Monday, February 10, 2020

Pediatric Module Assignment Example | Topics and Well Written Essays - 750 words

Pediatric Module - Assignment Example Proteins are essential for body growth and tissue requirements in infants. There has been much debate over the years on how much proteins the body should take. For infants below 6 months, they should take 1-13gms, 6 momths-1 year- 0.75-14gms, 1-6 years-. 16-24 Gms (Swearingen, 2009). The estimated calories in a pre-term infant is 4.5 -4.9 kcal/g. This has been reached due to the fact that development of new tissues that is, growth and repair needs more energy intake. The estimated protein in pre-term infant is 1per kg per day. Proteins are very important for the normal growth and development. The lean body mass is dependent on the protein intake. Pre-term infants therefore, if given enough protein show levels of enhanced growth. Milk based formula-It contains cow milk as its protein source. It has a fat source which is oil, lactose as the carbohydrate source, a mix of vitamin and minerals. The rationale for use is when the baby shows signs of hunger. Examples is Similac Go and Grow and Entamil. Milk based formula-It contains cow milk as its protein source. It has a fat source which is oil, lactose as the carbohydrate source, a mix of vitamin and minerals. The rationale for use is when the baby shows signs of hunger. Examples are Similac Go and Grow and Entamil. Soy-based infant Formula-It was developed for infants who cannot take formula made form cow’s milk. It contains protein from soybean, oils from vegetables, carbohydrates, vitamins and minerals. The rationale for use is when the infants shows signs of hunger. Examples are; Enfamil R and Similac Go and Grow. Protein hydrolysate formula-This formula contains protein which has been broken down into smaller sizes than the ones found in cow’s milk. It is given to babies that cannot take either milk based formula or soy-based formula. It is used whenever the infants shows signs of hunger. Examples are; Similac and PurAmino. Elementa formula- This

Sunday, February 9, 2020

The Killing of Jordanian Pilot Moaz Kasasbeh Essay

The Killing of Jordanian Pilot Moaz Kasasbeh - Essay Example In his article in the Los Angeles Times, Miller provides an objective headline that stamps his views over the issue. â€Å"Will Islamic State pay for killing a Jordanian pilot?† is an objective headline that illustrates the writers opinion for revenge or stern action in response to the inhumane act (Miller). On the other hand, Woodward depicts an outright thought and approach to solving the challenge. The headline â€Å"Patience is better than revenge† describes the writer’s view that there exists a peaceful approach to dealing with the situation (Woodward 1). Woodward draws his sentiments from the report by Mitchell Prothero about the killing. He then explains the chronological events as they happened after the assassination. In addition, he gives his perspectives about Jordan’s rejoinder of prisoner’s execution and concludes that it is not an appropriate move. To justify his facts that revenge is not right, Woodward compares the prevailing situation to the Quran teachings of peace and love. Conversely, Miller relates the potential outcomes of the ISIS acts to past events. Notably, he points out the Arab Spring and events triggered World War I. Both insights show that the writers are well equipped with historical facts and data about conflicts. Nonetheless, they provide alternative approaches to handling the situation. Woodward message to the readers comprises of importance of peace and collaboration to solving world problems. He also stamps his peaceful approach to solving conflicts by teaching his readers the spiritual aspect of conflict resolution. On the other hand, Miller to an extent wants the readers to pressurize the state to seek revenge. He gives an analysis of the on-going operations and points out areas of faults. In doing this, he is informing the readers about the seriousness or lack of focus on the side of the federal government and other concerned states. For example, he explains

Thursday, January 30, 2020

The Case Against Aid that Harms By Garrett Hardin Essay Example for Free

The Case Against Aid that Harms By Garrett Hardin Essay After reading the above reading by Mr. Hardin, I had come to the conclusion that in life there are many choices that must be made. In correlation to my Environmental Science class I can understand more of what his thought process is. In comparison, he could be talking about world hunger. His strongest points in the article were each rich nation can be seen as a lifeboat full of comparatively rich people, and in the ocean outside each lifeboat swim the poor world, who should be allowed to get in to share the wealth? By breaking down the population of many countries and showing that their population rate is expanding past their ability to feed the hungry is also another strong point. He also brings out the point of if poor countries were not given assistance with food sharing; it may possibly stabilize their population growth. But would it? According to many countries standards, women are having so many babies to try and have sons who can be strong enough to do work to bring food into the home. So by shutting out the poor would produce greater risks to their health. But there were several parts of his story I could have cared less about. For instance when he began talking about the fundamental error of spaceship ethics, I was lost and had no clue what this had to do with the rest of the article. Who cares about immigrants? This was not changing my world view on the issue of world hunger. He goes into the slang words for generations ago such as Dagos, Wops, Polacks, Chinks, and Krauts, what are half of these slang words referring to and who cares what they are referring to, it has nothing to do with the issue of preserving your life boat, what to do when there are too many mouths to feed and not enough food to go around. He also started mentioning the concept of pure justice produces an infinite regression to absurdity, okay where was he going on this one? There were a lot of things he stated in his article that I had no clue where he was going  with it and it seemed as if he was just trying to take up more space. Overall the whole article had no bearing on my worldview of my lifeboat.

