Liver Regeneration in Surgical Animal Models - A Historical Perspective and Clinical Implications

Methods/Aims: Despite improved preoperative evaluation, surgical techniques and perioperative intensive care, some patients still experience postoperative liver failure in part due to insufficient regeneration. The aim of this review is to give the reader a historical synopsis of the major trends in animal research on liver regeneration from the early experiments in 1877 up to modern investigation. A major focus is placed on the translational value of experimental surgery. Methods: A systematic review of the English literature published in Medline was undertaken with the search words ‘pig, porcine, dog, canine, liver regeneration, experimental’. Results: The evolution of the various models tentatively explaining the process of liver regeneration is described. Conclusions: We conclude by emphasizing the importance of large-animal surgical research on liver regeneration as it offers a more integrated, systemic biological understanding of this complex process. Furthermore, in our opinion, a closer collaboration between the hepatologist, liver surgeon/transplant surgeon and the laboratory scientist may advance clinically relevant research in liver regeneration

Tourism industry participation in national park management : a study of visitor management strategies for national parks in Southern Norway

Nasjonalparkene er viktige turistattraksjoner og har et stort antall besøkende hvert år. Videre er det et ønske om økt bruk av nasjonalparkene i reiselivssammenheng. For å unngå at dette fører til en forringelse av natur- og opplevelsesverdi har det blitt et økende behov for besøksforvaltning i nasjonalparkene. I 2016 ble besøksstrategier innført som verktøy i besøksforvaltningen av nasjonalparker og andre store verneområder. Besøksstrategiene er ment som planer for hvordan forvaltningen vil gjennomføre besøksforvaltningen, og utarbeides på grunnlag av kunnskap om verneområdet med samarbeid fra den lokale reiselivsnæringen. Denne oppgaven viser resultatene fra en undersøkelse av hvordan reiselivet medvirker til besøksstrategiene for nasjonalparker i Sør-Norge, og hvilke erfaringer reiselivet og nasjonalparkforvaltningen har fra denne medvirkningen. Første del av undersøkelsen ble gjort som en dokumentanalyse av de femten besøksstrategiene som hadde blitt godkjente og vedtatte innen slutten av juni 2021. Andre del bestod av en casestudie av nasjonalparkene Færder og Reinheimen hvor det ble gjennomført åtte dybdeintervjuer med informanter fra nasjonalparkforvaltningene og reiselivsnæringene. Undersøkelsen viser at den formelle reiselivsmedvirkningen foregår gjennom representasjon i de rådgivende utvalgene for nasjonalparkene. Det stilles samtidig spørsmålstegn ved om denne representasjonen favner om reiselivsinteressene godt nok, og om utvalgene har reell innvirkning på utformingen av besøksstrategiene. I tillegg har det blitt avholdt møter utenom de rådgivende utvalgene hvor reiselivsaktører har vært invitert, av både formell og uformell karakter. Av disse har noen vært rettet mot medvirkning i besøksstrategiarbeidet. De undersøkte besøksstrategiene viser stor variasjon i hvordan medvirkning har foregått. Diskusjonen knytter denne variasjonen til kontekstavhengige forskjeller mellom nasjonalparkene. Hos noen fremgår det ingenting om medvirkning, og hos andre presenteres opptil flere medvirkningsformer. Likevel innebærer ingen av medvirkningsformene en reell overføring av makt, dermed kan det settes spørsmåltegn ved om det foregår ekte medvirkning. Dette er den første undersøkelsen av reiselivsmedvirkning i besøksstrategiarbeidet for nasjonalparkene. Basert på resultatene fra denne undersøkelsen pekes det derfor på implikasjoner for besøksforvaltningen i verneområdene og behovet for nye undersøkelser for å øke forståelsen av fenomenet.National parks are important tourist attractions and receive numerous visitors annually. At the same time there is an increased interest in the use of national parks for tourism purposes. To avoid this leading to deterioration of natural and recreational values there is an increased need for visitor management in the national parks. In 2016 the visitor management strategies were introduced as visitor management instruments for national parks and other large, protected areas. The visitor management strategies show how the management authority plan to carry out the visitor management, they are developed from knowledge about the protected area through collaboration with local tourism businesses. This thesis report on the results from a study of how the tourism industry participate in development of the visitor management strategies of national parks in Southern Norway and the experiences linked to this participation process. A document analysis of the visitor management strategies of national parks in Southern Norway that were approved and passed before the end of June 2021, form the first part of the study. The second part is made up of a case study of Færder and Reinheimen national parks where eight in depth interviews were conducted with informants from the national park management and the tourism industry. The study shows formal participation from the tourism industry through representation in the advisory boards for the national parks. However, the actual representation of the tourism businesses and the de facto influence from the advisory boards is questioned. In addition to this, other meetings have been organized where tourism businesses have been invited, of both formal and informal character. Some of these have been directed towards participation in development of the visitor management strategies. The analysed visitor management strategies show great variety in how and whether participation have taken place. The discussion connects this variation with context related differences between the national parks. In some strategies there is no sign of participation, and in others there are different participation methods described. Despite this, none of the participation methods described involve distribution of power, thus it is questioned whether true participation is taking place. This is the first study of tourism industry participation in the visitor management strategy development for national parks. Based on the results of this study implications for the visitor management in protected areas is pointed out, together with a need for new studies to increase the knowledge about the phenomenon.M-REI

