonvulsant effects of active compounds were antagonized by flumazenil, a benzodiazepine antagonist, which establish the involvement of benzodiazepine receptors in these effects