The argument regarding guns Essay Example for Free

The argument regarding guns Essay The argument regarding guns on campus has been a hot topic ever since the widely covered tragedies at Columbine High School and Virginia Tech University. Since those and other shootings have taken place, some states have decided to make provisions to gun carrying laws on campuses allowing students and faculty to arm themselves. However, not all states have been able to pass changes on such a polarizing issue, and are met by heavy opposition from anti-gun groups. The debate is one with deeply rooted emotions for some, as family members and friends have been directly affected by campus violence. No matter which side of the debate you stand on, one has to admit it is interesting to look at how past events may have been altered with different gun laws, as well as the possible risks and prevention that provisions could cause in the future. On April 16, 2007, a single gunman killed 32 people and injured 17 others before taking his own life on the campus of Virginia Tech. Armed with a backpack full of ammunition, Seung-Hui Cho went from classroom to classroom murdering students and teachers before the campus police finally broke down the barricade to the building he was in, and he turned the gun on himself. Although there is not a way to be sure of how something so random could have been prevented, there is also no doubt in the minds of some that it could have been stopped sooner and more innocent lives saved had there been permission to carrying handguns into the classroom. The response in regards to gun laws following the events in Virginia included â€Å"politicians in at least 25 states have considered new laws or policies designed to protect college students† (Lewis 1) within three months of the attacks. The Virginia Tech Massacre opened old wounds and created new ones, all while sparking nationwide interest and debate on how campuses deal with gun restrictions. In 2008, Arizona Senator Karen S. Johnson, following a shooting on the campus of Northern Illinois University, said that â€Å"the police, [Johnson] said, respond too slowly to such incidents and, beside, who better than the people staring down the barrel to take action? † (Archibold 1) Had there been a student or professor armed during the VTech massacre, there is a chance that the gunman could have been taken down much sooner, saving maybe even dozens of lives and preventing multiple injuries sustained during the attacks. Further supporting from 2008 were the figures on violent crimes on college campuses for the year. â€Å"There were 3,287 rapes, 60 killings, 5,026 assaults and 4,562 robberies across college campuses just in 2008†¦experts tell us campus crime is underreported. † (Burnett 1) Those numbers support the thinking that many self defense advocates use in believing they are at a higher risk of attack knowing that most likely someone walking around a college campus will not be armed. Also following the shooting at VTech, the â€Å"Students for Concealed Carry† was formed. Within 6 months of forming the group had chapters at 60 colleges across the nation, and today now has over 350 chapters in 46 states. In 2010 a Colorado court ruled in favor of the SCC claiming there were inconsistencies between laws enforced by the state and by campuses regarding the right to self defense. (Colorado Court of Appeals 3) Those opposed to the notion of allowing firearms on campus often hang onto the risks that such enactments could possibly result in. Eliminating gun-free campuses nationwide could, in a sense, create very tense and hostile environments in schools and brings up several questions such as, â€Å"Will students feel safer knowing that the student sitting next to them could be ’packing’? † (Siebel 1) The atmosphere created by the chance that there are people armed with guns sitting in close proximity to one another could spoil the integrity of academic debate (Rogers 1) in some if not most college campus settings, much pride is invested into the ability to share differing and sometimes controversial ideas on what could be an emotion stirring topic. [School] is a place where we depend on being able to speak our minds and offer sometimes controversial opinions in a free and open place, she said. The feeling among a percentage of faculty is that this will create a climate of fear and intimidation. (Frosch) The prospect of having guns in the classroom makes many people nervous or fearful for understandable reasons, and would likely lead to less focus on actual instruction and academic participation. This immediate effect could also hold larger ramifications as time goes on with the possibility of affected grades, graduation rates, and possibly even lower enrollment rate from state to state depending on the campus gun laws. However, contrary to this belief shared by anti-gun groups, there does not seem to be any major spike in crime on those campuses that do allow concealed weapons to be carried, â€Å"71 campuses in three states already allow licensed concealed carry on campus†¦done so for years without a single resulting incident. †(Burnett) This leads some to believe that the idea that allowing guns campus would turn them into potential warzones may be greatly exaggerated. Another major criticism gun carrying provisions say that the amount of issues that could arise from it outweigh the prevention possibilities suggested by supporters and would cause more gun related incidents to occur as a result. â€Å"[Critics] argue that the guns would make it easier for people barely out of adolescence, or perhaps emotionally troubled, to respond lethally to typical campus frustrations like poor grades or failed romances. † (Archibold 2) That mode of thinking was supported in 2002 when University of Arizona student Robert Flores Jr. shot and killed 3 of his professors then himself after reportedly being barred from taking an exam by one of his professors. It was determined to be premeditated and likely emotion driven as â€Å"police said [he] specifically targeted the instructors, killing one in her office on the second floor and shooting the others in a fourth-floor classroom as students dove for cover. † (Holguin) Of course in the same respect one could say that had the professors been armed some, if not all, lives could have been saved. One will almost never be able to definitively determine the emotions and motives behind someone going off on a rampage through a university, however it is not farfetched to predict that someone willing to take innocent lives would not be as likely to follow the laws concerning guns whether it was legal to carry or not. Opposition to the pro-gun stance in this issue claim that the chances of catastrophic events such as the VTech Massacre are â€Å"rare† and usually â€Å"either last long enough for armed campus security to respond, or are over before anyone can react. † (USA Today) While the likelihood of an attack occurring is admittedly not very high, it would be interesting to hear a Senator explain those odds to the family of someone killed in one of the many tragedies taken place across the country. Also needed to take into account is the fact that some reports on violent crime â€Å"excludes incidents in which only one person was killed or injured, as well as incidents in which a student started shooting, but no one died. † (CLAJ 291) Therefore leaving some reports skewed to the notion that an attack is not likely. The amounts of varying factors weighed in the decision whether or not to permit guns on campuses, causes a major divide between stances in the issue. Such a polarizing issue as gun control can never come to complete compromise. Those uneasy about the idea of having live firearms in a classroom are unlikely to ever feel completely comfortable, and those who feel their right to self defense is being violated will continue to do so. Whether or not more states decide to adopt changes to gun laws, there will continue to be close attention paid to the measures campuses and states take to ensure the safety of students and staff. We can always speculate what could have been prevented, however much can be taken from tragedies in the past in an effort to deter any future attacks and prevent any more senseless acts of violence.

