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"Beer, C"
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A systematic review exploring youth peer support for young people with mental health problems
Youth peer support workers (YPSWs) are young adults with lived experience of mental illness during childhood or adolescence who support young people receiving treatment in mental health services. The contributions made by YPSWs are a promising development to facilitate consumer-centered and recovery-oriented care. Although the youth peer support workforce is expanding rapidly, structurally embedding YPSWs in practice is challenging. To overcome these challenges and thereby improve care for young people, insight into YPSW roles, barriers and facilitators for implementing and pursuing youth peer support (YPS) is a necessity. This systematic review examined the published literature to identify existing knowledge on YPSW roles in treatment settings, and the barriers and facilitators for implementing and pursuing YPS in practice. A total of 24 studies from a variety of youth serving contexts were included in this review. Thematic synthesis resulted in six YPSW roles and five themes with barriers and facilitators. The roles included the: engagement role, emotional support role, navigating and planning role, advocacy role, research role and the educational role. The themes explored the needs of YPSWs, experiences of YPSWs, relationships between service users and YPSWs, the collaboration process between YPSWs and non-peer staff, and organizational readiness. This review underlines that YPSWs likely are a valuable addition to numerous youth treatment contexts. Overall, the implementation of YPSWs is a multifaceted operation that requires careful planning. We recommend services to set clear and realistic expectations for YPSWs, to consider potential power imbalances between YPSWs and non-peer staff, to provide adequate resources to pursue YPS, and to approach the implementation of YPSWs with a growth mindset.
Journal Article
Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data
While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.
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Journal Article
Hepatocytes direct the formation of a pro-metastatic niche in the liver
The liver is the most common site of metastatic disease
1
. Although this metastatic tropism may reflect the mechanical trapping of circulating tumour cells, liver metastasis is also dependent, at least in part, on the formation of a ‘pro-metastatic’ niche that supports the spread of tumour cells to the liver
2
,
3
. The mechanisms that direct the formation of this niche are poorly understood. Here we show that hepatocytes coordinate myeloid cell accumulation and fibrosis within the liver and, in doing so, increase the susceptibility of the liver to metastatic seeding and outgrowth. During early pancreatic tumorigenesis in mice, hepatocytes show activation of signal transducer and activator of transcription 3 (STAT3) signalling and increased production of serum amyloid A1 and A2 (referred to collectively as SAA). Overexpression of SAA by hepatocytes also occurs in patients with pancreatic and colorectal cancers that have metastasized to the liver, and many patients with locally advanced and metastatic disease show increases in circulating SAA. Activation of STAT3 in hepatocytes and the subsequent production of SAA depend on the release of interleukin 6 (IL-6) into the circulation by non-malignant cells. Genetic ablation or blockade of components of IL-6–STAT3–SAA signalling prevents the establishment of a pro-metastatic niche and inhibits liver metastasis. Our data identify an intercellular network underpinned by hepatocytes that forms the basis of a pro-metastatic niche in the liver, and identify new therapeutic targets.
Pancreatic cancer activates IL-6–STAT3 signalling in hepatocytes to induce the formation of a pro-metastatic niche in the liver.
Journal Article
Mitigating site effects in covariance for machine learning in neuroimaging data
To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site‐related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi‐center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group. Multi‐site neuroimaging studies are hindered by differences in images acquired across multiple sites, often referred to as site effects. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for machine learning. After applying our proposed harmonization method CovBat, we find that machine learning methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group.
Journal Article
Calculating dissolved marine oxygen values based on an enhanced Benthic Foraminifera Oxygen Index
Marine oxygen minimum zones (OMZs) trap greenhouse gases, reduce livable habitats, a critical factor for these changes is the amount of dissolved oxygen (DO). The frequently used tool to reconstruct DO values, the Benthic Foraminifera Oxygen Index (BFOI), showed major shortcomings and lacks effectiveness. Therefore, we enhanced the BFOI and introduce enhanced BFOI (EBFOI) formulas by using all available data benthic foraminifers provide, calculating the whole livable habitat of benthic foraminifers, including bottom water oxygenation (BWO) and pore water oxygenation (PWO). Further, we introduce for the first time a transfer function to convert EBFOI vales directly into DO values, increasing efficiency by up to 38%. All formulas are calibrated on modern samples and applied to fossil datasets. Our new approach provides a major improvement in defining and reconstructing marine oxygen levels and eutrophication, by, providing a new toolset for understanding past changes and tracking actual and predicted future expanding OMZs.
