Topological embedding and directional feature importance in ensemble classifiers for multi-class classification.

Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an interpretable manner to provide meaningful insights into the underlying biological mechanisms We developed a class-based directional feature importance (CLIFI) metric for decision tree methods and demonstrated its use for The Cancer Genome Atlas proteomics data. The CLIFI metric was incorporated into four algorithms, Random Forest (RF), LAtent VAriable Stochastic Ensemble of Trees (LAVASET), and Gradient Boosted Decision Trees (GBDTs), and a new extension incorporating the LAVA step into GBDTs (LAVABOOST).

Macrophage-Expressed Coagulation Factor VII Promotes Adverse Cardiac Remodeling.

Background: Excess fibrotic remodeling causes cardiac dysfunction in ischemic heart disease, driven by MAP (mitogen-activated protein) kinase-dependent TGF-ß1 (transforming growth factor-ß1) activation by coagulation signaling of myeloid cells. How coagulation-inflammatory circuits can be specifically targeted to achieve beneficial macrophage reprogramming after myocardial infarction (MI) is not completely understood.

Methods: Mice with permanent ligation of the left anterior descending artery were used to model nonreperfused MI and analyzed by single-cell RNA sequencing, protein expression changes, confocal microscopy, and longitudinal monitoring of recovery.

Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models.

The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

Context: The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood.

Objective: We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D.

Methods: We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D.

LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies.

Motivation: Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting.

Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound.

Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images).

Targeting the tissue factor coagulation initiation complex prevents antiphospholipid antibody development.

Antiphospholipid antibodies (aPL) in primary or secondary antiphospholipid syndrome (APS) are a major cause for acquired thrombophilia, but specific interventions preventing autoimmune aPL development are an unmet clinical need. Although autoimmune aPL cross react with various coagulation regulatory proteins, lipid-reactive aPL, including those derived from patients with COVID-19, recognize the endolysosomal phospholipid lysobisphosphatidic acid presented by the cell surface-expressed endothelial protein C receptor. This specific recognition leads to complement-mediated activation of tissue factor (TF)-dependent proinflammatory signaling and thrombosis.

Diet-induced Weight Loss and Phenotypic Flexibility Among Healthy Overweight Adults: A Randomized Trial.

Background: The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state.

Aim: In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility.

Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer.

Background And Aims: The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes.

Methods: A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61).

Thrombin-mediated activation of PAR1 enhances doxorubicin-induced cardiac injury in mice.

The chemotherapeutic drug doxorubicin is cardiotoxic and can cause irreversible heart failure. In addition to being cardiotoxic, doxorubicin also induces the activation of coagulation. We determined the effect of thrombin-mediated activation of protease-activated receptor 1 (PAR1) on doxorubicin-induced cardiac injury.

Discovery of four plasmatic biomarkers potentially predicting cardiovascular outcome in peripheral artery disease.

Peripheral artery disease (PAD) patients have an increased cardiovascular risk despite pharmacological treatment strategies. Biomarker research improving risk stratification only focused on known atherothrombotic pathways, but unexplored pathways might play more important roles. To explore the association between a broad cardiovascular biomarker set and cardiovascular risk in PAD.

Multi-omic phenotyping reveals host-microbe responses to bariatric surgery, glycaemic control and obesity.

Background: Resolution of type 2 diabetes (T2D) is common following bariatric surgery, particularly Roux-en-Y gastric bypass. However, the underlying mechanisms have not been fully elucidated.

Methods: To address this we compare the integrated serum, urine and faecal metabolic profiles of participants with obesity ± T2D ( = 80, T2D = 42) with participants who underwent Roux-en-Y gastric bypass or sleeve gastrectomy (pre and 3-months post-surgery;  = 27), taking diet into account.

Characterization of diet-dependent temporal changes in circulating short-chain fatty acid concentrations: A randomized crossover dietary trial.

Background: Production of SCFAs from food is a complex and dynamic saccharolytic fermentation process mediated by both human and gut microbial factors. Knowledge of SCFA production and of the relation between SCFA profiles and dietary patterns is lacking.

Objectives: Temporal changes in SCFA concentrations in response to 2 contrasting diets were investigated using a novel GC-MS method.

Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs.