Wednesday, January 22, 2020

Frogs :: essays research papers fc

Frogs are needed for everyday life. They are part of pond life. Each animal is important because even in the pond, there is a food chain. Frogs are amphibians, animals that spend half of their lives under water, and remainder on land. The first frog appeared in the early Jurassic period about 200 million years ago.   Ã‚  Ã‚  Ã‚  Ã‚  Frogs live on every continent except Antarctica, but tropical regions have the largest amount. Like all amphibians, frogs spend half their lives near water because they must return to the water to lay their eggs. Frogs live underwater mostly when the are growing up to be an adult frog and when they are laying their eggs. When they hatch under water they are tadpoles and the breath with gills and swim using a tail. As they mature they loose their tail and they develop to be able to breathe air. During an extensive period of heat, a drought, frogs can enter a period of damancy similar to hibernation called starvation. Most of the frogs live in tropical and semitropical regions, most species of frogs breed in the spring or in early summer. Although the different species my vary in size and color, mostly all frogs have basic body structure. They have large hind legs, short front legs and flat head and body with no neck.   Ã‚  Ã‚  Ã‚  Ã‚  Most frogs have teeth only on their upper jaw. Toads swallow their prey in one piece. To aid in the swallowing process, the frog’s eyes sink through the openings in the skull and force the food down its throat. Frogs eat insects, catching them with their long sticky tongue. They also eat small fish and worms. They also absorb concentrate to make them stronger, and toxins (poisonous substances) in their fatty tissues.   Ã‚  Ã‚  Ã‚  Ã‚  Since the 1980’s scientists have noted the decline of many frog species. People do not know for certain what has caused these declines. A possible factor is pollution, disease, habitat destruction, and acid rain. Another factor may be the thinning of the earth’s protective ozone layer, which allows more harmful ultraviolet radiation from the sun to reach the earth. Because frogs have thin, moist skin and an aquatic tadpole stage, they are easily affected by pollution and changes in the environment.

Tuesday, January 21, 2020

Putting a Face on Freedom :: Philosophy Philosophical Papers

Putting a Face on Freedom What is Freedom? Freedom in and of itself defies definition since its very essence and parameters can be shown only with its constraints and limitations. If one state of being is unable to stand on its own merits and must rely on another to have structure, can it never truly be â€Å"free.† Therein lies the irony: freedom is forever encapsulated by its limitations, regardless of its range. Without evident barriers, it could not exist. If it is within barriers, can it truly exist? This paper will discuss the existence of freedom and several of its forms. Although much sought after, gaining freedom usually involves strife and war. With the splitting of the Catholic Church in the Reformation, many restraints on science, religion and education vanish, causing a rebirth of knowledge and philosophical thought. Although fragmented, the Catholic Church is by no means vanquished, so inevitable conflicts arise. The conflicts are not confined to the battlefield, though, they occur more often than not in the form of books and documents. With wars, famine and social upheaval accompanying the Reformation, men sometimes lament the passing of the former system (a unified Church) which, though imperfect, at least provides some stability. Thomas More, a critic of King Henry VIII, cleverly disguises his criticisms in his literature. He writes about a tightly regulated society, Utopia, where there are no social strata and people of all religious persuasions can live harmoniously with one another, to freely practice their religions without fear of reproach. Though there seems to be an abundance of freedom, including religious, there are a few hitches—such as having to obtain permission from your father and wife before exploring the countryside, wearing the same clothing as everyone else, having no possibility for social advancement and compulsory theism (your choice of deities, however): â€Å"†¦he (Utopus) made a solemn and severe law against such as should†¦think that our souls died with our bodies, o r that the world was governed by chance, without a wise overruling Providence† (More, p. 747). More’s Utopia offers freedoms some might cherish, but others would find it unbearably restrictive. Even those of a religious bent might have looked askance at Utopia’s forced theism policy. Is it freedom? Some might think so if they believed freedom of religion didn’t include freedom from religion.

