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  • 1.
    Andersson-Evelönn, Emma
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Vidman, Linda
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Källberg, David
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Landfors, Mattias
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Ljungberg, Börje
    Umeå universitet, Medicinska fakulteten, Institutionen för kirurgisk och perioperativ vetenskap, Urologi och andrologi.
    Hultdin, Magnus
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Degerman, Sofie
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi. Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Rydén, Patrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Combining epigenetic and clinicopathological variables improves prognostic prediction in clear cell Renal Cell CarcinomaManuskript (preprint) (Övrigt vetenskapligt)
  • 2.
    Andersson-Evelönn, Emma
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Vidman, Linda
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Källberg, David
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Landfors, Mattias
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Ljungberg, Börje
    Umeå universitet, Medicinska fakulteten, Institutionen för kirurgisk och perioperativ vetenskap, Urologi och andrologi.
    Hultdin, Magnus
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Rydén, Patrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Degerman, Sofie
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi. Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma2020Ingår i: Journal of Translational Medicine, ISSN 1479-5876, E-ISSN 1479-5876, Vol. 18, nr 1, artikel-id 435Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.

    Methods: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.

    Results: The “triple classifier” which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (pCIP5yr) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.

    Conclusions: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.

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  • 3.
    Bayisa, Fekadu L.
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Garpebring, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Computed Tomography Image Estimation by Statistical Learning MethodsManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images canbe utilised for attenuation correction, patient positioning, and dose planningin diagnostic and radiotherapy workflows. This study presents a statisticallearning method for CT image estimation. We have used predefined tissuetype information in a Gaussian mixture model to explore the estimation.The performance of our method was evaluated using cross-validation on realdata. In comparison with the existing model-based CT image estimationmethods, the proposed method has improved the estimation, particularly inbone tissues. Evaluation of our method shows that it is a promising methodto generate CT image substitutes for the implementation of fully MR-basedradiotherapy and PET/MRI applications.

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  • 4.
    Bayisa, Fekadu
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Garpebring, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Statistical learning in computed tomography image estimation2018Ingår i: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, nr 12, s. 5450-5460Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance of our method with the existing model-based CT image estimation methods.

    Methods: The statistical learning approach proposed here consists of two stages. At the trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.

    Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 5% and 23% on the whole brain and bone tissues, respectively.

    Conclusions: Evaluation of our method shows that it is a promising method to generate CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications

  • 5.
    Ecker, Kreske
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Liu, Xijia
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Schelin, Lina
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    de Luna, Xavier
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Double robust estimation of functional outcomes with data missing at randomManuskript (preprint) (Övrigt vetenskapligt)
  • 6.
    Estévez-Silva, Héctor M.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för integrativ medicinsk biologi (IMB). Departamento de Ciencias Médicas Básicas, Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, Santa Cruz de Tenerife, Spain.
    Mediavilla, Tomás
    Umeå universitet, Medicinska fakulteten, Institutionen för integrativ medicinsk biologi (IMB).
    Giacobbo, Bruno Lima
    Umeå universitet, Medicinska fakulteten, Institutionen för integrativ medicinsk biologi (IMB).
    Liu, Xijia
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet.
    Sultan, Fahad R.
    Umeå universitet, Medicinska fakulteten, Institutionen för integrativ medicinsk biologi (IMB).
    Marcellino, Daniel
    Umeå universitet, Medicinska fakulteten, Institutionen för integrativ medicinsk biologi (IMB).
    Pridopidine modifies disease phenotype in a SOD1 mouse model of amyotrophic lateral sclerosis2022Ingår i: European Journal of Neuroscience, ISSN 0953-816X, E-ISSN 1460-9568, Vol. 55, nr 5, s. 1356-1372Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Amyotrophic lateral sclerosis (ALS) is a lethal and incurable neurodegenerative disease due to the loss of upper and lower motor neurons, which leads to muscle weakness, atrophy, and paralysis. Sigma-1 receptor (σ-1R) is a ligand-operated protein that exhibits pro-survival and anti-apoptotic properties. In addition, mutations in its codifying gene are linked to development of juvenile ALS pointing to an important role in ALS. Here, we investigated the disease-modifying effects of pridopidine, a σ-1R agonist, using a delayed onset SOD1 G93A mouse model of ALS. Mice were administered a continuous release of pridopidine (3.0 mg/kg/day) for 4 weeks starting before the appearance of any sign of muscle weakness. Mice were monitored weekly and several behavioural tests were used to evaluate muscle strength, motor coordination and gait patterns. Pridopidine-treated SOD1 G93A mice showed genotype-specific effects with the prevention of cachexia. In addition, these effects exhibited significant improvement of motor behaviour 5 weeks after treatment ended. However, the survival of the animals was not extended. In summary, these results show that pridopidine can modify the disease phenotype of ALS-associated cachexia and motor deficits in a SOD1 G93A mouse model.

