Molekularno-genetička analiza osteogenesis imperfecta u kliničkoj praksi

Osteogenesis imperfecta (OI) is characterized by fractures with minimal or absent trauma, representing a continuum ranging from perinatal lethality through individuals with severe skeletal deformities to nearly asymptomatic individuals with mild predisposition to fractures. Diagnosis of OI is an interdisciplinary task based on family and/or patient history of fractures combined with characteristic physical fi ndings. Radiographic examination reveals fractures of varying ages and stages of healing, wormian bones, and osteopenia. As there is no defi nitive test for OI, molecular genetic testing by next generation sequencing (NGS) of COL1A1 and COL1A2 and up to 12 other genes is essential to confi rm the genetic background. Therefore, we designed a NGS gene panel comprising 12 genes involved in OI or severe osteoporosis. Here we report results in a cohort of 11 apparently sporadic young patients with OI, all off spring of unaff ected parents, who were referred to orthopaedic surgery at Sv. Katarina Special Hospital (Zabok/Zagreb, Croatia). Ten of these 11 patients could be classifi ed genetically. Overall, three genes with diff erent percent relating to the whole cohort were involved: COL1A1 (63.6%), COL1A2 (18.18%) and WNT1 (9.09%).Osteogenesis imperfecta (OI) je obilježena prijelomima uz minimalnu ili odsutnu traumu i predstavlja kontinuum u rasponu od perinatalne smrtnosti, osobe s teškim skeletnim deformitetima do gotovo asimptomatskih osoba s niskom sklonošću prijelomima. Dijagnosticiranje OI je interdisciplinski zadatak koji se temelji na obiteljskoj i/ili bolesnikovoj povijesti prijeloma u kombinaciji sa znakovitim fi zičkim nalazima. Radiografsko snimanje otkriva prijelome različite starosti i stadija zaraštanja, Wormove kosti i osteopeniju. Kako nema konačnog testa za OI, molekularno genetsko testiranje pomoću next generation sequencing (NGS) gena COL1A1 i COL1A2 te do 12 drugih gena bitno je za potvrdu genetske podloge. Stoga smo izradili NGS genski panel koji sadrži 12 gena uključenih u OI ili tešku osteoporozu. Prikazujemo rezultate dobivene u nizu od 11 očito sporadičnih mladih bolesnika s OI, svi potomci nezahvaćenih roditelja, koji su bili upućeni na ortopedsku kirurgiju u Specijalnoj bolnici sv. Katarina u Zaboku/Zagrebu. Deset od tih 11 bolesnika mogli smo genetski klasifi cirati. Sveukupno, uključena su bila tri gena u različitim postocima u našem nizu bolesnika: COL1A1 (63,6%), COL1A2 (18,18%) i WNT1 (9,09%)

Ergoterapeutiska åtgärder i förskola och grundskola : en forskningsöversikt

Syftet med detta examensarbete var att kartlägga vilka ergoterapeutiska åtgärder för elever i behov av särskilt stöd i för- och grundskola som finns dokumenterade i forskning. Detta examensarbete gjordes som en systematisk litteraturstudie, och analyserades med hjälp av en innehållsanalys. Arbetets frågeställningar var: Vilka åtgärder med fokus på aktivitet finns dokumenterade i forskning? Vilka åtgärder med fokus på miljöfaktorer finns dokumenterade i forskning? Vilka åtgärder med fokus på personfaktorer finns dokumenterade i forskning? Den teoretiska referensramen som användes var Person-Environment-Occupation-Performance Model (PEOP). PEOP är en klientcentrerad modell där fokuset ligger på interaktionen mellan personen och dess miljö, som påverkar aktivitets utförande och delaktighet. 14 artiklar inkulderades i detta arbete, som sedan analyserades och tematiserades, teman som användes var aktivitet, omgivning och person. Det framkommer i forskningarna att ergoterapeutiska interventioner som gjorts i skolmiljön har haft en positiv inverkan på barnets aktivitetsutförande och deltagande i social samvaro. Det framkommer dock även att barnens rätt till skolbaserad ergoterapi varierar, och att en del barn lämnas utan det stöd som de skulle behöva.Tämän opinnäytetyön tarkoituksena oli kartoittaa tutkimusta toimintaterapeuttisista interventioista erityistuen tarpeessa oleville oppilaille esi- ja peruskoulussa. Menetelmänä käytettiin systemaattista kirjallisuuskatsausta. Artikkeleiden analysoinnissa edettiin sisältöanalyysin kautta. Kysymyksensasettelu oli: Mitä aktiviteettiin keskittyviä interventioita on tutkittu? Mitä ympäristötekijöihin keskittyviä interventioita on tutkittu? Mitä henkilötekijöihin keskittyviä interventioita on tutkittu? Teoreettisena viitekehyksenä oli käytössä Person-Environment-Occupation-Performance Model (PEOP). PEOP on asiakaskeskeinen malli, joka keskittyy yksilöön ja sen ympäristön vuorovaikutuksen vaikutuksiin tämän suorituksissa ja osallisuudessa. Tiedonkeruun jälkeen 14 artikkelia analysoitiin, tematisoitiin ja sisällytettiin työhön. Teemat olivat toiminta, ympäristö ja yksilö. Tutkimuksissa tuli esille, että toimintaterapeuttiset interventiot kouluympäristössä ovat edesauttaneet lapsen sosiaalista osallistumista ja toiminnallista suorittamista. Tutkimuksessa tuli tosin ilmi, että kaikilla lapsilla ei ole tasavertaista oikeutta saada toimintaterapiaa kouluympäristössä. Tämä johtaa siihen, että osa lapsista, jotka ovat avun tarpeessa, jäävät ilman terapiaa.The purpose of this thesis was to map documented research on occupational therapy in-terventions aimed at special needs students in pre- and elementary school. The study was conducted as a literature study, and was analyzed through a content analysis. The questions for the thesis were formulated as following: Which interventions focusing on activity are documented in research? Which interventions focusing on environmental factors are documented in research? Which interventions focusing on personal factors are documented in research? The theoretical frame of reference used was Person-Environment-Occupation-Performance Model (PEOP). PEOP is a client-centric model focusing on how the client’s interactions with the environment impacts on activity performance and participation. 14 articles were included in this study, which then were an-alyzed and put into themes. The themes used were activity, environment and person. The research brings up evidence of the positive affects occupational therapy interven-tions have on the child’s occupational performance and participation in social situations. Although the research also points out variations in the rights for school based occupational therapy, implying that some children in need of therapy are unable to receive it

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose!#!While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.!##!Methods!#!We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).!##!Results!#!The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.!##!Conclusion!#!We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19