Tuesday, January 14, 2020

Literature-based discovery of diabetes

Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 54 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets.Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature- derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabet es-related targets from the 54 biomedical papers (http://Jdrf. eurology. med. umich. edu/ROSDiabetes/ webcite). Fifty-three targets were significantly over-represented in the ROS-diabetes literature ompared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice.For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the athogenesis of diabetic neuropathy. Diabetes is a metabolic disease in which the body does not produce or properly respond to insulin, a hormone required to convert ca rbohydrates into energy for daily life. According to the American Diabetes Association, 23. million children and adults, approximately 7. 8% of the population in the United States, have diabetes [1]. The cost of diabetes in 2007 was estimated to be $174 billion [1]. The micro- and macro-vascular complications of diabetes are the most common causes of renal tailure, blindness and amputations leading to significant morta y, morbidity poor quality of life; however, incomplete understanding of the causes of diabetic complications hinders the development of mechanism-based therapies.In vivo and in vitro experiments implicate a number of enzymatic and non-enzymatic metabolic pathways in the initiation and progression of diabetic complications [2] including: (1) increased polyol pathway activity leading to sorbitol and fructose accumulation, NAD(P)-redox imbalances and changes in signal transduction; (2) non- enzymatic glycation of proteins yielding â€Å"advanced glycation end-productsâ €  (AGES); (3) ctivation of protein kinase C (PKC), initiating a cascade of intracellular stress responses; and (4) increased hexosamine pathway flux [2,3].Only recently has a link among these pathways been established that provides a unified mechanism of tissue damage. Each of these pathways directly and indirectly leads to overproduction of reactive oxygen species (ROS) [23]. ROS are highly reactive ions or small molecules including oxygen ions, free radicals and peroxides, formed as natural byproducts of cellular energy metabolism. ROS are implicated in multiple cellular pathways such as mitogen-activated protein kinase MAPK) signaling, c-Jun amino-terminal kinase ONK), cell proliferation and apoptosis [4-6].Due to the highly reactive properties of ROS, excessive ROS may cause significant damage to proteins, DNA, RNA and lipids. All cells express enzymes capable of neutralizing ROS. In addition to the maintenance of antioxidant systems such as glutathione and thioredoxins, pri mary sensory neurons express two main detoxifying enzymes: superoxide dismutase (SOD) [7] and catalase [8]. SOD converts superoxide (02-) to H202, which is reduced to H20 by glutathione and catalase [8].SODI is the main form of SOD in the cytoplasm; SOD2 is located within the itochondria. In neurons, SODI activity represents approximately 90% of total SOD activity and SOD2 approximately 10% [9]. Under diabetic conditions, this protective mechanism is overwhelmed due to the substantial increase in ROS, leading to cellular damage and dysfunction [10]. The idea that increased ROS and oxidative stress contribute to the pathogenesis of diabetic complications has led scientists to investigate different oxidative stress pathways [7,11].Inhibition of ROS or maintenance of euglycemia restores metabolic and vascular imbalances and blocks both the initiation and progression of omplications [1 2,13]. Despite the significant implications and extensive research into the role of ROS in diabetes, n o comprehensive database regarding ROS-related genes or proteins is currently available. In the present study, a comprehensive list of ROS- and diabetes-related targets (genes/proteins) was compiled from the biomedical literature through text mining technology.SciMiner, a web-based literature mining tool [14], was used to retrieve and process documents and identify targets from the text. SciMiner provides a convenient web-based platform for target-identification within the biomedical iterature, similar to other tools including EBIMed [1 5], ALI BABA [16], and Polysearch [1 7]; however, SciMiner is unique in that it searches tull text documents, suppo free-text PubMed query style, and allows the comparison of target lists from multiple queries.The ROS-diabetes targets collected by SciMiner were further tested against randomly selected non-ROS-diabetes literature to identify targets that are significantly over- represented in the ROS-diabetes literature. Functional enrichment analyses were performed on these targets to identify significantly over-represented biological unctions in terms of Gene Ontology (GO) terms and pathways. In order to confirm the biological relevance of the over-represented ROS-diabetes targets, the gene expression levels of nine selected targets were measured in dorsal root ganglia (DRG) from mice with and without diabetes.DRG contain primary sensory neurons that relay information from the periphery to the central nervous system (CNS) Unlike the CNS, DRG are not protected by a blood-nerve barrier, and are consequently vulnerable to metabolic and toxic injury [19]. We hypothesize that differential expression of identified targets in DRG would confirm heir involvement in the pathogenesis of diabetic neuropathy. Defining ROS-diabetes literature To retrieve the list of biomedical literature associated with ROS and diabetes, PubMed was queried using (â€Å"Reactive Oxygen Species†[MeSH] AND