21 research outputs found
Posterior Segment Approach for Subretinal Transplantation or Injection in the Canine Model
A posterior segment approach for cell transplantation or injection into the subretinal space of the dog has been developed. Controlled penetration to the subretinal space was achieved using a 29-gauge injection cannula, either blunted or with a 30° sharpened bevel, and partially ensheathed with moveable plastic tubing. Depending on the injection volume used, the retina detached, and the fluid was reabsorbed within 1â3 weeks, although for smaller volumes the retina reattached within a matter of days. The optimal injection volume used was between 100 and 150 ÎŒl, or two injections of 55 ÎŒl each. By ophthalmoscopy following the surgery, it was possible to serially monitor the injection site and retinal bleb through fundus photography. Light microscopy demonstrates the distribution of stable, viable RPE cells in the subretinal space up to 6 months. The transplantation technique developed for the dog is atraumatic and free from any major surgical or clinical complications. It can be readily used to deliver cells or fluids to localized regions of the subretinal space
Posterior Segment Approach for Subretinal Transplantation or Injection in the Canine Model
Gasellepopulasjon i klynger : en metodetriangulert studie av gasellebedrifter i norske fylker
Gasellebedrifter kjennetegnes ved at de er smĂ„ unge bedrifter som vokser kraftig i lĂžpet av kort tid, og det har opp gjennom Ă„rene blitt diskutert hva som kan vĂŠre âsuksessoppskriftenâ.
I studien har jeg fÄtt tilgang til et datasett fra en spÞrreundersÞkelse rundt gasellebedrifter utfÞrt av analyse- og kommunikasjonsbyrÄet Perduco pÄ vegne av rÄdgivnings- og revisjonsselskapet PWC. Ved hjelp av datasettet har jeg fÄtt et innblikk i noen gasellebedrifter i Norge. Ved en gjennomgang av datasettet oppdaget jeg at det var store forskjeller i gasellepopulasjon mellom fylkene i Norge. Jeg stilte da spÞrsmÄl om hvorfor det var sÄ stor forskjell i populasjonene og hva som kunne forklare det. Kunne kanskje noen av fylkene med stor populasjon defineres som klynger, og var det i sÄ fall en forklarende faktor?
I selve analysen har jeg brukt en metodetriangulering for Ä undersÞke hvorvidt elementer hentet fra klyngeteorien kunne forklare forskjellene i gasellepopulasjon mellom fylkene i Norge. Jeg har fÞrst benyttet en kvantitativ analyse til Ä undersÞke datasettet jeg nevnte ovenfor, for sÄ Ä samle inn egne data gjennom dybdeintervjuer for Ä utfÞre en dypere kvalitativ analyse. FormÄlet med studien var Ä svare pÄ fÞlgende problemstilling:
Kan ulike forekomster av gasellepopulasjoner mellom fylker i Norge forklares av elementer hentet fra klyngeteori?
I den kvantitative analysen besluttet jeg Ä utelukke 2 av de 28 variablene som var med, da jeg mener disse ikke ga relevant nok informasjon til studien. Jeg plukket sÄ ut 8 av 19 fylker, 4 fylker med lav gasellepopulasjon og 4 fylker med stor gasellepopulasjon, til Ä benyttes videre i undersÞkelsen. Videre paret jeg sammen et fylke med liten gasellepopulasjon med et fylke med stor gasellepopulasjon slik at jeg fikk 4 grupper. Det jeg Þnsket Ä undersÞke var om fylket med liten gasellepopulasjon hadde signifikant forskjellige svar i forhold til fylket med stor gasellepopulasjon.
Jeg formulerte deretter 8 hypoteser som jeg fordelte de 26 variablene pÄ. For Ä kunne forkaste en hypotese for en variabel satt jeg et krav til at det mÄtte finnes signifikante forskjeller for mer enn en gruppe og signifikansnivÄet mÄtte vÊre relativt hÞyt for minst en gruppe. Kun en av variablene viste seg Ä kunne forkastes pÄ et tilfredsstillende nivÄ. Variabelen som kunne forkastes viste at fylker med hÞy gasellepopulasjon la mer vekt pÄ privat risikokapital enn fylker med lav gasellepopulasjon.
