AnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways.

Introduction: Cardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases, latent confounding and limited causal discovery methods hinder understanding of their causal relationships, essential for mechanistic insights and developing effective prevention and intervention strategies.

Methods: We introduce anchorFCI, a novel adaptation of the conservative Really Fast Causal Inference (RFCI) algorithm, designed to enhance robustness and discovery power in causal learning by strategically selecting and integrating reliable anchor variables from a set of variables known not to be caused by the variables of interest.

Diffusion capacity and static hyperinflation as markers of disease progression predict 3-year mortality in COPD: Results from COSYCONET.

Background And Objective: Chronic obstructive pulmonary disease (COPD) exhibits diverse patterns of disease progression, due to underlying disease activity. We hypothesized that changes in static hyperinflation or KCO % predicted would reveal subgroups with disease progression unidentified by preestablished markers (FEV, SGRQ, exacerbation history) and associated with unique baseline biomarker profiles. We explored 18-month measures of disease progression associated with 18-54-month mortality, including changes in hyperinflation parameters and transfer factor, in a large German COPD cohort.

Large language models and their applications in bioinformatics.

Recent advancements in Natural Language Processing (NLP) have been significantly driven by the development of Large Language Models (LLMs), representing a substantial leap in language-based technology capabilities. These models, built on sophisticated deep learning architectures, typically transformers, are characterized by billions of parameters and extensive training data, enabling them to achieve high accuracy across various tasks. The transformer architecture of LLMs allows them to effectively handle context and sequential information, which is crucial for understanding and generating human language.

Privacy-preserving decentralized learning methods for biomedical applications.

In recent years, decentralized machine learning has emerged as a significant advancement in biomedical applications, offering robust solutions for data privacy, security, and collaboration across diverse healthcare environments. In this review, we examine various decentralized learning methodologies, including federated learning, split learning, swarm learning, gossip learning, edge learning, and some of their applications in the biomedical field. We delve into the underlying principles, network topologies, and communication strategies of each approach, highlighting their advantages and limitations.

Missense genetic variants in major bitter taste receptors are associated with diet quality and food intake in a highly admixed underrepresented population.

Background And Aims: To investigate associations between Single Nucleotide Polymorphisms (SNPs) in the TAS1R and TAS2R taste receptors and diet quality, intake of alcohol, added sugar, and fat, using linear regression and machine learning techniques in a highly admixed population.

Methods: In the ISA-Capital health survey, 901 individuals were interviewed and had socioeconomic, demographic, health characteristics, along with dietary information obtained through two 24-h recalls. Data on 12 components related to food groups, nutrients, and calories was combined into a diet quality score (BHEI-R).

Development and implementation of a treatment pathway to reduce coronary angiograms - lessons from a failure.

Background: The rates of coronary angiograms (CA) and related procedures (percutaneous intervention [PCI]) are significantly higher in Germany than in other Organisation for Economic Co-ordination and Development (OECD) countries. The current guidelines recommend non-invasive diagnosis of coronary heart disease (CHD); CA should only have a limited role in choosing the appropriate revascularisation procedure. The aim of the present study was to explore whether improvements in guideline adherence can be achieved through the implementation of regional treatment pathways.

Turbo autoencoders for the DNA data storage channel with Autoturbo-DNA.

DNA, with its high storage density and long-term stability, is a potential candidate for a next-generation storage device. The DNA data storage channel, composed of synthesis, amplification, storage, and sequencing, exhibits error probabilities and error profiles specific to the components of the channel. Here, we present Autoturbo-DNA, a PyTorch framework for training error-correcting, overcomplete autoencoders specifically tailored for the DNA data storage channel.

Fetal liver macrophages contribute to the hematopoietic stem cell niche by controlling granulopoiesis.

During embryogenesis, the fetal liver becomes the main hematopoietic organ, where stem and progenitor cells as well as immature and mature immune cells form an intricate cellular network. Hematopoietic stem cells (HSCs) reside in a specialized niche, which is essential for their proliferation and differentiation. However, the cellular and molecular determinants contributing to this fetal HSC niche remain largely unknown.

Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning.

Background: Streptococcus agalactiae, commonly known as Group B Streptococcus (GBS), exhibits a broad host range, manifesting as both a beneficial commensal and an opportunistic pathogen across various species. In humans, it poses significant risks, causing neonatal sepsis and meningitis, along with severe infections in adults. Additionally, it impacts livestock by inducing mastitis in bovines and contributing to epidemic mortality in fish populations.

NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search.

The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens.

Telephone Health Coaching and Remote Exercise Monitoring (TeGeCoach) in Peripheral Arterial Occlusive Disease—a Randomized Controlled Trial.

Background: Supervised exercise programs are used to treat intermittent claudication (IC). Home-based exercise programs have been developed to lower barriers to participation. We studied the effects of one such exercise program (TeGeCoach) on self-reported walking ability in patients with IC.

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

Summary: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features.

Interactive polar diagrams for model comparison.

Objective: Evaluating the performance of multiple complex models, such as those found in biology, medicine, climatology, and machine learning, using conventional approaches is often challenging when using various evaluation metrics simultaneously. The traditional approach, which relies on presenting multi-model evaluation scores in the table, presents an obstacle when determining the similarities between the models and the order of performance.

Methods: By combining statistics, information theory, and data visualization, juxtaposed Taylor and Mutual Information Diagrams permit users to track and summarize the performance of one model or a collection of different models.

LAMPPrimerBank, a manually curated database of experimentally validated loop-mediated isothermal amplification primers for detection of respiratory pathogens.

Purpose And Methods: The emergence of coronavirus disease 2019 (COVID-19) has once again affirmed the significant threat of respiratory infections to global public health and the utmost importance of prompt diagnosis in managing and mitigating any pandemic. The nucleic acid amplification test (NAAT) is the primary detection method for most pathogens. Loop-mediated isothermal amplification (LAMP) is a rapid, simple, sensitive, and specific epitome of isothermal NAAT performed using a set of four to six primers.

Gender-specific association of loneliness and health care use in community-dwelling older adults.

Background: Loneliness in older adults is common, particularly in women. In this article, gender differences in the association of loneliness and health care use are investigated in a large sample of community-dwelling older adults.

Methods: Data of 2525 persons (ages 55-85 years)-participants of the fourth follow- up (2011-2014) of the ESTHER study- were analyzed.

Prehabilitation in older patients prior to elective cardiac procedures (PRECOVERY): study protocol of a multicenter randomized controlled trial.

Background: Previous studies have demonstrated the efficacy of rehabilitation after a cardiovascular procedure. Especially older and multimorbid patients benefit from rehabilitation after a cardiac procedure. Prehabilitation prior to cardiac procedures may also have positive effects on patients' pre- and postoperative outcomes.

Comparative analysis of full-length 16s ribosomal RNA genome sequencing in human fecal samples using primer sets with different degrees of degeneracy.

Next-generation sequencing has revolutionized the field of microbiology research and greatly expanded our knowledge of complex bacterial communities. Nanopore sequencing provides distinct advantages, combining cost-effectiveness, ease of use, high throughput, and high taxonomic resolution through its ability to process long amplicons, such as the entire 16s rRNA genome. We examine the performance of the conventional 27F primer (27F-I) included in the 16S Barcoding Kit distributed by Oxford Nanopore Technologies (ONT) and that of a more degenerate 27F primer (27F-II) in the context of highly complex bacterial communities in 73 human fecal samples.

AI-based multi-PRS models outperform classical single-PRS models.

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

Background: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data.