â€Å"Diabetes Mellitus†[MeSH]).This que ry yielded 54 articles as of April 27, 2009. SciMiner, a web-based literature mining tool [14], was used to retrieve and process the abstracts and available full text documents to identify targets (full text documents were available for approximately 40% of the 1 , 1 54 articles). SciMiner-identified targets, eported in the form of HGNC [HUGO (Human Genome Organization) Gene Nomenclature Committee] genes, were confirmed by manual review of the text. Comparison with human curated data (NCBI Gene2PubMed) The NCBI Gene database provides links between Gene and PubMed.The links are the result of (1) manual curation within the NCBI via literature analysis as part of generating a Gene record, (2) integration of information from other public databases, and (3) GeneRlF (Gene Reference Into Function) in which human experts provide a brief summary of gene functions and make the connections between citation PubMed) and Gene databases. For the 54 ROS-diabetes articles, gene-paper associations we re retrieved from the NCBI Gene database. Non-human genes were mapped to homologous human genes through the NCBI HomoloGene database.The retrieved genes were compared against the SciMiner derived targets. Any genes missed by SciMiner were added to the ROS-diabetes target set. Protein-protein interactions among ROS-diabetes targets To indirectly examine the association of literature derived targets (by SciMiner and NCBI Gene2PubMed) with ROS and diabetes, protein-protein interactions (PPIs) mong the targets were surveyed This was based on an assumption that targets are more likely to have PPIs with each other if they are truly associated within the same biological functions/pathways.A PPI network of the ROS-diabetes targets was generated using the Michigan Molecular Interactions (MIMI, http://mimi. ncibi. org/ webcite) database [20] and compared against 100 PPI networks of randomly drawn sets (the same number of the ROS-diabetes target set) from HUGO. A standard Z-test and one sample T-test were used to calculate the statistical significance of the ROS- diabetes PPI network with respect to the random PPI networks.Functional enrichment analysis Literature derived ROS-diabetes targets (by SciMiner and NCBI Gene2PubMed) were subject to functional enrichment analyses to identify significantly over-represented biological functions in terms of Gene Ontology [21], pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www. genome. ]p/kegg/ webcite) [22] and Reactome http://www. reactome. org/ webcite[23]). Fisher's exact test [24] was used to calculate the statistical significance of these biological functions with BenJamini-Hochberg (BH) adjusted p-value ; 0. 5 [25] as the cut-off. Over-represented ROS-diabetes targets Defining background corpora To identify a subset of targets that are highly over-represented within the ROS- diabetes targets, the frequency of each target (defined as the number of documents in which the target was identified divided by the n umber of total documents in the query) was compared against the frequencies in randomly selected background corpora.Depending on how the background set is defined, over-represented targets may vary widely; therefore, to maintain the background corpora close to the ROS and diabetes context, documents were selected from the same Journal, volume, and issue f the 54 ROS-diabetes documents, but were NOT indexed with â€Å"Reactive Oxygen Species†[MeSH] nor â€Å"Diabetes Mellitus†[MeSH]. For example, one of the ROS-diabetes articles (PMID: 18227068), was published in the Journal of Biological Chemistry, Volume 283, Issue 16. This issue contained 85 papers, 78 of which were not indexed with either â€Å"Reactive Oxygen Species†[MeSH] or â€Å"Diabetes Mellitus†[MeSH] indexed.One of these 78 papers was randomly selected as a background document. Three sets of 54 documents were selected using this approach and processed using SciMiner. Identified targets were con firmed by manual review for accuracy. Identifying significantly over-represented targets ROS-diabetes targets were tested for over-representation against targets identified from the three background sets. Fisher's exact test was used to determine if the frequency of each target in the ROS-diabetes target set was significantly different from that of the background sets. Any targets with a BH adjusted p-value < 0. 5 in at least two of the three comparisons were deemed to be an over-represented ROS- diabetes target. Functional enrichment analyses were performed on these over- represented ROS-diabetes targets as described above. Selecting targets tor real-time R A subset of targets were selected for RT-PCR from the top 10 over-represented ROS- diabetes targets excluding insulin and NADPH oxidase 5 (NOX5), which does not have a mouse ortholog. Nitric oxide synthase 1 (NOSI), the main generator of nitric oxide, ranked at the 1 5th position and was additionally selected for inclusion in th e test set.Differential gene expression by real-time RT-PCR Mice DBA/2J mice were purchased from the Jackson Laboratory (Bar Harbor, ME). Mice were housed in a pathogen-free environment and cared for following the University of Michigan Committee on the Care and Use of Animals guidelines. Mice were fed AIN76A chow (Research Diets, New Brunswick, NJ). Male mice were used for this study. Induction of diabetes Two treatment groups were defined: control (n = 4) and diabetic (n = 4). Diabetes was induced at 13 weeks of age by low-dose streptozotocin (STZ) injections, 50 mg/kg/day for five consecutive days.All diabetic mice received LinBit sustained release insulin implants (LinShin, Toronto, Canada) at 8 weeks post-STZ treatment. Insulin