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  • 7.
    Furberg, Maria
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Epidemiologi och global hälsa.
    Xijia, Liu
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Ahlm, Clas
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Infektionssjukdomar.
    Nystedt, Anders
    Stenmark, Stephan
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Elisasson, Mats
    Sellin, Mats
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Klinisk bakteriologi.
    Johansson, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Infektionssjukdomar.
    Tularemia in northern Sweden: sero-prevalence and a case-control study of risk factorsManuskript (preprint) (Övrigt vetenskapligt)
  • 8.
    Harbs, Justin
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Rinaldi, Sabina
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Gicquiau, Audrey
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Keski-Rahkonen, Pekka
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Mori, Nagisa
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Liu, Xijia
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Kaaks, Rudolf
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Katzke, Verena
    Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    Schulze, Matthias B.
    Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Potsdam, Germany.
    Agnoli, Claudia
    Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.
    Tumino, Rosario
    Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), Ragusa, Italy.
    Bueno-De-Mesquita, Bas
    Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
    Crous-Bou, Marta
    Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain; Department of Epidemiology, Harvard T.H. Chan School of Public Health, MA, Boston, United States.
    Sánchez, Maria-Jose
    Escuela Andaluza de Salud Pública (EASP), Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain.
    Aizpurua, Amaia
    Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain.
    Chirlaque, María-Dolores
    Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain.
    Gurrea, Aurelio Barricarte
    Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Navarra Public Health Institute, Pamplona, Spain; Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.
    Travis, Ruth C.
    Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
    Watts, Eleanor L.
    Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
    Christakoudi, Sofia
    Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Transplantation, Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom.
    Tsilidis, Konstantinos K.
    Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Hygiene and Epidemiology, Faculty of Medicine, University of Ioannina School of Medicine, Ioannina, Greece.
    Weiderpass, Elisabete
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Gunter, Marc J.
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    van Guelpen, Bethany
    Umeå universitet, Medicinska fakulteten, Wallenberg centrum för molekylär medicin vid Umeå universitet (WCMM). Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Murphy, Neil
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Harlid, Sophia
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Circulating Sex Hormone Levels and Colon Cancer Risk in Men: A Nested Case–Control Study and Meta-Analysis2022Ingår i: Cancer Epidemiology, Biomarkers and Prevention, ISSN 1055-9965, E-ISSN 1538-7755, Vol. 31, nr 4, s. 793-803Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Endogenous sex hormones may contribute to higher colorectal cancer incidence rates in men compared with women, but despite an increased number of studies, clear evidence is lacking.

    Methods: We conducted a comprehensive nested case–control study of circulating concentrations of sex hormones, sex hormone precursors, and sex hormone binding globulin (SHBG) in relation to subsequent colon cancer risk in European men. Concentrations were measured using liquid LC/MS-MS in prospectively collected plasma samples from 690 cases and 690 matched controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS) cohorts. Multivariable conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). In addition, we conducted a meta-analysis of previous studies on men.

    Results: Circulating levels of testosterone (OR, 0.68; 95% CI, 0.51–0.89) and SHBG (OR, 0.77; 95% CI, 0.62–0.96) were inversely associated with colon cancer risk. For free testosterone, there was a nonsignificant inverse association (OR, 0.83; 95% CI, 0.58–1.18). In a dose–response meta-analysis of endogenous sex hormone levels, inverse associations with colorectal/colon cancer risk were found for testosterone [relative risks (RR) per 100 ng/dL ¼ 0.98; 95% CI, 0.96–1.00; I2 ¼ 22%] and free testosterone (RR per 1 ng/dL ¼ 0.98; 95% CI, 0.95–1.00; I2 ¼ 0%).

    Conclusions: Our results provide suggestive evidence for the association between testosterone, SHBG, and male colon cancer development.

    Impact: Additional support for the involvement of sex hormones in male colon cancer.

