Consensus molecular subtype classification of colorectal adenomas

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC

An EEG-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children

STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit (PICU), it's currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography (EEG) data are used to derive a simple index for sleep classification.METHODS: Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography (PSG) recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years and 13-18 years.UNLABELLED: Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed EEG. With the best performing index, sleep classification models were developed for two, three and four states via decision tree and five-fold nested-cross validation. Model performance was assessed across age categories and EEG channels.RESULTS: In total 90 patients with PSG were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio (gamma:delta-ratio) of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74 and 0.57 for two, three and four state classification. Across age categories, balanced accuracy ranged between 0.83 - 0.92 and 0.72 - 0.77 for two and three state classification, respectively.CONCLUSIONS: We propose an interpretable and generalizable sleep index derived from single-channel-EEG for automated sleep monitoring at the bedside in non-critically ill children aged 6 months to 18 years, with good performance for two and three state classification.</p

Transport of dissolved Si from soil to river: a conceptual mechanistic model

This paper reviews the processes which determine the concentrations of dissolved silicon (DSi) in soil water and proposes a mechanistic model for understanding the transport of Si through a typical podzol soil to the river. DSi present in natural waters originates from the dissolution of mineral and amorphous Si sources in the soil. However, the DSi concentration in natural waters will be dependent on both dissolution and deposition/precipitation processes. The net DSi export is controlled by soil composition like (mineralogy and saturated porosity) as well as water composition (pH, concentrations of organic acids, CO2 and electrolytes). These state variables together with production, polymerization and adsorption equations constitute a mechanistic framework determining DSi concentrations. For a typical soil profile in a temperate climate, we discuss how the values of these key controls differ in each soil horizon and how it influences the DSi transport. Additionally, the impact of external forcings such as seasonal climatic variations and land use, is evaluated. This model is a first step to better understand Si transport processes in soils and should be further validated with field measurements