implants were replaced every 4 weeks, at 12 and 16 weeks post-STZ treatment. At 20 weeks post-STZ treatment, mice were euthanized by sodium pentobarbital overdose and DRG were harvested as previously described [26]. Real-time RT-PCR The gene expression o f the selected nine literature-derived ROS-diabetes targets in DRG was measured using real-time RT-PCR in duplicate.The amount of mRNA isolated from each DRG was normalized to an endogenous reference [Tbp: TATA box binding protein; A cycle threshold (CT)]. Identification of ROS-diabetes targets A total of 1,021 unique targets were identified by SciMiner from the 1,154 ROS- diabetes papers defined by the query of (â€Å"Reactive Oxygen Species†[MeSH] AND â€Å"Diabetes Mellitus†[MeSH]) and confirmed by manual review. Table 1 contains the op 10 most frequently mentioned targets in the ROS-diabetes papers. Insulin was the most frequently mentioned target, followed by superoxide dismutase 1 and catalase. Table 1 .Top 10 most frequent ROS-diabetes targets The NCBI Gene2PubMed database, containing expert-curated associations between the NCBI Gene and PubMed databases, revealed 90 unique genes associated with the 54 ROS-diabetes papers (Additional File 1). SciMiner identified 85 out of these 90 targets, indicating a 94% recall rate. Five targets missed by SciMiner were added to the initial ROS-diabetes target set to result in 1,026 unique targets (Additional File 2). Additional tile 1. The list ot 90 genes trom the NCBI Gene2PubMed database tor the ROS-Diabetes literature (1 , 1 54 papers).Format: XLS Size: 35KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Additional file 2. The list of 1,026 ROS-Diabetes targets. Format: XLS Size: 229KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data PPI network of the ROS-diabetes targets The PPI network among the ROS-diabetes targets was evaluated using MIMI interaction data. This was based on the assumption that targets commonly related to certain topic are more likely to have frequent interactions with each other.One hundred PPI networks were generated for comparison using the same number of genes (1,026) randomly selected from the complete HUGO gene set (2 5,254). The PPI network of the ROS-diabetes targets was significantly different from the randomly generated networks indicating their strong association with the topic â€Å"ROS and Diabetes†. Table 2 demonstrates that the mean number of targets with any PPI interaction in the randomly generated target sets was 528. 9 (approximately 52% of 1,026 targets), while the number of targets with any PPI interaction in the ROS- iabetes target was 983 (96%).The number of targets interacting with each other was also significantly different between the random networks (mean = 155. 4) and the ROS-diabetes network (mean = 879). Figure 1 illustrates the distributions of these measurements from the 100 random networks with the ROS-diabetes set depicted as a red vertical line. It is obvious that the PPI network of the ROS-diabetes targets is significantly different from the random networks. Table 2. Summary of 100 randomly generated PPI networks thumbnailFigure 1 . Histograms of randomly gene rated PPI networks.The histograms llustrate the distributions of 100 randomly generated networks, while the red line indicates the ROS-diabetes targets. The network of the ROS-diabetes targets is significantly different from the 100 randomly generated networks, indicating the overlap of ROS-diabetes targets with respect to the topic â€Å"Reactive Oxygen Species and Diabetes†. Functional enrichment analyses of the ROS-diabetes targets Functional enrichment analyses of the 1,026 ROS-diabetes targets were performed to identify over-represented biological functions of the ROS-diabetes targets.After BenJamini-Hochberg correction, a total of 189 molecular functions, 450 biological rocesses, 73 cellular components and 341 pathways were significantly enriched in the ROS-diabetes targets when compared against all the HUGO genes (see Additional Files 3, 4, 5 and 6 for the full lists). Table 3 lists the top 3 most over-represented GO terms and pathways ranked by p-values of Fisher's ex act test: e. g. , apoptosis, oxidoreductase activity and insulin signaling pathway. Additional file 3. The enriched Molecular Functions Gene Ontology Terms in the 1,026 ROS-Diabetes targets.Format: XLS Size: 91 KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Additional file 4. The nriched Biological Processes Gene Ontology Terms in the 1,026 ROS-Diabetes targets. Format: XLS Size: 95KB Download file This tile can be viewed wit Microsott Excel Vieweropen Data Additional tile enriched Cellular Components Gene Ontology Terms in the 1,026 ROS-Diabetes targets. Format: XLS Size: 61 KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Additional file 6. The enriched pathways in the 1,026 ROS-Diabetes targets.Format: XLS Size: 104KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Table 3. Enriched functions of 1,026 ROS-diabetes targets Identification of over-represented ROS-diabetes targets To identify th e ROS-diabetes targets highly over-represented in ROS-diabetes literature, three sets of background corpora of the same size (n = 1 , 1 54 documents) were generated using the same Journal, volume and issue approach. The overlap among the three background sets in terms of documents and identified targets are illustrated in Figure 2.Approximately 90% of the selected background documents were unique to the individual set, while 50% of the identified targets were identified in at least one of the three background document sets. The frequencies of the identified targets were compared among the background sets for significant differences. None of the targets had a BH adjusted p-value ; 0. 