I den kvalitative studien brukte jeg 4 kategorier knyttet opp mot klyngeteori nĂ„r jeg utformet intervjuguiden: samarbeid, beliggenhet, kompetent arbeidskraft og rivalisering. Deretter intervjuet jeg totalt 12 daglige ledere fra gasellebedrifter fordelt likt pĂ„ 3 fylker: Oslo â som en kunnskapsklynge, Rogaland â som en nĂŠringsklynge og Vestfold â som en kontrollgruppe. Alle intervjuene ble tatt opp pĂ„ bĂ„nd og det er brukt direkte sitater i den kvalitative analysen. Studien viste at det var noe klyngeeffekt tilknyttet beliggenhet og kompetent arbeidskraft, men at resultatene var for svake til at jeg kunne generalisere funnene.
Den endelige konklusjonen av studien var derfor at det ikke er signifikante nok klyngeeffekter til at vi kan pÄstÄ at de fÞrer til forskjellige populasjonsstÞrrelser av gasellebedrifter mellom fylkene i Norge. ..........................................................................................
Gazelle Enterprises are characterized in that they are small young companies that grow rapidly in a short period of time, and it has been discussed, over the years, what might be "the recipeâ for their success.
In this study, I have received access to a dataset from a survey, regarding gazelle firms, conducted by analysis- and communications agency Perduco on behalf of advisory- and audit firm PWC. Using this dataset, I got a insight into gazelle companies in Norway. In a review of the dataset, I discovered that there were large differences in the gazelle population between counties in Norway. I questioned why these substantially differences in gazelle populations occurred and what might explain it. Could perhaps some of the counties with large population be defined as clusters, and in that case could that be an explanatory factor?
In this study I therefore used a mixed-method analysis to determine whether the elements taken from cluster theory could explain the differences in the gazelle population between counties in Norway. I first used a quantitative analysis to examine the dataset I mentioned above, and then collected my own data through in-depth interviews that were used in a qualitative analysis. The purpose of this mixed-method study was to answer the following research question:
Can different occurrences of gazelle populations between counties in Norway be explained by elements taken from cluster theory?
In the quantitative analysis, I decided to exclude 2 of the 28 variables that were used in the survey, since I believed these did not provide information that were relevant enough to the study. I then selected 8 of 19 counties, four counties with low gazelle population and 4 counties with large gazelle population, to be used further in the study. Furthermore, I paired-up a county with small gazelle population with a county with large gazelle population so that I got 4 groups. What I wanted to examine was whether the county with small gazelle population had significantly different responses in relation to the county with large gazelle population.
I then formulated 8 hypotheses, which I divided the 26 variables between. To reject a hypothesis for a variable I set a requirement that there had to be significant differences in more than one group and that the significance level had to be relatively high for at least one of the groups. Only one of the variables proved to have enough significantly different answers to be discarded at a satisfactory level. The variable that could be discarded showed that counties with high gazelle population put more emphasis on private risk than counties with low gazelle population.
In the qualitative study, I used 4 categories that I linked to cluster theory when I designed the interview guide: collaboration, location, skilled labor and rivalry. I then interviewed a total of 12 general managers of gazelle companies distributed equally among three counties: Oslo â as a knowledge cluster, Rogaland â as a industrial cluster and Vestfold â as a control group. All interviews were audio taped and I have used direct quotes in the qualitative analysis. The study showed that there was some clustering effect associated with location and skilled labor, but the results were too weak for me to generalize the findings.
The final conclusion of the study was that the cluster effects are not significant enough for me to claim that they cause different population sizes of gazelle companies between counties in Norway
Gazelle population in clusters : a mixed-method study of gazelle companies in norwegian counties
Gasellebedrifter kjennetegnes ved at de er smĂ„ unge bedrifter som vokser kraftig i lĂžpet av kort tid, og det har opp gjennom Ă„rene blitt diskutert hva som kan vĂŠre âsuksessoppskriftenâ.