Tiltro og forventning som endringsfaktorer i CBT : - En kvantiativ undersøkelse av 21 barn og unge med OCD

Problemstilling og formål: Forventning og tiltro til behandling har lenge vært ansett som betydningsfulle faktorer for vellykket resultat. Kanskje er det visse kjennetegn ved kognitiv atferdsterapi (CBT) for tvangslidelse (OCD) som gjør tiltro til en spesielt relevant endringsfaktor når det gjelder denne behandlingsformen. Med et overordnet formål om å belyse hvilke forutsetninger CBT for barn og unge med OCD fungerer optimalt under, ønskes det med denne studien å undersøke om økt tiltro til behandling fører til bedre utfall. Tiltro til behandling er interessant både fra foreldrenes, terapeutens og barnets eget perspektiv. Klientens handlinger i tråd med behandlingsprinsippene (compliance) ansees som en annen viktig endringsfaktor. Det er derfor i tillegg et formål med denne studien å undersøke om en eventuell effekt av tiltro er avhengig av compliance. Metode: Deltagerne bestod av 21 barn og unge mellom 8 og 18 år, behandlet med 14 timers CBT med E/RP. Tiltro og compliance ble målt etter 2., 7. og 13. time, både hos barnet, foreldrene og terapeuten. Endringer i tvangssymptomer ble målt med CY-BOCS før, midtveis og mot slutten av behandlingen. Endringene ble analysert ved hjelp av korrelasjonelle analyser og ANOVA repeterte målingers design. Resultater: Til støtte for hypotese I ble det funnet at barnets tiltro var forbundet med senere symptomendring, både når den ble målt tidlig og midtveis i behandlingen. Imidlertid var det bare sistnevnte som kunne forklare senere endring på en forventet måte. Sammenhengene viste seg, i motsetning til hva som ble forventet, ikke å medieres av compliance. Foreldrenes tiltro viste kun svake sammenhenger med tiltro og compliance. Terapeutens tiltro så først og fremst ut til å relatere seg til symptomendring på en retrospektiv måte. Konklusjon: Resultatene kan tyde på at det er hensiktsmessig å øke barnets tiltro til behandling. Muligens forutsettes dette av en økt forståelse av rasjonalen. Selv om mye tyder på at tiltro i seg selv kan ha en selvoppfyllende virkning, ble det også gitt indikasjoner på at det å fremme barnets involvering og samarbeid tidlig i behandlingen bør være en prioritet i CBT for OCD

Scavenger properties of cultivated pig liver endothelial cells

BACKGROUND: The liver sinusoidal endothelial cells (LSEC) and Kupffer cells constitute the most powerful scavenger system in the body. Various waste macromolecules, continuously released from tissues in large quantities as a consequence of normal catabolic processes are cleared by the LSEC. In spite of the fact that pig livers are used in a wide range of experimental settings, the scavenger properties of pig LSEC has not been investigated until now. Therefore, we studied the endocytosis and intracellular transport of ligands for the five categories of endocytic receptors in LSEC. RESULTS: Endocytosis of five (125)I-labelled molecules: collagen α-chains, FITC-biotin-hyaluronan, mannan, formaldehyde-treated serum albumin (FSA), and aggregated gamma globulin (AGG) was substantial in cultured LSEC. The endocytosis was mediated via the collagen-, hyaluronan-, mannose-, scavenger-, or IgG Fc-receptors, respectively, as judged by the ability of unlabelled ligands to compete with labelled ligands for uptake. Intracellular transport was studied employing a morphological pulse-chase technique. Ninety minutes following administration of red TRITC-FSA via the jugular vein of pigs to tag LSEC lysosomes, cultures of the cells were established, and pulsed with green FITC-labelled collagen, -mannan, and -FSA. By 10 min, the FITC-ligands was located in small vesicles scattered throughout the cytoplasm, with no co-localization with the red lysosomes. By 2 h, the FITC-ligands co-localized with red lysosomes. When LSEC were pulsed with FITC-AGG and TRITC-FSA together, co-localization of the two ligands was observed following a 10 min chase. By 2 h, only partial co-localization was observed; TRITC-FSA was transported to lysosomes, whereas FITC-AGG only slowly left the endosomes. Enzyme assays showed that LSEC and Kupffer cells contained equal specific activities of hexosaminidase, aryl sulphates, acid phosphatase and acid lipase, whereas the specific activities of α-mannosidase, and glucuronidase were higher in LSEC. All enzymes measured showed considerably higher specific activities in LSEC compared to parenchymal cells. CONCLUSION: Pig LSEC express the five following categories of high capacity endocytic receptors: scavenger-, mannose-, hyaluronan-, collagen-, and IgG Fc-receptors. In the liver, soluble ligands for these five receptors are endocytosed exclusively by LSEC. Furthermore, LSEC contains high specific activity of lysosomal enzymes needed for degradation of endocytosed material. Our observations suggest that pig LSEC have the same clearance activity as earlier described in rat LSEC