05, indicating no significant difference among the targets from the three different background sets (See Additional File 7). thumbnailFigure 2. Venn diagrams of document compositions and identified targets of the randomly generated background sets.Approximately 90% of the selected background documents we re unique to individual set (A), while 50% of the identified targets were identified in at least one of the three background document sets (B). Additional file 7. Comparisons of target frequencies among three background sets. Format: XLS Size: 22KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Comparisons of the ROS-diabetes targets against these background sets revealed 53 highly over- represented ROS-diabetes targets as listed in Table 4.These 53 targets were significant (p-value ; 0. 05) against all three background sets and significant following BenJamini-Hochberg multiple testing correction (BH adjusted p-value ; 0. 05) against at least two of the three background sets. SODI was the most over-represented in he ROS-diabetes targets. Table 4. 53 targets over-represented in ROS-diabetes literature Functional enrichment analyses of the over-represented ROS-diabetes targets Functional enrichment analyses of the 53 ROS-diabetes targets were performed to identify over- represented biological functions.Following BenJamini-Hochberg correction, a total of 65 molecular functions, 209 biological processes, 26 cellular components and 108 pathways were significantly over-represented when compared against all the HUGO genes (see Additional Files 8, 9, 10 and 11 for the full lists). Table 5 shows the top 3 ost significantly over-represented GO terms and pathways ranked by p-values of Fisher's exact test. GO terms related to oxidative stress such as â€Å"superoxide metabolic process†, â€Å"superoxide release†, â€Å"electron carrier activity† and â€Å"mitochondrion† were highly over-represented 53 ROS-diabetes targets Additional file 8.The enriched Molecular Functions Gene Ontology Terms in the Over- represented 53 ROS-Diabetes targets. Format: XLS Size: 46KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Additional file 9. The enriched Biological Processes Gene Ontology Terms in the Over-represented 53 ROS- Diabetes targets. Format: XLS Size: 95KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Additional file 10. The enriched Cellular Components Gene Ontology Terms in the Over-represented 53 ROS-Diabetes targets.Format: XLS Size: 66KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Additional file 1 1 . The enriched pathways in the Over-represented 53 ROS-Diabetes targets. Format: XLS Size: 75KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data Table 5. Enriched functions of the 53 over-represented targets in diabetes Gene expression change in iabetes Two groups of DBA/2J mice exhibited significantly different levels of glycosylated hemoglobin (%GHb). The mean ? ± SEM were 6. 2 ? ± 0. for the non-diabetic control group and for 14. 0 ? ± 0. 8 for the diabetic group (p-value < 0. 001), indicative of prolonged hyperglycemia in the diabetic group [26]. DRG were harvested from these animals for gene expression assays. Nine genes were selected from the top ranked ROS-diabetes targets: superoxide dismutase 1 (Sodl), catalase (Cat), xanthine dehydrogenase (Xdh), protein kinase C alpha (Prkca), neutrophil cytosolic factor 1 Ncfl), nitric oxide synthase 3 (Nos3), superoxide dismutase 2 (Sod2), cytochrome b-245 alpha (Cyba), and nitric oxide synthase 1 (Nosl).Eight genes exhibited differential expression between diabetic and non-diabetic mice (p-value < 0. 05) as shown in Figure 3. Cat, Sodl, Sod2, Prkca, and NOSI expression levels were decreased, while Ncfl , Xdh, and Cyba expression levels were increased in diabetes. thumbnailFigure 3. Gene expression levels of selected ROS-diabetes targets in DRG examined by real-time RT-PCR. Expression levels are relative to Tbp, an internal control (error bar = SEM) (*, p < 0. 05; **, p < 0. 01; ***, p < 0. 01). Eight (Cat, Sodl, Ncfl , Xdh, Sod2, Cyba, Prkca, and Nosl) out of the nine selected ROS-diabetes genes were sign ificantly regulated by diabetes. Discussion Reactive oxygen species (ROS) are products of normal energy metabolism and play important roles in many other biological processes such as the immune response and signaling cascades [4-6]. As mediators of cellular damage, ROS are implicated in pathogenesis of multiple diseases including diabetic complications [27-30].With the aid of literature mining technology, we collected 1 ,026 possible ROS-related targets from a set of biomedical literature indexed with both ROS and diabetes. Fifty-three targets were significantly over-represented in the ROS-diabetes papers when compared against three background sets. Depending on how the background set is defined, the over-represented targets may vary widely. An ideal background set would be the entire PubMed set; however, this is not possible due to limited access to tull texts and intense data processing.An alternative method wou d be to use only abstracts in PubMed, but this may not fully represen t the literature. Using only the abstracts, our target identification method resulted in 21 (39%) of the 53 key ROS- iabetes targets (Additional File 12), suggesting the benefit of rich information in full text documents. In the present study, background documents were randomly selected from the same Journal, volume, and issue of the 54 ROS-diabetes documents, which were not indexed with â€Å"Reactive Oxygen Species†[MeSH] nor â€Å"Diabetes Mellitus†[MeSH].This approach maintained the background corpora not far from the