I studien har jeg fÄtt tilgang til et datasett fra en spÞrreundersÞkelse rundt gasellebedrifter utfÞrt av analyse- og kommunikasjonsbyrÄet Perduco pÄ vegne av rÄdgivnings- og revisjonsselskapet PWC. Ved hjelp av datasettet har jeg fÄtt et innblikk i noen gasellebedrifter i Norge. Ved en gjennomgang av datasettet oppdaget jeg at det var store forskjeller i gasellepopulasjon mellom fylkene i Norge. Jeg stilte da spÞrsmÄl om hvorfor det var sÄ stor forskjell i populasjonene og hva som kunne forklare det. Kunne kanskje noen av fylkene med stor populasjon defineres som klynger, og var det i sÄ fall en forklarende faktor?
I selve analysen har jeg brukt en metodetriangulering for Ä undersÞke hvorvidt elementer hentet fra klyngeteorien kunne forklare forskjellene i gasellepopulasjon mellom fylkene i Norge. Jeg har fÞrst benyttet en kvantitativ analyse til Ä undersÞke datasettet jeg nevnte ovenfor, for sÄ Ä samle inn egne data gjennom dybdeintervjuer for Ä utfÞre en dypere kvalitativ analyse. FormÄlet med studien var Ä svare pÄ fÞlgende problemstilling:
Kan ulike forekomster av gasellepopulasjoner mellom fylker i Norge forklares av elementer hentet fra klyngeteori?
I den kvantitative analysen besluttet jeg Ä utelukke 2 av de 28 variablene som var med, da jeg mener disse ikke ga relevant nok informasjon til studien. Jeg plukket sÄ ut 8 av 19 fylker, 4 fylker med lav gasellepopulasjon og 4 fylker med stor gasellepopulasjon, til Ä benyttes videre i undersÞkelsen. Videre paret jeg sammen et fylke med liten gasellepopulasjon med et fylke med stor gasellepopulasjon slik at jeg fikk 4 grupper. Det jeg Þnsket Ä undersÞke var om fylket med liten gasellepopulasjon hadde signifikant forskjellige svar i forhold til fylket med stor gasellepopulasjon.
Jeg formulerte deretter 8 hypoteser som jeg fordelte de 26 variablene pÄ. For Ä kunne forkaste en hypotese for en variabel satt jeg et krav til at det mÄtte finnes signifikante forskjeller for mer enn en gruppe og signifikansnivÄet mÄtte vÊre relativt hÞyt for minst en gruppe. Kun en av variablene viste seg Ä kunne forkastes pÄ et tilfredsstillende nivÄ. Variabelen som kunne forkastes viste at fylker med hÞy gasellepopulasjon la mer vekt pÄ privat risikokapital enn fylker med lav gasellepopulasjon.
I den kvalitative studien brukte jeg 4 kategorier knyttet opp mot klyngeteori nĂ„r jeg utformet intervjuguiden: samarbeid, beliggenhet, kompetent arbeidskraft og rivalisering. Deretter intervjuet jeg totalt 12 daglige ledere fra gasellebedrifter fordelt likt pĂ„ 3 fylker: Oslo â som en kunnskapsklynge, Rogaland â som en nĂŠringsklynge og Vestfold â som en kontrollgruppe. Alle intervjuene ble tatt opp pĂ„ bĂ„nd og det er brukt direkte sitater i den kvalitative analysen. Studien viste at det var noe klyngeeffekt tilknyttet beliggenhet og kompetent arbeidskraft, men at resultatene var for svake til at jeg kunne generalisere funnene.
Den endelige konklusjonen av studien var derfor at det ikke er signifikante nok klyngeeffekter til at vi kan pÄstÄ at de fÞrer til forskjellige populasjonsstÞrrelser av gasellebedrifter mellom fylkene i Norge. ..........................................................................................