Journal Article
Serum amyloid A impairs the antiinflammatory properties of HDL
HDL from healthy humans and lean mice inhibits palmitate-induced adipocyte inflammation; however, the effect of the inflammatory state on the functional properties of HDL on adipocytes is unknown. Here, we found that HDL from mice injected with AgNO3 fails to inhibit palmitate-induced inflammation and reduces cholesterol efflux from 3T3-L1 adipocytes. Moreover, HDL isolated from obese mice with moderate inflammation and humans with systemic lupus erythematosus had similar effects. Since serum amyloid A (SAA) concentrations in HDL increase with inflammation, we investigated whether elevated SAA is a causal factor in HDL dysfunction. HDL from AgNO3-injected mice lacking Saa1.1 and Saa2.1 exhibited a partial restoration of antiinflammatory and cholesterol efflux properties in adipocytes. Conversely, incorporation of SAA into HDL preparations reduced antiinflammatory properties but not to the same extent as HDL from AgNO3-injected mice. SAA-enriched HDL colocalized with cell surface-associated extracellular matrix (ECM) of adipocytes, suggesting impaired access to the plasma membrane. Enzymatic digestion of proteoglycans in the ECM restored the ability of SAA-containing HDL to inhibit palmitate-induced inflammation and cholesterol efflux. Collectively, these findings indicate that inflammation results in a loss of the antiinflammatory properties of HDL on adipocytes, which appears to partially result from the SAA component of HDL binding to cell-surface proteoglycans, thereby preventing access of HDL to the plasma membrane.
Journal Article
High potential for weathering and climate effects of non-vascular vegetation in the Late Ordovician
It has been hypothesized that predecessors of today’s bryophytes significantly increased global chemical weathering in the Late Ordovician, thus reducing atmospheric CO
2
concentration and contributing to climate cooling and an interval of glaciations. Studies that try to quantify the enhancement of weathering by non-vascular vegetation, however, are usually limited to small areas and low numbers of species, which hampers extrapolating to the global scale and to past climatic conditions. Here we present a spatially explicit modelling approach to simulate global weathering by non-vascular vegetation in the Late Ordovician. We estimate a potential global weathering flux of 2.8 (km
3
rock) yr
−1
, defined here as volume of primary minerals affected by chemical transformation. This is around three times larger than today’s global chemical weathering flux. Moreover, we find that simulated weathering is highly sensitive to atmospheric CO
2
concentration. This implies a strong negative feedback between weathering by non-vascular vegetation and Ordovician climate.
Early non-vascular vegetation may have caused an interval of glaciations in the Late Ordovician by enhancing global chemical weathering. Here, by simulating the organisms with a spatially explicit, process-based model, the authors propose that Ordovician vegetation had a high potential for chemical weathering.
Journal Article
NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing
The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range of the plasma proteome. Here we address these challenges with NUcleic acid Linked Immuno-Sandwich Assay (NULISA™), which improves the sensitivity of traditional proximity ligation assays by ~10,000-fold to attomolar level, by suppressing assay background via a dual capture and release mechanism built into oligonucleotide-conjugated antibodies. Highly multiplexed quantification of both low- and high-abundance proteins spanning a wide dynamic range is achieved by attenuating signals from abundant targets with unconjugated antibodies and next-generation sequencing of barcoded reporter DNA. A 200-plex NULISA containing 124 cytokines and chemokines and other proteins demonstrates superior sensitivity to a proximity extension assay in detecting biologically important low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA makes broad and in-depth proteomic analysis easily accessible for research and diagnostic applications.
Unlocking the blood proteome requires exquisite sensitivity and multiplexing to detect low and high abundance proteins simultaneously. Here the authors describe a 200-plex immunoassay with attomolar sensitivity to detect important low abundance proteins in inflammatory diseases and COVID-19.
Journal Article