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  • 9.
    Harbs, Justin
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Rinaldi, Sabina
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Keski-Rahkonen, Pekka
    International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Liu, Xijia
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Palmqvist, Richard
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    van Guelpen, Bethany
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi. Umeå universitet, Medicinska fakulteten, Wallenberg centrum för molekylär medicin vid Umeå universitet (WCMM).
    Harlid, Sophia
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    An epigenome-wide analysis of sex hormone levels and DNA methylation in male blood samples2023Ingår i: Epigenetics, ISSN 1559-2294, E-ISSN 1559-2308, Vol. 18, nr 1, artikel-id 2196759Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Endogenous sex hormones and DNA methylation both play important roles in various diseases. However, their interplay is largely unknown. A deeper understanding of their interrelationships could provide new insights into the pathology of disease development. We, therefore, investigated associations between circulating sex hormones, sex hormone binding globulin (SHBG), and DNA methylation in blood, using samples from 77 men (65 with repeated samples), from the population-based Northern Sweden Health and Disease Study (NSHDS). DNA methylation was measured in buffy coat using the Infinium Methylation EPIC BeadChip (Illumina). Sex hormone (oestradiol, oestrone, testosterone, androstenedione, dehydroepiandrosterone, and progesterone) and SHBG concentrations were measured in plasma using a high-performance liquid chromatography tandem mass spectrometry (LC/MS-MS) method and an enzyme-linked immunoassay, respectively. Associations between sex hormones, SHBG, and DNA methylation were estimated using both linear regression and mixed-effects models. Additionally, we used the comb-p method to identify differentially methylated regions based on nearby P values. We identified one novel CpG site (cg14319657), at which DNA methylation was associated with dehydroepiandrosterone, surpassing a genome-wide significance level. In addition, more than 40 differentially methylated regions were associated with levels of sex hormones and SHBG and several of these mapped to genes involved in hormone-related diseases. Our findings support a relationship between circulating sex hormones and DNA methylation and suggest that further investigation is warranted, both for validation, further exploration and to gain a deeper understanding of the mechanisms and potential consequences for health and disease.

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  • 10.
    Harlid, Sophia
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Harbs, Justin
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Myte, Robin
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Brunius, Carl
    Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, Gothenburg, Sweden.
    Gunter, Marc J.
    Section of Nutrition and Metabolism, International Agency for Research On Cancer, World Health Organization, Lyon, France.
    Palmqvist, Richard
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Patologi.
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    van Guelpen, Bethany
    Umeå universitet, Medicinska fakulteten, Wallenberg centrum för molekylär medicin vid Umeå universitet (WCMM). Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    A two-tiered targeted proteomics approach to identify pre-diagnostic biomarkers of colorectal cancer risk2021Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 11, nr 1, artikel-id 5151Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Colorectal cancer prognosis is dependent on stage, and measures to improve early detection are urgently needed. Using prospectively collected plasma samples from the population-based Northern Sweden Health and Disease Study, we evaluated protein biomarkers in relation to colorectal cancer risk. Applying a two-tiered approach, we analyzed 160 proteins in matched sequential samples from 58 incident colorectal cancer case–control pairs. Twenty-one proteins selected from both this discovery phase and the literature were then analyzed in a validation set of 450 case–control pairs. Odds ratios were estimated by conditional logistic regression. LASSO regression and ROC analysis were used for multi-marker analyses. In the main validation analysis, no proteins retained statistical significance. However, exploratory subgroup analyses showed associations between FGF-21 and colon cancer risk (multivariable OR per 1 SD: 1.23 95% CI 1.03–1.47) as well as between PPY and rectal cancer risk (multivariable OR per 1 SD: 1.47 95% CI 1.12–1.92). Adding protein markers to basic risk predictive models increased performance modestly. Our results highlight the challenge of developing biomarkers that are effective in the asymptomatic, prediagnostic window of opportunity for early detection of colorectal cancer. Distinguishing between cancer subtypes may improve prediction accuracy. However, single biomarkers or small panels may not be sufficient for effective precision screening.