Gazelle Enterprises are characterized in that they are small young companies that grow rapidly in a short period of time, and it has been discussed, over the years, what might be "the recipeâ for their success.
In this study, I have received access to a dataset from a survey, regarding gazelle firms, conducted by analysis- and communications agency Perduco on behalf of advisory- and audit firm PWC. Using this dataset, I got a insight into gazelle companies in Norway. In a review of the dataset, I discovered that there were large differences in the gazelle population between counties in Norway. I questioned why these substantially differences in gazelle populations occurred and what might explain it. Could perhaps some of the counties with large population be defined as clusters, and in that case could that be an explanatory factor?
In this study I therefore used a mixed-method analysis to determine whether the elements taken from cluster theory could explain the differences in the gazelle population between counties in Norway. I first used a quantitative analysis to examine the dataset I mentioned above, and then collected my own data through in-depth interviews that were used in a qualitative analysis. The purpose of this mixed-method study was to answer the following research question:
Can different occurrences of gazelle populations between counties in Norway be explained by elements taken from cluster theory?
In the quantitative analysis, I decided to exclude 2 of the 28 variables that were used in the survey, since I believed these did not provide information that were relevant enough to the study. I then selected 8 of 19 counties, four counties with low gazelle population and 4 counties with large gazelle population, to be used further in the study. Furthermore, I paired-up a county with small gazelle population with a county with large gazelle population so that I got 4 groups. What I wanted to examine was whether the county with small gazelle population had significantly different responses in relation to the county with large gazelle population.
I then formulated 8 hypotheses, which I divided the 26 variables between. To reject a hypothesis for a variable I set a requirement that there had to be significant differences in more than one group and that the significance level had to be relatively high for at least one of the groups. Only one of the variables proved to have enough significantly different answers to be discarded at a satisfactory level. The variable that could be discarded showed that counties with high gazelle population put more emphasis on private risk than counties with low gazelle population.
In the qualitative study, I used 4 categories that I linked to cluster theory when I designed the interview guide: collaboration, location, skilled labor and rivalry. I then interviewed a total of 12 general managers of gazelle companies distributed equally among three counties: Oslo â as a knowledge cluster, Rogaland â as a industrial cluster and Vestfold â as a control group. All interviews were audio taped and I have used direct quotes in the qualitative analysis. The study showed that there was some clustering effect associated with location and skilled labor, but the results were too weak for me to generalize the findings.
The final conclusion of the study was that the cluster effects are not significant enough for me to claim that they cause different population sizes of gazelle companies between counties in Norway
In Vitro Evaluation of Novel Nitazoxanide Derivatives against Mycobacterium tuberculosis
Synthesis and Evaluation of the 2-Aminothiazoles as Anti-Tubercular Agents.
The 2-aminothiazole series has anti-bacterial activity against the important global pathogen Mycobacterium tuberculosis. We explored the nature of the activity by designing and synthesizing a large number of analogs and testing these for activity against M. tuberculosis, as well as eukaryotic cells. We determined that the C-2 position of the thiazole can accommodate a range of lipophilic substitutions, while both the C-4 position and the thiazole core are sensitive to change. The series has good activity against M. tuberculosis growth with sub-micromolar minimum inhibitory concentrations being achieved. A representative analog was selective for mycobacterial species over other bacteria and was rapidly bactericidal against replicating M. tuberculosis. The mode of action does not appear to involve iron chelation. We conclude that this series has potential for further development as novel anti-tubercular agents
Whose perspective is it anyway?:Dilemmas of patient involvement in the development of a randomized clinical trial â a qualitative study
Chemical synthesis of analogs comprised of thiazole core replacement.
<p>Reagents:: (i) 4-(t-butyl)PhCOCl, EtOH, 1 h; (ii) EtOH, reflux, 16 h; (iii) H<sub>2</sub>SO<sub>4</sub>, r.t., 16 h; (iv) EDC, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 16 h.</p