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1.
Implementing quality indicators for diabetes and hypertension in family medicine in Slovenia
Zalika Klemenc-Ketiš, Igor Švab, Tonka Poplas-Susič, 2017, izvirni znanstveni članek

Opis: Introduction: A new form of family practices was introduced in 2011 through a pilot project introducing nurse practitioners as members of team and determining a set of quality indicators. The aim of this article was to assess the quality of diabetes and hypertension management. Methods: We included all family medicine practices that were participating in the project in December 2015 (N=584). The following data were extracted from automatic electronic reports on quality indicators: gender and specialisation of the family physician, status (public servant/self-contracted), duration of participation in the project, region of Slovenia, the number of inhabitants covered by a family medicine practice, the name of IT provider, and levels of selected quality indicators. Results: Out of 584 family medicine practices that were included in this project at the end of 2015, 568 (97.3%) had complete data and could be included in this analysis. The highest values were observed for structure quality indicator (list of diabetics) and the lowest for process and outcome quality indicators. The values of the selected quality indicators were independently associated with the duration of participation in the project, some regions of Slovenia where practices were located, and some IT providers of the practices. Conclusion: First, the analysis of data on quality indicators for diabetes and hypertension in this primary care project pointed out the problems which are currently preventing higher quality of chronic patient management at the primary health care level.
Ključne besede: family practices, healthcare quality indicator, diabetes mellitus, hypertension, Slovenia
Objavljeno v DKUM: 03.11.2017; Ogledov: 983; Prenosov: 309
.pdf Celotno besedilo (424,82 KB)
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2.
Razvoj spletne aplikacije in napovednega modela za napoved nediagnosticirane sladkorne bolezni tipa 2
Andrej Fajfar, 2017, magistrsko delo

Opis: V magistrski nalogi smo s pomočjo metode strojnega učenja »Random Forest« skušali napovedati stopnjo tveganja za nastanek sladkorne bolezni oz. verjetnost prisotnosti nediagnosticirane sladkorne bolezni na podlagi podatkov iz Slovenije. Za izbrano metodo smo določili optimalno število in vrsto spremenljivk za posamezni model. Za evalvacijo modela smo uporabili povprečno območje pod krivuljo (AUC), točnost in F-mero. Za model populacije s povečanim tveganjem smo dosegli povprečno AUC 0,823, točnost 0,824 in F-mero 0,804. V modelu za napoved nediagnostirane sladkorne bolezni smo dosegli povprečno AUC in točnost 0,749 in F-mero 0,654. Na podlagi podatkov smo pokazali, da je možno z veliko uspešnostjo določiti osebe z visokim tveganjem, ki predstavljajo preddiabetike in nediagnostirane diabetike oz. skupino s tveganjem za nastanek sladkorne bolezni tipa 2. Pokazali smo uporabo tehnik uravnoteženja odločitvenega razreda in rezultate primerjali z neuravnoteženim razredom. Uravnoteženje razreda zviša klasifikacijsko uspešnost modela. Rezultate smo primerjali z rezultati drugih znanstvenih objav in zasledili podobnost med rezultati. Tuje raziskave navajajo, da je klasifikator Random Forest najpogosteje izbran model, v primerjavi z drugimi modeli za napovedovanje kroničnih bolezni. S korelacijskim testom smo pokazali, da napovedna uspešnost modela ne korelira s številom dreves v ansamblu (p = 0,00015).
Ključne besede: strojno učenje, Random Forest, neuravnoteženi podatki, diabetes mellitus
Objavljeno v DKUM: 19.10.2017; Ogledov: 907; Prenosov: 142
.pdf Celotno besedilo (1,38 MB)

3.
Intracellular serotonin modulates insulin secretion from pancreatic ß-cells by protein serotonylation
Nils Paulmann, Maik Grohmann, Jörg-Peter Voigt, Bettina Bert, Jakob Vowinckel, Michael Bader, Maša Skelin, Marko Jevšek, Heidrun Fink, Marjan Rupnik, Diego Walther, 2009, izvirni znanstveni članek

Opis: While serotonin (5-HT) co-localization with insulin in granules of pancreatic ß-cells was demonstrated more than three decades ago, its physiological role in the etiology of diabetes is stili unclear. We combined biochemical and electrophysiological analyses of mice selectively deficient in peripheral tryptophan hydroxylase (Tph1-/-) and 5-HT to show that intracellular 5-HT regulates insulin secretion. We found that these mice are diabetic and have an impaired insulin secretion due to the lack of 5-HT in the pancreas. The pharmacological restoration of peripheral 5-HT levels rescued the impaired insulin secretion in vivo. These findings were further evidenced by patch clamp experiments with isolated Tph1-/- ß-cells, which clearly showed that the secretory defect is downstream of Ca2+ -signaling and can be rescued by direct intracellular application of 5-HT via the clamp pipette. In elucidating the underlying mechanism further, we demonstrate the covalent coupling of 5-HT by transglutaminases during insulin exocytosis to two key players in insulin secretion, the small GTPases Rab3a and Rab27a. This renders them constitutively active in a receptor-independent signaling mechanism we have recently termed serotonylation. Concordantly, an inhibition of such activating serotonylation in ß-cells abates insulin secretion. We also observed inactivation of serotonylated Rab3a by enhanced proteasomal degradation, which is in line with the inactivation of other serotonylated GTPases. Our results demonstrate that 5-HT regulates insulin secretion by serotonylation of GTPases within pancreatic ß-cells and suggest that intracellular 5-HT functions in various microenvironments via this mechanism in concert with the known receptor-mediated signaling.
Ključne besede: insulin secretion, serotonin, insulin, glucose, diabetes mellitus, guanosine triphosphatase, exocytosis, pancreas
Objavljeno v DKUM: 16.06.2017; Ogledov: 1204; Prenosov: 143
.pdf Celotno besedilo (774,75 KB)
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4.
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