Prediction of late-onset preeclampsia using plasma proteomics: a longitudinal multi-cohort study.

Preeclampsia is a pregnancy disorder with substantial perinatal and maternal morbidity and mortality. Pregnant women at risk of preeclampsia would benefit from early detection for follow-up, timely interventions and delivery. Several attempts have been made to identify protein biomarkers of preeclampsia, but findings vary with demographics, clinical characteristics, and time of sampling.

Protein biomarker signatures of preeclampsia - a longitudinal 5000-multiplex proteomics study.

We aimed to explore novel biomarker candidates and biomarker signatures of late-onset preeclampsia (LOPE) by profiling samples collected in a longitudinal discovery cohort with a high-throughput proteomics platform. Using the Somalogic 5000-plex platform, we analyzed proteins in plasma samples collected at three visits (gestational weeks (GW) 12-19, 20-26 and 28-34 in 35 women with LOPE (birth ≥ 34 GW) and 70 healthy pregnant women). To identify biomarker signatures, we combined Elastic Net with Stability Selection for stable variable selection and validated their predictive performance in a validation cohort.

Outcomes of 10 years of PSA screening for prostate cancer in Norwegian men with Lynch syndrome.

Background: Pathogenic germline variants in the mismatch repair (MMR) genes are associated with an increased risk of prostate cancer (PCa). Since 2010 we have recommended MMR carriers annual PSA testing from the age of 40. Prospective studies of the outcome of long-term PSA screening are lacking.

Tutorial on survival modeling with applications to omics data.

Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.

Functional and Molecular Heterogeneity in Glioma Stem Cells Derived from Multiregional Sampling.

Glioblastoma (GBM) is an aggressive and highly heterogeneous primary brain tumor. Glioma stem cells represent a subpopulation of tumor cells with stem cell traits that are presumed to be the cause of tumor relapse. There exists complex tumor heterogeneity in drug sensitivity patterns between glioma stem cell (GSC) cultures derived from different patients.

Real-world data on niraparib maintenance treatment in patients with non-gBRCA mutated platinum-sensitive recurrent ovarian cancer.

Objectives: The aim of this study was to provide real-world efficacy and safety data on niraparib maintenance treatment in patients with non-germline (gBRCA)1/2 mutated platinum-sensitive recurrent ovarian cancer.

Methods: This retrospective multi-center cohort study included 94 platinum-sensitive recurrent ovarian cancer patients without known gBRCA1/2 mutation treated in an individual patient access program in Norway. The primary outcome was time from start of niraparib treatment to first subsequent treatment.

Predictive value of DNA methylation patterns in AML patients treated with an azacytidine containing induction regimen.

Background: Acute myeloid leukemia (AML) is a heterogeneous disease with a poor prognosis. Dysregulation of the epigenetic machinery is a significant contributor to disease development. Some AML patients benefit from treatment with hypomethylating agents (HMAs), but no predictive biomarkers for therapy response exist.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.

Motivation: Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability.

Effect of a one-year personalized intensive dietary intervention on body composition in colorectal cancer patients: Results from a randomized controlled trial.

Background & Aims: Changes in body composition may affect colorectal cancer (CRC) patient's risk of cancer recurrence, secondary cancer, and other chronic diseases. The suggested interventions for changes in body composition such as low muscle mass or high fat mass, are diet and physical activity. Nevertheless, there is limited evidence of how dietary intervention alone can impact body composition.

Long-Term Use of Amoxicillin Is Associated with Changes in Gene Expression and DNA Methylation in Patients with Low Back Pain and Modic Changes.

Long-term antibiotics are prescribed for a variety of medical conditions, recently including low back pain with Modic changes. The molecular impact of such treatment is unknown. We conducted longitudinal transcriptome and epigenome analyses in patients ( = 100) receiving amoxicillin treatment or placebo for 100 days in the Antibiotics in Modic Changes (AIM) study.

Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach.

In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed.

Development of Hand Use with and Without Intensive Training Among Children with Unilateral Cerebral Palsy in Scandinavia.

