Evaluation of Global Wave Climate Based on the JMA/MRI-AGCM Climate Change Projection

In this study global wave climates for present and future climates are simulated with the WAM model, based on wind fields from the JMA/MRI-AGCM3.2 climate projection. The projected wave fields are analysed and compared for the two periods of 1979-2003 and 2075-2099, and climate change induced differences are identified. It is found that the wave climate is strongly dependent on latitude, with the largest waves, as well as most significant seasonal variations, located at the mid to high latitude regions. These areas are also where the climate induced changes from present to future climate are most noteworthy. The largest increases of significant wave height of approximately +5%, is experienced in the southern parts of the Indian, Pacific and Atlantic Oceans as well as in the Antarctic Ocean. The largest decreases are of the same order, and found to the northern Atlantic Ocean. In addition, a slightly smaller but widespread decrease is seen in the tropical storm affected region around Japan, in the western Pacific Ocean. In contrast to this reduction of the everyday wave climate, an evaluation of the annual maximum waves at this location indicates that the extreme wave climate might become more severe due to the projected climate change. In the central and lower parts of the mid latitude regions the projected wave climate remain fairly stable and show only minor changes between present and future climates. It is noted that the processes behind the everyday wave climate differ significantly from those causing extreme events, and that the phenomena should therefore be evaluated separately. Since this study focuses on the climate change induced effects on the wave climate under normal weather conditions, it is recommended that in future research evaluate the extremes in a more thorough manner. For that analysis using the full potential of the JMA/MRI-AGCM high-resolution wind fields output is recommended

Obesity Modifies the Performance of Fibrosis Biomarkers in Nonalcoholic Fatty Liver Disease

Context: Guidelines recommend blood-based fibrosis biomarkers to identify advanced nonalcoholic fatty liver disease (NAFLD), which is particularly prevalent in patients with obesity. Objective: To study whether the degree of obesity affects the performance of liver fibrosis biomarkers in NAFLD. Design: Cross-sectional cohort study comparing simple fibrosis scores [Fibrosis-4 Index (FIB-4); NAFLD Fibrosis Score (NFS); aspartate aminotransferase to platelet ratio index; BARD (body mass index, aspartate-to-alanine aminotransferase ratio, diabetes); Hepamet Fibrosis Score (HFS)] and newer scores incorporating neo-epitope biomarkers PRO-C3 (ADAPT, FIBC3) or cytokeratin 18 (MACK-3). Setting: Tertiary referral center. Patients: We recruited overweight/obese patients from endocrinology (n = 307) and hepatology (n = 71) clinics undergoing a liver biopsy [median body mass index (BMI) 40.3 (interquartile range 36.0-44.7) kg/m(2)]. Additionally, we studied 859 less obese patients with biopsy-proven NAFLD to derive BMI-adjusted cutoffs for NFS. Main Outcome Measures: Biomarker area under the receiver operating characteristic (AUROC), sensitivity, specificity, and predictive values to identify histological stage >= F3 fibrosis or nonalcoholic steatohepatitis with >= F2 fibrosis [fibrotic nonalcoholic steatohepatitis (NASH)]. Results: The scores with an AUROC >= 0.85 to identify >= F3 fibrosis were ADAPT, FIB-4, FIBC3, and HFS. For fibrotic NASH, the best predictors were MACK-3 and ADAPT. The specificities of NFS, BARD, and FIBC3 deteriorated as a function of BMI. We derived and validated new cutoffs for NFS to rule in/out >= F3 fibrosis in groups with BM Is = 40.0 kg/m(2). This optimized its performance at all levels of BMI. Sequentially combining FIB-4 with ADAPT or FIBC3 increased specificity to diagnose >= F3 fibrosis. Conclusions: In obese patients, the best-performing fibrosis biomarkers are ADAPT and the inexpensive FIB-4, which are unaffected by BMI. The widely used NFS loses specificity in obese individuals, which may be corrected with BMI-adjusted cutoffs.Peer reviewe

Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BackgroundHistologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.MethodsThis was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0–4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0–2 vs F3 vs F4; LSM: 2·67; NFS: 0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.FindingsOf 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44–63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33–91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62–0·81) for histology, 0·76 (0·70–0·83) for LSM-VCTE, 0·74 (0·64–0·82) for FIB-4, and 0·70 (0·63–0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.InterpretationSimple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases

Distriktssköterskans möjligheter och begränsningar att arbeta hälsofrämjande i primärvården : En systematisk litteraturstudie

Introduktion: Som distriktssköterska ska man främja och arbeta för en jämlik hälsa. I primärvården har distriktssköterskan många arbetsuppgifter och det gäller att finna balans mellan hälsofrämjande och medicinska uppgifter. Syfte: Syftet var att belysa möjligheter och begränsningar för distriktssköterskan i det hälsofrämjande arbetet i primärvården. Metod: Metoden var en systematisk litteraturstudie. Studien har baserats på tolv kvalitativa artiklar. Databaserna Cinahl och PubMed användes vid sökning. Artiklarna bearbetades enligt kvalitativ innehållsanalys. Resultat: Resultatet mynnade ut i tre teman: Första temat var Organisatoriska förutsättningar med kategorierna Brist på resurser, Brist på stöd och Riktlinjer &amp; verktyg. Andra temat var Personcentrerad vård med kategorierna Relationsskapande, Kulturella aspekter och Patientens attityd. Tredje temat var Sjuksköterskans syn på sin roll med kategorierna Känna ansvar, Hålla sig uppdaterad, Tillfredsställelse och Sjuksköterskans attityd. Konklusion: Resultatet visade att sjuksköterskorna kände ett stort ansvar och vilja att arbeta hälsofrämjande. De främsta begränsande faktorerna var hög arbetsbelastning och tidsbrist. Sjuksköterskorna behövde stöd från ledningen för att bibehålla motivationen att ta sig an de faktorer som skapade barriärer i det hälsofrämjande arbetet

Kulturstyrning i en mekanistisk organisation : En fallstudie på Gröna Lund

Syftet med denna studie var att undersöka hur kulturstyrning yttrar sig i en mekanistisk organisation samt att ge en djupare förståelse kring hur det används och hur den eventuella kulturstyrningen uppfattas av anställda inom organisationen. Resultatet utgörs av data från kvalitativa intervjuer samt granskning av interna dokument som satts i förhållande till definitionen att kultur blir ett styrmedel först när det används för att kontrollera beteenden. För att utföra studien samt tolka resultatet användes en teorimodell vars huvudfokus låg vid värde-, symbol- och klanbaserad styrning. Resultatet av studien visade att trots viss bristande kommunikation från ledningen till de längst ner i organisationen så utgjorde organisationens värdegrund basen för regler och beteendemönster. Även om en del av respondenterna inte kände till värdegrunden och dess innehåll så förmedlades trots detta en tydlig bild av vad som förväntades av den anställde

Kulturstyrning i en mekanistisk organisation : En fallstudie på Gröna Lund

Syftet med denna studie var att undersöka hur kulturstyrning yttrar sig i en mekanistisk organisation samt att ge en djupare förståelse kring hur det används och hur den eventuella kulturstyrningen uppfattas av anställda inom organisationen. Resultatet utgörs av data från kvalitativa intervjuer samt granskning av interna dokument som satts i förhållande till definitionen att kultur blir ett styrmedel först när det används för att kontrollera beteenden. För att utföra studien samt tolka resultatet användes en teorimodell vars huvudfokus låg vid värde-, symbol- och klanbaserad styrning. Resultatet av studien visade att trots viss bristande kommunikation från ledningen till de längst ner i organisationen så utgjorde organisationens värdegrund basen för regler och beteendemönster. Även om en del av respondenterna inte kände till värdegrunden och dess innehåll så förmedlades trots detta en tydlig bild av vad som förväntades av den anställde

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis