| 1. |
- Frisk, Henrik, et al.
(author)
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Acts of Creation : Introduction
- 2015
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In: Acts of Creation : Thoughts on artistic research supervision - Thoughts on artistic research supervision. - 9789187483165 ; , s. 7-18
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Book chapter (other academic/artistic)
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| 2. |
- Austeng, Dordi, et al.
(author)
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Incidence of and risk factors for neonatal morbidity after active perinatal care : extremely preterm infants study in Sweden (EXPRESS)
- 2010
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In: Acta Paediatrica. - : Wiley. - 0803-5253 .- 1651-2227. ; 99:7, s. 978-992
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Journal article (peer-reviewed)abstract
- Aims: The aim of this study was to determine the incidence of neonatal morbidity in extremely preterm infants and to identify associated risk factors. Methods: Population based study of infants born before 27 gestational weeks and admitted for neonatal intensive care in Sweden during 2004-2007. Results: Of 638 admitted infants, 141 died. Among these, life support was withdrawn in 55 infants because of anticipation of poor long-term outcome. Of 497 surviving infants, 10% developed severe intraventricular haemorrhage (IVH), 5.7% cystic periventricular leucomalacia (cPVL), 41% septicaemia and 5.8% necrotizing enterocolitis (NEC); 61% had patent ductus arteriosus (PDA) and 34% developed retinopathy of prematurity (ROP) stage >= 3. Eighty-five per cent needed mechanical ventilation and 25% developed severe bronchopulmonary dysplasia (BPD). Forty-seven per cent survived to one year of age without any severe IVH, cPVL, severe ROP, severe BPD or NEC. Tocolysis increased and prolonged mechanical ventilation decreased the chances of survival without these morbidities. Maternal smoking and higher gestational duration were associated with lower risk of severe ROP, whereas PDA and poor growth increased this risk. Conclusion: Half of the infants surviving extremely preterm birth suffered from severe neonatal morbidities. Studies on how to reduce these morbidities and on the long-term health of survivors are warranted.
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| 3. |
- Backman, Max, et al.
(author)
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Infiltration of NK and plasma cells is associated with a distinct immune subset in non‐small cell lung cancer
- 2021
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In: Journal of Pathology. - : John Wiley & Sons. - 0022-3417 .- 1096-9896. ; 255:3, s. 243-256
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Journal article (peer-reviewed)abstract
- Immune cells of the tumor microenvironment are central but erratic targets for immunotherapy. The aim of this study was to characterize novel patterns of immune cell infiltration in non-small cell lung cancer (NSCLC) in relation to its molecular and clinicopathologic characteristics. Lymphocytes (CD3+, CD4+, CD8+, CD20+, FOXP3+, CD45RO+), macrophages (CD163+), plasma cells (CD138+), NK cells (NKp46+), PD1+, and PD-L1+ were annotated on a tissue microarray including 357 NSCLC cases. Somatic mutations were analyzed by targeted sequencing for 82 genes and a tumor mutational load score was estimated. Transcriptomic immune patterns were established in 197 patients based on RNA sequencing data. The immune cell infiltration was variable and showed only poor association with specific mutations. The previously defined immune phenotypic patterns, desert, inflamed, and immune excluded, comprised 30, 13, and 57% of cases, respectively. Notably, mRNA immune activation and high estimated tumor mutational load were unique only for the inflamed pattern. However, in the unsupervised cluster analysis, including all immune cell markers, these conceptual patterns were only weakly reproduced. Instead, four immune classes were identified: (1) high immune cell infiltration, (2) high immune cell infiltration with abundance of CD20+ B cells, (3) low immune cell infiltration, and (4) a phenotype with an imprint of plasma cells and NK cells. This latter class was linked to better survival despite exhibiting low expression of immune response-related genes (e.g. CXCL9, GZMB, INFG, CTLA4). This compartment-specific immune cell analysis in the context of the molecular and clinical background of NSCLC reveals two previously unrecognized immune classes. A refined immune classification, including traits of the humoral and innate immune response, is important to define the immunogenic potency of NSCLC in the era of immunotherapy. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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| 4. |
- Backman, Max, 1987-, et al.
(author)
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Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer
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Other publication (other academic/artistic)abstract
- Introduction: Immune cells in the tumor microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterize the spatial immune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC).Methods: We established a multiplexed fluorescence multispectral imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4 Eff), CD4 regulatory cells (CD4 Treg), CD8 effector cells (CD8 Eff), CD8 regulatory cells (CD8 Treg), B-cells, NK-cells, NKT-cells, M1 macrophages (M1), CD163+ myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs). Results: CD4 Eff cells, CD8 Eff cells, and M1 macrophages were the most abundant immune cells invading the tumor cell compartment and indicated a patient group with a favorable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4 Eff, CD4 Treg, CD8 Treg, and B-cells), as well as pDCs, were independently associated with longer survival. However, when these immune cells were located close to CD8 Treg cells, the favorable impact was attenuated. In the multivariate Cox regression model including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8 Treg–B-cells, CD8 Eff–cancer cells, and B-cells–CD4 Treg) demonstrated positive prognostic impact, while short M2–M1 distances were prognostically unfavorable.Conclusion: We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is also crucial for diagnostic use.
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| 5. |
- Backman, Max, 1987-, et al.
(author)
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Spatial immunophenotyping of the tumour microenvironment in non-small cell lung cancer
- 2023
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In: European Journal of Cancer. - : Elsevier. - 0959-8049 .- 1879-0852. ; 185, s. 40-52
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Journal article (peer-reviewed)abstract
- Introduction: Immune cells in the tumour microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterise the spatial im-mune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC).Methods: We established a multiplexed fluorescence imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4-Eff), CD4 regulatory cells (CD4-Treg), CD8 effector cells (CD8-Eff), CD8 regulatory cells (CD8-Treg), B-cells, natural killer cells, natural killer T-cells, M1 macrophages (M1), CD163 thorn myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs).Results: CD4-Eff cells, CD8-Eff cells and M1 macrophages were the most abundant immune cells invading the tumour cell compartment and indicated a patient group with a favourable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4-Eff, CD4-Treg, CD8-Treg, B-cells and pDCs) were independently associated with longer survival. However, when these immune cells were located close to CD8-Treg cells, the favourable impact was attenuated. In the multivariable Cox regression model, including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8-Treg-B-cells, CD8-Eff-cancer cells and B-cells-CD4-Treg) demonstrated positive prognostic impact, whereas short M2-M1 distances were prognostically unfavourable.Conclusion: We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is crucial for diagnostic use.
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| 6. |
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| 7. |
- Dijken, Jan W.V. van, 1947-, et al.
(author)
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Samarbete breddar forskning : Oral Biomaterialgruppen, Umeå
- 2008
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In: Tandläkartidningen. - : Sveriges Tandläkarförbund. ; 100:5, s. 74-79
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Journal article (pop. science, debate, etc.)abstract
- Vid institutionen för odontologi vid Umeå Universitet finns en lång tradition av biomaterialforskning. För drygt två år sedan samlades större delen av den forskningen i ett vetenskapligt nätverk. Här beskrivs ett axplock av det breda forskningsarbetet.
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| 8. |
- Enroth, Stefan, 1976-, et al.
(author)
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Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment
- 2022
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In: Communications Medicine. - : Springer Nature. - 2730-664X. ; 2:1
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Journal article (peer-reviewed)abstract
- BackgroundOvarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30–50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.MethodsProtein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher’s Exact tests on achieved sensitivity and specificity.ResultsThe assay’s performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.ConclusionThe robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.
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| 9. |
- Fellman, Vineta, et al.
(author)
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One-year survival of extremely preterm infants after active perinatal care in Sweden.
- 2009
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In: JAMA : the journal of the American Medical Association. - : American Medical Association (AMA). - 1538-3598 .- 0098-7484. ; 301:21, s. 2225-33
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Journal article (peer-reviewed)abstract
- Up-to-date information on infant survival after extremely preterm birth is needed for assessing perinatal care services, clinical guidelines, and parental counseling.
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| 10. |
- Gyllensten, Ulf B., et al.
(author)
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Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer
- 2022
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In: Cancers. - : MDPI AG. - 2072-6694. ; 14:7
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Journal article (peer-reviewed)abstract
- Simple Summary Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4-7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort. Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.
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