Aim: To describe hand use development in children with unilateral cerebral palsy who did/did not participate in constraint-induced movement therapy (CIMT) before 7 years of age.

Method: The study included 334 participants (18 months-12 years) who were assessed with 1,565 Assisting Hand Assessments (AHAs) and categorized into no intensive training (NIT), CIMT (18 months-7 years), and Baby-CIMT (<18 months) groups.

Results: AHA performance at 18 months (AHA-18) was positively associated with development regardless of training.

Caressed by music: Related preferences for velocity of touch and tempo of music?

Given that both hearing and touch are 'mechanical senses' that respond to physical pressure or mechanical energy and that individuals appear to have a characteristic internal or spontaneous tempo, individual preferences in musical and touch rhythms might be related. We explored this in two experiments probing individual preferences for tempo in the tactile and auditory modalities. Study 1 collected ratings of received stroking on the forearm and measured the velocity the participants used for stroking a fur.

: an R package for custom set enrichment analysis and interactive visualization of intersecting sets.

Summary: Enrichment analysis has been widely used to study whether predefined sets of genes or other molecular features are over-represented in a ranked list associated with a disease or other phenotype. However, computational tools that perform enrichment analysis and visualization are usually limited to predefined sets available from public databases. To make such analyses more flexible, we introduce an R package, , which enables enrichment analyses among any ranked features and user-defined custom sets.

screenwerk: a modular tool for the design and analysis of drug combination screens.

Motivation: There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications.

Results: screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens.

Transcriptomic pan-cancer analysis using rank-based Bayesian inference.

The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features.

Identification of SNPs associated with methotrexate treatment outcomes in patients with early rheumatoid arthritis.

Methotrexate is one of the cornerstones of rheumatoid arthritis (RA) therapy. Genetic factors or single nucleotide polymorphisms (SNPs) are responsible for 15%-30% of the variation in drug response. Identification of clinically effective SNP biomarkers for predicting methotrexate (MTX) sensitivity has been a challenge.

Tissue-specific identification of multi-omics features for pan-cancer drug response prediction.

Current statistical models for drug response prediction and biomarker identification fall short in leveraging the shared and unique information from various cancer tissues and multi-omics profiles. We developed mix-lasso model that introduces an additional sample group penalty term to capture tissue-specific effects of features on pan-cancer response prediction. The mix-lasso model takes into account both the similarity between drug responses (i.

Maternal body mass index and placental weight: a role for fetal insulin, maternal insulin and leptin.

Purpose: Placental weight (PW) has been found to mediate the main effect of maternal BMI on fetal size. Still, the BMI-PW association is poorly understood. Therefore, we aimed to explore potential explanatory variables, including gestational weight gain (GWG), early- and late-pregnancy circulating levels of maternal glucose, insulin, leptin, adiponectin, triglycerides, LDL-C, and HDL-C, and fetal insulin.

Placenta-derived proteins across gestation in healthy pregnancies-a novel approach to assess placental function?

Background: Placenta-derived proteins in the systemic maternal circulation are suggested as potential biomarkers for placental function. However, the identity and longitudinal patterns of such proteins are largely unknown due to the inaccessibility of the human placenta and limitations in assay technologies. We aimed to identify proteins derived from and taken up by the placenta in the maternal circulation.

"Psychometric properties of the Norwegian foot function index revised short form".

Background: Foot disorders affect up to one quarter of the adult population. Plantar fasciopathy is a common cause of foot pain associated with decreased activity level and quality of life. Patient-reported outcome measures are important in assessing the burden of a condition as well as in research on the effects of interventions.

Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression.

Background: Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes).

Identification and Validation of Leucine-rich α-2-glycoprotein 1 as a Noninvasive Biomarker for Improved Precision in Prostate Cancer Risk Stratification.

Background: More accurate risk assessments are needed to improve prostate cancer management.

Objective: To identify blood-based protein biomarkers that provided prognostic information for risk stratification.

Design Setting And Participants: Mass spectrometry was used to identify biomarker candidates from blood, and validation studies were performed in four independent cohorts retrospectively collected between 1988 and 2015.