Antenatal breastmilk expression for women with diabetes in pregnancy - a feasibility study

Background: Mothers with diabetes are less likely to achieve successful breastfeeding. Antenatal breastmilk expression (ABE) may facilitate earlier breastfeeding, but feasibility of introducing ABE and its acceptance among Scandinavian women have previously not been investigated. Methods: This observational trial was conducted between the 1 January 2019 and the 12 March 2020 in Tromsø, Norway. We aimed to determine the feasibility of ABE in terms of practicality and acceptability among women with medically (metformin or insulin) treated diabetes. Women were invited to participate during antenatal visits from 32 weeks gestation. Participants received instruction and started ABE from gestation week 37 + 0. Participants, and their infants, were followed until 6–8 weeks after birth. We collected data on breastfeeding rates, infant hypoglycemia, transfer to the neonatal unit, and the women’s overall experience and satisfaction with antenatal breastmilk expression. Results: Twenty-eight of 34 (82%) invited women consented to participate. All started ABE from week 37 + 0, and continued until hospital admission. No women reported any discomfort or side effects. Labor was induced at 38 weeks gestation. Twenty-four women brought harvested colostrum to the maternity ward, which was given to their infants during the first 24 h of life. Breastfeeding rates at discharge were 24/28 (86%) and 21/27 (78%) at 6–8 weeks after delivery. Seven (25%) infants were transferred to the neonatal unit; four because of hypoglycemia. Maternal satisfaction assessed 6–8 weeks after delivery revealed that all participants felt positive about the ABE, but one woman would not recommend it to other pregnant women. Conclusions: Implementing a structured ABE guideline for women with medically treated diabetes was feasible. The intervention was associated with high level of satisfaction among study participants. No obvious side effects were observed, and breastfeeding rates at discharge and 6–8 weeks after delivery were higher than in comparable studies. Trial registration: The study was registered at the research study registry at the University Hospital of North Norway (Nr 2018/7181)

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is critical for clinical tasks. However, existing explainability techniques lack an important component for trustworthy and reliable decision support, namely a notion of uncertainty. In this paper, we address this lack of uncertainty by proposing a deep ensemble approach where a collection of DNNs are trained independently. A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable. The class activation mapping method is used to assign a relevance score for each time step in the time series. Results demonstrate that the proposed ensemble is more accurate in locating relevant time steps and is more consistent across random initializations, thus making the model more trustworthy. The proposed methodology paves the way for constructing trustworthy and dependable support systems for processing clinical time series for healthcare related tasks.Comment: 11 pages, 9 figures, code at https://github.com/Wickstrom/TimeSeriesXA

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping

Time series cluster kernels to exploit informative missingness and incomplete label information

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, making it particularly well suited for unsupervised learning. However, TCK assumes missing at random and that the underlying missingness mechanism is ignorable, i.e. uninformative, an assumption that does not hold in many real-world applications, such as e.g. medicine. To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data. In our approach, we create a representation of the missing pattern, which is incorporated into mixed mode mixture models in such a way that the information provided by the missing patterns is effectively exploited. Moreover, we also propose a semi-supervised kernel, capable of taking advantage of incomplete label information to learn more accurate similarities. Experiments on benchmark data, as well as a real-world case study of patients described by longitudinal electronic health record data who potentially suffer from hospital-acquired infections, demonstrate the effectiveness of the proposed method