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  • 11.
    Liu, Xijia
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Nassar, Hiba
    Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
    Podgórski, Krzysztof
    Department of Statistics, Lund University, Sweden.
    Dyadic diagonalization of positive definite band matrices and efficient B-spline orthogonalization2022Ingår i: Journal of Computational and Applied Mathematics, ISSN 0377-0427, E-ISSN 1879-1778, Vol. 414, artikel-id 114444Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A dyadic algorithm for diagonalizing an arbitrary positive definite band matrix, referred to as a band Gramian, is obtained to efficiently orthogonalize the B-splines. The algorithm can be also used as a fast inversion method for a band Gramian characterized by remarkable sparsity of the diagonalizing matrix. There are two versions of the algorithm: the first one is more efficient and is applicable to a Toeplitz band Gramian while the second one is more general, works with any Gramian matrix, but is more computationally intensive. In the context of the B-splines, these two cases result in new symmetric orthogonalization procedures and correspond to equally and arbitrarily spaced knots, respectively. In the algorithm, the sparsity of a band Gramian is utilized to produce a natural dyadic net of orthogonal splines, rather than a sequence of them. Such a net is thus naturally referred to as a splinet. The splinets exploit “near-orthogonalization” of the B-splines and feature locality expressed through a small size of the total support set and computational efficiency that is a result of a small number of inner product evaluations needed for their construction. These and other efficiencies are formally quantified by upper bounds and asymptotic rates with respect to the number of splines in a splinet. An additional assessment is provided through numerical experiments. They suggest that the theoretical bounds are rather conservative and the method is even more efficient than the bounds indicate. The dyadic net-like structures and the locality bear some resemblance to wavelets but in fact, the splinets are fundamentally different because they do not aim at capturing the resolution scales. The orthogonalization method together with efficient spline algebra and calculus has been implemented in R-package Splinets available on CRAN.

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  • 12.
    Liu, Xijia
    et al.
    Department of Statistics, Uppsala University, Uppsala, Sweden.
    Preve, Daniel
    Measure of location-based estimators in simple linear regression2016Ingår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 86, nr 9, s. 1771-1784Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this note we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error is assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroskedastic and multiple regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study.

  • 13.
    Liu, Xijia
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Tabibzada, Nazanin
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Lindgren, Helena
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Sjöstedt, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Utility of Borrelia-specific IgM and IgG antibody titer determinations during a 12-year period: results from a clinical laboratory in Northern Sweden2023Ingår i: Frontiers in Cellular and Infection Microbiology, E-ISSN 2235-2988, Vol. 13, artikel-id 1192038Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Interpretation of serological findings in suspected Lyme borreliosis (LB) is challenging and IgM reactivities may have low predictive value. Therefore, if used indiscriminately, there is a risk for incorrect diagnosis of LB. To evaluate the usefulness of IgM titer determination, we performed a study of the prevalence of Borrelia-specific antibodies in serological samples from patients with suspected LB analyzed during the period 2010 - 2021 at the University Hospital of Umeå in Sweden. In total, 19,335 samples had been analyzed for the presence of IgG and IgM antibodies. Overall, there were higher percentages of IgM positive or borderline titers, 1,847 (9.6%) and 905 (4.7%), respectively, than IgG positive or borderline titers, 959 (5.0%) and 406 (2.1%), respectively. Peak number of samples were recorded 2012 - 2013, exceeding 1,800, whereas there were around 1,200 during 2020 - 2021. The peak number of positive IgG and/or positive IgM samples were observed during the period 2015 - 2017 with close to, or above 400, and concomitantly, the proportion of IgG positive samples increased markedly. For IgG positive samples, the increase followed a positive linear time trend (P< 0.001). Peak monthly numbers were observed during August, September, and October. This seasonal increase was significant for the IgG positive group (P< 0.05), but not for the IgM positive/IgG negative group. Repeated samples were obtained from 3,188 individuals and of the initial samples 2,817 were (88%) IgG negative and 2,315 (72%) were IgM negative and of these, 130 (4%) showed IgG seroconversion and 300 (9%) IgM seroconversion. Collectively, the data demonstrate that IgG and/or IgM positive samples represented a minority of all samples, even when repeated sampling had occurred, and IgM positive samples were much more common than IgG positive samples. Thus, the accuracy of the clinical suspicion was low and this will lead to a low predictive value of the analysis, in particular of IgM. These findings question the use of IgM titer determination as a routine analysis.