ROS and diabetes context. Additional file 12. The Key 53 ROS-Diabetes Targets Identifiable Using Only the Abstracts. Format: XLS Size: 23KB Download file This file can be viewed with: Microsoft Excel Vieweropen Data The gene expression evels of nine targets selected from the 53 over-represented ROS-diabetes targets were measured in diabetic and non-diabetic DRG. Our laboratory is particularly interested in deciphering the underlying mechanisms of diab etic neuropathy, a major complication of diabetes.Data published by our laboratory both in vitro and in vivo confirm the negative impact of oxidative stress in complication-prone neuron tissues like DRG In an effort to obtain diabetic neuropathy specific targets, SciMiner was employed to further analyze a subset of the ROS-diabetes papers (data not shown). Nerve growth factor (NGF) was identified as the most over- epresented target in this subset when compared to the full ROS-diabetes set; however, NGF did not have statistical significance (BH adjusted p-value = 0. 06). The relatively small numbers of papers and associated targets may have contributed to this non-significance.Therefore, the candidate targets for gene expression validation were selected from among the 53 over-represented ROS-diabetes targets derived from the full ROS-diabetes corpus. Among the tested genes, the expression levels of Cat, Sodl , Sod2, Prkca, and NOSI were decreased, while the expression levels of Ncfl , Xdh, and Cyba were increased nder diabetic conditions. Cat, Sodl , and Sod2 are responsible for protecting cells from oxidative stress by destroying superoxides and hydrogen peroxides [8-11]. Decreased expression of these genes may result in oxidative stress [32].Increased expression of Cyba and Ncfl , subunits of superoxide-generating nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex [30], also supports enhanced oxidative stress. Xdh and its inter-convertible form, Xanthine oxidase (Xod), showed increased activity in various rat tissues under oxidative stress conditions ith diabetes [33], and also showed increased expression in diabetic DRG in the current study. Unlike the above concordant genes, protein kinase C and nitric oxide synthases did not exhibit predicted expression changes in diabetes.Protein kinase C activates NADPH oxidase, further promoting oxidative stress in the cell [34,35]. Decreased expression of Prkca in our diabetic DRG is not parallel with expression levels of other enzymes expected to increase oxidative stress. Between the two nitric oxide synthases tested in the present study, NOSI (neuronal) expression was significantly decreased (p-value < 0. 01) in diabetes, while Nos3 (endothelial) expression was not significant (p-value = 0. 06). The neuronal NOSI is expected to play a major role in producing nitric oxide, another type of highly reactive free radical.Thus, with some exceptions, the majority of the differentially expressed genes in DRG show parallel results to the known activities of these targets in diabetes, suggesting enhanced oxidative stress in the diabetic DRG. Assessment of antioxidant enzyme expression in diabetes has yielded a variety of results [36-40] depending upon the duration of diabetes, the tissue studied and other factors. In diabetic mice and rats, it is commonly reported that superoxide dismutases are down-regulated [37-40], where data regarding catalase are variable [36,40].PKC is activated i n diabetes, but most papers that examined mRNA demonstrated that its expression is largely unchanged [41]. Among the 53 over-represented ROS-diabetes targets, SODI was the most over- represented and was differentially expressed under diabetic and non-diabetic conditions. To the best of our knowledge, no published study has investigated the role of SODI in the onset and/or progression of diabetic neuropathy. Mutations of SODI have long been associated with the inherited form of amyotrophic lateral sclerosis (ALS) [42] and the theory of oxidative stress-based aging [43].Early reports indicate that knockout of the SODI gene does not affect nervous system development [44], although recovery following injury is slow and incomplete [45,46]. With respect to diabetes, SODI KO accelerates the development of diabetic nephropathy [47] and cataract formation [48]. Thus, examining the SODI KO mouse as a model of diabetic neuropathy would be a reasonable follow-up study. One limitation of the cur rent approach using literature mining technology is incorrect r missed identification of the mentioned targets within the literature.Based on a performance evaluation using a standard text set BioCreAtlvE (Critical Assessment of Information Extraction systems in Biology) version 2 [49], SciMiner achieved 87. 1% recall (percentage identification of targets in the given text), 71. 3% precision (percentage accuracy of identified target) and 75. 8% F-measure (harmonious average of recall and precision = (2 x recall x precision)/(recall + precision)) before manual revision [14]. In order to improve the accuracy of SciMiner's results, each target was anually reviewed and corrected by checking the sentences in which each target was identified.Approximately, 120 targets (†10% of the initially identified targets from the ROS-diabetes papers) were removed during the manual review process. The overall accuracy is expected to improve through the review process; however, the review process did not address targets missed by SciMiner, since we did not thoroughly review individual papers. Instead, 5 missed targets, whose associations with ROS-diabetes literature were available in the NCBI Gene2PubMed database, were added to the final ROS-diabetes target list (Additional File 2).