Aim: To develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs.

Materials And Methods: The described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm.

MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.

Reviewing the metabolomics literature is becoming increasingly difficult because of the rapid expansion of relevant journal literature. Text-mining technologies are therefore needed to facilitate more efficient literature reviews. Here we contribute a standardised corpus of full-text publications from metabolomics studies and describe the development of two metabolite named entity recognition (NER) methods.

Integrated fecal microbiome-metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome.

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS), several modalities of biological and clinical data must be combined. We aimed to identify profiles of fecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Fecal metabolites were measured using proton nuclear magnetic resonance (H-NMR) spectroscopy and gut microbiome using shotgun metagenomic sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HCs) with extensive clinical, biological and phenotype information.

Blood pressure interactions with the DASH dietary pattern, sodium, and potassium: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP).

Background: Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet enhances potassium intake and reduces sodium intake and blood pressure (BP), but the underlying metabolic pathways are unclear.

Objectives: Among free-living populations, we delineated metabolic signatures associated with the DASH diet adherence, 24-hour urinary sodium and potassium excretions, and the potential metabolic pathways involved.

Methods: We used 24-hour urinary metabolic profiling by proton nuclear magnetic resonance spectroscopy to characterize the metabolic signatures associated with the DASH dietary pattern score (DASH score) and 24-hour excretion of sodium and potassium among participants in the United States (n = 2164) and United Kingdom (n = 496) enrolled in the International Study of Macro- and Micronutrients and Blood Pressure (INTERMAP).

Auto-CORPus: A Natural Language Processing Tool for Standardizing and Reusing Biomedical Literature.

To analyse large corpora using machine learning and other Natural Language Processing (NLP) algorithms, the corpora need to be standardized. The BioC format is a community-driven simple data structure for sharing text and annotations, however there is limited access to biomedical literature in BioC format and a lack of bioinformatics tools to convert online publication HTML formats to BioC. We present Auto-CORPus (Automated pipeline for Consistent Outputs from Research Publications), a novel NLP tool for the standardization and conversion of publication HTML and table image files to three convenient machine-interpretable outputs to support biomedical text analytics.

Effect of combining aspirin and rivaroxaban on atherosclerosis in mice.

Background And Aims: The platelet inhibitor aspirin reduces inflammation and atherosclerosis in both apolipoprotein E deficient (apoE) mice and low-density lipoprotein receptor deficient (Ldlr) mice. Similarly, the factor Xa inhibitor rivaroxaban reduces atherosclerosis in both apoE and Ldlr mice. We tested the hypothesis that the combination of aspirin and rivaroxaban reduces atherosclerosis in mice to a greater extent than either agent alone.

Evidence-Based Tools for Dietary Assessments in Nutrition Epidemiology Studies for Dementia Prevention.

Increasing evidence proposes diet as a notable modifiable factor and viable target for the reduction of Alzheimer's Disease risk and age-related cognitive decline. However, assessment of dietary exposures is challenged by dietary capture methods that are prone to misreporting and measurement errors. The utility of -omics technologies for the evaluation of dietary exposures has the potential to improve reliability and offer new insights to pre-disease indicators and preventive targets in cognitive aging and dementia.

Odd Chain Fatty Acids Are Not Robust Biomarkers for Dietary Intake of Fiber.

Scope: Prior investigation has suggested a positive association between increased colonic propionate production and circulating odd-chain fatty acids (OCFAs; pentadecanoic acid [C15:0], heptadecanoic acid [C17:0]). As the major source of propionate in humans is the microbial fermentation of dietary fiber, OCFAs have been proposed as candidate biomarkers of dietary fiber. The objective of this study is to critically assess the plausibility, robustness, reliability, dose-response, time-response aspects of OCFAs as potential biomarkers of fermentable fibers in two independent studies using a validated analytical method.

Effects of storage and leukocyte reduction on the concentration and procoagulant activity of extracellular vesicles in canine packed red cells.

Objectives: To characterize the size and procoagulant activity of extracellular vesicles (EV) that accumulate in canine packed red blood cells (pRBCs) over time and the effect of leukocyte reduction on these characteristics.

Design: Prospective cohort study.

Setting: Private small animal specialty referral hospital and university research laboratories.