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Introduction: For a majority of tularemia patients, serology is the basis for the diagnosis. The aim of this study was to perform an analysis of the samples analyzed at a Swedish reference laboratory for the presence of Francisella tularensis-specific antibody levels in sera from individuals with suspected tularemia. Annual and monthly variations of the total number of samples and proportions of positive samples were analyzed, as well as the influence of age and gender.

Methods: We performed a retrospective analysis of the presence of F. tularensis-specific antibodies in serological samples from patients with suspected tularemia analyzed during the period 2010 - 2022 at the University Hospital of Umeå in Sweden, a national reference laboratory, by use of various statistical methods. In total, some 15,100 serum samples had been analyzed for the presence of IgG and IgM antibodies by ELISA during the 13-year period.

Results: Overall, there were higher number of samples with IgG positive or borderline titers, 2,522 and 921, respectively, than with IgM positive or borderline titers, 1,802 and 409, respectively. Repeated samples were obtained from some 1,930 individuals and approximately a third of the cases, which were initially seronegative, had seroconverted when resampled. Peak number of monthly samples were recorded in August and September, > 3,000. Annual numbers varied greatly and peak numbers were observed in 2015 and 2019, 1,832 and 2,250, respectively, whereas some other years the numbers were 700 – 800. There was also much variation in the annual and monthly percentages of positive samples and they varied between less than 10% to greater than 20%. The highest percentages of positive samples were recorded in September and October. IgG and IgM titers declined with age and these differences were highly significant for IgG titers, with decreasing average titers for each 20-year interval.

Discussion: Collectively, the data demonstrate the marked annual and seasonal variations in tularemia sampling occurring in Sweden. Also, the proportion of positive samples increased during months and years with peak number of samples. Another notable finding was that average antibody titers decreased with increased age.

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.

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.

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.

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.

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.

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.

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 (_pCIP_5yr) 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.

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.

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