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  • 14.
    Meyer, Lena
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi. Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS).
    Bröms, Jeanette
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi. Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS).
    Liu, Xijia
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi. Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS).
    Rottenberg, M.E.
    Sjöstedt, Anders
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Klinisk bakteriologi.
    Microinjection of Francisella tularensis and Listeria monocytogenes reveals the importance of bacterial and host factors for successful replication2015Ingår i: Infection and Immunity, ISSN 0019-9567, E-ISSN 1098-5522, Vol. 83, nr 8, s. 3233-3242Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    Certain intracellular bacteria use the host cell cytosol as the replicative niche. Although it has been hypothesized that the successful exploitation of this compartment requires a unique metabolic adaptation, supportive evidence is lacking. For Francisella tularensis, many genes of the Francisella pathogenicity island (FPI) are essential for intracellular growth, and therefore, FPI mutants are useful tools for understanding the prerequisites of intracytosolic replication. We compared the growth of bacteria taken up by phagocytic or nonphagocytic cells with that of bacteria microinjected directly into the host cytosol, using the live vaccine strain (LVS) of F. tularensis; five selected FPI mutants thereof, i.e., Delta iglA, Delta iglC, Delta iglG, Delta iglI, and Delta pdpE strains; and Listeria monocytogenes. After uptake in bone marrow-derived macrophages (BMDM), ASC(-/-) BMDM, MyD88(-/-) BMDM, J774 cells, or HeLa cells, LVS, Delta pdpE and Delta iglG mutants, and L. monocytogenes replicated efficiently in all five cell types, whereas the Delta iglA and Delta iglC mutants showed no replication. After microinjection, all 7 strains showed effective replication in J774 macrophages, ASC(-/-) BMDM, and HeLa cells. In contrast to the rapid replication in other cell types, L. monocytogenes showed no replication in MyD88(-/-) BMDM and LVS showed no replication in either BMDM or MyD88(-/-) BMDM after microinjection. Our data suggest that the mechanisms of bacterial uptake as well as the permissiveness of the cytosolic compartment per se are important factors for the intracytosolic replication. Notably, none of the investigated FPI proteins was found to be essential for intracytosolic replication after microinjection.

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  • 15.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Garpebring, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Brynolfsson, Patrik
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRIManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BHMs) are applied to smaller images (10×10×10) such that spatial information can be incorporated. Then exploratory analysis is done for larger images (64×64×64) by using maximum a posteriori (MAP).

    For smaller images: the estimators of proposed BHMs show improvements in terms of the root mean squared error compared to the estimators in existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperforms Besag models. For larger images: MAP estimators also show improvements by assigning Leroux prior.

  • 16.
    Wu, Wendy Yi-Ying
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Törnberg, Sven
    Elfström, Klara Miriam
    Liu, Xijia
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Nyström, Lennarth
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Epidemiologi och global hälsa.
    Jonsson, Håkan
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up2018Ingår i: Breast Cancer Research, ISSN 1465-5411, E-ISSN 1465-542X, Vol. 20, artikel-id 153Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of overdiagnosis, but a contemporary control group is usually not available in organized screening programs.

    Methods: We estimated the frequency of overdiagnosis of breast cancer due to screening in women 50–69 years old by using individual screening data from the population-based organized screening program in Stockholm County 1989–2014. A hidden Markov model with four latent states and three observed states was constructed to estimate the natural progression of breast cancer and the test sensitivity. Piecewise transition rates were used to consider the time-varying transition rates. The expected number of detected non-progressive breast cancer cases was calculated.

    Results: During the study period, 2,333,153 invitations were sent out; on average, the participation rate in the screening program was 72.7% and the average recall rate was 2.48%. In total, 14,648 invasive breast cancer cases were diagnosed; among the 8305 screen-detected cases, the expected number of non-progressive breast cancer cases was 35.9, which is equivalent to 0.43% (95% confidence interval (CI) 0.10%–2.2%) overdiagnosis. The corresponding estimates for the prevalent and subsequent rounds were 15.6 (0.87%, 95% CI 0.20%–4.3%) and 20.3 (0.31%, 95% CI 0.07%–1.6%), respectively. The likelihood ratio test showed that the non-homogeneous model fitted the data better than an age-homogeneous model (P<0.001).

    Conclusions: Our findings suggest that overdiagnosis in the organized biennial mammographic screening for women 50–69 in Stockholm County is a minor phenomenon. The frequency of overdiagnosis in the prevalent screening round was higher than that in subsequent rounds. The non-homogeneous model performed better than the simpler, traditional homogeneous model.

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